reported in Fig. 1, this point will result in the z coordinate equal to 84.7 mm after the occurred distortion of 6.53 mm. The Dy values obtained for the V1 and V2 cases are reported in Table 1, equal to 6.03 and 5.72 respectively.

<table><caption>Table 2. Output of the numerical model in terms of displacement, accuracy, and simulation time.</caption><thead><tr><th></th><th>V1</th><th>V2</th></tr></thead><tbody><tr><td>Dy [mm]</td><td>6.03</td><td>5.72</td></tr><tr><td>Err(Dy)%</td><td>7.6</td><td>12.4</td></tr><tr><td>Simulation Time (gg:hh:mm:ss)</td><td>02:07:39:52 (200392 s)</td><td>00:20:44:25 (74665 s)</td></tr><tr><td>Time Reduction [%]</td><td>53.6</td><td>82.7</td></tr></tbody></table>

Moreover, the accuracy of the simulation model was expressed in terms of percentage error (Err(Dy)%) between the Dy measured and simulated at z = 85.5 mm. As shown in Table 1, an Err(Dy) % value of 7.6 was found for the V1 voxel mesh and 12.4 for the V2 one. A further analysis regarding the simulation times to simulate the entire process was carried out. In detail, the simulation time was compared to the real printing time introducing a time reduction factor. This factor was calculated as the percentage difference between the printed time and the simulation time of the L-PBF process, by considering a printing time of 432000 s. The V1 voxel mesh resulted in a simulation time of 200392 s and a time reduction of 53.6%, while the V2 resulted in a simulation time of 74665 s and a time reduction of 82.7%. It can be observed how the simulation time is significantly reduced by using a voxel mesh dimension of 0.6 mm (V2) including 20 real layers in one computational layer. The considerable time reduction obtained by using half of the V1 voxel mesh dimension results in one eight of V1 voxel mesh elements. The reduction of the computational time is at the expense of the accuracy of the model as can be seen in Table 1.

## Conclusions

In this study, the simulation of the L-PBF process to produce a large Ti-6Al-4V Bipolar Plate was carried out. The influence of the residual stress on the distortion of this thin-walled structure was evaluated together with the accuracy of the numerical model. The main findings of this study are the following:

- Due to the significant development of the residual stresses along the z-direction is not possible to print a Bipolar Plate of the dimension used in this study without any further strategy.

- Even if it is not possible to reproduce exactly when the printing process will end prematurely, the reported displacement from the numerical simulation after the cooling time is very close to the real ones.

- The number of real printed layers included in the computational one has a big impact on the numerical model results both in terms of accuracy and simulation time. In detail, the V1 voxel mesh with 10 real printed layers in one computational layer leads to a better fit with the experimental results. The V2 voxel mesh with 20 real layers in one computational layer, instead, leads to a greater simulation time reduction.

- The good compromise obtained from the numerical model in the case of V1 voxel mesh makes this model a suitable tool for helping engineers in the design of the L-PBF process.
## Acknowledgment

Part of this research was funded by Regione Sicilia under the scheme "Azione 1.2.1_03 del PO FESR SICILIA 2014-2020" – Progetto PON03 PE_00206_1 AMELIE "Advanced framework for Manufacturing Engineering and Product Lifecycle Enhancement." CUP G76I20000060007.

## References

[1] M.K. Singla, P. Nijhawan, A.S. Oberoi, Hydrogen fuel and fuel cell technology for cleaner future: a review, Environmental Science and Pollution Research 28 (2021) 15607-15626. https://doi.org/10.1007/s11356-020-12231-8

[2] S. Karimi, N. Fraser, B. Roberts, F.R. Foulkes, A review of metallic bipolar plates for proton exchange membrane fuel cells: Materials and fabrication methods, Advances in Materials Science and Engineering 2012 (2012). https://doi.org/10.1155/2012/828070

[3] B.G. Pollet, S.S. Kocha, I. Staffell, Current status of automotive fuel cells for sustainable transport, Curr Opin Electrochem 16 (2019) 90-95. https://doi.org/10.1016/j.coelec.2019.04.021

[4] S. Celik, B. Timurkutluk, U. Aydin, M. Yagiz, Development of titanium bipolar plates fabricated by additive manufacturing for PEM fuel cells in electric vehicles, Int J Hydrogen Energy 47 (2022) 37956-37966. https://doi.org/10.1016/j.ijhydene.2022.08.282

[5] T. DebRoy, H.L. Wei, J.S. Zuback, T. Mukherjee, J.W. Elmer, J.O. Milewski, A.M. Beese, A. Wilson-Heid, A. De, W. Zhang, Additive manufacturing of metallic components – Process, structure and properties, Prog Mater Sci 92 (2018) 112-224. https://doi.org/10.1016/j.pmatsci.2017.10.001

[6] S.H. Wang, J. Peng, W.B. Lui, J.S. Zhang, Performance of the gold-plated titanium bipolar plates for the light weight PEM fuel cells, J Power Sources 162 (2006) 486-491. https://doi.org/10.1016/j.jpowsour.2006.06.084

[7] G. Buffa, D. Palmeri, G. Pollara, F. Di Franco, M. Santamaria, L. Fratini, Process parameters and surface treatment effects on the mechanical and corrosion resistance properties of Ti6Al4V components produced by laser powder bed fusion, Progress in Additive Manufacturing (2023). https://doi.org/10.1007/s40964-023-00440-9

[8] C. Chen, Z. Xiao, H. Zhu, X. Zeng, Deformation and control method of thin-walled part during laser powder bed fusion of Ti-6Al-4V alloy, The International Journal of Advanced Manufacturing Technology 110 (2020) 3467-3478. https://doi.org/10.1007/s00170-020-06104-0

[9] G. Buffa, A. Costa, D. Palmeri, G. Pollara, A. Barcellona, L. Fratini, A new control parameter to predict micro-warping-induced job failure in LPBF of Ti6Al4V titanium alloy, International Journal of Advanced Manufacturing Technology 126 (2023). https://doi.org/10.1007/s00170-023-11179-6

[10] T. Lee, U. Auyeskhan, N.-H. Kim, D.-H. Kim, Residual Stress and Dimensional Deviation in a Commercially Pure Titanium Thin Bipolar Plate for a Fuel Cell Using Laser Power Bed Fusion, Metals (Basel) 13 (2023) 1840. https://doi.org/10.3390/met13111840

[11] D. Palmeri, G. Pollara, R. Licari, F. Micari, Finite Element Method in L-PBF of Ti-6Al-4V: Influence of Laser Power and Scan Speed on Residual Stress and Part Distortion, Metals (Basel) 13 (2023) 1907. https://doi.org/10.3390/met13111907
## Acknowledgment

Part of this research was funded by Regione Sicilia under the scheme "Azione 1.2.1_03 del PO FESR SICILIA 2014-2020" – Progetto PON03 PE_00206_1 AMELIE "Advanced framework for Manufacturing Engineering and Product Lifecycle Enhancement." CUP G76I20000060007.

## References

[1] M.K. Singla, P. Nijhawan, A.S. Oberoi, Hydrogen fuel and fuel cell technology for cleaner future: a review, Environmental Science and Pollution Research 28 (2021) 15607-15626. https://doi.org/10.1007/s11356-020-12231-8

[2] S. Karimi, N. Fraser, B. Roberts, F.R. Foulkes, A review of metallic bipolar plates for proton exchange membrane fuel cells: Materials and fabrication methods, Advances in Materials Science and Engineering 2012 (2012). https://doi.org/10.1155/2012/828070

[3] B.G. Pollet, S.S. Kocha, I. Staffell, Current status of automotive fuel cells for sustainable transport, Curr Opin Electrochem 16 (2019) 90-95. https://doi.org/10.1016/j.coelec.2019.04.021

[4] S. Celik, B. Timurkutluk, U. Aydin, M. Yagiz, Development of titanium bipolar plates fabricated by additive manufacturing for PEM fuel cells in electric vehicles, Int J Hydrogen Energy 47 (2022) 37956-37966. https://doi.org/10.1016/j.ijhydene.2022.08.282

[5] T. DebRoy, H.L. Wei, J.S. Zuback, T. Mukherjee, J.W. Elmer, J.O. Milewski, A.M. Beese, A. Wilson-Heid, A. De, W. Zhang, Additive manufacturing of metallic components – Process, structure and properties, Prog Mater Sci 92 (2018) 112-224. https://doi.org/10.1016/j.pmatsci.2017.10.001

[6] S.H. Wang, J. Peng, W.B. Lui, J.S. Zhang, Performance of the gold-plated titanium bipolar plates for the light weight PEM fuel cells, J Power Sources 162 (2006) 486-491. https://doi.org/10.1016/j.jpowsour.2006.06.084

[7] G. Buffa, D. Palmeri, G. Pollara, F. Di Franco, M. Santamaria, L. Fratini, Process parameters and surface treatment effects on the mechanical and corrosion resistance properties of Ti6Al4V components produced by laser powder bed fusion, Progress in Additive Manufacturing (2023). https://doi.org/10.1007/s40964-023-00440-9

[8] C. Chen, Z. Xiao, H. Zhu, X. Zeng, Deformation and control method of thin-walled part during laser powder bed fusion of Ti-6Al-4V alloy, The International Journal of Advanced Manufacturing Technology 110 (2020) 3467-3478. https://doi.org/10.1007/s00170-020-06104-0

[9] G. Buffa, A. Costa, D. Palmeri, G. Pollara, A. Barcellona, L. Fratini, A new control parameter to predict micro-warping-induced job failure in LPBF of Ti6Al4V titanium alloy, International Journal of Advanced Manufacturing Technology 126 (2023). https://doi.org/10.1007/s00170-023-11179-6

[10] T. Lee, U. Auyeskhan, N.-H. Kim, D.-H. Kim, Residual Stress and Dimensional Deviation in a Commercially Pure Titanium Thin Bipolar Plate for a Fuel Cell Using Laser Power Bed Fusion, Metals (Basel) 13 (2023) 1840. https://doi.org/10.3390/met13111840

[11] D. Palmeri, G. Pollara, R. Licari, F. Micari, Finite Element Method in L-PBF of Ti-6Al-4V: Influence of Laser Power and Scan Speed on Residual Stress and Part Distortion, Metals (Basel) 13 (2023) 1907. https://doi.org/10.3390/met13111907
# Machine learning application for optimization of laser directed energy deposition process for aerospace component rapid prototyping in additive manufacturing

ERTUGRUL Gökhan¹,ª*, ALIMOV Artem¹,ᵇ, SVIRIDOV Alexander¹,ᶜ and
HÄRTEL Sebastian¹,ᵈ

¹Chair of Hybrid Manufacturing, Brandenburg University of Technology, Cottbus, Germany

ªGökhan.Ertugrul@b-tu.de, ᵇalimov@b-tu.de, ᵇviridov@b-tu.de, ᵈSebastian.Haertel@b-tu.de

**Keywords:** Directed Energy Deposition, Additive Manufacturing, Machine Learning, Digital Twin, Rapid Prototyping

**Abstract.** The paper proposes a methodology for determining the optimal L-DED parameters based on the minimum number planned of L-DED trials. A dataset compiled from planned L-DED experiments was used to train a machine learning model. The algorithm demonstrated a robust ability to predict the output metrics with notable accuracy and proposed a theoretical framework that modeled the complex relationships between the input variables and the resulting critical welding properties for AM. The application of the developed model and its comparison with conventional methods thus offers a methodical approach to determining the optimum process parameters in advance. This is a step towards the development and production of additively manufactured components for future digital twin application in the aerospace industry.

## Introduction

The sustained development of additive manufacturing (AM) technologies such as laser directed energy deposition (L-DED) has evolved significantly, promoting extensive research into the improvement of processes and materials. Production of functionally graded materials, tool industry, repair and maintenance, medical implants, and production of aerospace parts are some of the applications of the L-DED process [1]. To achieve a deposition, a laser heat source has to provide sufficient energy to melt the substrate and the powder simultaneously in this process. Traditionally, the width, height, and penetration depth of weld beads have been used as essential metrics to evaluate the overall strength and quality of 3D parts made using AM [2]. The laser power, travel speed, powder feed rate, hatch distance, z-axis distance, and combination of these parameters are crucial to identify the geometry of the deposition with sufficient heat input (HI) in AM with L-DED. Considering these parameters and their combinations, the high precision and complexity of the L-DED process require highly efficient and accurate use of optimization techniques to achieve identified deposition such as artificial neural network (ANN) and machine learning (ML) methods. However, small datasets often make it difficult for learning algorithms to achieve accurate predictions, and an oversampling technique is needed to overcome this problem in ANN and ML [3]. ML provides higher accurate results than ANN for a limited training dataset [4]. New methodologies are required to decrease experimental costs and the number of datasets to store and process it. Therefore, the effectiveness of the experimental design to generate high quality datasets is important. Central composite design (CDD) and response surface methodology (RSM) are often used to design the experiment matrix in the weld process involving the lowest possible number of experiments [2]. Yi et al [4] discussed the use of ML with small datasets for wear prediction in AM. Zhu et al [5] applied ML with a small dataset for the inspection of surface morphology in DED. Xiong et al [6] used ANN with a small dataset to predict the bead geometry of GMAW.

<figure><img src="image_265.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
# Machine learning application for optimization of laser directed energy deposition process for aerospace component rapid prototyping in additive manufacturing

ERTUGRUL Gökhan¹,ª*, ALIMOV Artem¹,ᵇ, SVIRIDOV Alexander¹,ᶜ and
HÄRTEL Sebastian¹,ᵈ

¹Chair of Hybrid Manufacturing, Brandenburg University of Technology, Cottbus, Germany

ªGökhan.Ertugrul@b-tu.de, ᵇalimov@b-tu.de, ᶜsviridov@b-tu.de, ᵈSebastian.Haertel@b-tu.de

**Keywords:** Directed Energy Deposition, Additive Manufacturing, Machine Learning, Digital Twin, Rapid Prototyping

**Abstract.** The paper proposes a methodology for determining the optimal L-DED parameters based on the minimum number planned of L-DED trials. A dataset compiled from planned L-DED experiments was used to train a machine learning model. The algorithm demonstrated a robust ability to predict the output metrics with notable accuracy and proposed a theoretical framework that modeled the complex relationships between the input variables and the resulting critical welding properties for AM. The application of the developed model and its comparison with conventional methods thus offers a methodical approach to determining the optimum process parameters in advance. This is a step towards the development and production of additively manufactured components for future digital twin application in the aerospace industry.

## Introduction

The sustained development of additive manufacturing (AM) technologies such as laser directed energy deposition (L-DED) has evolved significantly, promoting extensive research into the improvement of processes and materials. Production of functionally graded materials, tool industry, repair and maintenance, medical implants, and production of aerospace parts are some of the applications of the L-DED process [1]. To achieve a deposition, a laser heat source has to provide sufficient energy to melt the substrate and the powder simultaneously in this process. Traditionally, the width, height, and penetration depth of weld beads have been used as essential metrics to evaluate the overall strength and quality of 3D parts made using AM [2]. The laser power, travel speed, powder feed rate, hatch distance, z-axis distance, and combination of these parameters are crucial to identify the geometry of the deposition with sufficient heat input (HI) in AM with L-DED. Considering these parameters and their combinations, the high precision and complexity of the L-DED process require highly efficient and accurate use of optimization techniques to achieve identified deposition such as artificial neural network (ANN) and machine learning (ML) methods. However, small datasets often make it difficult for learning algorithms to achieve accurate predictions, and an oversampling technique is needed to overcome this problem in ANN and ML [3]. ML provides higher accurate results than ANN for a limited training dataset [4]. New methodologies are required to decrease experimental costs and the number of datasets to store and process it. Therefore, the effectiveness of the experimental design to generate high quality datasets is important. Central composite design (CDD) and response surface methodology (RSM) are often used to design the experiment matrix in the weld process involving the lowest possible number of experiments [2]. Yi et al [4] discussed the use of ML with small datasets for wear prediction in AM. Zhu et al [5] applied ML with a small dataset for the inspection of surface morphology in DED. Xiong et al [6] used ANN with a small dataset to predict the bead geometry of GMAW.

<figure><img src="image_269.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
Although there are some reports on DED concerning process dynamics, materials, and test planning, only a few of them focus on process or quality optimization involving a small dataset ML approach from the initial to the final product [2]. In this study, process parameter optimization is performed and compared using a small dataset applying statistical methods, ML, and a hybrid approach, which is a combination of these methods. Additive manufacturing was carried out with the optimized data and the final product was manufactured for the aerospace industry.

## Material and Methods

The used material was spherical powder made of 316L (1.4404) with a grain size of 45 - 90 µm. The substrate plate made of the austenitic CrNi material 1.4301 had the dimensions 100 X 100 X 10 mm and was free of oxide layers.

A LUNOVU e-LMD system was used for the L-DED process. The L-DED system consisted of a 2.5 kW solid-state laser source, a coaxial nozzle with corresponding powder feed, 5-axis kinematics and a line scanner (Figure 1). The laser wavelength and spot diameter were 960 – 1080 nm and 1.6 mm respectively. Argon (99.998%) with a volume flow of 12 l/min was used as the shielding gas. The volume flow of the powder carrier gas was 3 l/min. The distance between the substrate and the nozzle was 13 mm in order to focus the laser and the powder on the molten pool. Rhino and LunoCAM slicer were used for computer aided design (CAD) and computer aided manufacture (CAM).

<figure><img src="image_266.png" /><figcaption>Figure 1. Labelled photograph of the L-DED system which was used to produce the samples characterises in this work.</figcaption></figure>

Figure 2 shows the steps involved in optimizing the welding parameters for AM. The laser power, travel speed, and powder flow rate of 1.4404 material were investigated for a single layer related to weld width, weld height, aspect ratio (Weld width/Weld height), and dilution (100 * Penetration Area / (Penetration Area + Weld Bead Area)). The penetration area is the weld deposition area below the surface of the substrate and the weld bead area is the deposition area above the surface of the substrate. Subsequently, the optimized single weld parameters were adapted first coating with different hatch distances and AM with optimization of z-axis distance.

<figure><img src="image_267.png" /><figcaption>Figure 2. Optimization steps of the additive manufacturing.</figcaption></figure>

A full factorial 2³ (three factors with the two levels) central composite design with an axial point was used to produce the test matrix to optimize a single weld bead (Table 1). This design was augmented with an axial point (α), which is a strategically placed value at a specific distance ratio
Although there are some reports on DED concerning process dynamics, materials, and test planning, only a few of them focus on process or quality optimization involving a small dataset ML approach from the initial to the final product [2]. In this study, process parameter optimization is performed and compared using a small dataset applying statistical methods, ML, and a hybrid approach, which is a combination of these methods. Additive manufacturing was carried out with the optimized data and the final product was manufactured for the aerospace industry.

## Material and Methods

The used material was spherical powder made of 316L (1.4404) with a grain size of 45 - 90 µm. The substrate plate made of the austenitic CrNi material 1.4301 had the dimensions 100 X 100 X 10 mm and was free of oxide layers.

A LUNOVU e-LMD system was used for the L-DED process. The L-DED system consisted of a 2.5 kW solid-state laser source, a coaxial nozzle with corresponding powder feed, 5-axis kinematics and a line scanner (Figure 1). The laser wavelength and spot diameter were 960 – 1080 nm and 1.6 mm respectively. Argon (99.998%) with a volume flow of 12 l/min was used as the shielding gas. The volume flow of the powder carrier gas was 3 l/min. The distance between the substrate and the nozzle was 13 mm in order to focus the laser and the powder on the molten pool. Rhino and LunoCAM slicer were used for computer aided design (CAD) and computer aided manufacture (CAM).

<figure><img src="image_270.png" /><figcaption>Figure 1. Labelled photograph of the L-DED system which was used to produce the samples characterises in this work.</figcaption></figure>

Figure 2 shows the steps involved in optimizing the welding parameters for AM. The laser power, travel speed, and powder flow rate of 1.4404 material were investigated for a single layer related to weld width, weld height, aspect ratio (Weld width/Weld height), and dilution (100 * Penetration Area / (Penetration Area + Weld Bead Area)). The penetration area is the weld deposition area below the surface of the substrate and the weld bead area is the deposition area above the surface of the substrate. Subsequently, the optimized single weld parameters were adapted first coating with different hatch distances and AM with optimization of z-axis distance.

<figure><img src="image_271.png" /><figcaption>Figure 2. Optimization steps of the additive manufacturing.</figcaption></figure>

A full factorial 2³ (three factors with the two levels) central composite design with an axial point was used to produce the test matrix to optimize a single weld bead (Table 1). This design was augmented with an axial point (α), which is a strategically placed value at a specific distance ratio
from the center of the design space, to enhance the test matrix. The axial point 1.215 was used in order not to exceed the boundary conditions required for weld bead formation. Response surface regression method as a statistical method with Minitab Statistical software was utilized to analyze and optimize the process conditions. Regression learner with neural networks (NN) and Gaussian process regression (GPR) approaches as a supervised machine learning method were used with MATLAB R2021b software. As a nonparametric Bayesian method, GPR was developed using Gaussian probability distribution and it is particularly suitable for small amounts of data sets. The neural network used was a bi-layered fitting neural network. It can generalize an input-output relationship after training the data [7]. For machine learning, the training dataset in the central composite design was used as in the response surface method. The number of samples fabricated and results used for the training of the predictive algorithms consisted of 18 trials. The machine learning system was tested with an external dataset, which consist of 12 trials. Due to the small amount of data sets the 4-fold cross validation was chosen. The criteria for determining the discontinuities of the optimum welding parameters for a single weld bead were considered by DIN EN ISO 13919-1.

<table><caption>Table 1. Design of experiment of the single weld bead.</caption><thead><tr><td>Parameters</td><td>Low Level</td><td>Central Point</td><td>High Level</td></tr></thead><tbody><tr><td>Laser Power, W</td><td>500</td><td>1000</td><td>1500</td></tr><tr><td>Speed, mm/min</td><td>500</td><td>1000</td><td>1500</td></tr><tr><td>Powder Flow, g/min</td><td>5</td><td>10</td><td>15</td></tr></tbody></table>

### Axial point (α):1.215

The evaluated dimensions of the weld are shown in Figure 2. The aspect ratio is in the acceptable range between 3 - 5. The accuracy of the weld was measured with a Lunovu linear laser scanner and a Keyence VK-X1000 laser scanner. The optimal dilution is between 2% - 15%, preferably between 5%-10% [8].

The weld width is a key parameter to transfer the technology to the coating application due to hatch distance. Therefore, the one factor at a time (OFAT) method with three levels was used for the coating optimization. After the coating, the two types of scanning strategy (Figure 3) were carried out and then the z-axis distance was determined.

