# Improvement of the surface quality of titanium-based design objects produced through WAAM technology using chemical machining: A preliminary study

PERNA Alessia Serena¹,ª*, SCHERILLO Fabio¹,ᵇ*, SQUILLACE Antonino¹,ᶜ, CAMPATELLI Gianni²,ᵈ, CARVALHO H.S.F.L. Gustavo²,ᵉ

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

²Department of Industrial Engineering - University of Firenze - 50139 Firenze, Italy

**Keywords:** Additive Manufacturing, WAAM, Chemical Machining, Titanium, Design

**Abstract.** The quality of the surface is one of the most important factors in the fabrication of a component via additive manufacturing (AM). In particular, when considering the manufacture of workpieces in titanium and its alloys the successful use of surface treatments is essential. In fact, many fracture-related events, in particular fatigue cracks, start near the surface of the component. Numerous techniques based on machining, shot peening, or laser polishing have been proposed to enhance the surface quality. The limitations of these treatments stem from the challenges posed by focusing on complex form components. One of the most promising approaches for achieving homogenous smoothing of intricate objects with internal channels and lattice structure continues to be chemical-based surface treatments. It is a pivotal method to remove material that has been polluted by oxygen during processing. In this instance, the resistance to crack initiation and fracture is fundamentally improved by the removal of a hard, brittle top layer. In this work, HF/HNO₃-based treatment tailored for 3D printed design products is presented.

**Introduction**

The quality of the surface is one of the most important factors in the fabrication of a component via additive manufacturing (AM) [1]. Surface quality has a significant impact on the mechanical characteristics of the component: sharp peaks and valleys which serve as stress concentrators are typically found on the surface of an additive-manufactured product [2]. In particular, when considering the manufacture of workpieces in titanium and its alloys the successful use of surface treatments is essential [3]. This is due to the fact that many fracture-related events, in particular fatigue cracks, start near the surface of the component [4]. Numerous techniques based on machining, shot peening, or laser polishing have been proposed to enhance the surface quality [5]. Steel has long been subjected to shot peening processes to increase its resistance to the onset of fatigue cracks by applying a compressive force to the surface. Although not novel, the use of this approach to titanium alloy components is more recent than it is to steel [6–8]. Titanium components with stress-free surfaces have a relatively poor resistance to fatigue: the fatigue strength at 10 cycles to failure, expressed as a percentage of the yield stress is typically between 0.4 and 0.5 [9]. This value is further diminished when damage from production procedures like milling is added. Local plastic deformation during shot peening introduces compressive stresses into the material’s near-surface region to prevent this additional decrease in fatigue resistance [8].

More recently, it has been demonstrated that applying a laser to induce compressive stress on the surface is quite advantageous [10,11]. This method applies a localized pressure to the material's surface using a high-intensity pulsed laser. This pressure causes residual compressive stress since

<figure><img src="image_297.png" /></figure>

<footnote>ªalessiaserena.perna@unina.it, ᵇfabio.scherillo@unina.it, ᶜsquillac@unina.it,</footnote>

<footnote>ᶜgianni.campatelli@unifi.it, ᵈgustavo.carvalho@unifi.it</footnote>
# Improvement of the surface quality of titanium-based design objects produced through WAAM technology using chemical machining: A preliminary study

PERNA Alessia Serena¹,ª*, SCHERILLO Fabio¹,ᵇ*, SQUILLACE Antonino¹,ᶜ, CAMPATELLI Gianni²,ᵈ, CARVALHO H.S.F.L. Gustavo²,ᵉ

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

²Department of Industrial Engineering - University of Firenze - 50139 Firenze, Italy

**Keywords:** Additive Manufacturing, WAAM, Chemical Machining, Titanium, Design

**Abstract.** The quality of the surface is one of the most important factors in the fabrication of a component via additive manufacturing (AM). In particular, when considering the manufacture of workpieces in titanium and its alloys the successful use of surface treatments is essential. In fact, many fracture-related events, in particular fatigue cracks, start near the surface of the component. Numerous techniques based on machining, shot peening, or laser polishing have been proposed to enhance the surface quality. The limitations of these treatments stem from the challenges posed by focusing on complex form components. One of the most promising approaches for achieving homogenous smoothing of intricate objects with internal channels and lattice structure continues to be chemical-based surface treatments. It is a pivotal method to remove material that has been polluted by oxygen during processing. In this instance, the resistance to crack initiation and fracture is fundamentally improved by the removal of a hard, brittle top layer. In this work, HF/HNO₃-based treatment tailored for 3D printed design products is presented.

**Introduction**

The quality of the surface is one of the most important factors in the fabrication of a component via additive manufacturing (AM) [1]. Surface quality has a significant impact on the mechanical characteristics of the component: sharp peaks and valleys which serve as stress concentrators are typically found on the surface of an additive-manufactured product [2]. In particular, when considering the manufacture of workpieces in titanium and its alloys the successful use of surface treatments is essential [3]. This is due to the fact that many fracture-related events, in particular fatigue cracks, start near the surface of the component [4]. Numerous techniques based on machining, shot peening, or laser polishing have been proposed to enhance the surface quality [5]. Steel has long been subjected to shot peening processes to increase its resistance to the onset of fatigue cracks by applying a compressive force to the surface. Although not novel, the use of this approach to titanium alloy components is more recent than it is to steel [6–8]. Titanium components with stress-free surfaces have a relatively poor resistance to fatigue: the fatigue strength at 10 cycles to failure, expressed as a percentage of the yield stress is typically between 0.4 and 0.5 [9]. This value is further diminished when damage from production procedures like milling is added. Local plastic deformation during shot peening introduces compressive stresses into the material’s near-surface region to prevent this additional decrease in fatigue resistance [8].

More recently, it has been demonstrated that applying a laser to induce compressive stress on the surface is quite advantageous [10,11]. This method applies a localized pressure to the material's surface using a high-intensity pulsed laser. This pressure causes residual compressive stress since

<figure><img src="image_303.png" /></figure>

<footnote>ªalessiaserena.perna@unina.it, ᵇfabio.scherillo@unina.it, ᶜsquillac@unina.it,</footnote>

<footnote>ᶜgianni.campatelli@unifi.it, ᵈgustavo.carvalho@unifi.it</footnote>
it is greater than the yield stress. The enhanced depth of the compressive stress layer with about the same stress magnitude is the main benefit of laser shock processing over traditional shot peening [12].

The limitations of these treatments stem from the challenges posed by focusing on complex form components [13–15]. One of the most promising approaches for achieving homogenous smoothing of intricate objects with internal channels and lattice structures continues to be chemical and electrochemical-based surface treatments [16–18]. It is a pivotal method to remove material that has been polluted by oxygen during processing. In this instance, the resistance to crack initiation and fracture is fundamentally improved by the removal of a hard, brittle top layer. The fundamental benefit of these methods arises from the high mobility of the chemically aggressive solutions, whose smoothing effects can, in certain situations, be more precisely regulated and enhanced if driven by an electric current [19]. As a result, this class of treatments offers the greatest employment opportunity. Several authors exploited chemical polishing to conduct experimental research on the surface finishing of AM components. In a recent study, Tyagi et al. [20] evaluated the chemical polishing of SLM-produced SS316L components and achieved surface roughness reductions of around 92% for both the inner and outer surfaces. Scherillo [21] studied the impact of chemical polishing on AlSi10Mg samples produced by SLM, yielding notable surface quality improvements assessed by a number of surface texture indicators on less complicated geometries. Furthermore, Wysocki et al. [22] demonstrated that chemical machining was efficient in enhancing the surface quality of Ti6Al4V scaffolds produced by EBM while the treatment did not influence the capacity of the surface to integrate bone cells. In a more recent activity, Scherillo et al. [23] proposed a chemical machining approach to treat components produced by Ti6Al4V using EBM. Since HF and HNO₃ are typically used to treat this alloy, the goal of this work was to determine how the ratio of HF to HNO₃ affects the final surface quality, with a particular emphasis on the chemical-physical phenomena that take place throughout the process. The surface quality of Ti6Al4V components produced by EBM has been shown to be drastically improved by chemical machining, and all the solutions used, with varied HF/HNO₃ ratios, have been shown to have smoothing properties. The ultimate quality of a metal surface is improved by the addition of HNO₃, and the decrease of the studied surface parameter increases with increasing HNO₃ concentration. Moreover, it was demonstrated that there are two main phases in the smoothing process. The unfused particles that were left on the surface are removed in the first stage, and the distinctive sharp peaks are selectively dissolved in the second. The solution's transport characteristics have proved to be crucial in producing this outcome; as the concentration of HNO₃ rises, the viscosity increases, and the smoothing effect changes appropriately. Without HNO₃, gaseous H₂ is formed on the metal surface, with the gas bubbles serving as a barrier between the surface and the solution, resulting in a lower material removal rate and worse final quality. A passivation step was further introduced by the injection of HNO₃, and during this stage, ligands were found to be redistributed on the surface, reducing the change in surface composition relative to the bulk alloy. Following what has been done in the previous literature studies, it is therefore important to determine the best approach for reducing surface defects and removing oxides from the surface of titanium components manufactured using different AM technologies and characterized by more complicated geometries. It is also required to identify treatments with a minimal environmental effect that improve aesthetics (in terms of roughness, gloss, etc.) for the creation of design items in titanium alloys.

## Materials and methods.

Titanium alloy samples were produced using WAAM technology. In order to improve both the aesthetic appearance and functionality of the samples, a chemical machining and brightening treatment was performed on the samples. Each sample was cut into sections by making two cuts using a metallographic cut machine, using a K-B type grinding wheel. The larger-area specimen
it is greater than the yield stress. The enhanced depth of the compressive stress layer with about the same stress magnitude is the main benefit of laser shock processing over traditional shot peening [12].

The limitations of these treatments stem from the challenges posed by focusing on complex form components [13–15]. One of the most promising approaches for achieving homogenous smoothing of intricate objects with internal channels and lattice structures continues to be chemical and electrochemical-based surface treatments [16–18]. It is a pivotal method to remove material that has been polluted by oxygen during processing. In this instance, the resistance to crack initiation and fracture is fundamentally improved by the removal of a hard, brittle top layer. The fundamental benefit of these methods arises from the high mobility of the chemically aggressive solutions, whose smoothing effects can, in certain situations, be more precisely regulated and enhanced if driven by an electric current [19]. As a result, this class of treatments offers the greatest employment opportunity. Several authors exploited chemical polishing to conduct experimental research on the surface finishing of AM components. In a recent study, Tyagi et al. [20] evaluated the chemical polishing of SLM-produced SS316L components and achieved surface roughness reductions of around 92% for both the inner and outer surfaces. Scherillo [21] studied the impact of chemical polishing on AlSi10Mg samples produced by SLM, yielding notable surface quality improvements assessed by a number of surface texture indicators on less complicated geometries. Furthermore, Wysocki et al. [22] demonstrated that chemical machining was efficient in enhancing the surface quality of Ti6Al4V scaffolds produced by EBM while the treatment did not influence the capacity of the surface to integrate bone cells. In a more recent activity, Scherillo et al. [23] proposed a chemical machining approach to treat components produced by Ti6Al4V using EBM. Since HF and HNO₃ are typically used to treat this alloy, the goal of this work was to determine how the ratio of HF to HNO₃ affects the final surface quality, with a particular emphasis on the chemical-physical phenomena that take place throughout the process. The surface quality of Ti6Al4V components produced by EBM has been shown to be drastically improved by chemical machining, and all the solutions used, with varied HF/HNO₃ ratios, have been shown to have smoothing properties. The ultimate quality of a metal surface is improved by the addition of HNO₃, and the decrease of the studied surface parameter increases with increasing HNO₃ concentration. Moreover, it was demonstrated that there are two main phases in the smoothing process. The unfused particles that were left on the surface are removed in the first stage, and the distinctive sharp peaks are selectively dissolved in the second. The solution's transport characteristics have proved to be crucial in producing this outcome; as the concentration of HNO₃ rises, the viscosity increases, and the smoothing effect changes appropriately. Without HNO₃, gaseous H₂ is formed on the metal surface, with the gas bubbles serving as a barrier between the surface and the solution, resulting in a lower material removal rate and worse final quality. A passivation step was further introduced by the injection of HNO₃, and during this stage, ligands were found to be redistributed on the surface, reducing the change in surface composition relative to the bulk alloy. Following what has been done in the previous literature studies, it is therefore important to determine the best approach for reducing surface defects and removing oxides from the surface of titanium components manufactured using different AM technologies and characterized by more complicated geometries. It is also required to identify treatments with a minimal environmental effect that improve aesthetics (in terms of roughness, gloss, etc.) for the creation of design items in titanium alloys.