<figure><img src="image_268.png" /><figcaption>Figure 3. Bidirectional and scaling scanning strategy for additive manufacturing.</figcaption></figure>

In Figure 4, the diagram shows the workflow in additive manufacturing, starting with the creation of a 3D model using CAD. This model is subsequently used to build manufacturing instructions through CAM with the optimized single weld parameters. The component was manufactured in the additive manufacturing stage with optimization of the hatch distance and z-axis distance as well as scanning strategy, followed by digitalization and evaluation of the component surface using a 3D scanning and metrology tool (ATOS-GOM Metronome System) to compare the manufactured product with the original CAD design for accuracy. After digitalization and evaluation of the AM part, the CAM values were optimized to achieve high accuracy near-net shape part. The final stage was the production of the final product and an iterative optimization process was applied to refine and improve the result.
from the center of the design space, to enhance the test matrix. The axial point 1.215 was used in order not to exceed the boundary conditions required for weld bead formation. Response surface regression method as a statistical method with Minitab Statistical software was utilized to analyze and optimize the process conditions. Regression learner with neural networks (NN) and Gaussian process regression (GPR) approaches as a supervised machine learning method were used with MATLAB R2021b software. As a nonparametric Bayesian method, GPR was developed using Gaussian probability distribution and it is particularly suitable for small amounts of data sets. The neural network used was a bi-layered fitting neural network. It can generalize an input-output relationship after training the data [7]. For machine learning, the training dataset in the central composite design was used as in the response surface method. The number of samples fabricated and results used for the training of the predictive algorithms consisted of 18 trials. The machine learning system was tested with an external dataset, which consist of 12 trials. Due to the small amount of data sets the 4-fold cross validation was chosen. The criteria for determining the discontinuities of the optimum welding parameters for a single weld bead were considered by DIN EN ISO 13919-1.

<table><caption>Table 1. Design of experiment of the single weld bead.</caption><thead><tr><td>Parameters</td><td>Low Level</td><td>Central Point</td><td>High Level</td></tr></thead><tbody><tr><td>Laser Power, W</td><td>500</td><td>1000</td><td>1500</td></tr><tr><td>Speed, mm/min</td><td>500</td><td>1000</td><td>1500</td></tr><tr><td>Powder Flow, g/min</td><td>5</td><td>10</td><td>15</td></tr></tbody></table>

### Axial point (α):1.215

The evaluated dimensions of the weld are shown in Figure 2. The aspect ratio is in the acceptable range between 3 - 5. The accuracy of the weld was measured with a Lunovu linear laser scanner and a Keyence VK-X1000 laser scanner. The optimal dilution is between 2% - 15%, preferably between 5%-10% [8].

The weld width is a key parameter to transfer the technology to the coating application due to hatch distance. Therefore, the one factor at a time (OFAT) method with three levels was used for the coating optimization. After the coating, the two types of scanning strategy (Figure 3) were carried out and then the z-axis distance was determined.

<figure><img src="image_272.png" /><figcaption>Figure 3. Bidirectional and scaling scanning strategy for additive manufacturing.</figcaption></figure>

In Figure 4, the diagram shows the workflow in additive manufacturing, starting with the creation of a 3D model using CAD. This model is subsequently used to build manufacturing instructions through CAM with the optimized single weld parameters. The component was manufactured in the additive manufacturing stage with optimization of the hatch distance and z-axis distance as well as scanning strategy, followed by digitalization and evaluation of the component surface using a 3D scanning and metrology tool (ATOS-GOM Metronome System) to compare the manufactured product with the original CAD design for accuracy. After digitalization and evaluation of the AM part, the CAM values were optimized to achieve high accuracy near-net shape part. The final stage was the production of the final product and an iterative optimization process was applied to refine and improve the result.
<figure><img src="image_269.png" /><figcaption>Figure 4. Rapid prototyping workflow of the additive manufacturing component in this study.</figcaption></figure>

## Analysis of as-deposited specimen

The samples were cut with a cutting machine. The cut sample was ground on silicon carbide grinding papers with grit sizes from 120 to 2500, polished using three steps of diamond suspension (6 µm, 3 µm, and 1 µm), and oxide polished (0.05 µm silica solution, Struers OP-S Suspension). The Digital Microscope (DM) VHX-7000 (Keyence Deutschland GmbH) was used to investigate the presence, weld bead geometry, and dilution of the cross section of it.

## Results and discussion

### Optimization of the Single Weld Bead

Weld width, weld height, aspect ratio, and dilution were optimized concerning laser power, travel speed, and powder flow rate. Response surface and machine learning methods were used for each output to optimize weld bead geometry.

### Response Surface Method

The regression analysis carried out for the parameters of the L-DED process shows a robust model with a coefficient of determination (Table 2). A high value of R-squared and low value of standard error (S), mean squared error (MSE), and mean absolute error (MAE) indicate that the model can explain a significant proportion of the variability and high predictive capabilities. According to Minitab, R²(adj) is the percentage of variation in the response that was explained by the model, adjusted for the number of predictors in the model relative to the number of observations. R²(pred) was calculated using a formula equivalent to systematically removing each observation from the dataset, estimating the regression equation, and determining how accurately the model predicted the removed observation.

The equation of the weld width, weld height, aspect ratio, and dilution and their process window related to laser power, travel speed, and powder flow are shown in Equation (1) – Equation (4) and Figure 5, respectively. Equations exhibit a positive trended relationship with the laser power for the weld width, weld height, and dilution, and a negative trended relationship with aspect ratio. An increase in laser power leads to an increase in weld width, weld height, and unlikely, an increase in aspect ratio and dilution due to high heat input resulting in a higher melting rate.

Travel speed has a negative trend with weld width, and weld height and a positive trend with aspect ratio and dilution. High travel speed causes less heat input as well as powder feed on the substrate. Therefore, the weld with and height both decrease. The decreasing rate of the weld width is higher than to weld height, thus, the aspect ratio increases. However, dilution has a more complex relation in Figure 5. Due to this complex relation, Table 2 shows poor values of R², MSE, and MAE for dilution. On one hand, reducing the heat input per unit length causes decreasing the penetration area with reducing welding speed, on the other hand, less powder material per unit length causes decreasing of the weld bead area. The combination of geometrical parameters ensures that the dilution increases and then decreases.

In case increasing the powder feed rate, the weld height increases, however, the weld width decreases, slightly. This is because of a reduction in the unit heat required to melt large amount of powder which causes the low viscosity of melt pool and non-spread melt pool. Therefore, the
<figure><img src="image_273.png" /><figcaption>Figure 4. Rapid prototyping workflow of the additive manufacturing component in this study.</figcaption></figure>

## Analysis of as-deposited specimen

The samples were cut with a cutting machine. The cut sample was ground on silicon carbide grinding papers with grit sizes from 120 to 2500, polished using three steps of diamond suspension (6 µm, 3 µm, and 1 µm), and oxide polished (0.05 µm silica solution, Struers OP-S Suspension). The Digital Microscope (DM) VHX-7000 (Keyence Deutschland GmbH) was used to investigate the presence, weld bead geometry, and dilution of the cross section of it.

## Results and discussion

### Optimization of the Single Weld Bead

Weld width, weld height, aspect ratio, and dilution were optimized concerning laser power, travel speed, and powder flow rate. Response surface and machine learning methods were used for each output to optimize weld bead geometry.

### Response Surface Method

The regression analysis carried out for the parameters of the L-DED process shows a robust model with a coefficient of determination (Table 2). A high value of R-squared and low value of standard error (S), mean squared error (MSE), and mean absolute error (MAE) indicate that the model can explain a significant proportion of the variability and high predictive capabilities. According to Minitab, R²(adj) is the percentage of variation in the response that was explained by the model, adjusted for the number of predictors in the model relative to the number of observations. R²(pred) was calculated using a formula equivalent to systematically removing each observation from the dataset, estimating the regression equation, and determining how accurately the model predicted the removed observation.

The equation of the weld width, weld height, aspect ratio, and dilution and their process window related to laser power, travel speed, and powder flow are shown in Equation (1) – Equation (4) and Figure 5, respectively. Equations exhibit a positive trended relationship with the laser power for the weld width, weld height, and dilution, and a negative trended relationship with aspect ratio. An increase in laser power leads to an increase in weld width, weld height, and unlikely, an increase in aspect ratio and dilution due to high heat input resulting in a higher melting rate.

Travel speed has a negative trend with weld width, and weld height and a positive trend with aspect ratio and dilution. High travel speed causes less heat input as well as powder feed on the substrate. Therefore, the weld with and height both decrease. The decreasing rate of the weld width is higher than to weld height, thus, the aspect ratio increases. However, dilution has a more complex relation in Figure 5. Due to this complex relation, Table 2 shows poor values of R², MSE, and MAE for dilution. On one hand, reducing the heat input per unit length causes decreasing the penetration area with reducing welding speed, on the other hand, less powder material per unit length causes decreasing of the weld bead area. The combination of geometrical parameters ensures that the dilution increases and then decreases.

In case increasing the powder feed rate, the weld height increases, however, the weld width decreases, slightly. This is because of a reduction in the unit heat required to melt large amount of powder which causes the low viscosity of melt pool and non-spread melt pool. Therefore, the
aspect ratio increases. Because of the high material transfer, the dilution decreases with the increase of the powder flow due to less heat transfer on the substrate.

<figure><img src="image_270.png" /><figcaption>Figure 5 Process window of the laser power, travel speed, and powder flow rate concerning weld width (a), weld height (b), aspect ratio (c), and dilution (d) of the L-DED process.</figcaption></figure>

<table><caption>Table 2. Model summary of response surface method.</caption><thead><tr><td>Parameter</td><td>S</td><td>R<sup>2</sup></td><td>R<sup>2</sup> (adj)</td><td>R<sup>2</sup> (pred)</td><td>MSE Test</td><td>MAE Test</td></tr></thead><tbody><tr><td>Weld width</td><td>0.0483008</td><td>99.16%</td><td>98.41%</td><td>93.15%</td><td>0.01289</td><td>0.0877</td></tr><tr><td>Weld height</td><td>0.0330986</td><td>99.27%</td><td>98.62%</td><td>94.20%</td><td>0.03832</td><td>0.1824</td></tr><tr><td>Aspect ratio</td><td>0.659615</td><td>96.90%</td><td>94.10%</td><td>65.57%</td><td>0.80002</td><td>0.7761</td></tr><tr><td>Dilution</td><td>6.35228</td><td>95.81%</td><td>92.04%</td><td>68.66%</td><td>114.35455</td><td>8.9996</td></tr></tbody></table>
aspect ratio increases. Because of the high material transfer, the dilution decreases with the increase of the powder flow due to less heat transfer on the substrate.

<figure><img src="image_274.png" /><figcaption>Figure 5 Process window of the laser power, travel speed, and powder flow rate concerning weld width (a), weld height (b), aspect ratio (c), and dilution (d) of the L-DED process.</figcaption></figure>

<table><caption>Table 2. Model summary of response surface method.</caption><thead><tr><td>Parameter</td><td>S</td><td>R<sup>2</sup></td><td>R<sup>2</sup> (adj)</td><td>R<sup>2</sup> (pred)</td><td>MSE Test</td><td>MAE Test</td></tr></thead><tbody><tr><td>Weld width</td><td>0.0483008</td><td>99.16%</td><td>98.41%</td><td>93.15%</td><td>0.01289</td><td>0.0877</td></tr><tr><td>Weld height</td><td>0.0330986</td><td>99.27%</td><td>98.62%</td><td>94.20%</td><td>0.03832</td><td>0.1824</td></tr><tr><td>Aspect ratio</td><td>0.659615</td><td>96.90%</td><td>94.10%</td><td>65.57%</td><td>0.80002</td><td>0.7761</td></tr><tr><td>Dilution</td><td>6.35228</td><td>95.81%</td><td>92.04%</td><td>68.66%</td><td>114.35455</td><td>8.9996</td></tr></tbody></table>
$$ \text{Weld Width} = 0.885 + 0.002101 \text{ Laser Power} - 0.000275 \text{ Travel Speed} \\ - 0.0266 \text{ Powder Flow} - 0.000001 \text{ Laser Power} * \text{Laser Power} \\ + 0.0000001 \text{ Travel Speed} * \text{Travel Speed} \\ - 0.000297 \text{ Powder Flow} * \text{Powder Flow} + 0.0000001 \text{ Laser Power} * \text{Speed} \\ + 0.000035 \text{ Laser Power} * \text{Powder Flow} - 0.000021 \text{ Travel Speed} * \text{Powder Flow} \tag{1} $$

$$ \text{Weld Height} = 0.380 + 0.000667 \text{ Laser Power} - 0.001204 \text{ Travel Speed} \\ + 0.0608 \text{ Powder Flow} - 0.000000 \text{ Laser Power} * \text{Laser Power} \\ + 0.000001 \text{ Travel Speed} * \text{Travel Speed} \\ + 0.000335 \text{ Powder Flow} * \text{Powder Flow} \\ - 0.0000001 \text{ Laser Power} * \text{Travel Speed} \\ + 0.000004 \text{ Laser Power} * \text{Powder Flow} \\ - 0.000034 \text{ Travel Speed} * \text{Powder Flow} \tag{2} $$

$$ \text{Aspect Ratio} = 3.90 - 0.00209 \text{ Laser Power} + 0.01197 \text{ Travel Speed} \\ - 0.787 \text{ Powder Flow} - 0.0000001 \text{ Laser Power} * \text{Laser Power} \\ - 0.000001 \text{ Travel Speed} * \text{Travel Speed} \\ + 0.0288 \text{ Powder Flow} * \text{Powder Flow} \\ - 0.0000001 \text{ Laser Power} * \text{Travel Speed} \\ + 0.000250 \text{ Laser Power} * \text{Powder Flow} \\ - 0.000519 \text{ Travel Speed} * \text{Powder Flow} \tag{3} $$

$$ \text{Dilution} = -48.8 + 0.1232 \text{ Laser Power} + 0.0147 \text{ Travel Speed} \\ + 0.21 \text{ Powder Flow} - 0.000028 \text{ Laser Power} * \text{Laser Power} \\ - 0.000011 \text{ Travel Speed} * \text{Travel Speed} + 0.010 \text{ Powder} * \text{Powder Flow} \\ + 0.000011 \text{ Laser Power} * \text{Travel Speed} - 0.002661 \text{ Laser Power} * \text{Powder Flow} \\ + 0.000105 \text{ Travel Speed} * \text{Powder Flow} \tag{4} $$

## Machine Learning Optimization of Single Weld Bead

By training the model using the dataset, it was observed that the most effective models for prediction of the input parameters the weld width and the weld height on the training and test set were the models generated using the GPR method (Figure 6 and Figure 7).
$$ \text{Weld Width} = 0.885 + 0.002101 \text{ Laser Power} - 0.000275 \text{ Travel Speed} \\ - 0.0266 \text{ Powder Flow} - 0.000001 \text{ Laser Power} * \text{Laser Power} \\ + 0.0000001 \text{ Travel Speed} * \text{Travel Speed} \\ - 0.000297 \text{ Powder Flow} * \text{Powder Flow} + 0.0000001 \text{ Laser Power} * \text{Speed} \\ + 0.000035 \text{ Laser Power} * \text{Powder} - 0.000021 \text{ Travel Speed} * \text{Powder Flow} \tag{1} $$

$$ \text{Weld Height} = 0.380 + 0.000667 \text{ Laser Power} - 0.001204 \text{ Travel Speed} \\ + 0.0608 \text{ Powder Flow} - 0.000000 \text{ Laser Power} * \text{Laser Power} \\ + 0.000001 \text{ Travel Speed} * \text{Travel Speed} \\ + 0.000335 \text{ Powder Flow} * \text{Powder Flow} \\ - 0.0000001 \text{ Laser Power} * \text{Travel Speed} \\ + 0.000004 \text{ Laser Power} * \text{Powder Flow} \\ - 0.000034 \text{ Travel Speed} * \text{Powder Flow} \tag{2} $$

$$ \text{Aspect Ratio} = 3.90 - 0.00209 \text{ Laser Power} + 0.01197 \text{ Travel Speed} \\ - 0.787 \text{ Powder Flow} - 0.0000001 \text{ Laser Power} * \text{Laser Power} \\ - 0.000001 \text{ Travel Speed} * \text{Travel Speed} \\ + 0.0288 \text{ Powder Flow} * \text{Powder Flow} \\ - 0.0000001 \text{ Laser Power} * \text{Travel Speed} \\ + 0.000250 \text{ Laser Power} * \text{Powder Flow} \\ - 0.000519 \text{ Travel Speed} * \text{Powder Flow} \tag{3} $$

$$ \text{Dilution} = -48.8 + 0.1232 \text{ Laser Power} + 0.0147 \text{ Travel Speed} \\ + 0.21 \text{ Powder Flow} - 0.000028 \text{ Laser Power} * \text{Laser Power} \\ - 0.000011 \text{ Travel Speed} * \text{Travel Speed} + 0.010 \text{ Powder} * \text{Powder Flow} \\ + 0.000011 \text{ Laser Power} * \text{Travel Speed} - 0.002661 \text{ Laser Power} * \text{Powder Flow} \\ + 0.000105 \text{ Travel Speed} * \text{Powder Flow} \tag{4} $$

## Machine Learning Optimization of Single Weld Bead

By training the model using the dataset, it was observed that the most effective models for prediction of the input parameters the weld width and the weld height on the training and test set were the models generated using the GPR method (Figure 6 and Figure 7).
<figure><img src="image_271.png" /><figcaption>Figure 6. The training performance (a) and test (b) performance of the weld width prediction.</figcaption></figure>

In Figure 6 and Figure 7, the majority of the observations for both the training and test datasets are near the line of perfect prediction, suggesting that the model has a high degree of accuracy in the prediction of the weld bead width based on the given input parameters - laser power, welding speed, and powder feed rate (Table 3). At lower true response values, the model tends to predict slightly higher weld width and lower weld height than observed, while at higher true response values the predictions of the model are consistent.

<figure><img src="image_272.png" /><figcaption>Figure 7. The training performance (a) and test (b) performance of the weld height prediction.</figcaption></figure>

The response scatter plots highlight the robust ability of the bilayered neural network model to predict aspect ratio and dilution (Figure 8 and Figure 9).
<figure><img src="image_275.png" /><figcaption>Figure 6. The training performance (a) and test (b) performance of the weld width prediction.</figcaption></figure>

In Figure 6 and Figure 7, the majority of the observations for both the training and test datasets are near the line of perfect prediction, suggesting that the model has a high degree of accuracy in the prediction of the weld bead width based on the given input parameters - laser power, welding speed, and powder feed rate (Table 3). At lower true response values, the model tends to predict slightly higher weld width and lower weld height than observed, while at higher true response values the predictions of the model are consistent.

<figure><img src="image_276.png" /><figcaption>Figure 7. The training performance (a) and test (b) performance of the weld height prediction.</figcaption></figure>

The response scatter plots highlight the robust ability of the bilayered neural network model to predict aspect ratio and dilution (Figure 8 and Figure 9).
<figure><img src="image_273.png" /><figcaption>Figure 8. The training performance (a) and test (b) performance of the aspect ratio prediction.</figcaption></figure>

At lower true response values which is critical for AM with L-DED, the model tends to predict consistent dilution than observed, while at higher true response values the predictions of the model are slightly higher.

<figure><img src="image_274.png" /><figcaption>Figure 9. The training performance (a) and test (b) performance of the dilution.</figcaption></figure>

<table><caption>Table 3. Training and test results of the machine learning models.</caption><thead><tr><th rowspan="2">Parameter</th><th>RMSE</th><th>R<sup>2</sup></th><th>MSE</th><th>MAE</th></tr><tr><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th></tr></thead><tbody><tr><td>Weld width</td><td>0.14015 / 0.11452</td><td>0.88 / 0.80</td><td>0.019642 / 0.11452</td><td>0.096801 / 0.091415</td></tr><tr><td>Weld height</td><td>0.073121 / 0.045294</td><td>0.94 / 0.89</td><td>0.00053467 / 0.0020515</td><td>0.058013 / 0.033399</td></tr><tr><td>Aspect ratio</td><td>1.8346 / 0.39856</td><td>0.72 / 0.79</td><td>3.3658 / 0.15885</td><td>1.5785 / 0.33145</td></tr><tr><td>Dilution</td><td>10.404 / 5.1832</td><td>0.80 / 0.89</td><td>108.24 / 26.865</td><td>6.5527 / 3.3351</td></tr></tbody></table>

### Hybrid approach

In the hybrid approach, randomly augmented data from the RSM, constituting 25% of training data, was integrated with experimental data. The combined dataset was then utilized for the training of machine learning. To test the algorithm, the same test dataset as machine learning was used. The result of the training and test model of the hybrid approach is shown in Table 4.

Except for the weld width prediction, the hybrid model has a slightly lower performance in comparison to machine learning.
<figure><img src="image_277.png" /></figure>

<figure><img src="image_278.png" /><figcaption>Figure 8. The training performance (a) and test (b) performance of the aspect ratio prediction.</figcaption></figure>

At lower true response values which is critical for AM with L-DED, the model tends to predict consistent dilution than observed, while at higher true response values the predictions of the model are slightly higher.

<figure><img src="image_279.png" /></figure>

<figure><img src="image_280.png" /><figcaption>Figure 9. The training performance (a) and test (b) performance of the dilution.</figcaption></figure>

<table><caption>Table 3. Training and test results of the machine learning models.</caption><thead><tr><th rowspan="2">Parameter</th><th>RMSE</th><th>R<sup>2</sup></th><th>MSE</th><th>MAE</th></tr><tr><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th></tr></thead><tbody><tr><td>Weld width</td><td>0.14015 / 0.11452</td><td>0.88 / 0.80</td><td>0.019642 / 0.11452</td><td>0.096801 / 0.091415</td></tr><tr><td>Weld height</td><td>0.073121 / 0.045294</td><td>0.94 / 0.89</td><td>0.00053467 / 0.0020515</td><td>0.058013 / 0.033399</td></tr><tr><td>Aspect ratio</td><td>1.8346 / 0.39856</td><td>0.72 / 0.79</td><td>3.3658 / 0.15885</td><td>1.5785 / 0.33145</td></tr><tr><td>Dilution</td><td>10.404 / 5.1832</td><td>0.80 / 0.89</td><td>108.24 / 26.865</td><td>6.5527 / 3.3351</td></tr></tbody></table>

### Hybrid approach

In the hybrid approach, randomly augmented data from the RSM, constituting 25% of training data, was integrated with experimental data. The combined dataset was then utilized for the training of machine learning. To test the algorithm, the same test dataset as machine learning was used. The result of the training and test model of the hybrid approach is shown in Table 4.

Except for the weld width prediction, the hybrid model has a slightly lower performance in comparison to machine learning.
<table><caption>Table 4 Training and test results of the RSM-Machine learning hybrid approach.</caption><thead><tr><th rowspan="2">Parameter</th><th>RMSE</th><th>R<sup>2</sup></th><th>MSE</th><th>MAE</th></tr><tr><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th></tr></thead><tbody><tr><td>Weld width</td><td>0.09433/0.11472</td><td>0.94/0.80</td><td>0.008898/0.01316</td><td>0.059998/0.090264</td></tr><tr><td>Weld height</td><td>0.10487/0.062279</td><td>0.87/0.80</td><td>0.010997/0.074023</td><td>0.074023/0.047107</td></tr><tr><td>Aspect ratio</td><td>1.569/0.43455</td><td>0.66/0.75</td><td>2.4614/0.18883</td><td>0.75323/0.36265</td></tr><tr><td>Dilution</td><td>10.93/7.5793</td><td>0.81/0.77</td><td>119.47/57.446</td><td>6.7143/6.428</td></tr></tbody></table>

The parameters were optimized to achieve defect-free deposition with minimized dilution, causing minimized heat input. The chosen optimized input parameters by machine learning are for a lower response 650 W laser power, 500 mm/min travel speed, and 5 g/min powder flow which gives 1.628 mm weld width, 0.440 mm weld height, 3.70 aspect ratio and 2.1% dilution; for an upper response 750 W laser power, 500 mm/min travel speed and 5 g/min powder flow which gives 1.858 mm weld width, 0.514 mm weld height, 3.62 aspect ratio and 9.8% dilution.

The optimization was performed by machine learning. To minimize the process complexity, the optimum parameter range was determined by adjusting only laser power. Table 5 and Figure 10 show the optimized weld bead parameters and cross-section of the weld beads. As a lower response, a laser power with 650W, and an upper response a laser power with 750W were chosen. The travel speed of 500 mm/min and powder flow of 5g/min were remained constant. The powder efficiency of the 750W optimized parameters is 53%.

<figure><img src="image_275.png" /><figcaption>Figure 10. Microscope image of optimized weld beads with (a) a lower response (650W) and (b) an upper response (750W) based on Table 5.</figcaption></figure>

<table><caption>Table 5. Optimized weld bead parameter with a lower response and upper response.</caption><thead><tr><th>Laser Power, W</th><th>Speed, mm/min</th><th>Powder Flow, g/min</th><th>Width, mm</th><th>Height, mm</th><th>Aspect Ratio</th><th>Dilution, %</th></tr></thead><tbody><tr><td>650</td><td>500</td><td>5</td><td>1.628</td><td>0.440</td><td>3.70</td><td>2.1</td></tr><tr><td>750</td><td>500</td><td>5</td><td>1.858</td><td>0.514</td><td>3.62</td><td>9.8</td></tr></tbody></table>

### Determination of suitable Coating Parameters

The coating application was carried out with 5 stringer weld tracks and optimized by lower response parameters due to consider lack of fusion between the tracks. By optimization of the coating with three different hatch distances, it was observed that a narrow hatch distance with 40% of the weld width causes lack of fusion between tracks. The reason for this, if the focus position is not maintained, the steep flanks of the tracks can cause the laser beam to be shadowed during side-by-side welding with track overlap, which can result in bonding discontinues between adjacent tracks [9]. 50% and 60% hatch distances do not cause lack of fusion, however, the linearity of the surface of the cross-section between 1. track and 5. track is higher in 60% hatch distance than 50% (Figure 11). Therefore, the optimum hatch distance for coating is 60% of the weld width for coating
<table><caption>Table 4 Training and test results of the RSM-Machine learning hybrid approach.</caption><thead><tr><th rowspan="2">Parameter</th><th>RMSE</th><th>R<sup>2</sup></th><th>MSE</th><th>MAE</th></tr><tr><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th><th>Training/Test</th></tr></thead><tbody><tr><td>Weld width</td><td>0.09433/0.11472</td><td>0.94/0.80</td><td>0.008898/0.01316</td><td>0.059998/0.090264</td></tr><tr><td>Weld height</td><td>0.10487/0.062279</td><td>0.87/0.80</td><td>0.010997/0.074023</td><td>0.074023/0.047107</td></tr><tr><td>Aspect ratio</td><td>1.569/0.43455</td><td>0.66/0.75</td><td>2.4614/0.18883</td><td>0.75323/0.36265</td></tr><tr><td>Dilution</td><td>10.93/7.5793</td><td>0.81/0.77</td><td>119.47/57.446</td><td>6.7143/6.428</td></tr></tbody></table>

The parameters were optimized to achieve defect-free deposition with minimized dilution, causing minimized heat input. The chosen optimized input parameters by machine learning are for a lower response 650 W laser power, 500 mm/min travel speed, and 5 g/min powder flow which gives 1.628 mm weld width, 0.440 mm weld height, 3.70 aspect ratio and 2.1% dilution; for an upper response 750 W laser power, 500 mm/min travel speed and 5 g/min powder flow which gives 1.858 mm weld width, 0.514 mm weld height, 3.62 aspect ratio and 9.8% dilution.

The optimization was performed by machine learning. To minimize the process complexity, the optimum parameter range was determined by adjusting only laser power. Table 5 and Figure 10 show the optimized weld bead parameters and cross-section of the weld beads. As a lower response, a laser power with 650W, and an upper response a laser power with 750W were chosen. The travel speed of 500 mm/min and powder flow of 5g/min were remained constant. The powder efficiency of the 750W optimized parameters is 53%.

<figure><img src="image_281.png" /><figcaption>Figure 10. Microscope image of optimized weld beads with (a) a lower response (650W) and (b) an upper response (750W) based on Table 5.</figcaption></figure>

<table><caption>Table 5. Optimized weld bead parameter with a lower response and upper response.</caption><thead><tr><th>Laser Power, W</th><th>Speed, mm/min</th><th>Powder Flow, g/min</th><th>Width, mm</th><th>Height, mm</th><th>Aspect Ratio</th><th>Dilution, %</th></tr></thead><tbody><tr><td>650</td><td>500</td><td>5</td><td>1.628</td><td>0.440</td><td>3.70</td><td>2.1</td></tr><tr><td>750</td><td>500</td><td>5</td><td>1.858</td><td>0.514</td><td>3.62</td><td>9.8</td></tr></tbody></table>

### Determination of suitable Coating Parameters

The coating application was carried out with 5 stringer weld tracks and optimized by lower response parameters due to consider lack of fusion between the tracks. By optimization of the coating with three different hatch distances, it was observed that a narrow hatch distance with 40% of the weld width causes lack of fusion between tracks. The reason for this, if the focus position is not maintained, the steep flanks of the tracks can cause the laser beam to be shadowed during side-by-side welding with track overlap, which can result in bonding discontinues between adjacent tracks [9]. 50% and 60% hatch distances do not cause lack of fusion, however, the linearity of the surface of the cross-section between 1. track and 5. track is higher in 60% hatch distance than 50% (Figure 11). Therefore, the optimum hatch distance for coating is 60% of the weld width for coating
application. The average coating height of optimum hatch distance was measured as 0.74 mm which will be used for z axis optimization.

<figure><img src="image_276.png" /><figcaption>Figure 11. Cross-sections of the coating with hatch distance of (a) 40%, (b) 50% and (c) 60% of the weld width.</figcaption></figure>

## Optimization of Additive Manufacturing

The optimization of additive manufacturing samples was performed as a basic geometry in the form of a cylinder/cube. Figure 12 represents the cross-section of the basic shaped additive manufacturing samples. They were manufactured using optimized parameters that included a laser power of 650W. Each layer was built with a 0.7 mm z-axis offset distance employing a scaled and bidirectional scanning strategy (Figure 12a, Figure 12b). Instead of the upper part, the bottom part of both samples exhibits lack of fusion same as coating section (Figure 11a). The reason for not occurrence of lack of fusion in the upper layers is the increase in penetration as a result of heat accumulation. Increasing the layer height to 0.8 mm and using a bidirectional scanning strategy produced no bonding defects in the cube sample. (Figure 12c).

<figure><img src="image_277.png" /><figcaption>Figure 12. Cross-section of the basic shaped additive manufacturing samples. (a) 0.7 mm z-axis for each layer with scaled scanning strategy, (b) 0.7 mm z-axis for each layer with bidirectional scanning strategy, and (c) 0.8 mm z-axis for each layer with bidirectional scanning strategy.</figcaption></figure>

## Prototyping of the component

Figure 13 illustrates the additive manufacturing of the component. The prototyping process began with a digital model of the desired object, typically designed with CAD. Geometric adjustments of CAD can be performed at this stage due to avoid some design problems such as overhang. As a next step, the CAD model was processed using CAM software to plan the toolpath and simulate it with the optimized parameters. The blue lines represent the path planning with layers of deposited material. The actual production was carried out using the L-DED process. The laser was used to create a melt pool on the substrate material where powder material is deposited. The laser traveled along the planned path, transforming the powder into a final solid structure layer by layer. Between the lower (650W) and upper (750W) response parameters with an average 0.8 mm Z axis offset distance were used. The laser power is gradually reduced at the upper layers to prevent overheating.

The final product was machined using the CNC (Computer Numerical Control) milling process, a subtractive manufacturing process. The oversized material was removed from the additive manufactured near net-shaped sample using a cutting tool to achieve the desired shape and surface. The final product does not show any defects on the surface caused by porosity and lack of fusion.
application. The average coating height of optimum hatch distance was measured as 0.74 mm which will be used for z axis optimization.

<figure><img src="image_282.png" /><figcaption>Figure 11. Cross-sections of the coating with hatch distance of (a) 40%, (b) 50% and (c) 60% of the weld width.</figcaption></figure>

## Optimization of Additive Manufacturing

The optimization of additive manufacturing samples was performed as a basic geometry in the form of a cylinder/cube. Figure 12 represents the cross-section of the basic shaped additive manufacturing samples. They were manufactured using optimized parameters that included a laser power of 650W. Each layer was built with a 0.7 mm z-axis offset distance employing a scaled and bidirectional scanning strategy (Figure 12a, Figure 12b). Instead of the upper part, the bottom part of both samples exhibits lack of fusion same as coating section (Figure 11a). The reason for not occurrence of lack of fusion in the upper layers is the increase in penetration as a result of heat accumulation. Increasing the layer height to 0.8 mm and using a bidirectional scanning strategy produced no bonding defects in the cube sample. (Figure 12c).

<figure><img src="image_283.png" /><figcaption>Figure 12. Cross-section of the basic shaped additive manufacturing samples. (a) 0.7 mm z-axis for each layer with scaled scanning strategy, (b) 0.7 mm z-axis for each layer with bidirectional scanning strategy, and (c) 0.8 mm z-axis for each layer with bidirectional scanning strategy.</figcaption></figure>

## Prototyping of the component

Figure 13 illustrates the additive manufacturing of the component. The prototyping process began with a digital model of the desired object, typically designed with CAD. Geometric adjustments of CAD can be performed at this stage due to avoid some design problems such as overhang. As a next step, the CAD model was processed using CAM software to plan the toolpath and simulate it with the optimized parameters. The blue lines represent the path planning with layers of deposited material. The actual production was carried out using the L-DED process. The laser was used to create a melt pool on the substrate material where powder material is deposited. The laser traveled along the planned path, transforming the powder into a final solid structure layer by layer. Between the lower (650W) and upper (750W) response parameters with an average 0.8 mm Z axis offset distance were used. The laser power is gradually reduced at the upper layers to prevent overheating.

The final product was machined using the CNC (Computer Numerical Control) milling process, a subtractive manufacturing process. The oversized material was removed from the additive manufactured near net-shaped sample using a cutting tool to achieve the desired shape and surface. The final product does not show any defects on the surface caused by porosity and lack of fusion.
<figure><img src="image_278.png" /><figcaption>Figure 13. Additive manufacturing of the component.</figcaption></figure>

Figure 14 demonstrates the digitalization and surface evaluation of the component. The color map indicates surface deviation from the targeted CAD part. The scale on the right displays the range of these deviations in millimeters. The red and blue areas identify the oversized and undersized dimensions, respectively. The green area identifies where the component dimensions are close to the design dimensions. The blue area should not appear. The minimum, maximum, and average surface deviation of the additive manufacturing and CAD parts are +0.1 mm, +3.28 mm, and +1.23 mm, respectively. Material consumption decreases for the final production as the average deviation decreases to zero. The final product was achieved by applying the milling process.

<figure><img src="image_279.png" /><figcaption>Figure 14. Digitalization and surface evaluation of the component: a) Alignment of the scanned component with CAD, b) surface deviation of the scanned component.</figcaption></figure>

## Conclusion

This study demonstrates an L-DED approach from initial to final steps for rapid prototyping in additive manufacturing. The optimization part of the methodology has significantly improved the precision and prediction of welding parameters such as laser power, welding speed and powder flow with respect to critical output parameters such as weld bead width, height and dilution. RSM, machine learning and hybrid approaches were utilized and compared. The study focuses on determining optimal L-DED parameters with a minimum number of planned trials involving test data. The regression learner-based ML algorithm provides a robust ability with high R-squared and low MSE values to predict these output metrics with considerable accuracy compared to statistical approach and hybrid approach for test data. The prediction of dilution is less accurate than that of other factors, due to the highly complex behavior of dilution. Based on the optimized parameters, the appropriate coating with hatch distance of 60% of the weld bead was applied and subsequently additive manufacturing with a z-axis distance of 0.8 mm was performed. The evaluation of the near net-shaped part was carried out by digitization and the final product of the aerospace part was prototyped. This approach is a step towards developing rapid prototyping additive manufacturing for future digital twin applications by increasing the precision and reducing the complexity of the L-DED process for aerospace industry.
<figure><img src="image_284.png" /><figcaption>Figure 13. Additive manufacturing of the component.</figcaption></figure>

Figure 14 demonstrates the digitalization and surface evaluation of the component. The color map indicates surface deviation from the targeted CAD part. The scale on the right displays the range of these deviations in millimeters. The red and blue areas identify the oversized and undersized dimensions, respectively. The green area identifies where the component dimensions are close to the design dimensions. The blue area should not appear. The minimum, maximum, and average surface deviation of the additive manufacturing and CAD parts are +0.1 mm, +3.28 mm, and +1.23 mm, respectively. Material consumption decreases for the final production as the average deviation decreases to zero. The final product was achieved by applying the milling process.

<figure><img src="image_285.png" /><figcaption>Figure 14. Digitalization and surface evaluation of the component: a) Alignment of the scanned component with CAD, b) surface deviation of the scanned component.</figcaption></figure>

## Conclusion

This study demonstrates an L-DED approach from initial to final steps for rapid prototyping in additive manufacturing. The optimization part of the methodology has significantly improved the precision and prediction of welding parameters such as laser power, welding speed and powder flow with respect to critical output parameters such as weld bead width, height and dilution. RSM, machine learning and hybrid approaches were utilized and compared. The study focuses on determining optimal L-DED parameters with a minimum number of planned trials involving test data. The regression learner-based ML algorithm provides a robust ability with high R-squared and low MSE values to predict these output metrics with considerable accuracy compared to statistical approach and hybrid approach for test data. The prediction of dilution is less accurate than that of other factors, due to the highly complex behavior of dilution. Based on the optimized parameters, the appropriate coating with hatch distance of 60% of the weld bead was applied and subsequently additive manufacturing with a z-axis distance of 0.8 mm was performed. The evaluation of the near net-shaped part was carried out by digitization and the final product of the aerospace part was prototyped. This approach is a step towards developing rapid prototyping additive manufacturing for future digital twin applications by increasing the precision and reducing the complexity of the L-DED process for aerospace industry.
## Acknowledgements

The authors thank Avantika Jhanji for assisting with the GOM measurement.

## Funding

This work was supported by the European Union and the European Regional Development Fund and was carried out in the framework of the project GABRIEL - Erforschung GAnzheitlicher, HyBrid-ElektRIscher AntriebskomponEnten für die Luftfahrt (ProFIT Brandenburg, Application number: 80257607).

## References

[1] G. Piscopo and L. Iuliano, "Current research and industrial application of laser powder directed energy deposition," *Int J Adv Manuf Technol*, vol. 119, 11-12, pp. 6893-6917, 2022. https://doi.org/10.1007/s00170-021-08596-w

[2] I. Z. Era, M. A. Farahani, T. Wuest, and Z. Liu, "Machine learning in Directed Energy Deposition (DED) additive manufacturing: A state-of-the-art review," *Manufacturing Letters*, vol. 35, pp. 689-700, 2023. https://doi.org/10.1016/j.mfglet.2023.08.079

[3] A. Abdulraheem, R. Abdullah Arshah, and H. Qin, "Evaluating the Effect of Dataset Size on Predictive Model Using Supervised Learning Technique," *International Journal of Software Engineering & Computer Sciences*, vol. 1, pp. 75-84, 2015. https://doi.org/10.15282/ijsecs.1.2015.6.0006.

[4] Y. Zhu, Z. Yuan, M. M. Khonsari, S. Zhao, and H. Yang, "Small-Dataset Machine Learning for Wear Prediction of Laser Powder Bed Fusion Fabricated Steel," *Journal of Tribology*, vol. 145, no. 9, p. 91101, 2023. https://doi.org/10.1115/1.4062368

[5] X. Zhu, F. Jiang, C. Guo, de Xu, Z. Wang, and G. Jiang, "Surface morphology inspection for directed energy deposition using small dataset with transfer learning," *Journal of Manufacturing Processes*, vol. 93, pp. 101-115, 2023. https://doi.org/10.1016/j.jmapro.2023.03.016

[6] J. Xiong, G. Zhang, J. Hu, and L. Wu, "Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis," *Journal of Intelligent Manufacturing*, vol. 25, no. 1, pp. 157-163, 2014. https://doi.org/10.1007/s10845-012-0682-1

[7] PERNA Alessia Serena, CARRINO Luigi, CITARELLA Alessia Auriemma, De MARCO Fabiola, Di BIASI Luigi, TORTORA Genoveffa, VISCUSI Antonio, A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates, Materials Research Proceedings, Vol. 28, pp 57-64, 2023. https://doi.org/10.21741/9781644902479-7

[8] Raphaela Rauter, "Laserauftragschweißen von Wolframkarbidschichten in Nickelbasismatrizen zur Herstellung verschleißfester Beschichtungen von Warmformwerkzeugen," Technischen Universität Graz, 2016.

[9] J. Witzel, "Qualifizierung des Laserstrahl-Auftragschweißens zur generativen Fertigung von Luftfahrtkomponenten," Von der Fakultät für Maschinenwesen der Rheinisch-Westfälischen Technischen Hochschule Aachen zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften genehmigte Dissertation, Dec. 2014.
## Acknowledgements

The authors thank Avantika Jhanji for assisting with the GOM measurement.

## Funding

This work was supported by the European Union and the European Regional Development Fund and was carried out in the framework of the project GABRIEL - Erforschung GAnzheitlicher, HyBrid-ElektRIscher AntriebskomponEnten für die Luftfahrt (ProFIT Brandenburg, Application number: 80257607).

## References

[1] G. Piscopo and L. Iuliano, "Current research and industrial application of laser powder directed energy deposition," *Int J Adv Manuf Technol*, vol. 119, 11-12, pp. 6893-6917, 2022. https://doi.org/10.1007/s00170-021-08596-w

[2] I. Z. Era, M. A. Farahani, T. Wuest, and Z. Liu, "Machine learning in Directed Energy Deposition (DED) additive manufacturing: A state-of-the-art review," *Manufacturing Letters*, vol. 35, pp. 689-700, 2023. https://doi.org/10.1016/j.mfglet.2023.08.079

[3] A. Abdulraheem, R. Abdullah Arshah, and H. Qin, "Evaluating the Effect of Dataset Size on Predictive Model Using Supervised Learning Technique," *International Journal of Software Engineering & Computer Sciences*, vol. 1, pp. 75-84, 2015. https://doi.org/10.15282/ijsecs.1.2015.6.0006.

[4] Y. Zhu, Z. Yuan, M. M. Khonsari, S. Zhao, and H. Yang, "Small-Dataset Machine Learning for Wear Prediction of Laser Powder Bed Fusion Fabricated Steel," *Journal of Tribology*, vol. 145, no. 9, p. 91101, 2023. https://doi.org/10.1115/1.4062368

[5] X. Zhu, F. Jiang, C. Guo, de Xu, Z. Wang, and G. Jiang, "Surface morphology inspection for directed energy deposition using small dataset with transfer learning," *Journal of Manufacturing Processes*, vol. 93, pp. 101-115, 2023. https://doi.org/10.1016/j.jmapro.2023.03.016

[6] J. Xiong, G. Zhang, J. Hu, and L. Wu, "Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis," *Journal of Intelligent Manufacturing*, vol. 25, no. 1, pp. 157-163, 2014. https://doi.org/10.1007/s10845-012-0682-1

[7] PERNA Alessia Serena, CARRINO Luigi, CITARELLA Alessia Auriemma, De MARCO Fabiola, Di BIASI Luigi, TORTORA Genoveffa, VISCUSI Antonio, A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates, Materials Research Proceedings, Vol. 28, pp 57-64, 2023. https://doi.org/10.21741/9781644902479-7

[8] Raphaela Rauter, "Laserauftragschweißen von Wolframkarbidschichten in Nickelbasismatrizen zur Herstellung verschleißfester Beschichtungen von Warmformwerkzeugen," Technischen Universität Graz, 2016.

[9] J. Witzel, "Qualifizierung des Laserstrahl-Auftragschweißens zur generativen Fertigung von Luftfahrtkomponenten," Von der Fakultät für Maschinenwesen der Rheinisch-Westfälischen Technischen Hochschule Aachen zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften genehmigte Dissertation, Dec. 2014.
# Effect of infill percentage and pattern on compressive behavior of FDM-printed GF-CF PA6 composites

ANDREOZZI Marina¹,ª, BIANCHI Iacopo¹,ᵇ, FORCELLESE Archimede¹,ᶜ, MANCIA Tommaso¹,ᵈ*, MIGNANELLI Chiara¹,ᵉ, SIMONCINI Michela¹,ᵉ

¹Università Politecnica delle Marche, Via Brecce Bianche 12, 60123 Ancona, Italy

ªm.andreozzi@pm.univpm.it, ᵇi.bianchi@pm.univpm.it, ᶜa.forcellese@staff.univpm.it,

ᵈ*t.mancia@staff.univpm.it, ᵈc.mignanelli@pm.univpm.it, ᵉm.simoncini@staff.univpm.it

**Keywords:** FDM, Infill Pattern, Infill Percentage, Compression Test

**Abstract.** The present paper aims to assess the effect of different infill percentages and patterns on the compressive mechanical properties of specimens in Polyamide PA6 reinforced with 20% glass fibers (GF) and 10% carbon fibers (CF) printed using the Fused Deposition Modeling (FDM) technology. According to the ASTM D695-15 standard, cylindrical specimens were designed and processed through slicing software, configuring infill percentages and patterns. Three different typologies of infill pattern and two infill percentages were considered: a 100% grid infill, a 50% grid infill, a 100% concentric infill and a 50% honeycomb infill were printed. Then, compression tests were performed at room temperature to evaluate the properties of the different specimens. The comparison between the stress-strain compression curves has shown that the infill percentages and patterns significantly affect the mechanical compression properties of 3D printed components.