## Materials and methods.

Titanium alloy samples were produced using WAAM technology. In order to improve both the aesthetic appearance and functionality of the samples, a chemical machining and brightening treatment was performed on the samples. Each sample was cut into sections by making two cuts using a metallographic cut machine, using a K-B type grinding wheel. The larger-area specimen
was used for the chemical machining treatment, while the remaining two were used for metallographic preparation and as backup reference specimens before performing machining and treatments, as shown in Fig. 1.

<figure><img src="image_298.png" /><figcaption>Figure 1. Schematic representation of sample cutting.</figcaption></figure>

The purpose of the analyses performed before the treatment is to evaluate thicknesses to be removed. In fact, when titanium is exposed to an oxidizing atmosphere, in addition to the oxide layer, there is usually an additional layer called the "alpha case," which, due to the presence of oxygen, is harder and therefore more brittle than the bulk material. It is therefore necessary to assess the thickness of the alpha case and remove it completely to prevent the manufactured object from easily surface damage during use. The sample portions used for metallographic analysis were pre-embedded in hot mounting resin; afterwards, the samples were grinded and polished. Subsequently, in order to observe the microstructure, a chemical etching treatment was carried out using a solution made of 1 ml of hydrofluoric acid (48 wt%) and 100 ml of H₂O. The embedded samples were observed employing the optical microscope in order to evaluate the different microstructures present in the border and bulk before treatment and verify the removal of oxides and alpha case after treatment

The chemical treatment was carried out with a solution of hydrofluoric acid (48 wt%) and nitric acid (69 wt%) in a molar ratio of HF/HNO₃ equal to 1 to 3. Treatment times of 30, 60, and 90 minutes were evaluated. The samples were completely immersed in the solution, with the lateral walls shielded using epoxy resin to ensure material removal only on the top and bottom surfaces of the samples. The solution was kept agitated using a magnetic stirrer throughout the treatment. No auxiliary heating or cooling equipment was used, and a sketch of the equipment used for the treatment is shown in Fig.2
was used for the chemical machining treatment, while the remaining two were used for metallographic preparation and as backup reference specimens before performing machining and treatments, as shown in Fig. 1.

<figure><img src="image_304.png" /><figcaption>Figure 1. Schematic representation of sample cutting.</figcaption></figure>

The purpose of the analyses performed before the treatment is to evaluate thicknesses to be removed. In fact, when titanium is exposed to an oxidizing atmosphere, in addition to the oxide layer, there is usually an additional layer called the "alpha case," which, due to the presence of oxygen, is harder and therefore more brittle than the bulk material. It is therefore necessary to assess the thickness of the alpha case and remove it completely to prevent the manufactured object from easily surface damage during use. The sample portions used for metallographic analysis were pre-embedded in hot mounting resin; afterwards, the samples were grinded and polished. Subsequently, in order to observe the microstructure, a chemical etching treatment was carried out using a solution made of 1 ml of hydrofluoric acid (48 wt%) and 100 ml of H₂O. The embedded samples were observed employing the optical microscope in order to evaluate the different microstructures present in the border and bulk before treatment and verify the removal of oxides and alpha case after treatment

The chemical treatment was carried out with a solution of hydrofluoric acid (48 wt%) and nitric acid (69 wt%) in a molar ratio of HF/HNO₃ equal to 1 to 3. Treatment times of 30, 60, and 90 minutes were evaluated. The samples were completely immersed in the solution, with the lateral walls shielded using epoxy resin to ensure material removal only on the top and bottom surfaces of the samples. The solution was kept agitated using a magnetic stirrer throughout the treatment. No auxiliary heating or cooling equipment was used, and a sketch of the equipment used for the treatment is shown in Fig.2
<figure><img src="image_299.png" /><figcaption>Figure 2. Scheme of the setup for chemical machining treatments.</figcaption></figure>

In order to assess the effectiveness of chemical machining treatment, the samples were analysed, weighed, and measured before and after treatment. The samples were observed under an optical microscope to evaluate their microstructure and measure the thickness of oxide and alpha case. An example of a micrograph of an untreated observed sample is shown in Fig.3.

**Result and discussion.**

An example of a micrograph of an untreated observed sample is shown in Fig.3.

<figure><img src="image_300.png" /><figcaption>Figure 3. Optical micrograph of untreated specimen with evident the alpha case.</figcaption></figure>

Fig.3 shows the effect of the different reactivity to the metallographic etching solution between the alpha case and the bulk, as evidenced by the demarcation lines. This allows for a certain determination of the total thickness of the alpha case. It is possible to establish that the thickness of material to be removed, in order to ensure complete removal of the alpha case, is approximately 100 microns.
<figure><img src="image_305.png" /><figcaption>Figure 2. Scheme of the setup for chemical machining treatments.</figcaption></figure>

In order to assess the effectiveness of chemical machining treatment, the samples were analysed, weighed, and measured before and after treatment. The samples were observed under an optical microscope to evaluate their microstructure and measure the thickness of oxide and alpha case. An example of a micrograph of an untreated observed sample is shown in Fig.3.

**Result and discussion.**

An example of a micrograph of an untreated observed sample is shown in Fig.3.

<figure><img src="image_306.png" /><figcaption>Figure 3. Optical micrograph of untreated specimen with evident the alpha case.</figcaption></figure>

Fig.3 shows the effect of the different reactivity to the metallographic etching solution between the alpha case and the bulk, as evidenced by the demarcation lines. This allows for a certain determination of the total thickness of the alpha case. It is possible to establish that the thickness of material to be removed, in order to ensure complete removal of the alpha case, is approximately 100 microns.
In order to evaluate the amount of material removed during the treatment, the weights of the samples before and after the treatment were measured. The amount of material removed increases non-linearly with increasing treatment time, as shown in Fig.4, which shows the graphs of the percentage weight loss (Figure 4a) and percentage thickness reduction (Figure 4b) for the different treatment times.

<figure><img src="image_301.png" /><figcaption>Figure 4. a) Percentage weight loss after 30, 60, and 90 seconds of treatment b) Percentage thickness reduction after 30, 60, and 90 minutes of treatment.</figcaption></figure>

It is evident that the treatment carried out guarantees the removal of a thickness of material from the surface of the samples.

In order to appreciate the variation in brightness of the samples after the chemical treatment and make an initial assessment of the morphological changes of the samples, the images were acquired using a stereomicroscope of the samples before and after treatment. In Fig.5, it is possible to observe the comparisons between samples that underwent machining treatment for 90 minutes.

<figure><img src="image_302.png" /><figcaption>Figure 5. Observation through stereomicroscope of a) Untreated sample b) Sample after a 90-minute chemical machining treatment.</figcaption></figure>
In order to evaluate the amount of material removed during the treatment, the weights of the samples before and after the treatment were measured. The amount of material removed increases non-linearly with increasing treatment time, as shown in Fig.4, which shows the graphs of the percentage weight loss (Figure 4a) and percentage thickness reduction (Figure 4b) for the different treatment times.

<figure><img src="image_307.png" /><figcaption>Figure 4. a) Percentage weight loss after 30, 60, and 90 seconds of treatment b) Percentage thickness reduction after 30, 60, and 90 minutes of treatment.</figcaption></figure>

It is evident that the treatment carried out guarantees the removal of a thickness of material from the surface of the samples.

In order to appreciate the variation in brightness of the samples after the chemical treatment and make an initial assessment of the morphological changes of the samples, the images were acquired using a stereomicroscope of the samples before and after treatment. In Fig.5, it is possible to observe the comparisons between samples that underwent machining treatment for 90 minutes.

<figure><img src="image_308.png" /><figcaption>Figure 5. Observation through stereomicroscope of a) Untreated sample b) Sample after a 90-minute chemical machining treatment.</figcaption></figure>
It can be noticed that, as the treatment time increases, the peaks on the surface as well as on the side walls become smoother. After 90 minutes of treatment, the samples appear particularly shiny and smoothed.

The samples were observed under an optical microscope to evaluate the microstructure and the removal of alpha case present at the border. In Fig.6, Fig.7 and Fig.8 it is possible to observe the comparisons between micrographs of samples that underwent machining treatments lasting respectively 30,60 and 90 minutes.

<figure><img src="image_303.png" /><figcaption>Figure 6. Observation under optical microscope of a) Untreated Sample b) Sample after 30 minutes of chemical machining treatment.</figcaption></figure>

<figure><img src="image_304.png" /><figcaption>Figure 7. Observation under optical microscope of a) Untreated Sample b) Sample after 60 minutes of chemical machining treatment.</figcaption></figure>

<figure><img src="image_305.png" /><figcaption>Figure 8. Optical microscope observation of a) Untreated Sample b) Sample after 90 minutes of chemical machining treatment.</figcaption></figure>

It is evident that after 30 minutes of treatment, there is not complete removal of the alpha case. After 60 minutes of treatment, the microstructure at the border appears completely similar to that
It can be noticed that, as the treatment time increases, the peaks on the surface as well as on the side walls become smoother. After 90 minutes of treatment, the samples appear particularly shiny and smoothed.

The samples were observed under an optical microscope to evaluate the microstructure and the removal of alpha case present at the border. In Fig.6, Fig.7 and Fig.8 it is possible to observe the comparisons between micrographs of samples that underwent machining treatments lasting respectively 30,60 and 90 minutes.

<figure><img src="image_309.png" /><figcaption>Figure 6. Observation under optical microscope of a) Untreated Sample b) Sample after 30 minutes of chemical machining treatment.</figcaption></figure>

<figure><img src="image_310.png" /><figcaption>Figure 7. Observation under optical microscope of a) Untreated Sample b) Sample after 60 minutes of chemical machining treatment.</figcaption></figure>

<figure><img src="image_311.png" /><figcaption>Figure 8. Optical microscope observation of a) Untreated Sample b) Sample after 90 minutes of chemical machining treatment.</figcaption></figure>

It is evident that after 30 minutes of treatment, there is not complete removal of the alpha case. After 60 minutes of treatment, the microstructure at the border appears completely similar to that
of the bulk. After 90 minutes of treatment, in addition to the removal of the alpha case, it is also
possible to notice morphological modifications of the contour of the samples.

Conclusions

The proposed chemical treatment demonstrates to be effective in removing alpha layer, in particular 90 min are enough to remove 100 µm thickness of alpha layer. Even if more analysis are necessary the surface quality of the sample seems improved, the surface after chemical machining appears very shiny and smooth and this result is of particular interest in many applications.

This study was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.

References

[1] M. Pérez, A. García-Collado, D. Carou, G. Medina-Sánchez, R. Dorado-Vicente, On surface quality of engineered parts manufactured by additive manufacturing and postfinishing by machining, in: Addit Manuf, Elsevier, 2021: pp. 369–394. https://doi.org/10.1016/B978-0-12-818411-0.00015-X

[2] A. du Plessis, I. Yadroitsava, I. Yadroitsev, Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights, Mater Des. 187 (2020) 108385. https://doi.org/10.1016/j.matdes.2019.108385

[3] L. Wagner, Mechanical surface treatments on titanium, aluminum and magnesium alloys, Materials Science and Engineering: A. 263 (1999) 210–216. https://doi.org/10.1016/S0921-5093(98)01168-X

[4] Titanium, Springer Berlin Heidelberg, Berlin, Heidelberg, 2007. https://doi.org/10.1007/978-3-540-73036-1

[5] A.I. Nicolas-Silvente, E. Velasco-Ortega, I. Ortiz-Garcia, L. Monsalve-Guil, J. Gil, A. Jimenez-Guerra, Influence of the Titanium Implant Surface Treatment on the Surface Roughness and Chemical Composition, Materials. 13 (2020) 314. https://doi.org/10.3390/ma13020314

[6] O. Unal, A. Cahit Karaoglanli, R. Varol, A. Kobayashi, Microstructure evolution and mechanical behavior of severe shot peened commercially pure titanium, Vacuum. 110 (2014) 202–206. https://doi.org/10.1016/j.vacuum.2014.08.004

[7] P. Peyre, X. Scherpereel, L. Berthe, C. Carboni, R. Fabbro, G. Béranger, C. Lemaitre, Surface modifications induced in 316L steel by laser peening and shot-peening. Influence on pitting corrosion resistance, Materials Science and Engineering: A. 280 (2000) 294–302. https://doi.org/10.1016/S0921-5093(99)00698-X

[8] M. Kobayashi, T. Matsui, Y. Murakami, Mechanism of creation of compressive residual stress by shot peening, Int J Fatigue. 20 (1998) 351–357. https://doi.org/10.1016/S0142-1123(98)00002-4

[9] Y. Yıldıran Avcu, O. Yetik, M. Guney, E. Iakovakis, T. Sınmazçelik, E. Avcu, Surface, Subsurface and Tribological Properties of Ti6Al4V Alloy Shot Peened under Different Parameters, Materials. 13 (2020) 4363. https://doi.org/10.3390/ma13194363

[10] J.Z. Zhou, S. Huang, L.D. Zuo, X.K. Meng, J. Sheng, Q. Tian, Y.H. Han, W.L. Zhu, Effects of laser peening on residual stresses and fatigue crack growth properties of Ti–6Al–4V titanium alloy, Opt Lasers Eng. 52 (2014) 189–194. https://doi.org/10.1016/j.optlaseng.2013.06.011
of the bulk. After 90 minutes of treatment, in addition to the removal of the alpha case, it is also
possible to notice morphological modifications of the contour of the samples.

Conclusions

The proposed chemical treatment demonstrates to be effective in removing alpha layer, in particular 90 min are enough to remove 100 µm thickness of alpha layer. Even if more analysis are necessary the surface quality of the sample seems improved, the surface after chemical machining appears very shiny and smooth and this result is of particular interest in many applications.

This study was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.

References

[1] M. Pérez, A. García-Collado, D. Carou, G. Medina-Sánchez, R. Dorado-Vicente, On surface quality of engineered parts manufactured by additive manufacturing and postfinishing by machining, in: Addit Manuf, Elsevier, 2021: pp. 369–394. https://doi.org/10.1016/B978-0-12-818411-0.00015-X

[2] A. du Plessis, I. Yadroitsava, I. Yadroitsev, Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights, Mater Des. 187 (2020) 108385. https://doi.org/10.1016/j.matdes.2019.108385

[3] L. Wagner, Mechanical surface treatments on titanium, aluminum and magnesium alloys, Materials Science and Engineering: A. 263 (1999) 210–216. https://doi.org/10.1016/S0921-5093(98)01168-X

[4] Titanium, Springer Berlin Heidelberg, Berlin, Heidelberg, 2007. https://doi.org/10.1007/978-3-540-73036-1

[5] A.I. Nicolas-Silvente, E. Velasco-Ortega, I. Ortiz-Garcia, L. Monsalve-Guil, J. Gil, A. Jimenez-Guerra, Influence of the Titanium Implant Surface Treatment on the Surface Roughness and Chemical Composition, Materials. 13 (2020) 314. https://doi.org/10.3390/ma13020314

[6] O. Unal, A. Cahit Karaoglanli, R. Varol, A. Kobayashi, Microstructure evolution and mechanical behavior of severe shot peened commercially pure titanium, Vacuum. 110 (2014) 202–206. https://doi.org/10.1016/j.vacuum.2014.08.004

[7] P. Peyre, X. Scherpereel, L. Berthe, C. Carboni, R. Fabbro, G. Béranger, C. Lemaitre, Surface modifications induced in 316L steel by laser peening and shot-peening. Influence on pitting corrosion resistance, Materials Science and Engineering: A. 280 (2000) 294–302. https://doi.org/10.1016/S0921-5093(99)00698-X

[8] M. Kobayashi, T. Matsui, Y. Murakami, Mechanism of creation of compressive residual stress by shot peening, Int J Fatigue. 20 (1998) 351–357. https://doi.org/10.1016/S0142-1123(98)00002-4

[9] Y. Yıldıran Avcu, O. Yetik, M. Guney, E. Iakovakis, T. Sınmazçelik, E. Avcu, Surface, Subsurface and Tribological Properties of Ti6Al4V Alloy Shot Peened under Different Parameters, Materials. 13 (2020) 4363. https://doi.org/10.3390/ma13194363

[10] J.Z. Zhou, S. Huang, L.D. Zuo, X.K. Meng, J. Sheng, Q. Tian, Y.H. Han, W.L. Zhu, Effects of laser peening on residual stresses and fatigue crack growth properties of Ti–6Al–4V titanium alloy, Opt Lasers Eng. 52 (2014) 189–194. https://doi.org/10.1016/j.optlaseng.2013.06.011
[11] Q. Hongchao, Experimental investigation of laser peening on Ti17 titanium alloy for rotor blade applications, Appl Surf Sci. 351 (2015) 524-530.
https://doi.org/10.1016/j.apsusc.2015.05.098

[12] K.K. Liu, M.R. Hill, The effects of laser peening and shot peening on fretting fatigue in Ti-6Al-4V coupons, Tribol Int. 42 (2009) 1250-1262.
https://doi.org/10.1016/j.triboint.2009.04.005

[13] M.K. Thompson, G. Moroni, T. Vaneker, G. Fadel, R.I. Campbell, I. Gibson, A. Bernard, J. Schulz, P. Graf, B. Ahuja, F. Martina, Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints, CIRP Annals. 65 (2016) 737-760.
https://doi.org/10.1016/j.cirp.2016.05.004

[14] 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

[15] A. Diaz, Surface texture characterization and optimization of metal additive manufacturing-produced components for aerospace applications, in: Additive Manufacturing for the Aerospace Industry, Elsevier, 2019: pp. 341-374. https://doi.org/10.1016/B978-0-12-814062-8.00018-2

[16] E. Maleki, S. Bagherifard, M. Bandini, M. Guagliano, Surface post-treatments for metal additive manufacturing: Progress, challenges, and opportunities, Addit Manuf. 37 (2021) 101619.
https://doi.org/10.1016/j.addma.2020.101619

[17] D. Brent, T.A. Saunders, F. Garcia Moreno, P. Tyagi, Taguchi Design of Experiment for the Optimization of Electrochemical Polishing of Metal Additive Manufacturing Components, in: Volume 2: Advanced Manufacturing, American Society of Mechanical Engineers, 2016.
https://doi.org/10.1115/IMECE2016-67492

[18] U.S. Kim, J.W. Park, High-Quality Surface Finishing of Industrial Three-Dimensional Metal Additive Manufacturing Using Electrochemical Polishing, International Journal of Precision Engineering and Manufacturing-Green Technology. 6 (2019) 11-21.
https://doi.org/10.1007/s40684-019-00019-2

[19] U.S. Kim, J.W. Park, High-Quality Surface Finishing of Industrial Three-Dimensional Metal Additive Manufacturing Using Electrochemical Polishing, International Journal of Precision Engineering and Manufacturing-Green Technology. 6 (2019) 11-21.
https://doi.org/10.1007/s40684-019-00019-2

[20] P. Tyagi, T. Goulet, C. Riso, R. Stephenson, N. Chuenprateep, J. Schlitzer, C. Benton, F. Garcia-Moreno, Reducing the roughness of internal surface of an additive manufacturing produced 316 steel component by chempolishing and electropolishing, Addit Manuf. 25 (2019) 32-38.
https://doi.org/10.1016/j.addma.2018.11.001

[21] F. Scherillo, Chemical surface finishing of AlSi10Mg components made by additive manufacturing, Manuf Lett. 19 (2019) 5-9. https://doi.org/10.1016/j.mfglet.2018.12.002

[22] B. Wysocki, J. Idaszek, K. Szlązak, K. Strzelczyk, T. Brynk, K. Kurzydłowski, W. Święszkowski, Post Processing and Biological Evaluation of the Titanium Scaffolds for Bone Tissue Engineering, Materials. 9 (2016) 197. https://doi.org/10.3390/ma9030197

[23] F. Scherillo, E. Manco, A. El Hassanin, S. Franchitti, C. Pirozzi, R. Borrelli, Chemical surface finishing of electron beam melted Ti6Al4V using HF-HNO3 solutions, J Manuf Process. 60 (2020) 400-409. https://doi.org/10.1016/j.jmapro.2020.10.033
[11] Q. Hongchao, Experimental investigation of laser peening on Ti17 titanium alloy for rotor blade applications, Appl Surf Sci. 351 (2015) 524-530.
https://doi.org/10.1016/j.apsusc.2015.05.098

[12] K.K. Liu, M.R. Hill, The effects of laser peening and shot peening on fretting fatigue in Ti-6Al-4V coupons, Tribol Int. 42 (2009) 1250-1262.
https://doi.org/10.1016/j.triboint.2009.04.005

[13] M.K. Thompson, G. Moroni, T. Vaneker, G. Fadel, R.I. Campbell, I. Gibson, A. Bernard, J. Schulz, P. Graf, B. Ahuja, F. Martina, Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints, CIRP Annals. 65 (2016) 737-760.
https://doi.org/10.1016/j.cirp.2016.05.004

[14] 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

[15] A. Diaz, Surface texture characterization and optimization of metal additive manufacturing-produced components for aerospace applications, in: Additive Manufacturing for the Aerospace Industry, Elsevier, 2019: pp. 341-374. https://doi.org/10.1016/B978-0-12-814062-8.00018-2

[16] E. Maleki, S. Bagherifard, M. Bandini, M. Guagliano, Surface post-treatments for metal additive manufacturing: Progress, challenges, and opportunities, Addit Manuf. 37 (2021) 101619.
https://doi.org/10.1016/j.addma.2020.101619

[17] D. Brent, T.A. Saunders, F. Garcia Moreno, P. Tyagi, Taguchi Design of Experiment for the Optimization of Electrochemical Polishing of Metal Additive Manufacturing Components, in: Volume 2: Advanced Manufacturing, American Society of Mechanical Engineers, 2016.
https://doi.org/10.1115/IMECE2016-67492

[18] U.S. Kim, J.W. Park, High-Quality Surface Finishing of Industrial Three-Dimensional Metal Additive Manufacturing Using Electrochemical Polishing, International Journal of Precision Engineering and Manufacturing-Green Technology. 6 (2019) 11-21.
https://doi.org/10.1007/s40684-019-00019-2

[19] U.S. Kim, J.W. Park, High-Quality Surface Finishing of Industrial Three-Dimensional Metal Additive Manufacturing Using Electrochemical Polishing, International Journal of Precision Engineering and Manufacturing-Green Technology. 6 (2019) 11-21.
https://doi.org/10.1007/s40684-019-00019-2

[20] P. Tyagi, T. Goulet, C. Riso, R. Stephenson, N. Chuenprateep, J. Schlitzer, C. Benton, F. Garcia-Moreno, Reducing the roughness of internal surface of an additive manufacturing produced 316 steel component by chempolishing and electropolishing, Addit Manuf. 25 (2019) 32-38.
https://doi.org/10.1016/j.addma.2018.11.001

[21] F. Scherillo, Chemical surface finishing of AlSi10Mg components made by additive manufacturing, Manuf Lett. 19 (2019) 5-9. https://doi.org/10.1016/j.mfglet.2018.12.002