**Introduction**

In recent years, Additive Manufacturing (AM) has gained increasing relevance in different manufacturing sectors due to its capability to produce lightweight components with complex geometries, which are challenging to achieve through traditional techniques [1,2]. The AM approach also offers high efficiency, reducing raw materials costs, labor, and material waste, enabling on-demand manufacturing and subsequently reducing storage and logistical costs [3,4]. Furthermore, additive manufacturing promotes rapid and iterative design, allowing engineers and designers to swiftly test and refine their prototypes. This technology also holds potential to reduce energy demand, production process complexity, manufacturing lead times, and time to market. This industrial progress is reshaping production paradigms in different sectors, from aerospace, to automotive, biomedical, sports, and others [5].

Among various AM methodologies, Fused Deposition Modeling (FDM) has emerged as one of the most versatile and widely adopted techniques [6]. Its popularity derives from its simplicity, user-friendliness, and especially its extensive material flexibility, allowing the printing of a wide range of materials. Traditionally, FDM-printed parts primarily employ polymers, yet there is a growing interest in utilizing high-performance composite materials. Composites are produced by combining two or more constituent materials, characterized by dissimilar chemical or physical properties, in order to obtain a material with enhanced properties with respect to the individual elements. Specifically, fiber-reinforced polymer (FRP) composites, consisting of fibers, as reinforcing phase, embedded into a polymeric matrix, offer superior mechanical properties as compared with traditional metallic materials, such as higher tensile strength-weight ratio and modulus, making them suitable for many structural applications. Such materials can be also 3D printed using the FDM technology. Different studies have demonstrated that FDM process allows obtaining a significant fiber alignment in the longitudinal direction when short fiber-reinforced polymers are printed by means of a material extrusion-based process [7,8]. Such result demonstrates that the design of the printing process and, consequently, the deposition direction

<figure><img src="image_280.png" /></figure>
# Effect of infill percentage and pattern on compressive behavior of FDM-printed GF-CF PA6 composites

ANDREOZZI Marina¹,ª, BIANCHI Iacopo¹,ᵇ, FORCELLESE Archimede¹,ᶜ, MANCIA Tommaso¹,ᵈ*, MIGNANELLI Chiara¹,ᵉ, SIMONCINI Michela¹,ᵉ

¹Università Politecnica delle Marche, Via Brecce Bianche 12, 60123 Ancona, Italy

ªm.andreozzi@pm.univpm.it, ᵇi.bianchi@pm.univpm.it, ᶜa.forcellese@staff.univpm.it,

ᵈ*t.mancia@staff.univpm.it, ᵈc.mignanelli@pm.univpm.it, ᵉm.simoncini@staff.univpm.it

**Keywords:** FDM, Infill Pattern, Infill Percentage, Compression Test

**Abstract.** The present paper aims to assess the effect of different infill percentages and patterns on the compressive mechanical properties of specimens in Polyamide PA6 reinforced with 20% glass fibers (GF) and 10% carbon fibers (CF) printed using the Fused Deposition Modeling (FDM) technology. According to the ASTM D695-15 standard, cylindrical specimens were designed and processed through slicing software, configuring infill percentages and patterns. Three different typologies of infill pattern and two infill percentages were considered: a 100% grid infill, a 50% grid infill, a 100% concentric infill and a 50% honeycomb infill were printed. Then, compression tests were performed at room temperature to evaluate the properties of the different specimens. The comparison between the stress-strain compression curves has shown that the infill percentages and patterns significantly affect the mechanical compression properties of 3D printed components.

**Introduction**

In recent years, Additive Manufacturing (AM) has gained increasing relevance in different manufacturing sectors due to its capability to produce lightweight components with complex geometries, which are challenging to achieve through traditional techniques [1,2]. The AM approach also offers high efficiency, reducing raw materials costs, labor, and material waste, enabling on-demand manufacturing and subsequently reducing storage and logistical costs [3,4]. Furthermore, additive manufacturing promotes rapid and iterative design, allowing engineers and designers to swiftly test and refine their prototypes. This technology also holds potential to reduce energy demand, production process complexity, manufacturing lead times, and time to market. This industrial progress is reshaping production paradigms in different sectors, from aerospace, to automotive, biomedical, sports, and others [5].

Among various AM methodologies, Fused Deposition Modeling (FDM) has emerged as one of the most versatile and widely adopted techniques [6]. Its popularity derives from its simplicity, user-friendliness, and especially its extensive material flexibility, allowing the printing of a wide range of materials. Traditionally, FDM-printed parts primarily employ polymers, yet there is a growing interest in utilizing high-performance composite materials. Composites are produced by combining two or more constituent materials, characterized by dissimilar chemical or physical properties, in order to obtain a material with enhanced properties with respect to the individual elements. Specifically, fiber-reinforced polymer (FRP) composites, consisting of fibers, as reinforcing phase, embedded into a polymeric matrix, offer superior mechanical properties as compared with traditional metallic materials, such as higher tensile strength-weight ratio and modulus, making them suitable for many structural applications. Such materials can be also 3D printed using the FDM technology. Different studies have demonstrated that FDM process allows obtaining a significant fiber alignment in the longitudinal direction when short fiber-reinforced polymers are printed by means of a material extrusion-based process [7,8]. Such result demonstrates that the design of the printing process and, consequently, the deposition direction

<figure><img src="image_286.png" /></figure>
can significantly affect the mechanical properties of the 3D printed components in short FRP composites. The growing advancement of FDM technology in composite materials leads to the need of characterizing the printed materials performances, and to understand the effect of the process printing parameters on the mechanical properties of the additive manufactured components [9–12]. Current research is mainly focused on the influence of printing parameters, such as print speed, infill percentage, temperature, layer height, and infill geometry, on tensile, flexural and impact resistance behaviours of FRP components printed using FDM process. Lee & Wu [13] investigated the influence of printing parameters on the mechanical properties of carbon fiber reinforces PLA, identifying the printing bed temperature as the most significant parameter on tensile strength. They also demonstrated that parts printed at a 45° orientation exhibit superior mechanical properties as compared to those printed at 90°. The increase in infill density enhances strength. The impact of nozzle and print bed temperature, print speed, and layer thickness on the tensile and flexural behavior of CF/PEEK and GF/PEEK samples was studied by Wang et al. [14], highlighting that the increase in print speed and layer thickness negatively affect the mechanical properties. Kovan et al. [15] analysed the effect of orientation angle. Peng et al. [16] discovered that CF/PA6, printed along the tensile loading direction, exhibits higher tensile properties as compared to orientations at 45°/–45° and 90°, indicating strong anisotropy in mechanical properties linked to printing orientation. From the study conducted by Ramalingam et al. [17] on the effect of infill density (30%, 40%, and 50%) with various infill patterns (hexagonal, triangular, and rectangular) on 3D printed onyx glass fiber composites subjected to impact tests, it was found that there is an increase in energy absorption rate in the hexagonal pattern and 50% infill density model.

Unfortunately, few studies on the influence of process parameters on the compressive mechanical properties of 3D printed short fiber-reinforced composites are available in scientific literature. Fisher et al. [18] investigated that nylon samples reinforced with 14% by weight of short carbon fibers is characterized by an increase in compression strength by up to 130% when the printing path aligns parallel to the loading direction. Mei et al. [19] demonstrated that the yield load and elastic modulus of PLA and CF/PLA printed specimens increase with decreasing layer thickness.

In this framework, the effect of printing parameters on compressive mechanical properties needs to be further investigated. To this purpose, the present study focuses on optimizing the mechanical properties of polyamide PA6 reinforced with 20% glass fibers (GF) and 10% carbon fibers (CF) composite material. The influence of different filling percentages and patterns on the compression strength and on specific compression strength of cylindrical samples obtained through FDM was investigated. This approach aims to provide a deeper understanding of the relationships between filling geometries and mechanical performances, crucial for optimizing of the utilization of such composite materials in different industrial applications. Furthermore, this investigation is significant for the optimization of the balance between weight, durability, and production costs to meet the specific needs of industrial applications.

## Materials and methods

### 3D printer and material

The composite material investigated in this study is the polyamide PA6 reinforced with 20% short glass fiber and 10% short carbon fibers (PA6 CF-GF), provided by Tagin3D company in the form of a 1.75 mm diameter filament.

As reported in the technical datasheet, the material is characterized by a density of 1.27 g/cm³, and mechanical properties higher than a not reinforced PA6.

Compression cylindrical specimens were sized according to ASTM D695 standard with a diameter of 12.7 mm and a height of 25.4 mm. The model was designed using CAD software and then exported in STL format. It was imported into Simplify 3D slicing software, through which the main printing parameters, such as infill percentage and pattern, were defined.
can significantly affect the mechanical properties of the 3D printed components in short FRP composites. The growing advancement of FDM technology in composite materials leads to the need of characterizing the printed materials performances, and to understand the effect of the process printing parameters on the mechanical properties of the additive manufactured components [9–12]. Current research is mainly focused on the influence of printing parameters, such as print speed, infill percentage, temperature, layer height, and infill geometry, on tensile, flexural and impact resistance behaviours of FRP components printed using FDM process. Lee & Wu [13] investigated the influence of printing parameters on the mechanical properties of carbon fiber reinforces PLA, identifying the printing bed temperature as the most significant parameter on tensile strength. They also demonstrated that parts printed at a 45° orientation exhibit superior mechanical properties as compared to those printed at 90°. The increase in infill density enhances strength. The impact of nozzle and print bed temperature, print speed, and layer thickness on the tensile and flexural behavior of CF/PEEK and GF/PEEK samples was studied by Wang et al. [14], highlighting that the increase in print speed and layer thickness negatively affect the mechanical properties. Kovan et al. [15] analysed the effect of orientation angle. Peng et al. [16] discovered that CF/PA6, printed along the tensile loading direction, exhibits higher tensile properties as compared to orientations at 45°/–45° and 90°, indicating strong anisotropy in mechanical properties linked to printing orientation. From the study conducted by Ramalingam et al. [17] on the effect of infill density (30%, 40%, and 50%) with various infill patterns (hexagonal, triangular, and rectangular) on 3D printed onyx glass fiber composites subjected to impact tests, it was found that there is an increase in energy absorption rate in the hexagonal pattern and 50% infill density model.

Unfortunately, few studies on the influence of process parameters on the compressive mechanical properties of 3D printed short fiber-reinforced composites are available in scientific literature. Fisher et al. [18] investigated that nylon samples reinforced with 14% by weight of short carbon fibers is characterized by an increase in compression strength by up to 130% when the printing path aligns parallel to the loading direction. Mei et al. [19] demonstrated that the yield load and elastic modulus of PLA and CF/PLA printed specimens increase with decreasing layer thickness.

In this framework, the effect of printing parameters on compressive mechanical properties needs to be further investigated. To this purpose, the present study focuses on optimizing the mechanical properties of polyamide PA6 reinforced with 20% glass fibers (GF) and 10% carbon fibers (CF) composite material. The influence of different filling percentages and patterns on the compression strength and on specific compression strength of cylindrical samples obtained through FDM was investigated. This approach aims to provide a deeper understanding of the relationships between filling geometries and mechanical performances, crucial for optimizing of the utilization of such composite materials in different industrial applications. Furthermore, this investigation is significant for the optimization of the balance between weight, durability, and production costs to meet the specific needs of industrial applications.

## Materials and methods

### 3D printer and material

The composite material investigated in this study is the polyamide PA6 reinforced with 20% short glass fiber and 10% short carbon fibers (PA6 CF-GF), provided by Tagin3D company in the form of a 1.75 mm diameter filament.

As reported in the technical datasheet, the material is characterized by a density of 1.27 g/cm³, and mechanical properties higher than a not reinforced PA6.

Compression cylindrical specimens were sized according to ASTM D695 standard with a diameter of 12.7 mm and a height of 25.4 mm. The model was designed using CAD software and then exported in STL format. It was imported into Simplify 3D slicing software, through which the main printing parameters, such as infill percentage and pattern, were defined.
The Roboze One* 400 3D, equipped with Fused Deposition Modeling (FDM) technology, was used to produce the compression cylinders. Extrusion of the PA/6 CF-GF filament was carried out at a temperature of 250°C, using a nozzle of 0.4 mm in diameter. The temperature of the building plate was set at 70°C to enhance the adhesion of the specimens. Table 1 summarizes the main processes parameters.

<table><caption>Table 1: Printing parameters.</caption><thead><tr><th>Processes parameters</th><th></th></tr></thead><tbody><tr><td>Nozzle diameter</td><td>0.4 mm</td></tr><tr><td>Extrusion temperature</td><td>250 °C</td></tr><tr><td>Printing plate temperature</td><td>70 °C</td></tr><tr><td>Average printing speed</td><td>50 mm/s</td></tr><tr><td>Layer height</td><td>0.25 mm</td></tr><tr><td>Outer layer</td><td>2 (0.8mm)</td></tr></tbody></table>

Four different configurations were analyzed, obtained from the combination of three different types of infill patterns with two fill percentages: 100% concentric infill, 100% grid infill, 50% grid infill, and 50% honeycomb (Figure 1). For grid and honeycomb infill pattern specimens, 3D printing was performed with deposition layers alternating between raster angle of +- 45°, while a straight external infill (0°, 90°) was chosen for first and last 2 layers (external infill was not necessary in the concentric configuration).

Due to the high hygroscopic nature of polyamide, the filament was preheated in an oven for four hours at 100°C, 15°C below the softening temperature, to ensure better print quality and reduce void formation caused by moisture absorption. Furthermore, during the printing phase, the filament spool was stored in a dedicated dryer at a temperature of 50°C. To enhance adhesion, the print bed was coated with an adhesive film (blue tape).

<figure><img src="image_281.png" /><figcaption>Figure 1: Representation of the four configurations studied, and comparison between the slicing images and the real sections of the 3D printed specimens.</figcaption></figure>
The Roboze One* 400 3D, equipped with Fused Deposition Modeling (FDM) technology, was used to produce the compression cylinders. Extrusion of the PA/6 CF-GF filament was carried out at a temperature of 250°C, using a nozzle of 0.4 mm in diameter. The temperature of the building plate was set at 70°C to enhance the adhesion of the specimens. Table 1 summarizes the main processes parameters.

<table><caption>Table 1: Printing parameters.</caption><thead><tr><th>Processes parameters</th><th></th></tr></thead><tbody><tr><td>Nozzle diameter</td><td>0.4 mm</td></tr><tr><td>Extrusion temperature</td><td>250 °C</td></tr><tr><td>Printing plate temperature</td><td>70 °C</td></tr><tr><td>Average printing speed</td><td>50 mm/s</td></tr><tr><td>Layer height</td><td>0.25 mm</td></tr><tr><td>Outer layer</td><td>2 (0.8mm)</td></tr></tbody></table>

Four different configurations were analyzed, obtained from the combination of three different types of infill patterns with two fill percentages: 100% concentric infill, 100% grid infill, 50% grid infill, and 50% honeycomb (Figure 1). For grid and honeycomb infill pattern specimens, 3D printing was performed with deposition layers alternating between raster angle of +- 45°, while a straight external infill (0°, 90°) was chosen for first and last 2 layers (external infill was not necessary in the concentric configuration).

Due to the high hygroscopic nature of polyamide, the filament was preheated in an oven for four hours at 100°C, 15°C below the softening temperature, to ensure better print quality and reduce void formation caused by moisture absorption. Furthermore, during the printing phase, the filament spool was stored in a dedicated dryer at a temperature of 50°C. To enhance adhesion, the print bed was coated with an adhesive film (blue tape).

<figure><img src="image_287.png" /><figcaption>Figure 1: Representation of the four configurations studied, and comparison between the slicing images and the real sections of the 3D printed specimens.</figcaption></figure>
Compression test

Compression tests of 3D printed cylindrical specimens in polyamide PA6 reinforced with short carbon and glass fibers were performed using the MTS 810 servo-hydraulic testing machine, at a speed of 0.5 mm/s, following ASTM D695 standards. Load and displacement along the loading direction were captured using a load cell and a variable linear displacement transducer (LVDT). The results obtained by compression tests were plotted as stress-strain curves. To ensure result repeatability, five compression tests were performed for each specimen configuration.

The specific strength of the material was calculated as ratio between the strength of material and its density. To this purpose, before the compression tests, the average masses of the specimens were measured using an analytical balance; similarly, the average size of each specimen was measured. The density of each structure was calculated as ratio between the average mass and volume.

Results and discussion

Figure 2 shows nominal compression stress-strain curves obtained by short glass and carbon fiber reinforced polyamide specimens with the different infill patterns and percentages, produced through FDM printing process. Irrespective of the infill percentage and pattern, it can be observed that the typical compression stress-strain curve is characterized by an increase in compression stress with strain until a peak value. Then, the stress tends to decrease with rising strain until reaching a minimum value at which the stress tends to further rise. By comparing the two 100% configurations, no differences in terms of maximum compression strength and strain at the peak can be observed. However, a reduction of about 19% in the compression modulus appears when transitioning from 100% grid infill to the 100% concentric infill. There is no substantial difference in the yield point. Analyzing the results obtained from the 50% infill samples, the configuration with the highest compression strength is the grid arrangement. However, the maximum peak strain is about 50% honeycomb infill, which is only 2.3% lower than that of the 100% grid specimens.

<figure><img src="image_282.png" /><figcaption>Figure 2: Effect of the infill percentage and pattern on typical nominal compression stress-strain curves of 3D printed specimens in polyamide PA6 reinforced with 20% short glass fiber and 10% short carbon fibers.</figcaption></figure>

As far as the infill percentage is concerned, it can be observed that the strength levels reached by specimens printed at 100% infill are higher than those obtained by specimens printed at 50% infill. As a matter of fact, a reduction in compression strength and compression modulus of about 57% and 60%, respectively, can be observed as the infill percentage decreases from 100% to 50%.
Compression test

Compression tests of 3D printed cylindrical specimens in polyamide PA6 reinforced with short carbon and glass fibers were performed using the MTS 810 servo-hydraulic testing machine, at a speed of 0.5 mm/s, following ASTM D695 standards. Load and displacement along the loading direction were captured using a load cell and a variable linear displacement transducer (LVDT). The results obtained by compression tests were plotted as stress-strain curves. To ensure result repeatability, five compression tests were performed for each specimen configuration.

The specific strength of the material was calculated as ratio between the strength of material and its density. To this purpose, before the compression tests, the average masses of the specimens were measured using an analytical balance; similarly, the average size of each specimen was measured. The density of each structure was calculated as ratio between the average mass and volume.

Results and discussion

Figure 2 shows nominal compression stress-strain curves obtained by short glass and carbon fiber reinforced polyamide specimens with the different infill patterns and percentages, produced through FDM printing process. Irrespective of the infill percentage and pattern, it can be observed that the typical compression stress-strain curve is characterized by an increase in compression stress with strain until a peak value. Then, the stress tends to decrease with rising strain until reaching a minimum value at which the stress tends to further rise. By comparing the two 100% configurations, no differences in terms of maximum compression strength and strain at the peak can be observed. However, a reduction of about 19% in the compression modulus appears when transitioning from 100% grid infill to the 100% concentric infill. There is no substantial difference in the yield point. Analyzing the results obtained from the 50% infill samples, the configuration with the highest compression strength is the grid arrangement. However, the maximum peak strain is about 50% honeycomb infill, which is only 2.3% lower than that of the 100% grid specimens.

<figure><img src="image_288.png" /><figcaption>Figure 2: Effect of the infill percentage and pattern on typical nominal compression stress-strain curves of 3D printed specimens in polyamide PA6 reinforced with 20% short glass fiber and 10% short carbon fibers.</figcaption></figure>

As far as the infill percentage is concerned, it can be observed that the strength levels reached by specimens printed at 100% infill are higher than those obtained by specimens printed at 50% infill. As a matter of fact, a reduction in compression strength and compression modulus of about 57% and 60%, respectively, can be observed as the infill percentage decreases from 100% to 50%.
With regard to the yield stress analysis, the maximum value reached at the 100% grid setting is 61% higher than the minimum value measured in the case with 50% honeycomb infill. The yield point difference between the 50% grid condition and the 50% honeycomb condition, about 5.5 MPa lower than the former case, is also not significant. Table 2 summarizes the main results achieved.

<table><caption>Table 2: Compression results summary (averages, n=5).</caption><thead><tr><th></th><th>Compression Modulus [MPa]</th><th>Compression Strength [MPa]</th><th>Compression Yield (0.2%) [MPa]</th><th>Strain at the peak stress</th></tr></thead><tbody><tr><td>Concentric 100%</td><td>2157.8</td><td>151.2</td><td>53.0</td><td>0.3501</td></tr><tr><td>Grid 100%</td><td>1754.1</td><td>144.8</td><td>58.3</td><td>0.3624</td></tr><tr><td>Grid 50%</td><td>869.2</td><td>72.3</td><td>22.5</td><td>0.3247</td></tr><tr><td>Honeycomb 50%</td><td>653.1</td><td>65.1</td><td>16.1</td><td>0.3542</td></tr></tbody></table>

Figure 3 shows the maximum specific strength values achieved in each configuration. It becomes evident that samples exhibiting the highest specific strength are always those with 100% concentric configuration. By comparing the data gathered from the four studied cases, it emerges that the difference in terms of compression specific strength between the 100% and 50% infill structures is reduced as compared to the results obtained by Figure 2, in which the stress levels were evaluated without considering the density of the 3D printed specimens. This suggests that the advantage in terms of strength with doubling the infill is mitigated by the structure's weight. Specifically, cylindrical specimens with 50% honeycomb infill exhibit a reduction in specific stress compared to concentric cylinders of 37.4%.

<figure><img src="image_283.png" /><figcaption>Figure 3: Effect of the infill percentage and pattern on maximum specific strength of 3D printed specimens in polyamide PA6 reinforced with 20% short glass fiber and 10% short carbon fibers.</figcaption></figure>

Similarly to the results shown in Figure 2, there are no substantial differences in specific strength between the two 100% infill structures. Furthermore, the comparison between specimens with 50% grid infill and 50% honeycomb infill shows that a negligible difference in specific strength appears.
With regard to the yield stress analysis, the maximum value reached at the 100% grid setting is 61% higher than the minimum value measured in the case with 50% honeycomb infill. The yield point difference between the 50% grid condition and the 50% honeycomb condition, about 5.5 MPa lower than the former case, is also not significant. Table 2 summarizes the main results achieved.