[22] B. Wysocki, J. Idaszek, K. Szlązak, K. Strzelczyk, T. Brynk, K. Kurzydłowski, W. Święszkowski, Post Processing and Biological Evaluation of the Titanium Scaffolds for Bone Tissue Engineering, Materials. 9 (2016) 197. https://doi.org/10.3390/ma9030197

[23] F. Scherillo, E. Manco, A. El Hassanin, S. Franchitti, C. Pirozzi, R. Borrelli, Chemical surface finishing of electron beam melted Ti6Al4V using HF-HNO3 solutions, J Manuf Process. 60 (2020) 400-409. https://doi.org/10.1016/j.jmapro.2020.10.033
# Experimental and numerical study of heat transfer on an industrial FFF printer: Application to PEEK

BENARBIA Adel<sup>1,2,a*</sup>, SOBOTKA Vincent<sup>1,b</sup>, BOYARD Nicolas<sup>1,c</sup> and
ROUA Christophe<sup>2,d</sup>

<sup>1</sup>Nantes Université, CNRS, LTEN, UMR 6607, F-44000 Nantes, France

<sup>2</sup>Cogit Composites company, 9117 rue des Vignerons 18390 Saint-Germain-du-Puy, France

<sup>a</sup>adel.benarbia@univ-nantes.fr, <sup>b</sup>vincent.sobotka@univ-nantes.fr,

<sup>c</sup>nicolas.boyard@univ-nantes.fr, <sup>d</sup>christophe roua@cogit-composites.com

**Keywords:** Fused Filament Fabrication (FFF), Heat Transfer, Printer Characterization, Crystallization, Semicrystalline Polymer, Experimental Study, Numerical Simulation

**Abstract.** The FFF process is one of the most widely used additive manufacturing processes for shaping thermoplastic polymers. The recent development of industrial printers equipped with high-temperature ovens has made it possible to print high-performance thermoplastics from the PAEK family for applications in the aerospace, medical and other industries. Numerous studies have shown that thermal history is a key factor to improve the mechanical properties of printed parts. Nevertheless, the uniformity of mechanical properties of printed parts is generally poor and highly dependent on the homogeneity of the thermal oven used, which, to our knowledge, has never been properly characterized. For semi-crystalline polymers, the thermal driven crystallization process is also a key factor in adhesion. However, the coupling between phase transformation and heat transfer is often neglected in numerical modelling and its influence has not yet been clearly demonstrated. In this work, we will carry out a preliminary characterization of the printer by measuring air velocity and temperature gradients over the whole printing zone. Secondly, the comparison between simulation and experimental measurements will show the importance of correctly predicting crystallization kinetics to obtain more accurate predictions.

**Introduction**

In recent years, additive manufacturing has been the subject of many studies, regardless of the type of technology. The Fused Filament Fabrication (FFF) process being the most widespread, a great deal of attention has been paid to it. It is one of the most affordable additive manufacturing technologies which is simple and with a fast manufacturing speed. Nevertheless, one of the notable limitations of the additive manufacturing process is the decrease of the mechanical properties of the produced parts, compared to conventional processes like injection molding. These mechanical properties are low in the direction normal to the interfaces, resulting in high anisotropic properties [1]. Such behaviour is the consequence of the presence of porosities and the low interfacial strength between filaments. For this reason, a lot of researches have been performed to improve the quality of the parts manufactured with this process [1,2]. Industrial manufacturers have also developed large-scale printers equipped with high-temperature ovens to print PEAKs such as Polyetheretherketone (PEEK) (Figure 1) [3].

In the FFF process, the polymer material is passed through an extruder consisting of a heating system and a nozzle to form the filament. The melted filament is then deposited along a specific trajectory to produce a layer. The platform is successively lowered along the z axis by an increment equal to the layer thickness, allowing the process to proceed layer by layer until the desired geometry is achieved.

<figure><img src="image_306.png" /></figure>
# Experimental and numerical study of heat transfer on an industrial FFF printer: Application to PEEK

BENARBIA Adel<sup>1,2,a*</sup>, SOBOTKA Vincent<sup>1,b</sup>, BOYARD Nicolas<sup>1,c</sup> and
ROUA Christophe<sup>2,d</sup>

<sup>1</sup>Nantes Université, CNRS, LTEN, UMR 6607, F-44000 Nantes, France

<sup>2</sup>Cogit Composites company, 9117 rue des Vignerons 18390 Saint-Germain-du-Puy, France

<sup>a</sup>adel.benarbia@univ-nantes.fr, <sup>b</sup>vincent.sobotka@univ-nantes.fr,

<sup>c</sup>nicolas.boyard@univ-nantes.fr, <sup>d</sup>christophe roua@cogit-composites.com

**Keywords:** Fused Filament Fabrication (FFF), Heat Transfer, Printer Characterization, Crystallization, Semicrystalline Polymer, Experimental Study, Numerical Simulation

**Abstract.** The FFF process is one of the most widely used additive manufacturing processes for shaping thermoplastic polymers. The recent development of industrial printers equipped with high-temperature ovens has made it possible to print high-performance thermoplastics from the PAEK family for applications in the aerospace, medical and other industries. Numerous studies have shown that thermal history is a key factor to improve the mechanical properties of printed parts. Nevertheless, the uniformity of mechanical properties of printed parts is generally poor and highly dependent on the homogeneity of the thermal oven used, which, to our knowledge, has never been properly characterized. For semi-crystalline polymers, the thermal driven crystallization process is also a key factor in adhesion. However, the coupling between phase transformation and heat transfer is often neglected in numerical modelling and its influence has not yet been clearly demonstrated. In this work, we will carry out a preliminary characterization of the printer by measuring air velocity and temperature gradients over the whole printing zone. Secondly, the comparison between simulation and experimental measurements will show the importance of correctly predicting crystallization kinetics to obtain more accurate predictions.

**Introduction**

In recent years, additive manufacturing has been the subject of many studies, regardless of the type of technology. The Fused Filament Fabrication (FFF) process being the most widespread, a great deal of attention has been paid to it. It is one of the most affordable additive manufacturing technologies which is simple and with a fast manufacturing speed. Nevertheless, one of the notable limitations of the additive manufacturing process is the decrease of the mechanical properties of the produced parts, compared to conventional processes like injection molding. These mechanical properties are low in the direction normal to the interfaces, resulting in high anisotropic properties [1]. Such behaviour is the consequence of the presence of porosities and the low interfacial strength between filaments. For this reason, a lot of researches have been performed to improve the quality of the parts manufactured with this process [1,2]. Industrial manufacturers have also developed large-scale printers equipped with high-temperature ovens to print PEAKs such as Polyetheretherketone (PEEK) (Figure 1) [3].

In the FFF process, the polymer material is passed through an extruder consisting of a heating system and a nozzle to form the filament. The melted filament is then deposited along a specific trajectory to produce a layer. The platform is successively lowered along the z axis by an increment equal to the layer thickness, allowing the process to proceed layer by layer until the desired geometry is achieved.

<figure><img src="image_312.png" /></figure>
<figure><img src="image_307.png" /><figcaption>Figure 1 - Comparison of the printing volume of PEEK printers according to their chamber temperature. Only PEEK printers with a chamber temperature above 200°C and a volume greater than 0.05m³ are shown.</figcaption></figure>

Process adhesion takes place in several steps. During the deposition, intimate contact occurs and interfaces develop with the other filaments. This phenomenon called coalescence is followed by the healing process where polymer chains diffuse through the interfaces in order to create a real molecular bond with the others filaments [4]. These adhesion mechanisms are only possible if the molecular chains present at the interfaces have sufficient mobility [5]. Molecular mobility is governed by the thermodynamic state of polymers, which explains the significant influence of thermal history on adhesion kinetics [6].

Studies were conducted to predict the thermal history of the FFF process [4,7]. From these works, numerical Finite Element Method (FEM) models were developed and showed good agreement with experimental tests in the case of amorphous polymers. However, for semi-crystalline polymers, endothermal and exothermal phase transformation reactions are rarely taken into account and their influence has not yet been demonstrated numerically or experimentally.

Heat transfer being a primary importance on the quality of the printed parts, a good knowledge of thermal behavior of heated chamber is required. 3D printing ovens, have most of the time a poorly insulating upper wall due to the mobility function and require thus air circulation to prevent vertical thermal stratification. The use of a rotating fan ensures a uniform temperature, but can also result in a highly variable airflow. To avoid this, air curtain systems have been developed and integrated to ensure a constant, homogeneous and linear air flow over the upper part of the oven. However, multiple circulation systems and poorly insulated upper wall generally lead to significant temperature fluctuations, particularly on large scale printer.

As heat exchange between the air and the printed part has a major influence on heat transfer, a preliminary characterisation of the printer was carried out in order to determine the thermal homogeneity of the printing environment. This characterisation is necessary in order to define whether the temperature and the convective heat transfer coefficient can be considered constant over the whole printing volume for the numerical modelling. *In situ* thermal monitoring of the process was also carried out to measure the thermal history depending on process parameters. The experimental measurements were compared with the numerical simulation in order to validate the model and show the influence of the energy released during the crystallization on heat transfer.
<figure><img src="image_313.png" /><figcaption>Figure 1 - Comparison of the printing volume of PEEK printers according to their chamber temperature. Only PEEK printers with a chamber temperature above 200°C and a volume greater than 0.05m³ are shown.</figcaption></figure>

Process adhesion takes place in several steps. During the deposition, intimate contact occurs and interfaces develop with the other filaments. This phenomenon called coalescence is followed by the healing process where polymer chains diffuse through the interfaces in order to create a real molecular bond with the others filaments [4]. These adhesion mechanisms are only possible if the molecular chains present at the interfaces have sufficient mobility [5]. Molecular mobility is governed by the thermodynamic state of polymers, which explains the significant influence of thermal history on adhesion kinetics [6].

Studies were conducted to predict the thermal history of the FFF process [4,7]. From these works, numerical Finite Element Method (FEM) models were developed and showed good agreement with experimental tests in the case of amorphous polymers. However, for semi-crystalline polymers, endothermal and exothermal phase transformation reactions are rarely taken into account and their influence has not yet been demonstrated numerically or experimentally.

Heat transfer being a primary importance on the quality of the printed parts, a good knowledge of thermal behavior of heated chamber is required. 3D printing ovens, have most of the time a poorly insulating upper wall due to the mobility function and require thus air circulation to prevent vertical thermal stratification. The use of a rotating fan ensures a uniform temperature, but can also result in a highly variable airflow. To avoid this, air curtain systems have been developed and integrated to ensure a constant, homogeneous and linear air flow over the upper part of the oven. However, multiple circulation systems and poorly insulated upper wall generally lead to significant temperature fluctuations, particularly on large scale printer.

As heat exchange between the air and the printed part has a major influence on heat transfer, a preliminary characterisation of the printer was carried out in order to determine the thermal homogeneity of the printing environment. This characterisation is necessary in order to define whether the temperature and the convective heat transfer coefficient can be considered constant over the whole printing volume for the numerical modelling. *In situ* thermal monitoring of the process was also carried out to measure the thermal history depending on process parameters. The experimental measurements were compared with the numerical simulation in order to validate the model and show the influence of the energy released during the crystallization on heat transfer.
# Experimental section

## Industrial Printer

The tests were carried out on the COGIT AM 885-HT-PRO FFF industrial printer developed by COGIT AM (Figure 2). The printer is a Cartesian type machine with a printing volume of 800x800x500 mm³. The printer consists of an extruder that deposits the filament on a bed in an oven with a volume of 1.5m³. The oven has been dimensioned to reach 300°C in the whole chamber. The extruder moves along the X and Y axis and the bed is lowered along the Z axis. The COGIT 885-HT-PRO is a flexible and open machine. It has been designed to easily implement complementary modules for research purposes, such as in-situ instrumentation systems (optical and thermal cameras), pellet extruders, local heating (infrared lamps and laser), gripping and placement systems, etc.

## Air flow rate printer characterization

In order to determine whether the convective heat transfer coefficient can be considered independent of the printing position, the air velocity was measured over the whole printing surface. The air circulation system of the COGIT AM 885-HT-PRO consists of two fans positioned along the height of the oven. (areas 1 and 2 in Figure 3). The air curtain (area 3 in Figure 3) is positioned above the air circulation system at 90° to the circulation direction. The flow rate of the three mixing systems can be controlled separately. In this study, the three circulation systems were controlled at a constant flow rate. The air curtain is positioned at the height of the printing nozzle so that it is effective over the first fifty millimeters printed. As convective heat transfer between the air and the filaments has an influence on the thermal history only for the first printed layers, only the air curtain flow was characterized. Measurements were therefore taken at a fixed altitude Z=0mm.

<figure><img src="image_308.png" /><figcaption>Figure 2 – COGIT AM 885-HT-PRO printer.</figcaption></figure>

<figure><img src="image_309.png" /><figcaption>Figure 3 - Schematics of the COGIT AM 885-HT-PRO circulation air systems.</figcaption></figure>

The air velocity was characterized with a hot wire anemometer positioned on the tool holder instead of the extruder and then moved along the XY axes. The anemometer is moved along a specified path using a back and forth motion to map the air velocity over the full print area 800x800mm (Figure 4). The anemometer head is positioned at the same height as the nozzle to characterize the air curtain at Z=0mm.

The anemometer travel speed is 10mm/s and the sampling rate is 2Hz. The displacement speed is assumed to be slow enough to not disturb the airflow or the measurement. The anemometer is capable of measuring over a range of 0.1 to 30 m/s with an accuracy of ± 3% of the measurement. The tests were carried out at room temperature, assuming that the influence of temperature on airflow is negligible. The measurements will therefore be considered valid regardless of the printing temperature. The recording of the speed is triggered simultaneously with the anemometer movement in order to associate each measurement with a position in the XY plane. A 2D linear
# Experimental section

## Industrial Printer

The tests were carried out on the COGIT AM 885-HT-PRO FFF industrial printer developed by COGIT AM (Figure 2). The printer is a Cartesian type machine with a printing volume of 800x800x500 mm³. The printer consists of an extruder that deposits the filament on a bed in an oven with a volume of 1.5m³. The oven has been dimensioned to reach 300°C in the whole chamber. The extruder moves along the X and Y axis and the bed is lowered along the Z axis. The COGIT 885-HT-PRO is a flexible and open machine. It has been designed to easily implement complementary modules for research purposes, such as in-situ instrumentation systems (optical and thermal cameras), pellet extruders, local heating (infrared lamps and laser), gripping and placement systems, etc.

## Air flow rate printer characterization

In order to determine whether the convective heat transfer coefficient can be considered independent of the printing position, the air velocity was measured over the whole printing surface. The air circulation system of the COGIT AM 885-HT-PRO consists of two fans positioned along the height of the oven. (areas 1 and 2 in Figure 3). The air curtain (area 3 in Figure 3) is positioned above the air circulation system at 90° to the circulation direction. The flow rate of the three mixing systems can be controlled separately. In this study, the three circulation systems were controlled at a constant flow rate. The air curtain is positioned at the height of the printing nozzle so that it is effective over the first fifty millimeters printed. As convective heat transfer between the air and the filaments has an influence on the thermal history only for the first printed layers, only the air curtain flow was characterized. Measurements were therefore taken at a fixed altitude $Z=0$mm.

<figure><img src="image_314.png" /><figcaption>Figure 2 – COGIT AM 885-HT-PRO printer.</figcaption></figure>

<figure><img src="image_315.png" /><figcaption>Figure 3 - Schematics of the COGIT AM 885-HT-PRO circulation air systems.</figcaption></figure>

The air velocity was characterized with a hot wire anemometer positioned on the tool holder instead of the extruder and then moved along the XY axes. The anemometer is moved along a specified path using a back and forth motion to map the air velocity over the full print area 800x800mm (Figure 4). The anemometer head is positioned at the same height as the nozzle to characterize the air curtain at $Z=0$mm.

The anemometer travel speed is 10mm/s and the sampling rate is 2Hz. The displacement speed is assumed to be slow enough to not disturb the airflow or the measurement. The anemometer is capable of measuring over a range of 0.1 to 30 m/s with an accuracy of ± 3% of the measurement. The tests were carried out at room temperature, assuming that the influence of temperature on airflow is negligible. The measurements will therefore be considered valid regardless of the printing temperature. The recording of the speed is triggered simultaneously with the anemometer movement in order to associate each measurement with a position in the XY plane. A 2D linear
interpolation of the measurements was carried out to obtain the airflow map of the XY plane at height of Z=0mm (Figure 4).

<figure><img src="image_310.png" /><figcaption>Figure 4 - Diagram of the tool holder trajectory in the XY plane and 2D interpolation to obtain a 2D map.</figcaption></figure>

Temperature oven characterization

3D printing ovens have a poorly insulating upper wall due to the mobility function, so air circulation is necessary to avoid vertical temperature gradients. Despite the correct air circulation system, it is necessary to have a uniform temperature distribution throughout the printing volume to ensure uniform mechanical and geometric quality of the parts. The air temperature was therefore characterized over the entire 800x800x500mm printing volume. As the process window for PEEK is between 200 and 300°C, the temperature measurements were taken in the middle of the range at a fixed setpoint of 250°C.

Air temperature was measured using class 1 type J thermocouples capable of measuring from -40 to 600°C with an accuracy of ± 1.5°C. The thermocouples are made up of two 300µm diameter conductors giving them a short response time of 0.7s in air. The thermocouples are connected to an acquisition unit (Graphtec GL220) capable of reading and recording up to 10 thermocouples simultaneously with a sampling time of 5Hz.

In order to determine the temperature evolution as a function of height, several thermocouples were positioned at different levels on a support. The discretization of the measurement points was defined in such a way as to detect thermal gradients, particularly in the upper part of the oven. 9 thermocouples were therefore positioned at 0, 25, 50, 70, 100, 125, 250, 375 and 500mm in order to completely cover the printing height (Figure 5).

<figure><img src="image_311.png" /><figcaption>Figure 5 - Representation of the thermocouple positions on the support along the Z axis for 3 different configurations.</figcaption></figure>

The printing bed, even if it is not heated, can have an influence on the air flow and therefore on the heating system. In order to take this influence into account, the bed was positioned at 3 different altitudes 0, 50 and 100% of the Z axis, i.e. 0, 250 and 500mm. In this way, when the bed is at an altitude of 0mm, only 1 thermocouple is present on the support. When the bed is at 50%, 7
interpolation of the measurements was carried out to obtain the airflow map of the XY plane at height of Z=0mm (Figure 4).

<figure><img src="image_316.png" /><figcaption>Figure 4 - Diagram of the tool holder trajectory in the XY plane and 2D interpolation to obtain a 2D map.</figcaption></figure>

Temperature oven characterization

3D printing ovens have a poorly insulating upper wall due to the mobility function, so air circulation is necessary to avoid vertical temperature gradients. Despite the correct air circulation system, it is necessary to have a uniform temperature distribution throughout the printing volume to ensure uniform mechanical and geometric quality of the parts. The air temperature was therefore characterized over the entire 800x800x500mm printing volume. As the process window for PEEK is between 200 and 300°C, the temperature measurements were taken in the middle of the range at a fixed setpoint of 250°C.

Air temperature was measured using class 1 type J thermocouples capable of measuring from -40 to 600°C with an accuracy of ± 1.5°C. The thermocouples are made up of two 300µm diameter conductors giving them a short response time of 0.7s in air. The thermocouples are connected to an acquisition unit (Graphtec GL220) capable of reading and recording up to 10 thermocouples simultaneously with a sampling time of 5Hz.

In order to determine the temperature evolution as a function of height, several thermocouples were positioned at different levels on a support. The discretization of the measurement points was defined in such a way as to detect thermal gradients, particularly in the upper part of the oven. 9 thermocouples were therefore positioned at 0, 25, 50, 70, 100, 125, 250, 375 and 500mm in order to completely cover the printing height (Figure 5).

<figure><img src="image_317.png" /><figcaption>Figure 5 - Representation of the thermocouple positions on the support along the Z axis for 3 different configurations.</figcaption></figure>

The printing bed, even if it is not heated, can have an influence on the air flow and therefore on the heating system. In order to take this influence into account, the bed was positioned at 3 different altitudes 0, 50 and 100% of the Z axis, i.e. 0, 250 and 500mm. In this way, when the bed is at an altitude of 0mm, only 1 thermocouple is present on the support. When the bed is at 50%, 7
thermocouples are present to map the enclosure. Finally, when the bed is at Z=100%, all thermocouples are used on the sensor support, as shown in the figure.

In the same way as for the airflow characterization presented previously, the thermocouple support is fixed to the tool holder instead of the extruder. The support is moved in the same path along XY in order to measure the temperature over the whole printing area. The same interpolation method is used to obtain the 2D temperature map. As a result, 2D temperature mapping is obtained at each altitude where a sensor is positioned. For example, when the bed is at 100% height, we obtain 9 2D maps in the XY plane at altitudes of 0, 25, 50, 70, 100, 125, 250, 375 and 500mm.

The thermocouples are thermally insulated from its support by a silicone-coated glass sheath and positioned so that the weld point is only in contact with the air. The displacement speed of 10mm/s is considered to have no effect on the measurement, considering the fast response time of the sensor and the small temperature variations measured. As the thermocouples were installed before heating the printing enclosure, a sensor positioning accuracy along the Z axis of ± 2mm can be considered, taking into account the thermal expansion of the sensor support from 25 to 250°C.

## Thermal history measurement of printed part

The thermal history of printed filaments is carried out using an infrared thermal camera positioned inside the printing enclosure (Figure 6). The infrared camera has a measurement range between 150 and 900°C and detects wavelengths from 8 to 14 µm. The emissivity of PEEK has been measured and can be considered constant over the 8-14 µm wavelength range with a value of ε = 0.92. The thermal camera is mounted on the printing bed at a distance of 100mm from the part, where the pixel size corresponds to a surface of 90 x 90 µm².

<figure><img src="image_312.png" /><figcaption>Figure 6 – Diagram of the bench used to measure the thermal history of printed filament (a). Thermal image obtained (b).</figcaption></figure>

In order to take into account, the effects of environmental radiation on the measurement, the camera was calibrated beforehand. The calibration consisted in comparing temperatures between the camera and a thermocouple welded to the surface of the printed part. Temperature steps were taken from 150 to 250°C every 10°C to obtain the correction curve that was applied to the thermal measurements. The infrared camera is positioned in a water cooling housing to keep it below its maximum operating temperature.

The infrared images are acquired at 80Hz, allowing the cooling of the filament over time to be correctly observed. The thermal history of 5 filaments is measured between the 20th and 30th filaments where there is no further influence of the printing bed. The average of the 5 measurements is calculated and taken as a reference for comparison with the simulation results.

The prints were performed in the centre of the bed at a printing speed of 20mm/s. The walls consisted of a single filament across the width and measured 40mm high and 50mm long. The deposited and simulated filament is 1mm wide with a layer thickness of 0.5mm, so 5 pixels of the
thermocouples are present to map the enclosure. Finally, when the bed is at Z=100%, all thermocouples are used on the sensor support, as shown in the figure.

In the same way as for the airflow characterization presented previously, the thermocouple support is fixed to the tool holder instead of the extruder. The support is moved in the same path along XY in order to measure the temperature over the whole printing area. The same interpolation method is used to obtain the 2D temperature map. As a result, 2D temperature mapping is obtained at each altitude where a sensor is positioned. For example, when the bed is at 100% height, we obtain 9 2D maps in the XY plane at altitudes of 0, 25, 50, 70, 100, 125, 250, 375 and 500mm.