<table><caption>Table 2: Compression results summary (averages, n=5).</caption><thead><tr><th></th><th>Compression Modulus [MPa]</th><th>Compression Strength [MPa]</th><th>Compression Yield (0.2%) [MPa]</th><th>Strain at the peak stress</th></tr></thead><tbody><tr><td>Concentric 100%</td><td>2157.8</td><td>151.2</td><td>53.0</td><td>0.3501</td></tr><tr><td>Grid 100%</td><td>1754.1</td><td>144.8</td><td>58.3</td><td>0.3624</td></tr><tr><td>Grid 50%</td><td>869.2</td><td>72.3</td><td>22.5</td><td>0.3247</td></tr><tr><td>Honeycomb 50%</td><td>653.1</td><td>65.1</td><td>16.1</td><td>0.3542</td></tr></tbody></table>

Figure 3 shows the maximum specific strength values achieved in each configuration. It becomes evident that samples exhibiting the highest specific strength are always those with 100% concentric configuration. By comparing the data gathered from the four studied cases, it emerges that the difference in terms of compression specific strength between the 100% and 50% infill structures is reduced as compared to the results obtained by Figure 2, in which the stress levels were evaluated without considering the density of the 3D printed specimens. This suggests that the advantage in terms of strength with doubling the infill is mitigated by the structure's weight. Specifically, cylindrical specimens with 50% honeycomb infill exhibit a reduction in specific stress compared to concentric cylinders of 37.4%.

<figure><img src="image_289.png" /><figcaption>Figure 3: Effect of the infill percentage and pattern on maximum specific strength of 3D printed specimens in polyamide PA6 reinforced with 20% short glass fiber and 10% short carbon fibers.</figcaption></figure>

Similarly to the results shown in Figure 2, there are no substantial differences in specific strength between the two 100% infill structures. Furthermore, the comparison between specimens with 50% grid infill and 50% honeycomb infill shows that a negligible difference in specific strength appears.
## Conclusions

In this paper, the effect of different infill percentages and patterns on the compression behaviour of cylindrical specimens produced in Polyamide PA6 reinforced with 20% glass fibers and 10% carbon fibers, printed using the Fused Deposition Modeling technology, was investigated. Different infill percentages, equal to 100% and 50%, and patterns (grid, concentric, and honeycomb) were chosen.

The main results can be summarized as follows:

* The nominal compression stress-strain curves are significantly affected by the infill percentages, in particular in terms of strength level.

* The difference in specific strength between the 100% and 50% infill structures is less marked.

* The 100% concentric configuration demonstrates the highest specific compression strength. Among the 100% infill configurations, no substantial differences emerge in maximum compression strength. However, a reduction of approximately 19% in the compression modulus is observed between the 100% grid and 100% concentric specimens, with a higher peak deformation observed in the grid configuration.

* The 50% infill configuration shows high variability in mechanical properties depending on the infill geometry.

* Among the 50% infill configurations, the grid pattern exhibits the highest compression strength and a superior modulus. Maximum deformation is observed in the 50% honeycomb configuration, with a value only slightly lower than that of the 100% grid samples.

This study provides a deep understanding of the relationships between infill geometries and mechanical performances, crucial for optimizing the utilization of 3D printed composites in different industrial applications. Further research should focus on advanced characterization of mechanical properties and process parameter optimization to further enhance the performance of these composites in additive manufacturing. Nevertheless, the results offer valuable insights for optimizing the production of 3D printed components for various industrial applications.

## References

[1] M. Attaran, The rise of 3-D printing: The advantages of additive manufacturing over traditional manufacturing, Bus Horiz 60 (2017) 677-688.
https://doi.org/10.1016/J.BUSHOR.2017.05.011

[2] M. Pérez, D. Carou, E.M. Rubio, R. Teti, Current advances in additive manufacturing, Procedia CIRP 88 (2020) 439-444. https://doi.org/10.1016/J.PROCIR.2020.05.076

[3] S. Ford, M. Despeisse, Additive manufacturing and sustainability: an exploratory study of the advantages and challenges, J Clean Prod 137 (2016) 1573-1587.
https://doi.org/10.1016/JJCLEPRO.2016.04.150

[4] A. Bandyopadhyay, K.D. Traxel, M. Lang, M. Juhasz, N. Eliaz, S. Bose, Alloy design via additive manufacturing: Advantages, challenges, applications and perspectives, Materials Today 52 (2022) 207-224. https://doi.org/10.1016/J.MATTOD.2021.11.026

[5] H. Vasudev, H. In, G. Prashar, D. Bhuddhi, · Hitesh Vasudev, · Dharam Bhuddhi, Additive manufacturing: expanding 3D printing horizon in industry 4.0, International Journal on Interactive Design and Manufacturing (IJIDeM) 17 (2023) 2221-2235. https://doi.org/10.1007/s12008-022-00956-4

[6] S.F. Iftekar, A. Aabid, A. Amir, M. Baig, Advancements and Limitations in 3D Printing Materials and Technologies: A Critical Review, Polymers 2023, Vol. 15, Page 2519 15 (2023) 2519. https://doi.org/10.3390/POLYM15112519
## Conclusions

In this paper, the effect of different infill percentages and patterns on the compression behaviour of cylindrical specimens produced in Polyamide PA6 reinforced with 20% glass fibers and 10% carbon fibers, printed using the Fused Deposition Modeling technology, was investigated. Different infill percentages, equal to 100% and 50%, and patterns (grid, concentric, and honeycomb) were chosen.

The main results can be summarized as follows:

* The nominal compression stress-strain curves are significantly affected by the infill percentages, in particular in terms of strength level.

* The difference in specific strength between the 100% and 50% infill structures is less marked.

* The 100% concentric configuration demonstrates the highest specific compression strength. Among the 100% infill configurations, no substantial differences emerge in maximum compression strength. However, a reduction of approximately 19% in the compression modulus is observed between the 100% grid and 100% concentric specimens, with a higher peak deformation observed in the grid configuration.

* The 50% infill configuration shows high variability in mechanical properties depending on the infill geometry.

* Among the 50% infill configurations, the grid pattern exhibits the highest compression strength and a superior modulus. Maximum deformation is observed in the 50% honeycomb configuration, with a value only slightly lower than that of the 100% grid samples.

This study provides a deep understanding of the relationships between infill geometries and mechanical performances, crucial for optimizing the utilization of 3D printed composites in different industrial applications. Further research should focus on advanced characterization of mechanical properties and process parameter optimization to further enhance the performance of these composites in additive manufacturing. Nevertheless, the results offer valuable insights for optimizing the production of 3D printed components for various industrial applications.

## References

[1] M. Attaran, The rise of 3-D printing: The advantages of additive manufacturing over traditional manufacturing, Bus Horiz 60 (2017) 677-688.
https://doi.org/10.1016/J.BUSHOR.2017.05.011

[2] M. Pérez, D. Carou, E.M. Rubio, R. Teti, Current advances in additive manufacturing, Procedia CIRP 88 (2020) 439-444. https://doi.org/10.1016/J.PROCIR.2020.05.076

[3] S. Ford, M. Despeisse, Additive manufacturing and sustainability: an exploratory study of the advantages and challenges, J Clean Prod 137 (2016) 1573-1587.
https://doi.org/10.1016/JJCLEPRO.2016.04.150

[4] A. Bandyopadhyay, K.D. Traxel, M. Lang, M. Juhasz, N. Eliaz, S. Bose, Alloy design via additive manufacturing: Advantages, challenges, applications and perspectives, Materials Today 52 (2022) 207-224. https://doi.org/10.1016/J.MATTOD.2021.11.026

[5] H. Vasudev, H. In, G. Prashar, D. Bhuddhi, · Hitesh Vasudev, · Dharam Bhuddhi, Additive manufacturing: expanding 3D printing horizon in industry 4.0, International Journal on Interactive Design and Manufacturing (IJIDeM) 17 (2023) 2221-2235. https://doi.org/10.1007/s12008-022-00956-4

[6] S.F. Iftekar, A. Aabid, A. Amir, M. Baig, Advancements and Limitations in 3D Printing Materials and Technologies: A Critical Review, Polymers 2023, Vol. 15, Page 2519 15 (2023) 2519. https://doi.org/10.3390/POLYM15112519
[7] B. Brenken, E. Barocio, A. Favaloro, V. Kunc, R.B. Pipes, Fused filament fabrication of fiber-reinforced polymers: A review, Addit Manuf 21 (2018) 1-16.
https://doi.org/10.1016/J ADDMA.2018.01.002

[8] I. Bianchi, A. Forcellese, T. Mancia, M. Simoncini, A. Vita, Process parameters effect on environmental sustainability of composites FFF technology, Materials and Manufacturing Processes 37 (2022) 591-601. https://doi.org/10.1080/10426914.2022.2049300

[9] C. Abeykoon, P. Sri-Amphorn, A. Fernando, Optimization of fused deposition modeling parameters for improved PLA and ABS 3D printed structures, International Journal of Lightweight Materials and Manufacture 3 (2020) 284-297. https://doi.org/10.1016/J.IJLMM.2020.03.003

[10] S. Srinivasan Ganesh Iyer, O. Keles, Effect of raster angle on mechanical properties of 3D printed short carbon fiber reinforced acrylonitrile butadiene styrene, Composites Communications 32 (2022) 101163. https://doi.org/10.1016/J.COCO.2022.101163

[11] H. Al Khawaja, H. Alabdouli, H. Alqaydi, A. Mansour, W. Ahmed, H. Al Jassmi, Investigating the mechanical properties of 3D printed components, 2020 Advances in Science and Engineering Technology International Conferences, ASET 2020 (2020).
https://doi.org/10.1109/ASET48392.2020.9118307

[12] N. Vinoth Babu, N. Venkateshwaran, N. Rajini, S.O. Ismail, F. Mohammad, H.A. Al-Lohedan, S. Suchart, Influence of slicing parameters on surface quality and mechanical properties of 3D-printed CF/PLA composites fabricated by FDM technique, Materials Technology 37 (2022) 1008-1025. https://doi.org/10.1080/10667857.2021.1915056

[13] D. Lee, G.Y. Wu, Parameters Affecting the Mechanical Properties of Three-Dimensional (3D) Printed Carbon Fiber-Reinforced Polylactide Composites, Polymers 2020, Vol. 12, Page 2456 12 (2020) 2456. https://doi.org/10.3390/POLYM12112456

[14] P. WANG, B. ZOU, S. DING, L. LI, C. HUANG, Effects of FDM-3D printing parameters on mechanical properties and microstructure of CF/PEEK and GF/PEEK, Chinese Journal of Aeronautics 34 (2021) 236-246. https://doi.org/10.1016/J.CJA.2020.05.040

[15] K. V., T. T., C. H.E., T. E.S., Effect of printing parameters on mechanical properties of 3D printed PLA/carbon fibre composites, Materials Science. Non-Equilibrium Phase Transformations. 4 (2018) 126-128.

[16] X. Peng, M. Zhang, Z. Guo, L. Sang, W. Hou, Investigation of processing parameters on tensile performance for FDM-printed carbon fiber reinforced polyamide 6 composites, Composites Communications 22 (2020) 100478. https://doi.org/10.1016/J.COCO.2020.100478

[17] P.S. Ramalingam, K. Mayandi, V. Balasubramanian, K. Chandrasekar, V.M. Stalany, A.A. Munaf, Effect of 3D printing process parameters on the impact strength of onyx – Glass fiber reinforced composites, Mater Today Proc 45 (2021) 6154-6159.
https://doi.org/10.1016/J.MATPR.2020.10.467

[18] T.; Fisher, J.H.S.; Almeida, A.R. Zanjanijam, T. Fisher, J. Humberto, S. Almeida, B.G. Falzon, Z. Kazancı, Tension and Compression Properties of 3D-Printed Composites: Print Orientation and Strain Rate Effects, Polymers 2023, Vol. 15, Page 1708 15 (2023) 1708.
https://doi.org/10.3390/POLYM15071708

[19] H. Mei, X. Yin, J. Zhang, W. Zhao, Compressive Properties of 3D Printed Polylactic Acid Matrix Composites Reinforced by Short Fibers and SiC Nanowires, Adv Eng Mater 21 (2019) 1800539. https://doi.org/10.1002/ADEM.201800539
[7] B. Brenken, E. Barocio, A. Favaloro, V. Kunc, R.B. Pipes, Fused filament fabrication of fiber-reinforced polymers: A review, Addit Manuf 21 (2018) 1-16.
https://doi.org/10.1016/J ADDMA.2018.01.002

[8] I. Bianchi, A. Forcellese, T. Mancia, M. Simoncini, A. Vita, Process parameters effect on environmental sustainability of composites FFF technology, Materials and Manufacturing Processes 37 (2022) 591-601.
https://doi.org/10.1080/10426914.2022.2049300

[9] C. Abeykoon, P. Sri-Amphorn, A. Fernando, Optimization of fused deposition modeling parameters for improved PLA and ABS 3D printed structures, International Journal of Lightweight Materials and Manufacture 3 (2020) 284-297.
https://doi.org/10.1016/J.IJLMM.2020.03.003

[10] S. Srinivasan Ganesh Iyer, O. Keles, Effect of raster angle on mechanical properties of 3D printed short carbon fiber reinforced acrylonitrile butadiene styrene, Composites Communications 32 (2022) 101163.
https://doi.org/10.1016/J.COCO.2022.101163

[11] H. Al Khawaja, H. Alabdouli, H. Alqaydi, A. Mansour, W. Ahmed, H. Al Jassmi, Investigating the mechanical properties of 3D printed components, 2020 Advances in Science and Engineering Technology International Conferences, ASET 2020 (2020).
https://doi.org/10.1109/ASET48392.2020.9118307

[12] N. Vinoth Babu, N. Venkateshwaran, N. Rajini, S.O. Ismail, F. Mohammad, H.A. Al-Lohedan, S. Suchart, Influence of slicing parameters on surface quality and mechanical properties of 3D-printed CF/PLA composites fabricated by FDM technique, Materials Technology 37 (2022) 1008-1025.
https://doi.org/10.1080/10667857.2021.1915056

[13] D. Lee, G.Y. Wu, Parameters Affecting the Mechanical Properties of Three-Dimensional (3D) Printed Carbon Fiber-Reinforced Polylactide Composites, Polymers 2020, Vol. 12, Page 2456 12 (2020) 2456.
https://doi.org/10.3390/POLYM12112456

[14] P. WANG, B. ZOU, S. DING, L. LI, C. HUANG, Effects of FDM-3D printing parameters on mechanical properties and microstructure of CF/PEEK and GF/PEEK, Chinese Journal of Aeronautics 34 (2021) 236-246.
https://doi.org/10.1016/J.CJA.2020.05.040

[15] K. V., T. T., C. H.E., T. E.S., Effect of printing parameters on mechanical properties of 3D printed PLA/carbon fibre composites, Materials Science. Non-Equilibrium Phase Transformations. 4 (2018) 126-128.

[16] X. Peng, M. Zhang, Z. Guo, L. Sang, W. Hou, Investigation of processing parameters on tensile performance for FDM-printed carbon fiber reinforced polyamide 6 composites, Composites Communications 22 (2020) 100478.
https://doi.org/10.1016/J.COCO.2020.100478

[17] P.S. Ramalingam, K. Mayandi, V. Balasubramanian, K. Chandrasekar, V.M. Stalany, A.A. Munaf, Effect of 3D printing process parameters on the impact strength of onyx – Glass fiber reinforced composites, Mater Today Proc 45 (2021) 6154-6159.
https://doi.org/10.1016/J.MATPR.2020.10.467

[18] T.; Fisher, J.H.S.; Almeida, A.R. Zanjanijam, T. Fisher, J. Humberto, S. Almeida, B.G. Falzon, Z. Kazancı, Tension and Compression Properties of 3D-Printed Composites: Print Orientation and Strain Rate Effects, Polymers 2023, Vol. 15, Page 1708 15 (2023) 1708.
https://doi.org/10.3390/POLYM15071708

[19] H. Mei, X. Yin, J. Zhang, W. Zhao, Compressive Properties of 3D Printed Polylactic Acid Matrix Composites Reinforced by Short Fibers and SiC Nanowires, Adv Eng Mater 21 (2019) 1800539.
https://doi.org/10.1002/ADEM.201800539
# Microstructure mastering and fatigue behavior of duplex stainless steel obtained with laser powder bed fusion

PIRAS Maxime<fnref n="1" />,ᵃ*, HOR Anis<fnref n="1" />,ᵇ and CHARKALUK Eric<fnref n="2" />,ᶜ

**Keywords:** LPBF, Microstructure, Fatigue behavior, Duplex SAF 2507

**Abstract.** Microstructure and mechanical behavior of duplex stainless steels (DSS) are well known when elaborated with conventional processes. Nowadays, many studies aimed to characterize this material for additive manufacturing (AM) processes like laser powder bed fusion (LPBF) [1], [2]. The as-built LPBF microstructure of this alloy is totally ferritic [1]. Heat treatments are therefore necessary to recover the duplex microstructure. These heat treatments need to be optimized. The effects of annealing heat treatments on the microstructure and mechanical behavior of DSS are now known [2], but the consequences on its fatigue strength need to be explored. In this study, firstly, the duplex microstructure will be controlled by heat treatments to obtain austenitic phase with different grain morphology. Then, the influence of microstructure on tensile high cycle fatigue behavior will be investigated. As a result, a microstructure with coarser austenite grains has no effect on fatigue behavior compared to the fully ferritic As-built material whereas the finer microstructure allows an improvement. Even if there is austenite phase improving the material ductility, fatigue damage initiation is always due to pore defects.

**Introduction**

Since the first apparition of (DSS) in the XXth century, the interests in the properties of this grade of stainless steel leads to its well and further development [3]. Through the years, several grades of DSS were developed increasing their properties such as higher strength, better resistance against intergranular, pitting and crevice corrosion, higher resistance against hydrogen embrittlement, better fatigue and fatigue-corrosion behaviors [4], [5]. Current grades are DSS, super DSS (SDSS) and hyper DSS according to Pitting Resistance Equivalent (PRE) value [4]–[6]. Thanks to these high properties, DSS are used in several industrial fields like oil and gas, paper industry, desalination plant, chemical transport, storage tank [3], [5].

Secondly, AM technologies allow manufacturing parts with more freedom and more complexity. The layer-by-layer processes unlocks new possibilities of design and production. These technologies are available for different domains and materials and is an important subject of researches [7]–[9]. The additively manufactured DSS investigated in the literature are SAF 2205 and SAF 2507. The difficulty to obtain an as-built dual phase microstructure by LPBF process is highlighted by several authors [1], [10], [11]. Even if different energy densities are used, varying process parameters such as laser power, scan speed or layer thickness, a fully ferritic as-built structure is always obtained with this process [12], [13]. This result is mainly explained by the higher cooling rate of the LPBF process leaving no time for nucleation of austenite phase [7]. Thanks to heat treatments (HT), it is possible to obtain austenite phase with an austenite-ferrite phases ratio near to 50-50 and thus improve ductility [2], [10], [11].

<figure><img src="image_284.png" /></figure>

<footnote n="1">¹Institut Clément Ader (ICA), Université de Toulouse, CNRS, ISAE-SUPAERO, UPS, INSA, Mines-Albi, 3 rue Caroline Aigle, 31400 Toulouse, France</footnote>

<footnote n="2">²Laboratoire de Mécanique des Solides (LMS), CNRS, UMR 7649, Ecole Polytechnique, Palaiseau Cedex 91128, France</footnote>

<footnote>ªmaxime.piras@isae-supaero.fr, ᵇanis.hor@isae-supaero.fr, ᶜeric.charkaluk@polytechnique.edu</footnote>
# Microstructure mastering and fatigue behavior of duplex stainless steel obtained with laser powder bed fusion

PIRAS Maxime<fnref n="1" />,ᵃ*, HOR Anis<fnref n="1" />,ᵇ and CHARKALUK Eric<fnref n="2" />,ᶜ

**Keywords:** LPBF, Microstructure, Fatigue behavior, Duplex SAF 2507

**Abstract.** Microstructure and mechanical behavior of duplex stainless steels (DSS) are well known when elaborated with conventional processes. Nowadays, many studies aimed to characterize this material for additive manufacturing (AM) processes like laser powder bed fusion (LPBF) [1], [2]. The as-built LPBF microstructure of this alloy is totally ferritic [1]. Heat treatments are therefore necessary to recover the duplex microstructure. These heat treatments need to be optimized. The effects of annealing heat treatments on the microstructure and mechanical behavior of DSS are now known [2], but the consequences on its fatigue strength need to be explored. In this study, firstly, the duplex microstructure will be controlled by heat treatments to obtain austenitic phase with different grain morphology. Then, the influence of microstructure on tensile high cycle fatigue behavior will be investigated. As a result, a microstructure with coarser austenite grains has no effect on fatigue behavior compared to the fully ferritic As-built material whereas the finer microstructure allows an improvement. Even if there is austenite phase improving the material ductility, fatigue damage initiation is always due to pore defects.

**Introduction**

Since the first apparition of (DSS) in the XXth century, the interests in the properties of this grade of stainless steel leads to its well and further development [3]. Through the years, several grades of DSS were developed increasing their properties such as higher strength, better resistance against intergranular, pitting and crevice corrosion, higher resistance against hydrogen embrittlement, better fatigue and fatigue-corrosion behaviors [4], [5]. Current grades are DSS, super DSS (SDSS) and hyper DSS according to Pitting Resistance Equivalent (PRE) value [4]–[6]. Thanks to these high properties, DSS are used in several industrial fields like oil and gas, paper industry, desalination plant, chemical transport, storage tank [3], [5].

Secondly, AM technologies allow manufacturing parts with more freedom and more complexity. The layer-by-layer processes unlocks new possibilities of design and production. These technologies are available for different domains and materials and is an important subject of researches [7]–[9]. The additively manufactured DSS investigated in the literature are SAF 2205 and SAF 2507. The difficulty to obtain an as-built dual phase microstructure by LPBF process is highlighted by several authors [1], [10], [11]. Even if different energy densities are used, varying process parameters such as laser power, scan speed or layer thickness, a fully ferritic as-built structure is always obtained with this process [12], [13]. This result is mainly explained by the higher cooling rate of the LPBF process leaving no time for nucleation of austenite phase [7]. Thanks to heat treatments (HT), it is possible to obtain austenite phase with an austenite-ferrite phases ratio near to 50-50 and thus improve ductility [2], [10], [11].

<figure><img src="image_290.png" /></figure>

<footnote n="1">¹Institut Clément Ader (ICA), Université de Toulouse, CNRS, ISAE-SUPAERO, UPS, INSA, Mines-Albi, 3 rue Caroline Aigle, 31400 Toulouse, France</footnote>

<footnote n="2">²Laboratoire de Mécanique des Solides (LMS), CNRS, UMR 7649, Ecole Polytechnique, Palaiseau Cedex 91128, France</footnote>

<footnote>ªmaxime.piras@isae-supaero.fr, ᵇanis.hor@isae-supaero.fr, ᶜeric.charkaluk@polytechnique.edu</footnote>
In this study, a high-density material is obtained using a parametric optimization. Different HT will be realized to control the microstructure and obtain coarser grain than observed in literature. Then the influence of these microstructures on the high cycle fatigue (HCF) will be studied.