The thermocouples are thermally insulated from its support by a silicone-coated glass sheath and positioned so that the weld point is only in contact with the air. The displacement speed of 10mm/s is considered to have no effect on the measurement, considering the fast response time of the sensor and the small temperature variations measured. As the thermocouples were installed before heating the printing enclosure, a sensor positioning accuracy along the Z axis of ± 2mm can be considered, taking into account the thermal expansion of the sensor support from 25 to 250°C.

## Thermal history measurement of printed part

The thermal history of printed filaments is carried out using an infrared thermal camera positioned inside the printing enclosure (Figure 6). The infrared camera has a measurement range between 150 and 900°C and detects wavelengths from 8 to 14 µm. The emissivity of PEEK has been measured and can be considered constant over the 8-14 µm wavelength range with a value of ε = 0.92. The thermal camera is mounted on the printing bed at a distance of 100mm from the part, where the pixel size corresponds to a surface of 90 x 90 µm².

<figure><img src="image_318.png" /><figcaption>Figure 6 – Diagram of the bench used to measure the thermal history of printed filament (a). Thermal image obtained (b).</figcaption></figure>

In order to take into account, the effects of environmental radiation on the measurement, the camera was calibrated beforehand. The calibration consisted in comparing temperatures between the camera and a thermocouple welded to the surface of the printed part. Temperature steps were taken from 150 to 250°C every 10°C to obtain the correction curve that was applied to the thermal measurements. The infrared camera is positioned in a water cooling housing to keep it below its maximum operating temperature.

The infrared images are acquired at 80Hz, allowing the cooling of the filament over time to be correctly observed. The thermal history of 5 filaments is measured between the 20th and 30th filaments where there is no further influence of the printing bed. The average of the 5 measurements is calculated and taken as a reference for comparison with the simulation results.

The prints were performed in the centre of the bed at a printing speed of 20mm/s. The walls consisted of a single filament across the width and measured 40mm high and 50mm long. The deposited and simulated filament is 1mm wide with a layer thickness of 0.5mm, so 5 pixels of the
infrared camera can measure the temperature along this direction. The deposition time between each layer is 12.2s. These conditions and printing geometry are commonly used for PEEK.

## Modeling section

In this work, heat transfers and phase transformations are implemented in a coupled multiphysics model. The numerical solving was performed with COMSOL® FEM software on a 2D model. A 30 filament high wall was simulated in one step calculation. The thermal history is recorded on the 25th filament at mid-height on the outer surface in contact with the chamber air. The reading is taken at the same point where the temperature is measured with the infrared camera, so that the results can be compared. Model parameters such as layer thickness, printing speed, nozzle temperature are identical to the real process.

The thermal model is represented in Figure 7 in which we solve the energy equation coupled to crystallization and melting. The source term in the energy equation depends on ΔH, the crystallization and melting enthalpies and α, the crystalline degree. [8]. For more information on the multiphysic model, please refer to [9].

<figure><img src="image_313.png" /><figcaption>Figure 7 - Schematic representation of the numerical model.</figcaption></figure>

## Results and discussion

The air velocity norm map of the industrial printer COGIT AM 885-HT-PRO is shown in Figure 8.a. The velocity values are centered around 0.85 m/s with a standard deviation of 0.13 m/s (Figure 8.b). The velocity of the area was averaged along the X and Y directions shown in Figure 8.c and Figure 8.d respectively. It can be seen that for positions close to Y=0mm, the air velocity is greater to 1.8m/s. This is due to the proximity of the air curtain outlet. In Figure 8.a or Figure 8.d, a slight slowing of the air velocity is observed at both ends of the X axis (X<50mm and X>750mm). This is due to the edge effects of the air curtain which is positioned along the X axis. Overall, the heat transfer coefficient will be considered constant for positions of Y greater than 50mm and X between 50 and 750mm as indicated by the green rectangle in Figure 8.a.
infrared camera can measure the temperature along this direction. The deposition time between each layer is 12.2s. These conditions and printing geometry are commonly used for PEEK.

## Modeling section

In this work, heat transfers and phase transformations are implemented in a coupled multiphysics model. The numerical solving was performed with COMSOL® FEM software on a 2D model. A 30 filament high wall was simulated in one step calculation. The thermal history is recorded on the 25th filament at mid-height on the outer surface in contact with the chamber air. The reading is taken at the same point where the temperature is measured with the infrared camera, so that the results can be compared. Model parameters such as layer thickness, printing speed, nozzle temperature are identical to the real process.

The thermal model is represented in Figure 7 in which we solve the energy equation coupled to crystallization and melting. The source term in the energy equation depends on ΔH, the crystallization and melting enthalpies and α, the crystalline degree. [8]. For more information on the multiphysic model, please refer to [9].

<figure><img src="image_319.png" /><figcaption>Figure 7 - Schematic representation of the numerical model.</figcaption></figure>

## Results and discussion

The air velocity norm map of the industrial printer COGIT AM 885-HT-PRO is shown in Figure 8.a. The velocity values are centered around 0.85 m/s with a standard deviation of 0.13 m/s (Figure 8.b). The velocity of the area was averaged along the X and Y directions shown in Figure 8.c and Figure 8.d respectively. It can be seen that for positions close to Y=0mm, the air velocity is greater to 1.8m/s. This is due to the proximity of the air curtain outlet. In Figure 8.a or Figure 8.d, a slight slowing of the air velocity is observed at both ends of the X axis (X<50mm and X>750mm). This is due to the edge effects of the air curtain which is positioned along the X axis. Overall, the heat transfer coefficient will be considered constant for positions of Y greater than 50mm and X between 50 and 750mm as indicated by the green rectangle in Figure 8.a.
<figure><img src="image_314.png" /><figcaption>Figure 8 - Air velocity norm map (a), measure distribution (b), average air speed along Y (c) and X (d) axis.</figcaption></figure>

The thermal measurements of the printer are shown in Figure 9 for 3 different bed positions. For each configuration, a 3D map is defined where each plane corresponds to the position of a thermocouple that has been moved along XY. No real influence of the bed position could be observed.

<figure><img src="image_315.png" /><figcaption>Figure 9 - Thermal 3D map for 3 different bed height: 0% (a), 50% (b) and 100% (c). The bed and nozzle are shown in black as spatial references.</figcaption></figure>

In order to get the most out of the results, statistical calculations were carried out for each measurement plan. In Figure 10, the mean, standard deviation, minimum and maximum recorded temperatures were plotted for each measured height in the form of a whisker diagram for configuration Z<sub>BED</sub> = 100%. The results show a high degree of thermal stability in the chamber, with relatively low standard deviations of less than 1°C. It can also be seen that the standard deviation decreases with increasing Z, which means that the lower part of the oven is more homogeneous than the upper part. Over the full printing volume, the minimum temperature detected is 245°C and the maximum is 255°C. The machine's oven is therefore capable of keeping the chamber at 250±5°C over a volume of 800x800x500mm<sup>3</sup>.
<figure><img src="image_320.png" /><figcaption>Figure 8 - Air velocity norm map (a), measure distribution (b), average air speed along Y (c) and X (d) axis.</figcaption></figure>

The thermal measurements of the printer are shown in Figure 9 for 3 different bed positions. For each configuration, a 3D map is defined where each plane corresponds to the position of a thermocouple that has been moved along XY. No real influence of the bed position could be observed.

<figure><img src="image_321.png" /><figcaption>Figure 9 - Thermal 3D map for 3 different bed height: 0% (a), 50% (b) and 100% (c). The bed and nozzle are shown in black as spatial references.</figcaption></figure>

In order to get the most out of the results, statistical calculations were carried out for each measurement plan. In Figure 10, the mean, standard deviation, minimum and maximum recorded temperatures were plotted for each measured height in the form of a whisker diagram for configuration Z<sub>BED</sub> = 100%. The results show a high degree of thermal stability in the chamber, with relatively low standard deviations of less than 1°C. It can also be seen that the standard deviation decreases with increasing Z, which means that the lower part of the oven is more homogeneous than the upper part. Over the full printing volume, the minimum temperature detected is 245°C and the maximum is 255°C. The machine's oven is therefore capable of keeping the chamber at 250±5°C over a volume of 800x800x500mm³.
<figure><img src="image_316.png" /><figcaption>Figure 10 – Statistic data evolution along Z axis. Average temperature (—), standard deviation (—), minimum and maximum recorded temperatures (--) for each XY measured height map.</figcaption></figure>

3 different thermal cases were experimentally measured and numerically estimated. The thermal cases were carried out under the same conditions with only the chamber temperature changing: 180, 200 and 220°C. The superposition of the experimental measurements made with the pixel located in the middle of the layer thickness and the numerical results of the single-walls is shown in the Figure 11. It can be seen that the numerical modelling seems to be in good agreement with the experimental results.

<figure><img src="image_317.png" /><figcaption>Figure 11 – Thermal history experimentally measured (—), numerically computed with (- -) and without (· ·) crystallization coupling, on left axis. Volume fraction crystallinity evolution (—), on right axis.</figcaption></figure>

During cooling of the first deposited filament, the experimental measurements (red solid line) show an abrupt change in cooling rate after 4, 6 and 8 seconds for 180, 200 and 220°C respectively. This behaviour change is also predicted by the numerical simulation (blue dashed lines) and corresponds to the start of crystallization. Indeed, the enthalpy produced during crystallization appears to have a non-negligible effect on the thermal history of the material. In order to show the influence of crystallization enthalpy, the thermal simulation was simulated without crystallization
<figure><img src="image_322.png" /><figcaption>Figure 10 – Statistic data evolution along Z axis. Average temperature (—), standard deviation (—), minimum and maximum recorded temperatures (--) for each XY measured height map.</figcaption></figure>

3 different thermal cases were experimentally measured and numerically estimated. The thermal cases were carried out under the same conditions with only the chamber temperature changing: 180, 200 and 220°C. The superposition of the experimental measurements made with the pixel located in the middle of the layer thickness and the numerical results of the single-walls is shown in the Figure 11. It can be seen that the numerical modelling seems to be in good agreement with the experimental results.

<figure><img src="image_323.png" /><figcaption>Figure 11 – Thermal history experimentally measured (—), numerically computed with (- -) and without (· ·) crystallization coupling, on left axis. Volume fraction crystallinity evolution (—), on right axis.</figcaption></figure>

During cooling of the first deposited filament, the experimental measurements (red solid line) show an abrupt change in cooling rate after 4, 6 and 8 seconds for 180, 200 and 220°C respectively. This behaviour change is also predicted by the numerical simulation (blue dashed lines) and corresponds to the start of crystallization. Indeed, the enthalpy produced during crystallization appears to have a non-negligible effect on the thermal history of the material. In order to show the influence of crystallization enthalpy, the thermal simulation was simulated without crystallization
coupling (black dotted lines). It can be seen that as long as crystallization has not begun, the simulations with and without the coupling are perfectly superimposed. The simulation without the coupling to crystallization can underestimate the temperature with an error of up to 10K.

## Conclusion

The thermal environment of a high-temperature large scale industrial printer was characterized experimentally. Firstly, the air flow rate of the air curtain was measured and a good homogeneity of the velocity was observed over the entire printing surface, with the exception of the boundary. In addition, the air temperature was characterized in 3 dimensions over the full printing volume, taking into account the presence of the printing bed. No influence of the printing bed on temperature was observed. No significant fluctuations were also observed, with chamber stability of ± 5°C at a set temperature of 250°C.

The thermal history of printed walls was measured experimentally at different chamber temperatures on the high-temperature industrial printer. Good agreement between experimental and numerical results confirms the validity of the model. The abrupt change of the surface filament experimental temperature observed during cooling was correctly predicted by the model and demonstrated the importance of taking account of the enthalpies of crystallization in the heat equation.

In future work, adhesion measurements will be carried out experimentally and compared with the predictions of the multiphysics model, which takes into account a crystallization-coupled adhesion model.