## Material and methods

### Powder:

The material used in this study is a SDSS SAF 2507 (UNS S32750, X2CrNiMoN25-7-4) commercialized by Sandvik Osprey LTD. The powder is gas-atomized under nitrogen atmosphere with a granulometry of 20 - 45 µm. Figure 1a shows particle size distribution of the powder obtained using a laser diffraction analyzer. Figure 1b reveals that there are some satellites on the powder. The composition of the powder is given in Table 1.

<figure><img src="image_285.png" /><figcaption>Figure 1: (a) The cumulative particle size distribution obtained by laser granulometry and (b) SEM image of the SAF2507 powder.</figcaption></figure>

<table><caption>Table 1: Chemical composition (wt. %) of the SAF 2507 powder.</caption><tr><td>Element</td><td>Fe</td><td>Cr</td><td>Ni</td><td>Mo</td><td>Mn</td><td>Si</td><td>N</td><td>O</td><td>Cu</td><td>C</td><td>Co</td><td>Al</td></tr><tr><td>wt. %</td><td>Bal.</td><td>25.0</td><td>7.1</td><td>3.93</td><td>0.8</td><td>0.4</td><td>0.3</td><td>0.06</td><td>0.05</td><td>0.02</td><td>0.1</td><td>0.01</td></tr></table>

### Sample manufacturing:

Samples were manufactured using SLM 125HL machine from SLM Solutions. An inert atmosphere under argon was maintained allowing an oxygen level lower than 0.1 %. The substrate was pre-heated at 100 °C. Based on literature [1], [9]–[11], [14], [15], one set of parameters was selected according to its respective volumetric energy density (VED), defined by:

$$ VED = \frac{P}{v \times h \times t} \quad [J.mm^{-3}] \qquad (1) $$

Where, *P* is the laser power, *v* the laser scan speed, *h* the hatch distance and *t* the layer thickness. The values of the process parameters are illustrated in Table 2. Back-and-forth laser scanning strategy is used without contouring nor chess pattern. The 67° rotation of laser direction between each layer is also applied.

<table><caption>Table 2: Process parameters used for the LPBF process.</caption><tr><td>VED (J.mm<sup>-3</sup>)</td><td>P (W)</td><td>v (mm.s<sup>-1</sup>)</td><td>h (mm)</td><td>t (mm)</td></tr><tr><td>115</td><td>280</td><td>600</td><td>0.09</td><td>0.045</td></tr></table>
In this study, a high-density material is obtained using a parametric optimization. Different HT will be realized to control the microstructure and obtain coarser grain than observed in literature. Then the influence of these microstructures on the high cycle fatigue (HCF) will be studied.

## Material and methods

### Powder:

The material used in this study is a SDSS SAF 2507 (UNS S32750, X2CrNiMoN25-7-4) commercialized by Sandvik Osprey LTD. The powder is gas-atomized under nitrogen atmosphere with a granulometry of 20 - 45 µm. Figure 1a shows particle size distribution of the powder obtained using a laser diffraction analyzer. Figure 1b reveals that there are some satellites on the powder. The composition of the powder is given in Table 1.

<figure><img src="image_291.png" /><figcaption>Figure 1: (a) The cumulative particle size distribution obtained by laser granulometry and (b) SEM image of the SAF2507 powder.</figcaption></figure>

<table><caption>Table 1: Chemical composition (wt. %) of the SAF 2507 powder.</caption><tr><td>Element</td><td>Fe</td><td>Cr</td><td>Ni</td><td>Mo</td><td>Mn</td><td>Si</td><td>N</td><td>O</td><td>Cu</td><td>C</td><td>Co</td><td>Al</td></tr><tr><td>wt. %</td><td>Bal.</td><td>25.0</td><td>7.1</td><td>3.93</td><td>0.8</td><td>0.4</td><td>0.3</td><td>0.06</td><td>0.05</td><td>0.02</td><td>0.1</td><td>0.01</td></tr></table>

### Sample manufacturing:

Samples were manufactured using SLM 125HL machine from SLM Solutions. An inert atmosphere under argon was maintained allowing an oxygen level lower than 0.1 %. The substrate was pre-heated at 100 °C. Based on literature [1], [9]–[11], [14], [15], one set of parameters was selected according to its respective volumetric energy density (VED), defined by:

$$ VED = \frac{P}{v \times h \times t} \quad [J.mm^{-3}] \quad (1) $$

Where, *P* is the laser power, *v* the laser scan speed, *h* the hatch distance and *t* the layer thickness. The values of the process parameters are illustrated in Table 2. Back-and-forth laser scanning strategy is used without contouring nor chess pattern. The 67° rotation of laser direction between each layer is also applied.

<table><caption>Table 2: Process parameters used for the LPBF process.</caption><tr><td>VED (J.mm<sup>-3</sup>)</td><td>P (W)</td><td>v (mm.s<sup>-1</sup>)</td><td>h (mm)</td><td>t (mm)</td></tr><tr><td>115</td><td>280</td><td>600</td><td>0.09</td><td>0.045</td></tr></table>
**Heat treatments:**

As observed in literature it is mandatory to realize HT to achieve duplex microstructure [1], [10], [11], [16]. To study the effect of HT, temperature ranging from 1100 °C to 1275 °C and holding time ranging from 15 min to 3 h were tested. All the HT are followed by a water quenching to avoid deleterious precipitation (sigma phase).

**Characterization methods:**

To determine the defect population of the material, X-ray Computed Tomography (CT) scanning was realized with Easytom RX 130 device. A voxel size of (6.46)³ µm³ is obtained using a tension of 130 V, an intensity of 61 µA and an exposure time of 4 image.s⁻¹. The volume of the gauge length of the fatigue specimen (Figure 2) was analyzed: cylinder with a diameter of 6 mm and a length of 10 mm.

To get detailed information about microstructure and grains, Electron BackScatter Diffraction (EBSD) analysis is realized with FEG JSM 7100F TTLS LS SEM from JEOL and equipped with the CMOS Symmetry S2 EBSD detector from Oxford Instruments. Size map is 804 x 603 µm² and pixel size is 0.25 x 0.25 µm². The observed sections are parallel to the Building Direction (BD).

Tensile High Cycles Fatigue (HCF) tests are realized with a loading ratio of R = -1 and a frequency of f = 15 Hz. The test run-out is set at 2.10⁶ cycles. To determine the HCF limit, the staircase method is used with a step of 20 MPa. The fatigue specimens (Figure 2) are obtained by machining from as-built and heat-treated cylinders with a diameter of 13 mm and a length of 95 mm. After machining, all specimens were grinded and polished with silica paper from P600 to P4000 to avoid influence of surface roughness on fatigue behavior. Finally, Fractography analysis were realized with a Zeiss SEM EVO 10 to determine fracture mechanisms.

<figure><img src="image_286.png" /><figcaption>Figure 2: Geometry of fatigue specimen (dimensions in mm).</figcaption></figure>

**Results and discussions**

*Duplex microstructure control:*

Firstly, the relative density obtained is higher than 99.93 % for each batch. Samples can be considered as high-density materials. Nevertheless, Figure 3 shows that even with high-density material, there still are defects. Those have a Feret diameter lower than 200 µm and an overall sphericity higher than 0.5. It can be considered as pore defects.
**Heat treatments:**

As observed in literature it is mandatory to realize HT to achieve duplex microstructure [1], [10], [11], [16]. To study the effect of HT, temperature ranging from 1100 °C to 1275 °C and holding time ranging from 15 min to 3 h were tested. All the HT are followed by a water quenching to avoid deleterious precipitation (sigma phase).

**Characterization methods:**

To determine the defect population of the material, X-ray Computed Tomography (CT) scanning was realized with Easytom RX 130 device. A voxel size of (6.46)³ µm³ is obtained using a tension of 130 V, an intensity of 61 µA and an exposure time of 4 image.s⁻¹. The volume of the gauge length of the fatigue specimen (Figure 2) was analyzed: cylinder with a diameter of 6 mm and a length of 10 mm.

To get detailed information about microstructure and grains, Electron BackScatter Diffraction (EBSD) analysis is realized with FEG JSM 7100F TTLS LS SEM from JEOL and equipped with the CMOS Symmetry S2 EBSD detector from Oxford Instruments. Size map is 804 x 603 µm² ans pixel size is 0.25 x 0.25 µm². The observed sections are parallel to the Building Direction (BD).

Tensile High Cycles Fatigue (HCF) tests are realized with a loading ratio of R = -1 and a frequency of f = 15 Hz. The test run-out is set at 2.10⁶ cycles. To determine the HCF limit, the staircase method is used with a step of 20 MPa. The fatigue specimens (Figure 2) are obtained by machining from as-built and heat-treated cylinders with a diameter of 13 mm and a length of 95 mm. After machining, all specimens were grinded and polished with silica paper from P600 to P4000 to avoid influence of surface roughness on fatigue behavior. Finally, Fractography analysis were realized with a Zeiss SEM EVO 10 to determine fracture mechanisms.

<figure><img src="image_292.png" /><figcaption>Figure 2: Geometry of fatigue specimen (dimensions in mm).</figcaption></figure>

**Results and discussions**

*Duplex microstructure control:*

Firstly, the relative density obtained is higher than 99.93 % for each batch. Samples can be considered as high-density materials. Nevertheless, Figure 3 shows that even with high-density material, there still are defects. Those have a Feret diameter lower than 200 µm and an overall sphericity higher than 0.5. It can be considered as pore defects.
<figure><img src="image_287.png" /><figcaption>Figure 3: (a) Example of spatial distribution of defects obtained by CT scans of the analyzed volume, and (b) Feret diameter versus sphericity of the defects for each produced batch.</figcaption></figure>

Then, due to the very higher cooling rate characterizing the LPBF process [9], the as-built microstructure is nearly fully-ferritic. It is confirmed on the phase maps in Figure 4a where austenite phase ratio is lower than 1 %. HT are therefore necessary to create the austenite phase and thus obtain a duplex (dual phase) microstructure. The <001> preferred orientation is observed in the orientation map illustrated in Figure 4b and confirmed by the Inverse pole figures (IPF) in Figure 4c. It means that most grains [001] are oriented according to BD.

<figure><img src="image_288.png" /><figcaption>Figure 4: a) Phase map, b) orientation map and c) inverse pole figures of as-built microstructure.</figcaption></figure>

Additionally, as observed in literature, a HT with short holding time is enough to obtain a duplex microstructure with an austenite-ferrite phase fraction ratio near to 50-50 [2], [10], [11] (Figure 5a). Comparing Figure 4a and Figure 5, nucleated austenite grains are both intergranular
<figure><img src="image_293.png" /><figcaption>Figure 3: (a) Example of spatial distribution of defects obtained by CT scans of the analyzed volume, and (b) Feret diameter versus sphericity of the defects for each produced batch.</figcaption></figure>

Then, due to the very higher cooling rate characterizing the LPBF process [9], the as-built microstructure is nearly fully-ferritic. It is confirmed on the phase maps in Figure 4a where austenite phase ratio is lower than 1 %. HT are therefore necessary to create the austenite phase and thus obtain a duplex (dual phase) microstructure. The <001> preferred orientation is observed in the orientation map illustrated in Figure 4b and confirmed by the Inverse pole figures (IPF) in Figure 4c. It means that most grains [001] are oriented according to BD.

<figure><img src="image_294.png" /><figcaption>Figure 4: a) Phase map, b) orientation map and c) inverse pole figures of as-built microstructure.</figcaption></figure>

Additionally, as observed in literature, a HT with short holding time is enough to obtain a duplex microstructure with an austenite-ferrite phase fraction ratio near to 50-50 [2], [10], [11] (Figure 5a). Comparing Figure 4a and Figure 5, nucleated austenite grains are both intergranular
and intragranular. With SA₃₀ HT, temperature and time are too short to observe grain growth or recrystallization. Only small austenite grains have time to nucleate [10]. Coarser grain morphology is achievable by increasing temperature and holding time (Figure 5b). This grain growth is also accompanied by an imbalance between the two phases: austenite and ferrite. This second HT microstructure (Figure 5b) is composed by 40 % of austenite and 60 % of ferrite (Table 3). To regain the 50-50 balance a second HT is applied. The Double HT (DHT) microstructure is composed by coarser grain with a 50-50 phase ratio as illustrated in Table 3 and Figure 5c.

Finally, austenitic grains as big as those from wrought material (Figure 5d) can be obtained by both DHT and 1275 °C - 3 h HT. Nevertheless, these austenitic grains are not equiaxed.

<figure><img src="image_289.png" /><figcaption>Figure 5: Phase maps of microstructures obtained after different heat treatments and comparison with wrought microstructure (hot rolled and solution heat treated for 1 h at 1050 °C and then water-quenched).</figcaption></figure>
and intragranular. With SA₃₀ HT, temperature and time are too short to observe grain growth or recrystallization. Only small austenite grains have time to nucleate [10]. Coarser grain morphology is achievable by increasing temperature and holding time (Figure 5b). This grain growth is also accompanied by an imbalance between the two phases: austenite and ferrite. This second HT microstructure (Figure 5b) is composed by 40 % of austenite and 60 % of ferrite (Table 3). To regain the 50-50 balance a second HT is applied. The Double HT (DHT) microstructure is composed by coarser grain with a 50-50 phase ratio as illustrated in Table 3 and Figure 5c.

Finally, austenitic grains as big as those from wrought material (Figure 5d) can be obtained by both DHT and 1275 °C - 3 h HT. Nevertheless, these austenitic grains are not equiaxed.

<figure><img src="image_295.png" /><figcaption>Figure 5: Phase maps of microstructures obtained after different heat treatments and comparison with wrought microstructure (hot rolled and solution heat treated for 1 h at 1050 °C and then water-quenched).</figcaption></figure>
<table><caption>Table 3: Phase fraction obtained after different HT and comparison with wrought microstructure.</caption><thead><tr><td>HT</td><td>Austenite (%)</td><td>Ferrite (%)</td><td>Sigma (%)</td></tr></thead><tbody><tr><td>SA<sub>30</sub></td><td>50.7</td><td>49.2</td><td>&lt; 0.1</td></tr><tr><td>1275 °C - 3 h</td><td>39.5</td><td>60.4</td><td>&lt; 0.1</td></tr><tr><td>DHT</td><td>50.0</td><td>49.9</td><td>&lt; 0.1</td></tr><tr><td>Wrought</td><td>41</td><td>59</td><td>0</td></tr></tbody></table>

### Fatigue behavior:

Discussion about microstructure effect on fatigue behavior is possible because defect population is similar. As observed in Figure 7, at higher stress amplitude ($\sigma_a$ > 460 MPa), the As-built samples have better fatigue resistance than HT samples. It is in accordance with the higher $R_m$ (Figure 8). While decreasing the stress amplitude, the As-built state becomes less fatigue resistance than SA30 HT and then than DHT. It can be explained by the presence of austenite phase for both HT which increases the ductility. A microstructure with coarser grains doesn't improve the fatigue life resistance. Both As-built and DHT specimen show similar fatigue limit ($\sigma_{-1}$) even though their fatigue behavior is different (Figure 8a and Figure 8c). An increase of 11% of fatigue life resistance is obtained for SA30 compared to DHT. This is the effect of the smaller austenitic grains obtained for the SA30 microstructure (Figure 5a).

The HCF limit of wrought material is close to that obtained for the DHT microstructure. The two microstructure features are similar and the only difference is the presence of LPBF defects in the DHT state. This means that the small LPBF defects obtained in this study (<175 µm as illustrated in Figure 3) have little effect on the fatigue behavior.

<figure><img src="image_290.png" /><figcaption>Figure 6: S-N plots obtained for the studied microstructures: as-built, SA30 and DHT.</figcaption></figure>
<table><caption>Table 3: Phase fraction obtained after different HT and comparison with wrought microstructure.</caption><thead><tr><td>HT</td><td>Austenite (%)</td><td>Ferrite (%)</td><td>Sigma (%)</td></tr></thead><tbody><tr><td>SA<sub>30</sub></td><td>50.7</td><td>49.2</td><td>&lt; 0.1</td></tr><tr><td>1275 °C - 3 h</td><td>39.5</td><td>60.4</td><td>&lt; 0.1</td></tr><tr><td>DHT</td><td>50.0</td><td>49.9</td><td>&lt; 0.1</td></tr><tr><td>Wrought</td><td>41</td><td>59</td><td>0</td></tr></tbody></table>

### Fatigue behavior:

Discussion about microstructure effect on fatigue behavior is possible because defect population is similar. As observed in Figure 7, at higher stress amplitude ($\sigma_a$ > 460 MPa), the As-built samples have better fatigue resistance than HT samples. It is in accordance with the higher $R_m$ (Figure 8). While decreasing the stress amplitude, the As-built state becomes less fatigue resistance than SA30 HT and then than DHT. It can be explained by the presence of austenite phase for both HT which increases the ductility. A microstructure with coarser grains doesn't improve the fatigue life resistance. Both As-built and DHT specimen show similar fatigue limit ($\sigma_{-1}$) even though their fatigue behavior is different (Figure 8a and Figure 8c). An increase of 11% of fatigue life resistance is obtained for SA30 compared to DHT. This is the effect of the smaller austenitic grains obtained for the SA30 microstructure (Figure 5a).

The HCF limit of wrought material is close to that obtained for the DHT microstructure. The two microstructure features are similar and the only difference is the presence of LPBF defects in the DHT state. This means that the small LPBF defects obtained in this study (<175 µm as illustrated in Figure 3) have little effect on the fatigue behavior.

<figure><img src="image_296.png" /><figcaption>Figure 6: S-N plots obtained for the studied microstructures: as-built, SA30 and DHT.</figcaption></figure>
<figure><img src="image_291.png" /><figcaption>Figure 7: Microstructure, yield stress, ultimate tensile stress and HCF limit of each state: as-built, SA30, DHT and wrought.</figcaption></figure>

However, the crack initiation zone still contains surface pore defect as shown in Figure 9b for all the microstructures. Half of these defects are opening ones. The circularity of these defects is in accordance with the X-ray tomography observations illustrated in Figure 3b. The $\sqrt{area}$ of the pore defects ranges from 30 to 70 µm showing a low dispersion for each sample (Figure 9c). This small defect size confirms that microstructure effect on fatigue behavior can be discussed. The modified Kitagawa-Takachi diagram plotted in Figure 9c shows that there is no defect size effect on fatigue stress amplitude.
<figure><img src="image_297.png" /><figcaption>Figure 7: Microstructure, yield stress, ultimate tensile stress and HCF limit of each state: as-built, SA30, DHT and wrought.</figcaption></figure>

However, the crack initiation zone still contains surface pore defect as showed in Figure 9b for all the microstructures. Half of these defects are opening ones. The circularity of these defects is in accordance with the X-ray tomography observations illustrated in Figure 3b. The $\sqrt{area}$ of the pore defects ranges from 30 to 70 µm showing a low dispersion for each sample (Figure 9c). This small defect size confirms that microstructure effect on fatigue behavior can be discussed. The modified Kitagawa-Takachi diagram plotted in Figure 9c shows that there is no defect size effect on fatigue stress amplitude.
<figure><img src="image_292.png" /><figcaption>Figure 8: Example of fracture facies of (a) the overall surface, (b) magnification of the initiation zone and (c) the modified Kitagawa-Takachi diagram for the three microstructures: as-built, SA30 and DHT.</figcaption></figure>

**Conclusion**

After successfully building high density LPBF samples, this work aimed at controlling the microstructure of the material and to identify the influence on the fatigue behavior. The main conclusions of this study are:

* LPBF as-built microstructure is ferritic and HT provides a duplex microstructure with different grain morphologies and with austenite-ferrite ratio near to 50-50.

* The coarser duplex microstructure doesn't improve the fatigue life resistance when compared with the As-built material which is fully ferritic.

* In contrary, the smaller microstructure obtained with SA30 HT has an impact on the fatigue behavior since an increase of 11 % of the fatigue limit ($\sigma_{-1}$) is observed.
<figure><img src="image_298.png" /><figcaption>Figure 8: Example of fracture facies of (a) the overall surface, (b) magnification of the initiation zone and (c) the modified Kitagawa-Takachi diagram for the three microstructures: as-built, SA30 and DHT.</figcaption></figure>

Conclusion

After successfully building high density LPBF samples, this work aimed at controlling the microstructure of the material and to identify the influence on the fatigue behavior. The main conclusions of this study are:

* LPBF as-built microstructure is ferritic and HT provides a duplex microstructure with different grain morphologies and with austenite-ferrite ratio near to 50-50.

* The coarser duplex microstructure doesn't improve the fatigue life resistance when compared with the As-built material which is fully ferritic.

* In contrary, the smaller microstructure obtained with SA30 HT has an impact on the fatigue behavior since an increase of 11 % of the fatigue limit ($\sigma_{-1}$) is observed.
## References

[1] K. Saeidi, L. Kevetkova, F. Lofaj, and Z. Shen, ‘Novel ferritic stainless steel formed by laser melting from duplex stainless steel powder with advanced mechanical properties and high ductility’, *Mater. Sci. Eng. A*, vol. 665, pp. 59–65, May 2016. https://doi.org/10.1016/j.msea.2016.04.027

[2] L. Becker et al., ‘Influence of annealing time on the microstructure and properties of additively manufactured X2CrNiMoN25-7-4 duplex stainless steel: Experiment and simulation’, *Materialia*, p. 101720, Feb. 2023. https://doi.org/10.1016/j.mtla.2023.101720

[3] R. N. Gunn, *Duplex Stainless Steels Microstructure, Properties and Applications*. Woodhead Publishing, 1997. doi: 10.1533/9781845698775.24

[4] M. Knyazeva and M. Pohl, ‘Duplex Steels: Part I: Genesis, Formation, Structure’, *Metallogr. Microstruct. Anal.*, vol. 2, no. 2, pp. 113–121, Apr. 2013. https://doi.org/10.1007/s13632-013-0066-8

[5] I. Alvarez-Armas and S. Degallaix, *Les aciers inoxydables duplex*. in Traité MIM - Mécanique et Ingénierie des Matériaux Matériaux et métallurgie. Cachan: Hermès science publications, 2012.

[6] G. Chail and P. Kangas, ‘Super and hyper duplex stainless steels: structures, properties and applications’, *Procedia Struct. Integr.*, vol. 2, pp. 1755–1762, Jan. 2016. https://doi.org/10.1016/j.prostr.2016.06.221

[7] T. DebRoy et al., ‘Additive manufacturing of metallic components – Process, structure and properties’, *Prog. Mater. Sci.*, vol. 92, pp. 112–224, Mar. 2018. https://doi.org/10.1016/j.pmatsci.2017.10.001

[8] T. Mukherjee, J. S. Zuback, A. De, and T. DebRoy, ‘Printability of alloys for additive manufacturing’, *Sci. Rep.*, vol. 6, no. 1, Art. no. 1, Jan. 2016. https://doi.org/10.1038/srep19717

[9] K. Davidson and S. Singamneni, ‘Selective Laser Melting of Duplex Stainless Steel Powders: An Investigation’, *Mater. Manuf. Process.*, vol. 31, p. 150930095558007, Sep. 2015. https://doi.org/10.1080/10426914.2015.1090605

[10] F. Hengsbach et al., ‘Duplex stainless steel fabricated by selective laser melting - Microstructural and mechanical properties’, *Mater. Des.*, vol. 133, pp. 136–142, Nov. 2017. https://doi.org/10.1016/j.matdes.2017.07.046

[11] S. Papula et al., ‘Selective Laser Melting of Duplex Stainless Steel 2205: Effect of Post-Processing Heat Treatment on Microstructure, Mechanical Properties, and Corrosion Resistance’, *Materials*, vol. 12, no. 15, Art. no. 15, Jan. 2019. https://doi.org/10.3390/ma12152468

[12] K. P. Davidson and S. B. Singamneni, ‘Metallographic evaluation of duplex stainless steel powders processed by selective laser melting’, *Rapid Prototyp. J.*, vol. 23, no. 6, pp. 1146–1163, Jan. 2017. https://doi.org/10.1108/RPJ-04-2016-0053

[13] P. Murkute, S. Pasebani, and O. Burkan Isgor, ‘Metallurgical and Electrochemical Properties of Super Duplex Stainless Steel Clads on Low Carbon Steel Substrate produced with Laser Powder Bed Fusion’, *Sci. Rep.*, vol. 10, no. 1, Art. no. 1, Jun. 2020. https://doi.org/10.1038/s41598-020-67249-2

[14] M. Laleh, A. E. Hughes, W. Xu, P. Cizek, and M. Y. Tan, ‘Unanticipated drastic decline in pitting corrosion resistance of additively manufactured 316L stainless steel after high-temperature post-processing’, *Corros. Sci.*, vol. 165, p. 108412, Apr. 2020. https://doi.org/10.1016/j.corsci.2019.108412
## References

[1] K. Saeidi, L. Kevetkova, F. Lofaj, and Z. Shen, ‘Novel ferritic stainless steel formed by laser melting from duplex stainless steel powder with advanced mechanical properties and high ductility’, *Mater. Sci. Eng. A*, vol. 665, pp. 59–65, May 2016. https://doi.org/10.1016/j.msea.2016.04.027

[2] L. Becker et al., ‘Influence of annealing time on the microstructure and properties of additively manufactured X2CrNiMoN25-7-4 duplex stainless steel: Experiment and simulation’, *Materialia*, p. 101720, Feb. 2023. https://doi.org/10.1016/j.mtla.2023.101720

[3] R. N. Gunn, *Duplex Stainless Steels Microstructure, Properties and Applications*. Woodhead Publishing, 1997. doi: 10.1533/9781845698775.24

[4] M. Knyazeva and M. Pohl, ‘Duplex Steels: Part I: Genesis, Formation, Structure’, *Metallogr. Microstruct. Anal.*, vol. 2, no. 2, pp. 113–121, Apr. 2013. https://doi.org/10.1007/s13632-013-0066-8

[5] I. Alvarez-Armas and S. Degallaix, *Les aciers inoxydables duplex*. in Traité MIM - Mécanique et Ingénierie des Matériaux Matériaux et métallurgie. Cachan: Hermès science publications, 2012.

[6] G. Chail and P. Kangas, ‘Super and hyper duplex stainless steels: structures, properties and applications’, *Procedia Struct. Integr.*, vol. 2, pp. 1755–1762, Jan. 2016. https://doi.org/10.1016/j.prostr.2016.06.221

[7] T. DebRoy et al., ‘Additive manufacturing of metallic components – Process, structure and properties’, *Prog. Mater. Sci.*, vol. 92, pp. 112–224, Mar. 2018. https://doi.org/10.1016/j.pmatsci.2017.10.001

[8] T. Mukherjee, J. S. Zuback, A. De, and T. DebRoy, ‘Printability of alloys for additive manufacturing’, *Sci. Rep.*, vol. 6, no. 1, Art. no. 1, Jan. 2016. https://doi.org/10.1038/srep19717

[9] K. Davidson and S. Singamneni, ‘Selective Laser Melting of Duplex Stainless Steel Powders: An Investigation’, *Mater. Manuf. Process.*, vol. 31, p. 150930095558007, Sep. 2015. https://doi.org/10.1080/10426914.2015.1090605

[10] F. Hengsbach et al., ‘Duplex stainless steel fabricated by selective laser melting - Microstructural and mechanical properties’, *Mater. Des.*, vol. 133, pp. 136–142, Nov. 2017. https://doi.org/10.1016/j.matdes.2017.07.046

[11] S. Papula et al., ‘Selective Laser Melting of Duplex Stainless Steel 2205: Effect of Post-Processing Heat Treatment on Microstructure, Mechanical Properties, and Corrosion Resistance’, *Materials*, vol. 12, no. 15, Art. no. 15, Jan. 2019. https://doi.org/10.3390/ma12152468

[12] K. P. Davidson and S. B. Singamneni, ‘Metallographic evaluation of duplex stainless steel powders processed by selective laser melting’, *Rapid Prototyp. J.*, vol. 23, no. 6, pp. 1146–1163, Jan. 2017. https://doi.org/10.1108/RPJ-04-2016-0053

[13] P. Murkute, S. Pasebani, and O. Burkan Isgor, ‘Metallurgical and Electrochemical Properties of Super Duplex Stainless Steel Clads on Low Carbon Steel Substrate produced with Laser Powder Bed Fusion’, *Sci. Rep.*, vol. 10, no. 1, Art. no. 1, Jun. 2020. https://doi.org/10.1038/s41598-020-67249-2

[14] M. Laleh, A. E. Hughes, W. Xu, P. Cizek, and M. Y. Tan, ‘Unanticipated drastic decline in pitting corrosion resistance of additively manufactured 316L stainless steel after high-temperature post-processing’, *Corros. Sci.*, vol. 165, p. 108412, Apr. 2020. https://doi.org/10.1016/j.corsci.2019.108412
[15] J. Kunz, A. Boontanom, S. Herzog, P. Suwanpinij, A. Kaletsch, and C. Broeckmann, ‘Influence of hot isostatic pressing post-treatment on the microstructure and mechanical behavior of standard and super duplex stainless steel produced by laser powder bed fusion’, *Mater. Sci. Eng. A*, vol. 794, p. 139806, Sep. 2020. https://doi.org/10.1016/j.msea.2020.139806

[16] K. Saeidi, S. Alvi, F. Lofaj, V. I. Petkov, and F. Akhtar, ‘Advanced Mechanical Strength in Post Heat Treated SLM 2507 at Room and High Temperature Promoted by Hard/Ductile Sigma Precipitates’, *Metals*, vol. 9, no. 2, Art. no. 2, Feb. 2019. https://doi.org/10.3390/met9020199
[15] J. Kunz, A. Boontanom, S. Herzog, P. Suwanpinij, A. Kaletsch, and C. Broeckmann, ‘Influence of hot isostatic pressing post-treatment on the microstructure and mechanical behavior of standard and super duplex stainless steel produced by laser powder bed fusion’, *Mater. Sci. Eng. A*, vol. 794, p. 139806, Sep. 2020. https://doi.org/10.1016/j.msea.2020.139806

[16] K. Saeidi, S. Alvi, F. Lofaj, V. I. Petkov, and F. Akhtar, ‘Advanced Mechanical Strength in Post Heat Treated SLM 2507 at Room and High Temperature Promoted by Hard/Ductile Sigma Precipitates’, *Metals*, vol. 9, no. 2, Art. no. 2, Feb. 2019. https://doi.org/10.3390/met9020199
# Artificial intelligence approaches for enhanced coating performance

PERNA Alessia Serena¹,ª, CARRINO Luigi²,ᵇ, AURIEMMA CITARELLA Alessia²,ᶜ, DE MARCO Fabiola²,ᵈ*, DI BIASI Luigi²,ᵉ, TORTORA Genoveffa²,ᵉ and VISCUSI Antonio¹,⁹

¹Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Napoli, Italy

²Department of Computer Science, University of Salerno, Fisciano, SA, Italy

ªalessierena.perna@unina.it, ᵇluigi.carrino@unina.it, ᶜaauriemmacitarella@unisa.it,

ᵈfdemarco@unisa.it,ᵉldibiasi@unisa.it, ᶠtortora@unisa.it, ᵈantonio.viscusi@unina.it

**Keywords:** Cold Spray, Deep Learning, Genetic Algorithms

**Abstract.** Cold spray (CS) is an innovative manufacturing technology designed to produce metallic layers on diverse materials. This process involves propelling metallic particles at supersonic speeds using pressurized gas, causing them to impact the target surface and achieve adhesion through mechanical interlocking between the powders and the substrate. Integrating Artificial Intelligence (AI) techniques can enhance the understanding and quality of this additive manufacturing process. This work focuses on predicting the characteristics of particle deformation upon collision by exploring multiple Machine Learning (ML) and Deep Learning (DL) techniques with the aim of identifying the most suitable approach. The used dataset is mixed data, composed of experimental data and FEM data, generated by Finite Element models (FEM). The input parameters for the model are categorized into three macro-categories: process, powder, and substrate. The research aims to forecast particle behavior through this multidimensional approach and contribute valuable insights for optimizing the cold spray manufacturing process by applying DL methodologies.

## Introduction

As an innovative production technique, cold spray stands out for its capability to deposit metallic layers onto diverse materials, unlocking possibilities for creating robust and firmly adherent metal coatings. The broad applications span various industries, from aerospace to manufacturing and beyond [1]. A crucial aspect of exploring the potential of cold spray lies in conducting comprehensive coating studies to understand the behavior and performance of these coatings on different surfaces or objects. The intricate mechanisms of bonding formation and deposit strength are influenced by numerous factors [2]. Traditionally, material characterization has relied on manual efforts, often hindered by the need for a centralized database containing information on material properties. This bottleneck can pose challenges, mainly when performing FEM analyses [3] on newly introduced materials and substrates. Predicting the ultimate characteristics of a coating is a complex task. This complexity arises from the intricate interplay of numerous factors. These factors encompass the metallic powder's inherent properties, the polymeric substrates' characteristics, and the specific spraying parameters selected for the process. The challenge lies in the intricate synergy of these elements, making it presently tricky to forecast the behavior of metallic particles upon impact with diverse substrates precisely. The intricate nature of these interactions necessitates a comprehensive understanding of material properties, substrate dynamics, and process conditions, highlighting the current limitations in accurately predicting the outcomes of such interactions. Addressing these challenges requires innovative approaches and

<figure><img src="image_293.png" /></figure>
# Artificial intelligence approaches for enhanced coating performance

PERNA Alessia Serena¹,ª, CARRINO Luigi²,ᵇ,
AURIEMMA CITARELLA Alessia²,ᶜ, DE MARCO Fabiola²,ᵈ*, DI BIASI Luigi²,ᵉ,
TORTORA Genoveffa²,>f and VISCUSI Antonio¹,⁹

¹Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Napoli, Italy

²Department of Computer Science, University of Salerno, Fisciano, SA, Italy

ªalessierena.perna@unina.it, ᵇluigi.carrino@unina.it, ᶜaauriemmacitarella@unisa.it,

ᵈfdemarco@unisa.it,ᵉldibiasi@unisa.it, ᶠtortora@unisa.it, ⁹antonio.viscusi@unina.it

**Keywords:** Cold Spray, Deep Learning, Genetic Algorithms

**Abstract.** Cold spray (CS) is an innovative manufacturing technology designed to produce metallic layers on diverse materials. This process involves propelling metallic particles at supersonic speeds using pressurized gas, causing them to impact the target surface and achieve adhesion through mechanical interlocking between the powders and the substrate. Integrating Artificial Intelligence (AI) techniques can enhance the understanding and quality of this additive manufacturing process. This work focuses on predicting the characteristics of particle deformation upon collision by exploring multiple Machine Learning (ML) and Deep Learning (DL) techniques with the aim of identifying the most suitable approach. The used dataset is mixed data, composed of experimental data and FEM data, generated by Finite Element models (FEM). The input parameters for the model are categorized into three macro-categories: process, powder, and substrate. The research aims to forecast particle behavior through this multidimensional approach and contribute valuable insights for optimizing the cold spray manufacturing process by applying DL methodologies.

## Introduction

As an innovative production technique, cold spray stands out for its capability to deposit metallic layers onto diverse materials, unlocking possibilities for creating robust and firmly adherent metal coatings. The broad applications span various industries, from aerospace to manufacturing and beyond [1]. A crucial aspect of exploring the potential of cold spray lies in conducting comprehensive coating studies to understand the behavior and performance of these coatings on different surfaces or objects. The intricate mechanisms of bonding formation and deposit strength are influenced by numerous factors [2]. Traditionally, material characterization has relied on manual efforts, often hindered by the need for a centralized database containing information on material properties. This bottleneck can pose challenges, mainly when performing FEM analyses [3] on newly introduced materials and substrates. Predicting the ultimate characteristics of a coating is a complex task. This complexity arises from the intricate interplay of numerous factors. These factors encompass the metallic powder's inherent properties, the polymeric substrates' characteristics, and the specific spraying parameters selected for the process. The challenge lies in the intricate synergy of these elements, making it presently tricky to forecast the behavior of metallic particles upon impact with diverse substrates precisely. The intricate nature of these interactions necessitates a comprehensive understanding of material properties, substrate dynamics, and process conditions, highlighting the current limitations in accurately predicting the outcomes of such interactions. Addressing these challenges requires innovative approaches and

<figure><img src="image_299.png" /></figure>
advanced modeling techniques to enhance our ability to anticipate and control the behavior of
metallic particles during the coating process.

Recognizing this limitation, the current study focuses on leveraging deep learning and broader artificial intelligence techniques. This study uses DL models to analyze a comprehensive material dataset comprising experimentally validated results, and genetic algorithms are employed to design the network architecture. The objective is to enable accurate predictions of FEM results across multiple materials, even those not initially included in the original dataset. The input parameters incorporated into the model are systematically grouped into three categories: process parameters, powder parameters, and substrate parameters. The selected output parameters, chosen with precision to capture the fundamental traits of the coating, are flattening and penetration. These parameters are significant as they directly influence the coating's uniformity and adhesion characteristics. This research aims to pioneer the integration of DL models with GAs to achieve two main objectives:

- Firstly, the focus is on developing a sophisticated tool that predicts and enhances the characteristics of coatings. Through the iterative optimization facilitated by GAs, the DL models can refine their predictions and guide the enhancement of coating properties, thus advancing the capabilities of coating technologies;

- Secondly, the research endeavors to expand the scope of automation in the coating process by training additional DL models. The goal is to support the capacity for complete automation in coating applications.

## Materials and methodologies

*Input and output parameters.* The input parameters for the implemented strategies can be categorized into three main groups: impact velocity, encompassing various process parameters such as temperature, pressure, and stand-off distance; powder parameters (Yp), which, for metallic coatings on polymeric substrates, can be summarized by the yield strength of the powder material; and substrate parameters (Ys), that for polymeric substrates which are only deformed by the impact of the particles at low temperatures, can be characterized by the yield strength of the substrate material. The latter also considers the presence of fibers, which, when appropriately positioned beneath a matrix layer at least comparable in size to the powders, solely enhance the substrate stiffness, leading to a variation in yield strength. The output parameters under consideration include the particle penetration depth and flattening, measured by analyzing SEM micrographs of the coatings. Penetration depth refers to the extent to which a particle can traverse or penetrate a material or surface and evaluates the height of the particle upon the impact [Hs0].

The penetration depth was defined as $Hs [%] = \frac{(Hs_0-HS)}{HS} 100$. To assess the degree of powder flattening, the examination focused on the top surface of the coating. Using Image J software, the mean radius of the particles following a collision with the substrate (r) was measured. The percentage of particle flattening was calculated $r/r_0$ [%] = $\frac{(r-r_0)}{r_0} 100$, where r₀ represents the mean radius of the particle before the impact. As regards the formation of the dataset, the ones used in this study were created using both finite element method (FEM) simulations and experimental data. The first training dataset consisted of 30% experimental data and 70% FEM data, which were combined. Further information on the FEM analysis performed is presented in previous works of the authors [9]. The second dataset only included FEM data. The test dataset for both models only consisted of experimental data, for both FEM and experimental dataset, ABS, PEEK, and PA66 were considered as substrate materials, while copper, aluminum, steel, and titanium were the powders employed. LPW South Europe provided all the spherical powders used in the deposition process. A low-pressure cold spray equipment called DYCOMET was used for the depositions. Air was used as the carrier gas based on previous research indicating no significant
advanced modeling techniques to enhance our ability to anticipate and control the behavior of
metallic particles during the coating process.

Recognizing this limitation, the current study focuses on leveraging deep learning and broader artificial intelligence techniques. This study uses DL models to analyze a comprehensive material dataset comprising experimentally validated results, and genetic algorithms are employed to design the network architecture. The objective is to enable accurate predictions of FEM results across multiple materials, even those not initially included in the original dataset. The input parameters incorporated into the model are systematically grouped into three categories: process parameters, powder parameters, and substrate parameters. The selected output parameters, chosen with precision to capture the fundamental traits of the coating, are flattening and penetration. These parameters are significant as they directly influence the coating's uniformity and adhesion characteristics. This research aims to pioneer the integration of DL models with GAs to achieve two main objectives:

- Firstly, the focus is on developing a sophisticated tool that predicts and enhances the characteristics of coatings. Through the iterative optimization facilitated by GAs, the DL models can refine their predictions and guide the enhancement of coating properties, thus advancing the capabilities of coating technologies;

- Secondly, the research endeavors to expand the scope of automation in the coating process by training additional DL models. The goal is to support the capacity for complete automation in coating applications.

## Materials and methodologies

*Input and output parameters.* The input parameters for the implemented strategies can be categorized into three main groups: impact velocity, encompassing various process parameters such as temperature, pressure, and stand-off distance; powder parameters (Yp), which, for metallic coatings on polymeric substrates, can be summarized by the yield strength of the powder material; and substrate parameters (Ys), that for polymeric substrates which are only deformed by the impact of the particles at low temperatures, can be characterized by the yield strength of the substrate material. The latter also considers the presence of fibers, which, when appropriately positioned beneath a matrix layer at least comparable in size to the powders, solely enhance the substrate stiffness, leading to a variation in yield strength. The output parameters under consideration include the particle penetration depth and flattening, measured by analyzing SEM micrographs of the coatings. Penetration depth refers to the extent to which a particle can traverse or penetrate a material or surface and evaluates the height of the particle upon the impact [Hs0].

The penetration depth was defined as $Hs$ [%] = $\frac{(Hs_0-HS)}{HS}$ 100. To assess the degree of powder flattening, the examination focused on the top surface of the coating. Using Image J software, the mean radius of the particles following a collision with the substrate (r) was measured. The percentage of particle flattening was calculated $r/r_0$ [%] = $\frac{(r-r_0)}{r_0}$ 100, where r₀ represents the mean radius of the particle before the impact. As regards the formation of the dataset, the ones used in this study were created using both finite element method (FEM) simulations and experimental data. The first training dataset consisted of 30% experimental data and 70% FEM data, which were combined. Further information on the FEM analysis performed is presented in previous works of the authors [9]. The second dataset only included FEM data. The test dataset for both models only consisted of experimental data, for both FEM and experimental dataset, ABS, PEEK, and PA66 were considered as substrate materials, while copper, aluminum, steel, and titanium were the powders employed. LPW South Europe provided all the spherical powders used in the deposition process. A low-pressure cold spray equipment called DYCOMET was used for the depositions. Air was used as the carrier gas based on previous research indicating no significant
differences when using other carrier gases. The samples were positioned on a platform, and a spraying gun attached to a robot (HIGH-Z S-400/T-CNC-Technik) was used remotely to spray the substrates perpendicularly. Table 1 highlights the process parameters and characteristics of the powders used in the experiments.

<table><caption>Table 1. CS parameters employed for the experiments</caption><tr><td>Inlet Gas temperature [°C]</td><td>100 - 400</td><td>100 - 400</td><td>100 - 400</td></tr><tr><td>Standoff distance [mm]</td><td>10 - 4</td><td>10 - 4</td><td>10 - 4</td></tr><tr><td>Inlet gas pressure [MPa]</td><td>0.5 - 0.6</td><td>0.5 - 0.6</td><td>0.5 - 0.6</td></tr><tr><td>Gun traverse speed [mm s<sup>-1</sup>]</td><td>7.5</td><td>7.5</td><td>7.5</td></tr></table>

## DL models and Genetic algorithm approach

GAs are optimization techniques inspired by the principles of evolutionary theory in biology. These algorithms mimic the process of natural selection, evolution, and genetic recombination observed in living organisms. By emulating the mechanisms of genetic variation, reproduction, and survival of the fittest, genetic algorithms strive to find optimal solutions to complex problems. This computational approach involves the generation of diverse solutions, their evaluation based on a defined fitness criterion, and the evolution of increasingly refined solutions over successive generations. Genetic algorithms have proven effective in solving various optimization challenges across various fields, leveraging the inherent adaptability and efficiency inspired by the biological processes that govern evolution [4]. GAs aim to construct a population of potential solutions for a given problem, employing a fitness function to evaluate the merit of each candidate solution. The algorithm then identifies the best solutions to form a new population, perpetuating this iterative process and refining the solutions. Essential operations characterize each GA iteration:

- *initialization*: the algorithm initiates by randomly populating the pool of potential solutions;

- *selection*: optimal parents are chosen from the existing population based on their fitness.

- *crossover*: genetic material from the current population is recombined to generate novel solutions;

- *mutation*: random changes to the genetic material are introduced to prevent the algorithm from getting trapped in local optima, fostering new genetic variations;

- *evaluation*: the fitness of each solution is compared to the target, which could be based on factors such as the number of generations or the fitness of the best solution;

- *replacement*: the current population is replaced with a new generation of solutions, and this process is repeated until a satisfactory solution is found or after several generations.

In this scenario, we used the GAs and their related terminology and operations to design the architecture of the network, rather than optimizing the network's hyperparameters. A neural network is a computational model inspired by the structure and functioning of the human brain. It consists of interconnected nodes, often called neurons or artificial neurons, organized into layers.
differences when using other carrier gases. The samples were positioned on a platform, and a spraying gun attached to a robot (HIGH-Z S-400/T-CNC-Technik) was used remotely to spray the substrates perpendicularly. Table 1 highlights the process parameters and characteristics of the powders used in the experiments.