## References

[1] Popescu D, Zapciu A, Amza C, Baciu F, Marinescu R. FDM process parameters influence over the mechanical properties of polymer specimens: A review. Polymer Testing 2018;69:157-66. https://doi.org/10.1016/j.polymertesting.2018.05.020

[2] Lepoivre A, Boyard N, Levy A, Sobotka V. Heat Transfer and Adhesion Study for the FFF Additive Manufacturing Process. Procedia Manufacturing 2020;47:948-55. https://doi.org/10.1016/j.promfg.2020.04.291

[3] PEEK 3D PRINTING – The ultimade guide. All3dp 2023. https://all3dp.com/1/peek-3d-printer/

[4] Xu D, Zhang Y, Pigeonneau F. Thermal analysis of the fused filament fabrication printing process: Experimental and numerical investigations. Int J Mater Form 2021;14:763-76. https://doi.org/10.1007/s12289-020-01591-8

[5] Barocio E, Brenken B, Favaloro A, Pipes RB. Interlayer fusion bonding of semi-crystalline polymer composites in extrusion deposition additive manufacturing. Composites Science and Technology 2022;109334. https://doi.org/10.1016/j.compscitech.2022.109334

[6] Lo C-T, Laabs FC, Narasimhan B. Interfacial adhesion mechanisms in incompatible semicrystalline polymer systems. J Polym Sci B Polym Phys 2004;42:2667-79. https://doi.org/10.1002/polb.20148

[7] Costa SF, Duarte FM, Covas JA. Estimation of filament temperature and adhesion development in fused deposition techniques. Journal of Materials Processing Technology 2017;245:167-79. https://doi.org/10.1016/j.jmatprotec.2017.02.026

[8] Zanjanijam AR, Major I, Lyons JG, Lafont U, Devine DM. Fused Filament Fabrication of PEEK: A Review of Process-Structure-Property Relationships. Polymers 2020;12:1665. https://doi.org/10.3390/polym12081665

[9] Benarbia A, Sobotka V, Boyard N, Roua C. Fused filament fabrication: Numerical adhesion modeling suitable for semicrystalline polymers, 2023, p. 139-48. https://doi.org/10.21741/9781644902479-16.
coupling (black dotted lines). It can be seen that as long as crystallization has not begun, the simulations with and without the coupling are perfectly superimposed. The simulation without the coupling to crystallization can underestimate the temperature with an error of up to 10K.

## Conclusion

The thermal environment of a high-temperature large scale industrial printer was characterized experimentally. Firstly, the air flow rate of the air curtain was measured and a good homogeneity of the velocity was observed over the entire printing surface, with the exception of the boundary. In addition, the air temperature was characterized in 3 dimensions over the full printing volume, taking into account the presence of the printing bed. No influence of the printing bed on temperature was observed. No significant fluctuations were also observed, with chamber stability of ± 5°C at a set temperature of 250°C.

The thermal history of printed walls was measured experimentally at different chamber temperatures on the high-temperature industrial printer. Good agreement between experimental and numerical results confirms the validity of the model. The abrupt change of the surface filament experimental temperature observed during cooling was correctly predicted by the model and demonstrated the importance of taking account of the enthalpies of crystallization in the heat equation.

In future work, adhesion measurements will be carried out experimentally and compared with the predictions of the multiphysics model, which takes into account a crystallization-coupled adhesion model.

## References

[1] Popescu D, Zapciu A, Amza C, Baciu F, Marinescu R. FDM process parameters influence over the mechanical properties of polymer specimens: A review. Polymer Testing 2018;69:157-66. https://doi.org/10.1016/j.polymertesting.2018.05.020

[2] Lepoivre A, Boyard N, Levy A, Sobotka V. Heat Transfer and Adhesion Study for the FFF Additive Manufacturing Process. Procedia Manufacturing 2020;47:948-55. https://doi.org/10.1016/j.promfg.2020.04.291

[3] PEEK 3D PRINTING – The ultimade guide. All3dp 2023. https://all3dp.com/1/peek-3d-printer/

[4] Xu D, Zhang Y, Pigeonneau F. Thermal analysis of the fused filament fabrication printing process: Experimental and numerical investigations. Int J Mater Form 2021;14:763-76. https://doi.org/10.1007/s12289-020-01591-8

[5] Barocio E, Brenken B, Favaloro A, Pipes RB. Interlayer fusion bonding of semi-crystalline polymer composites in extrusion deposition additive manufacturing. Composites Science and Technology 2022;109334. https://doi.org/10.1016/j.compscitech.2022.109334

[6] Lo C-T, Laabs FC, Narasimhan B. Interfacial adhesion mechanisms in incompatible semicrystalline polymer systems. J Polym Sci B Polym Phys 2004;42:2667-79. https://doi.org/10.1002/polb.20148

[7] Costa SF, Duarte FM, Covas JA. Estimation of filament temperature and adhesion development in fused deposition techniques. Journal of Materials Processing Technology 2017;245:167-79. https://doi.org/10.1016/j.jmatprotec.2017.02.026

[8] Zanjanijam AR, Major I, Lyons JG, Lafont U, Devine DM. Fused Filament Fabrication of PEEK: A Review of Process-Structure-Property Relationships. Polymers 2020;12:1665. https://doi.org/10.3390/polym12081665

[9] Benarbia A, Sobotka V, Boyard N, Roua C. Fused filament fabrication: Numerical adhesion modeling suitable for semicrystalline polymers, 2023, p. 139-48. https://doi.org/10.21741/9781644902479-16.
# Influence of deposition strategy on porosity in powder-feed directed energy deposition (DED)

DHOONOOAH Neha Devi¹,ᵃ*, MOUSSAOUI Kamel¹,ᵇ, MONIES Frederic¹,ᶜ, RUBIO Walter¹,ᵈ, ZITOUNE Redouane¹,ᵉ

¹Institut Clément Ader (ICA), UMR-CNRS 5312, “INSA, UPS, Mines Albi, ISAE”, Université de Toulouse, 133 C Avenue de Rangueil, 31077 Toulouse, France

ᵃneha-devi.dhoonooah@univ-tlse3.fr, ᵇKamel.MOUSSAOUI@isae-supaero.fr,

ᶜfrederic.monies@univ-tlse3.fr, ᵈwalter.rubio@univ-tlse3.fr, ᶜredouane.zitoune@iut-tlse3.fr

**Keywords:** DED-Powder, Deposition Strategy, Automatic Toolpath Generation, Surface Roughness, Bearing Ratio Curve

**Abstract.** Directed energy deposition (DED) is an additive manufacturing process which is increasingly being used for direct manufacturing, added function application and component repair of high-value metals and alloys that are otherwise hard to machine conventionally [1]. However, most commercially available toolpath generators are not comprehensive with regards to the deposition strategy and process parameters, emphasis usually being placed solely on the overlap of adjacent weld beads rather than including the interaction between the contour curves and infill pattern. This increases the probability of gap formation in the deposition pattern, and hence the rate of porosity in the part. This study focuses on the influence of varying bead overlap on the resulting bead geometry and surface roughness for Inconel 718 workpieces deposited using the DED process. The deposition strategy was devised using Python programming language, and the code found to be robust for both single- and multi-layer deposits.

**Introduction**

The laser DED process uses a focused source of thermal energy to fuse material, generally in powder or wire form, for building 3D shapes through layer-by-layer deposition. Widely used in the aerospace, military and medical sectors, DED may be used for depositing titanium-, nickel- and iron-based alloys, as well as different grades of stainless steel, with a relatively higher deposition rate than other common AM processes [2]. Moreover, it usually results in a smaller heat affected zone (HAZ) than other welding-based repair methods such as submerged arc welding [2]. The DED process depends on the interaction of several process parameters such as laser power, scanning speed and material feed rate. Another parameter which remains crucial for the process optimisation is the deposition strategy. In fact, several authors have shown that this parameter is the key to obtaining a good visual and structural finish, as well as minimising the occurrence of porosities and voids in the printed part.

It is important to note that porosities are defects that commonly occur in metal AM parts, negatively affecting their mechanical properties. The origin of the porosity may come from the powder feedstock or the process itself [3]. Powder-induced pores result from the entrapment of the carrier gas inside the feedstock during powder atomisation and are generally spherical in shape. These can directly affect as-fabricated parts and degrade their overall performance. Process-induced pores occur when the applied thermal energy is insufficient for complete melting of the feedstock, or when spatter of the powdered material occurs. These pores are typically non-spherical in shape and occur in a variety of sizes, ranging from the sub-microscopic to the macroscopic size. As highlighted by Sames et al. [4], porosity is directly influenced by process methodology, and so process parameters need to be accurately tuned to minimise the risk of pore

<figure><img src="image_318.png" /></figure>
# Influence of deposition strategy on porosity in powder-feed directed energy deposition (DED)

DHOONOOAH Neha Devi¹,ᵃ*, MOUSSAOUI Kamel¹,ᵇ, MONIES Frederic¹,ᶜ, RUBIO Walter¹,ᵈ, ZITOUNE Redouane¹,ᵉ

¹Institut Clément Ader (ICA), UMR-CNRS 5312, “INSA, UPS, Mines Albi, ISAE”, Université de Toulouse, 133 C Avenue de Rangueil, 31077 Toulouse, France

ᵃneha-devi.dhoonooah@univ-tlse3.fr, ᵇKamel.MOUSSAOUI@isae-supaero.fr,

ᶜfrederic.monies@univ-tlse3.fr, ᵈwalter.rubio@univ-tlse3.fr, ᶜredouane.zitoune@iut-tlse3.fr

**Keywords:** DED-Powder, Deposition Strategy, Automatic Toolpath Generation, Surface Roughness, Bearing Ratio Curve

**Abstract.** Directed energy deposition (DED) is an additive manufacturing process which is increasingly being used for direct manufacturing, added function application and component repair of high-value metals and alloys that are otherwise hard to machine conventionally [1]. However, most commercially available toolpath generators are not comprehensive with regards to the deposition strategy and process parameters, emphasis usually being placed solely on the overlap of adjacent weld beads rather than including the interaction between the contour curves and infill pattern. This increases the probability of gap formation in the deposition pattern, and hence the rate of porosity in the part. This study focuses on the influence of varying bead overlap on the resulting bead geometry and surface roughness for Inconel 718 workpieces deposited using the DED process. The deposition strategy was devised using Python programming language, and the code found to be robust for both single- and multi-layer deposits.

**Introduction**

The laser DED process uses a focused source of thermal energy to fuse material, generally in powder or wire form, for building 3D shapes through layer-by-layer deposition. Widely used in the aerospace, military and medical sectors, DED may be used for depositing titanium-, nickel- and iron-based alloys, as well as different grades of stainless steel, with a relatively higher deposition rate than other common AM processes [2]. Moreover, it usually results in a smaller heat affected zone (HAZ) than other welding-based repair methods such as submerged arc welding [2]. The DED process depends on the interaction of several process parameters such as laser power, scanning speed and material feed rate. Another parameter which remains crucial for the process optimisation is the deposition strategy. In fact, several authors have shown that this parameter is the key to obtaining a good visual and structural finish, as well as minimising the occurrence of porosities and voids in the printed part.

It is important to note that porosities are defects that commonly occur in metal AM parts, negatively affecting their mechanical properties. The origin of the porosity may come from the powder feedstock or the process itself [3]. Powder-induced pores result from the entrapment of the carrier gas inside the feedstock during powder atomisation and are generally spherical in shape. These can directly affect as-fabricated parts and degrade their overall performance. Process-induced pores occur when the applied thermal energy is insufficient for complete melting of the feedstock, or when spatter of the powdered material occurs. These pores are typically non-spherical in shape and occur in a variety of sizes, ranging from the sub-microscopic to the macroscopic size. As highlighted by Sames et al. [4], porosity is directly influenced by process methodology, and so process parameters need to be accurately tuned to minimise the risk of pore

<figure><img src="image_324.png" /></figure>
formation. The occurrence of porosity or voids at the part surface or within the part effectively reduce density.