<table><caption>Table 1. CS parameters employed for the experiments</caption><tr><td>Inlet Gas temperature [°C]</td><td>100 - 400</td><td>100 - 400</td><td>100 - 400</td></tr><tr><td>Standoff distance [mm]</td><td>10 - 4</td><td>10 - 4</td><td>10 - 4</td></tr><tr><td>Inlet gas pressure [MPa]</td><td>0.5 - 0.6</td><td>0.5 - 0.6</td><td>0.5 - 0.6</td></tr><tr><td>Gun traverse speed [mm s<sup>-1</sup>]</td><td>7.5</td><td>7.5</td><td>7.5</td></tr></table>

## DL models and Genetic algorithm approach

GAs are optimization techniques inspired by the principles of evolutionary theory in biology. These algorithms mimic the process of natural selection, evolution, and genetic recombination observed in living organisms. By emulating the mechanisms of genetic variation, reproduction, and survival of the fittest, genetic algorithms strive to find optimal solutions to complex problems. This computational approach involves the generation of diverse solutions, their evaluation based on a defined fitness criterion, and the evolution of increasingly refined solutions over successive generations. Genetic algorithms have proven effective in solving various optimization challenges across various fields, leveraging the inherent adaptability and efficiency inspired by the biological processes that govern evolution [4]. GAs aim to construct a population of potential solutions for a given problem, employing a fitness function to evaluate the merit of each candidate solution. The algorithm then identifies the best solutions to form a new population, perpetuating this iterative process and refining the solutions. Essential operations characterize each GA iteration:

- *initialization*: the algorithm initiates by randomly populating the pool of potential solutions;

- *selection*: optimal parents are chosen from the existing population based on their fitness.

- *crossover*: genetic material from the current population is recombined to generate novel solutions;

- *mutation*: random changes to the genetic material are introduced to prevent the algorithm from getting trapped in local optima, fostering new genetic variations;

- *evaluation*: the fitness of each solution is compared to the target, which could be based on factors such as the number of generations or the fitness of the best solution;

- *replacement*: the current population is replaced with a new generation of solutions, and this process is repeated until a satisfactory solution is found or after several generations.

In this scenario, we used the GAs and their related terminology and operations to design the architecture of the network, rather than optimizing the network's hyperparameters. A neural network is a computational model inspired by the structure and functioning of the human brain. It consists of interconnected nodes, often called neurons or artificial neurons, organized into layers.
The fundamental building blocks are the input layer, hidden layers (if any), and the output layer. In a neural network, information flows through the network feedforward, with each node connection having an associated weight. The network processes input data through these weighted connections, applying activation functions at each node to introduce non-linearity and complexity. Neural networks can learn from data through training, where the weights are adjusted based on the error between predicted and actual outputs. This learning ability allows neural networks to generalize patterns and predict or classify new, unseen data. The entity $E_i$ is represented as a vector $E_i = \{F_1, ..., F_m\}$ comprising $m$ features. Each feature $F_j$ of $E_i$ indicates a gene. The Genome of $E_i$ encompasses the complete set of genes. The Population at time $t$, denoted as $P(t) = \{E_1, ..., E_n\}$, is the collection of entities. To achieve an adequately expanded network architecture in the experiment, the initial genome size was set to ten, ensuring the presence of the minimum layers needed for executing a CNN. Each gene corresponds to a specific Matlab CNN network layer: input, dropout, batch norm, cross-channel norm, 2D-convolution, RELU, softmax, and Fully Connected. Chromosomes are represented by arrays, with each cell indicating the presence or absence of a characteristic (feature) within the entity. A feature represents one of the CNN layers. Activation of a feature (array cell) incorporates the associated layer into the network, while inactivity excludes the layer when the feature is not expressed. Due to the operation mutation, each feature $F_j$ may or may not be expressed by $E_i$, resulting in the presence of both silent and expressed genes. In this context, the training was conducted using a 5% validation and 10% test cross-validation approach to assess the performance of the models and ensure robustness and generalization. This involved the division of the dataset into multiple subsets, with 5% of the data reserved for validation and 10% for testing.

To assess the effectiveness of the DL approaches, we computed several performance metrics, including the Root-mean-square error (RMSE) [5], R-squared [6], Mean Squared Error (MSE) [7], and Mean Absolute Error (MAE) [8]. RMSE is derived from the squared mean error and is a metric sensitive to outliers. R-squared serves as a goodness-of-fit metric for linear regression models, indicating the proportion of variance in the dependent variable explained by the independent variables. It quantifies the strength of the association between the model and the dependent variable. MSE reflects the mean squared difference between observed and estimated data values. On the other hand, MAE signifies the distance between predicted and measured values. Lower values for these metrics indicate higher model accuracy.

## Results and Discussion

In this section, we reported the results of the mixed data (FEM and experimental data). The execution of GAs disclosed the existence of two straightforward NN architectures. Specifically, for the flattening, the best network was a Wide Neural Network (WNN). In contrast, for the penetration, the best results were achieved by a Trilayered Neural Network (TNN), as reported in Table 2 and 3. In Fig. 1 and Fig. 2, we depicted the plot of the results.

The WNN refers to an artificial neural network architecture that typically has fewer hidden layers but a substantial number of nodes in each layer, providing it with the capacity to capture a wide range of features and relationships within the data. In this context, the TNN is formed by one fully connected layer and a ReLU activation function. The TNN is a form of artificial neural network, also known as a single-layer perceptron. It consists of three layers: an input layer, which receives the initial data; a hidden layer, which processes the input data using weighted connections and activation functions; and an output layer, which produces the final results. The obtained TNN comprises three fully connected layers and a ReLU activation function. The selected NN models demonstrate enhanced penetration values on the validation set but decreased penetration performance on the test set. On the other hand, on the test set, WNN reached the best performance. Additional studies and model evaluation metrics are required to understand this behavior.
The fundamental building blocks are the input layer, hidden layers (if any), and the output layer. In a neural network, information flows through the network feedforward, with each node connection having an associated weight. The network processes input data through these weighted connections, applying activation functions at each node to introduce non-linearity and complexity. Neural networks can learn from data through training, where the weights are adjusted based on the error between predicted and actual outputs. This learning ability allows neural networks to generalize patterns and predict or classify new, unseen data. The entity $E_i$ is represented as a vector $E_i = \{F_1, ..., F_m\}$ comprising $m$ features. Each feature $F_j$ of $E_i$ indicates a gene. The Genome of $E_i$ encompasses the complete set of genes. The Population at time $t$, denoted as $P(t) = \{E_1, ..., E_n\}$, is the collection of entities. To achieve an adequately expanded network architecture in the experiment, the initial genome size was set to ten, ensuring the presence of the minimum layers needed for executing a CNN. Each gene corresponds to a specific Matlab CNN network layer: input, dropout, batch norm, cross-channel norm, 2D-convolution, RELU, softmax, and Fully Connected. Chromosomes are represented by arrays, with each cell indicating the presence or absence of a characteristic (feature) within the entity. A feature represents one of the CNN layers. Activation of a feature (array cell) incorporates the associated layer into the network, while inactivity excludes the layer when the feature is not expressed. Due to the operation mutation, each feature $F_j$ may or may not be expressed by $E_i$, resulting in the presence of both silent and expressed genes. In this context, the training was conducted using a 5% validation and 10% test cross-validation approach to assess the performance of the models and ensure robustness and generalization. This involved the division of the dataset into multiple subsets, with 5% of the data reserved for validation and 10% for testing.

To assess the effectiveness of the DL approaches, we computed several performance metrics, including the Root-mean-square error (RMSE) [5], R-squared [6], Mean Squared Error (MSE) [7], and Mean Absolute Error (MAE) [8]. RMSE is derived from the squared mean error and is a metric sensitive to outliers. R-squared serves as a goodness-of-fit metric for linear regression models, indicating the proportion of variance in the dependent variable explained by the independent variables. It quantifies the strength of the association between the model and the dependent variable. MSE reflects the mean squared difference between observed and estimated data values. On the other hand, MAE signifies the distance between predicted and measured values. Lower values for these metrics indicate higher model accuracy.

## Results and Discussion

In this section, we reported the results of the mixed data (FEM and experimental data). The execution of GAs disclosed the existence of two straightforward NN architectures. Specifically, for the flattening, the best network was a Wide Neural Network (WNN). In contrast, for the penetration, the best results were achieved by a Trilayered Neural Network (TNN), as reported in Table 2 and 3. In Fig. 1 and Fig. 2, we depicted the plot of the results.

The WNN refers to an artificial neural network architecture that typically has fewer hidden layers but a substantial number of nodes in each layer, providing it with the capacity to capture a wide range of features and relationships within the data. In this context, the TNN is formed by one fully connected layer and a ReLU activation function. The TNN is a form of artificial neural network, also known as a single-layer perceptron. It consists of three layers: an input layer, which receives the initial data; a hidden layer, which processes the input data using weighted connections and activation functions; and an output layer, which produces the final results. The obtained TNN comprises three fully connected layers and a ReLU activation function. The selected NN models demonstrate enhanced penetration values on the validation set but decreased penetration performance on the test set. On the other hand, on the test set, WNN reached the best performance. Additional studies and model evaluation metrics are required to understand this behavior.
<table><caption>Table 2. Results for flattening on validation and test sets</caption><thead><tr><th>Model: WNN</th><th>RMSE</th><th>R-Squared</th><th>MSE</th><th>MAE</th></tr></thead><tbody><tr><td>Validation set</td><td>2.48</td><td>0.75</td><td>6.16</td><td>1.67</td></tr><tr><td>Test set</td><td>0.73</td><td>0.92</td><td>0.54</td><td>0.51</td></tr></tbody></table>

<table><caption>Table 3. Results for penetration depth on validation and test sets</caption><thead><tr><th>Model: TNN</th><th>RMSE</th><th>R-Squared</th><th>MSE</th><th>MAE</th></tr></thead><tbody><tr><td>Validation set</td><td>0.80</td><td>0.96</td><td>0.64</td><td>0.49</td></tr><tr><td>Test set</td><td>2.57</td><td>0.60</td><td>6.64</td><td>1.46</td></tr></tbody></table>

<figure><img src="image_294.png" /><figcaption>Fig. 1. Performance for the flattening prediction for mixed data</figcaption></figure>
<table><caption>Table 2. Results for flattening on validation and test sets</caption><thead><tr><th>Model: WNN</th><th>RMSE</th><th>R-Squared</th><th>MSE</th><th>MAE</th></tr></thead><tbody><tr><td>Validation set</td><td>2.48</td><td>0.75</td><td>6.16</td><td>1.67</td></tr><tr><td>Test set</td><td>0.73</td><td>0.92</td><td>0.54</td><td>0.51</td></tr></tbody></table>

<table><caption>Table 3. Results for penetration depth on validation and test sets</caption><thead><tr><th>Model: TNN</th><th>RMSE</th><th>R-Squared</th><th>MSE</th><th>MAE</th></tr></thead><tbody><tr><td>Validation set</td><td>0.80</td><td>0.96</td><td>0.64</td><td>0.49</td></tr><tr><td>Test set</td><td>2.57</td><td>0.60</td><td>6.64</td><td>1.46</td></tr></tbody></table>

<figure><img src="image_300.png" /><figcaption>Fig. 1. Performance for the flattening prediction for mixed data</figcaption></figure>
<figure><img src="image_295.png" /><figcaption>Fig. 2. Performance for the penetration prediction for mixed data</figcaption></figure>

In this scenario, DL can contribute to expediting finite element analyses, a process known for its resource-intensive computing requirements. Additionally, DL enables predictions for novel combinations of materials, eliminating the necessity for running extra simulations. This not only enhances computational efficiency but also broadens the scope of material analysis by providing insights into unexplored configurations, thus showcasing the versatility and potential of machine learning in the domain of FEM analyses.

## Comparison with our previous work

Our previous work tested machine learning (ML) techniques on mixed data. The best models were NN for the penetration on the test set and LR for the flattening prediction, as reported in Table 4 [9]. In this previous work, our results showed high performance for the penetration value and their decrease for flattening prediction, as confirmed in this research.

<table><caption>Table 4. Results for flattening and penetration on test set with ML techniques</caption><thead><tr><td>Models</td><td>RMSE</td><td>R-Squared</td><td>MSE</td><td>MAE</td></tr></thead><tbody><tr><td>LR</td><td>1.83</td><td>0.90</td><td>3.36</td><td>1.45</td></tr><tr><td>NN</td><td>0.58</td><td>0.96</td><td>0.34</td><td>0.41</td></tr></tbody></table>
<figure><img src="image_301.png" /><figcaption>Fig. 2. Performance for the penetration prediction for mixed data</figcaption></figure>

In this scenario, DL can contribute to expediting finite element analyses, a process known for its resource-intensive computing requirements. Additionally, DL enables predictions for novel combinations of materials, eliminating the necessity for running extra simulations. This not only enhances computational efficiency but also broadens the scope of material analysis by providing insights into unexplored configurations, thus showcasing the versatility and potential of machine learning in the domain of FEM analyses.

## Comparison with our previous work

Our previous work tested machine learning (ML) techniques on mixed data. The best models were NN for the penetration on the test set and LR for the flattening prediction, as reported in Table 4 [9]. In this previous work, our results showed high performance for the penetration value and their decrease for flattening prediction, as confirmed in this research.

<table><caption>Table 4. Results for flattening and penetration on test set with ML techniques</caption><thead><tr><td>Models</td><td>RMSE</td><td>R-Squared</td><td>MSE</td><td>MAE</td></tr></thead><tbody><tr><td>LR</td><td>1.83</td><td>0.90</td><td>3.36</td><td>1.45</td></tr><tr><td>NN</td><td>0.58</td><td>0.96</td><td>0.34</td><td>0.41</td></tr></tbody></table>
<table><caption>Table 5. Comparison of the models</caption><thead><tr><th>Output</th><th>Models</th><th>RMSE</th><th>R-Squared</th><th>MSE</th><th>MAE</th></tr></thead><tbody><tr><td rowspan="2">Flattening</td><td>LR</td><td>1.83</td><td>0.90</td><td>3.36</td><td>1.45</td></tr><tr><td>WNN</td><td>0.73</td><td>0.92</td><td>0.54</td><td>0.51</td></tr><tr><td rowspan="2">Penetration</td><td>NN</td><td>0.58</td><td>0.96</td><td>0.34</td><td>0.41</td></tr><tr><td>TNN</td><td>2.57</td><td>0.60</td><td>6.64</td><td>1.46</td></tr></tbody></table>

In Table 5, we compared the results of the previous work and the approach presented in this study. WNN achieved a high performance for the flattening of the evaluation metrics. For the penetration, the TNN designed using GA shows worse performance. We graphically represented this comparison in Fig. 3. The best model for penetration prediction is NN. We highlighted the best results for flattening and penetration with the color view in Table 4.

<figure><img src="image_296.png" /><figcaption>Fig. 3. Comparison of performance</figcaption></figure>

## Conclusions

This study applied DL techniques on a mixed dataset for CS to enhance the precision of predicting coating features, such as penetration and flattening, to optimize process efficiency. To design the
<table><caption>Table 5. Comparison of the models</caption><thead><tr><th>Output</th><th>Models</th><th>RMSE</th><th>R-Squared</th><th>MSE</th><th>MAE</th></tr></thead><tbody><tr><td rowspan="2">Flattening</td><td>LR</td><td>1.83</td><td>0.90</td><td>3.36</td><td>1.45</td></tr><tr><td>WNN</td><td>0.73</td><td>0.92</td><td>0.54</td><td>0.51</td></tr><tr><td rowspan="2">Penetration</td><td>NN</td><td>0.58</td><td>0.96</td><td>0.34</td><td>0.41</td></tr><tr><td>TNN</td><td>2.57</td><td>0.60</td><td>6.64</td><td>1.46</td></tr></tbody></table>

In Table 5, we compared the results of the previous work and the approach presented in this study. WNN achieved a high performance for the flattening of the evaluation metrics. For the penetration, the TNN designed using GA shows worse performance. We graphically represented this comparison in Fig. 3. The best model for penetration prediction is NN. We highlighted the best results for flattening and penetration with the color view in Table 4.

<figure><img src="image_302.png" /><figcaption>Fig. 3. Comparison of performance</figcaption></figure>

## Conclusions

This study applied DL techniques on a mixed dataset for CS to enhance the precision of predicting coating features, such as penetration and flattening, to optimize process efficiency. To design the
architecture of the models, GA is applied to construct the network. The best models are WNN for the prediction of the flattening and TNN for the prediction of the penetration depth. Specifically, WNN can outperform our previous AI models. The conducted experiments demonstrate that DL techniques have the potential to predict optimal parameter combinations, consequently amplifying the efficiency and effectiveness of the coating process. GA can help optimize the design model architectures to minimize the need for manual hyperparameter tuning. This process can be time-consuming and less than optimal. Exploring multi-output regression networks can be considered a potential approach in future works. By employing multi-output regression networks, the model can directly predict multiple outputs simultaneously, potentially enhancing the overall performance and applicability of the system.

## Acknowledgments

This study was carried out within the "OPTIMA: depOsition of cold sPray in the realm of green addiTIve manufacturing through construction of MAchine learning models" and received funding from the Research Projects of Significant National Interest (PRIN) 2022 PNRR, project n. D53D23017410001.

## References

[1] Assadi, H., Kreye, H., Gärtner, F., & Klassen, T. J. A. M. (2016). Cold spraying–A materials perspective. Acta Materialia, 116, 382-407.

[2] Singh, S., Raman, R. S., Berndt, C. C., & Singh, H. (2021). Influence of cold spray parameters on bonding mechanisms: A review. Metals, 11(12), 2016.

[3] Hughes, Thomas JR. The finite element method: linear static and dynamic finite element analysis. Courier Corporation, 2012.

[4] Kramer, Oliver, and Oliver Kramer. Genetic algorithms. Springer International Publishing, 2017.

[5] C.E. Rasmussen, Gaussian Processes in machine learning, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3176 (2004) 63–71. https://doi.org/10.1007/978-3-540-28650-9_4/COVER

[6] J. Nevitt, G.R. Hancock, Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling, 68 (2010) 251–268. https://doi.org/10.1080/00220970009600095

[7] G.I. Webb, C. Sammut, C. Perlich, T. Horváth, S. Wrobel, K.B. Korb, W.S. Noble, C. Leslie, M.G. Lagoudakis, N. Quadrianto, W.L. Buntine, N. Quadrianto, W.L. Buntine, L. Getoor, G. Namata, L. Getoor, X.J.J. Han, J.-A. Ting, S. Vijayakumar, S. Schaal, L. de Raedt, Encyclopedia of Machine Learning, Encyclopedia of Machine Learning. (2010) 613–624. https://doi.org/10.1007/978-0-387-30164-8_488

[8] J. Fürnkranz, P.K. Chan, S. Craw, C. Sammut, W. Uther, A. Ratnaparkhi, X. Jin, J. Han, Y. Yang, K. Morik, M. Dorigo, M. Birattari, T. Stützle, P. Brazdil, R. Vilalta, C. Giraud-Carrier, C. Soares, J. Rissanen, R.A. Baxter, I. Bruha, R.A. Baxter, G.I. Webb, L. Torgo, A. Banerjee, H. Shan, S. Ray, P. Tadepalli, Y. Shoham, R. Powers, Y. Shoham, R. Powers, G.I. Webb, S. Ray, S. Scott, H. Blockeel, L. De Raedt, Mean Absolute Error, Encyclopedia of Machine Learning. (2011) 652–652. https://doi.org/10.1007/978-0-387-30164-8_525

[9] Perna, Alessia Serena, et al. "A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates." Materials Research Proceedings 28 (2023): 57-64. https://doi.org/10.21741/9781644902479-7
architecture of the models, GA is applied to construct the network. The best models are WNN for the prediction of the flattening and TNN for the prediction of the penetration depth. Specifically, WNN can outperform our previous AI models. The conducted experiments demonstrate that DL techniques have the potential to predict optimal parameter combinations, consequently amplifying the efficiency and effectiveness of the coating process. GA can help optimize the design model architectures to minimize the need for manual hyperparameter tuning. This process can be time-consuming and less than optimal. Exploring multi-output regression networks can be considered a potential approach in future works. By employing multi-output regression networks, the model can directly predict multiple outputs simultaneously, potentially enhancing the overall performance and applicability of the system.

## Acknowledgments

This study was carried out within the "OPTIMA: depOsition of cold sPray in the realm of green addiTIve manufacturing through construction of MAchine learning models" and received funding from the Research Projects of Significant National Interest (PRIN) 2022 PNRR, project n. D53D23017410001.

## References

[1] Assadi, H., Kreye, H., Gärtner, F., & Klassen, T. J. A. M. (2016). Cold spraying–A materials perspective. Acta Materialia, 116, 382-407.

[2] Singh, S., Raman, R. S., Berndt, C. C., & Singh, H. (2021). Influence of cold spray parameters on bonding mechanisms: A review. Metals, 11(12), 2016.

[3] Hughes, Thomas JR. The finite element method: linear static and dynamic finite element analysis. Courier Corporation, 2012.

[4] Kramer, Oliver, and Oliver Kramer. Genetic algorithms. Springer International Publishing, 2017.

[5] C.E. Rasmussen, Gaussian Processes in machine learning, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3176 (2004) 63–71. https://doi.org/10.1007/978-3-540-28650-9_4/COVER

[6] J. Nevitt, G.R. Hancock, Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling, 68 (2010) 251–268. https://doi.org/10.1080/00220970009600095

[7] G.I. Webb, C. Sammut, C. Perlich, T. Horváth, S. Wrobel, K.B. Korb, W.S. Noble, C. Leslie, M.G. Lagoudakis, N. Quadrianto, W.L. Buntine, N. Quadrianto, W.L. Buntine, L. Getoor, G. Namata, L. Getoor, X.J.J. Han, J.-A. Ting, S. Vijayakumar, S. Schaal, L. de Raedt, Encyclopedia of Machine Learning, Encyclopedia of Machine Learning. (2010) 613–624. https://doi.org/10.1007/978-0-387-30164-8_488

[8] J. Fürnkranz, P.K. Chan, S. Craw, C. Sammut, W. Uther, A. Ratnaparkhi, X. Jin, J. Han, Y. Yang, K. Morik, M. Dorigo, M. Birattari, T. Stützle, P. Brazdil, R. Vilalta, C. Giraud-Carrier, C. Soares, J. Rissanen, R.A. Baxter, I. Bruha, R.A. Baxter, G.I. Webb, L. Torgo, A. Banerjee, H. Shan, S. Ray, P. Tadepalli, Y. Shoham, R. Powers, Y. Shoham, R. Powers, G.I. Webb, S. Ray, S. Scott, H. Blockeel, L. De Raedt, Mean Absolute Error, Encyclopedia of Machine Learning. (2011) 652–652. https://doi.org/10.1007/978-0-387-30164-8_525

[9] Perna, Alessia Serena, et al. "A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates." Materials Research Proceedings 28 (2023): 57-64. https://doi.org/10.21741/9781644902479-7