In a single printed layer, the overlap between adjacent weld beads needs to be monitored to prevent voids, and hence improve the density of the part. As layers are stacked, new process requirements arise due to the complex thermal activity occurring between consecutive layers. When the laser source moves over a previously deposited layer, thermal energy accumulates and this causes a partial re-melting of this particular layer, hence affecting the HAZ, formed microstructure and overall part density. The selected deposition strategy for a particular process has a significant influence on the density and microstructure of the resulting part.

This study looks at the influence of varying bead overlap on the resulting bead geometry and surface roughness for Inconel 718 workpieces deposited using the DED process. While the influence of adjacent bead overlap has already been studied extensively for the laser powder bed fusion (LPBF) process, our approach of using bearing ratios for characterising deposits achieved using varying bead overlaps in powder-feed laser metal deposition (LMD) is novel and could potentially prove to be significant. A toolpath generation approach for metal powder DED was developed using Python programming language and tested through the physical printing of individual weld beads, and single- and multi-layered geometries with Inconel 718 powder on a BeAM® laser deposition machine. The bead geometry, extent of porosity in the prints, and surface roughness were then evaluated using an Alicona® microscope.

## Selection of the deposition strategy

The majority of deposition strategies currently used in AM processes are based on those used in computer-aided machining (CAM) software, such as for milling operations. While the principles behind the strategies used for subtractive and additive processes remain the same, it is important to note that AM includes aspects such as adjacent weld bead overlap, laser spot size, weld bead cross-sectional diameter, etc., which determine the overall thermal activity occurring during a 3D build. These need to be optimised in order to produce parts with a good microstructure, minimum porosity and high density, as well as better mechanical properties, minimal distortion and thermal stresses.

Deposition strategies were experimentally assessed by Parimi et al. [6] to optimise the microstructure of Inconel 718 deposited through the DED process. They used unidirectional and bidirectional deposition paths, as well as different laser power values ranging from 390 to 910W but did not conclude on a preference for either deposition strategy. Ribeiro et al. [2] studied the influence of weld bead stepover on the production of stainless steel 316 cubes using the DED process and concluded that the contour toolpath resulted in the best performance, closely followed by the zigzag. Both these strategies were linked to more uniform heat distribution and less distortions for similar deposition heights. Calleja et al. [7] tested new process parameters and strategies for optimising multi-axis laser cladding operations with AISI 1045 structural steel as substrate material and an AISI 316L stainless steel as filler material. For cladding a hemispherical surface, they concluded that a hatching strategy comprising horizontal, vertical and 45°-diagonal strategies resulted in the best-quality deposition, the combination of angles creating a “mesh type” structure, with optimal overlapping and a uniform 3D structure [7].

In this study, we initially considered combinations of different modes (one-way, zigzag and oscillatory) and directions (horizontal, vertical, and 45°-diagonal) for the generation of automatic toolpaths, where the toolpath here refers to the set of movements of the deposition head during the layer-by-layer deposition process. The zigzag mode has been found to be the most commonly used in material deposition due to its ease of implementation and independence of the shape of the cross-section. It can therefore be used for depositing various thin or thick shapes [2]. In view of the oblong-shaped single- and multi-layer geometries that we intend to study, we thus selected the horizontal zigzag strategy for its relatively long strokes between the start and end points of the
formation. The occurrence of porosity or voids at the part surface or within the part effectively reduce density.

In a single printed layer, the overlap between adjacent weld beads needs to be monitored to prevent voids, and hence improve the density of the part. As layers are stacked, new process requirements arise due to the complex thermal activity occurring between consecutive layers. When the laser source moves over a previously deposited layer, thermal energy accumulates and this causes a partial re-melting of this particular layer, hence affecting the HAZ, formed microstructure and overall part density. The selected deposition strategy for a particular process has a significant influence on the density and microstructure of the resulting part.

This study looks at the influence of varying bead overlap on the resulting bead geometry and surface roughness for Inconel 718 workpieces deposited using the DED process. While the influence of adjacent bead overlap has already been studied extensively for the laser powder bed fusion (LPBF) process, our approach of using bearing ratios for characterising deposits achieved using varying bead overlaps in powder-feed laser metal deposition (LMD) is novel and could potentially prove to be significant. A toolpath generation approach for metal powder DED was developed using Python programming language and tested through the physical printing of individual weld beads, and single- and multi-layered geometries with Inconel 718 powder on a BeAM® laser deposition machine. The bead geometry, extent of porosity in the prints, and surface roughness were then evaluated using an Alicona® microscope.

## Selection of the deposition strategy

The majority of deposition strategies currently used in AM processes are based on those used in computer-aided machining (CAM) software, such as for milling operations. While the principles behind the strategies used for subtractive and additive processes remain the same, it is important to note that AM includes aspects such as adjacent weld bead overlap, laser spot size, weld bead cross-sectional diameter, etc., which determine the overall thermal activity occurring during a 3D build. These need to be optimised in order to produce parts with a good microstructure, minimum porosity and high density, as well as better mechanical properties, minimal distortion and thermal stresses.

Deposition strategies were experimentally assessed by Parimi et al. [6] to optimise the microstructure of Inconel 718 deposited through the DED process. They used unidirectional and bidirectional deposition paths, as well as different laser power values ranging from 390 to 910W but did not conclude on a preference for either deposition strategy. Ribeiro et al. [2] studied the influence of weld bead stepover on the production of stainless steel 316 cubes using the DED process and concluded that the contour toolpath resulted in the best performance, closely followed by the zigzag. Both these strategies were linked to more uniform heat distribution and less distortions for similar deposition heights. Calleja et al. [7] tested new process parameters and strategies for optimising multi-axis laser cladding operations with AISI 1045 structural steel as substrate material and an AISI 316L stainless steel as filler material. For cladding a hemispherical surface, they concluded that a hatching strategy comprising horizontal, vertical and 45°-diagonal strategies resulted in the best-quality deposition, the combination of angles creating a “mesh type” structure, with optimal overlapping and a uniform 3D structure [7].

In this study, we initially considered combinations of different modes (one-way, zigzag and oscillatory) and directions (horizontal, vertical, and 45°-diagonal) for the generation of automatic toolpaths, where the toolpath here refers to the set of movements of the deposition head during the layer-by-layer deposition process. The zigzag mode has been found to be the most commonly used in material deposition due to its ease of implementation and independence of the shape of the cross-section. It can therefore be used for depositing various thin or thick shapes [2]. In view of the oblong-shaped single- and multi-layer geometries that we intend to study, we thus selected the horizontal zigzag strategy for its relatively long strokes between the start and end points of the
toolpath. As compared to the vertical zigzag, this is expected to result in lower heating, fewer burn marks, and fewer voids at the edges of the infill pattern.

## Methodology

### Deposition of single-layer geometries

In order to evaluate the influence of deposition strategy and bead overlap on the final geometry of DED deposited parts, Inconel 718 workpieces in the shape of oblongs with length 20mm and width 10mm were deposited onto a 4mm-thick substrate of the same material, using a BeAM® machine. Based on previous tests, the laser power (P) was set to 250W, with scanning speed (v) of 1300mm/min and powder feed rate of 6.50g/min – all parameters remaining constant throughout the process. With these parameters, the average bead width and height resulted in 0.56mm and 0.2mm respectively.

Prior to the deposition process, a control set of three individual weld beads were deposited at the edge of the substrate to set a reference value of bead height for the selected values of P and v. This was repeated at the end of the deposition process to compare the measurements and hence assess the reproducibility of the process. Using an Alicona® microscope, the average bead height was found to be 0.25mm prior to deposition and 0.26mm post-deposition, indicating good repeatability over the duration of the process.

The overlap between adjacent weld beads, R_cent, was set to 10%, 30% and 50% to determine the effective average height of the deposited pattern, the deposition quality, and the roughness characteristics. For each value of R_cent, the oblong pattern was deposited three times to ensure the repeatability and reproducibility of the process.

<figure><img src="image_319.png" /><figcaption>Fig. 1: A diagram illustrating the overlap between adjacent beads in the deposit zone</figcaption></figure>

### Deposition of multi-layer geometries

The same values of laser power, scanning speed and powder feed rate were further used to deposit 10-layer-high stacks, with a constant Z-increment of 0.2mm, and overall nominal height of 2.0mm. According to Ribeiro et al. [2], this Z-increment likely needs to be adjusted to compensate for decreases in layer thickness as layers are stacked to build higher workpieces. However, this was not considered in this study so that any reduction in the resulting average height of the stacks could be detected.

During deposition in Z, the focal distance for both the powder feedstock and laser source was set to 3.5mm. It was verified that small variations around the focal point of the laser did not significantly influence the laser spot size and geometry of the weld beads, even when varying the values of bead overlap.

## Results and discussion

The single- and multi-layer geometries were both analysed for dimensional accuracy in terms of bead width and height, and quality of deposition according to roughness characteristics.

Similarly to the measurement process for the individual beads, the average height of each layer was measured at three points along each of three sections under the Alicona® microscope. Measurements were taken at the highest point of the profile of a given section. They were averaged first across the section, then across the given deposit. To characterise the roughness of the
toolpath. As compared to the vertical zigzag, this is expected to result in lower heating, fewer burn marks, and fewer voids at the edges of the infill pattern.

## Methodology

### Deposition of single-layer geometries

In order to evaluate the influence of deposition strategy and bead overlap on the final geometry of DED deposited parts, Inconel 718 workpieces in the shape of oblongs with length 20mm and width 10mm were deposited onto a 4mm-thick substrate of the same material, using a BeAM® machine. Based on previous tests, the laser power (P) was set to 250W, with scanning speed (v) of 1300mm/min and powder feed rate of 6.50g/min – all parameters remaining constant throughout the process. With these parameters, the average bead width and height resulted in 0.56mm and 0.2mm respectively.

Prior to the deposition process, a control set of three individual weld beads were deposited at the edge of the substrate to set a reference value of bead height for the selected values of P and v. This was repeated at the end of the deposition process to compare the measurements and hence assess the reproducibility of the process. Using an Alicona® microscope, the average bead height was found to be 0.25mm prior to deposition and 0.26mm post-deposition, indicating good repeatability over the duration of the process.

The overlap between adjacent weld beads, R_cent, was set to 10%, 30% and 50% to determine the effective average height of the deposited pattern, the deposition quality, and the roughness characteristics. For each value of R_cent, the oblong pattern was deposited three times to ensure the repeatability and reproducibility of the process.

<figure><img src="image_325.png" /><figcaption>Fig. 1: A diagram illustrating the overlap between adjacent beads in the deposit zone</figcaption></figure>

### Deposition of multi-layer geometries

The same values of laser power, scanning speed and powder feed rate were further used to deposit 10-layer-high stacks, with a constant Z-increment of 0.2mm, and overall nominal height of 2.0mm. According to Ribeiro et al. [2], this Z-increment likely needs to be adjusted to compensate for decreases in layer thickness as layers are stacked to build higher workpieces. However, this was not considered in this study so that any reduction in the resulting average height of the stacks could be detected.

During deposition in Z, the focal distance for both the powder feedstock and laser source was set to 3.5mm. It was verified that small variations around the focal point of the laser did not significantly influence the laser spot size and geometry of the weld beads, even when varying the values of bead overlap.

## Results and discussion

The single- and multi-layer geometries were both analysed for dimensional accuracy in terms of bead width and height, and quality of deposition according to roughness characteristics.

Similarly to the measurement process for the individual beads, the average height of each layer was measured at three points along each of three sections under the Alicona® microscope. Measurements were taken at the highest point of the profile of a given section. They were averaged first across the section, then across the given deposit. To characterise the roughness of the
deposited surface, the average roughness of the profile through a given cross-section, R<sub>a</sub>, and the maximum peak-to-valley height of the same given profile, R<sub>t</sub>, were also measured using the microscope.

As in Fig. 2 below, no open and/or closed pores were visibly present in the deposits, indicating that the deposition process did not generate important defects in the printed parts. The increasing bead overlap resulted in adjacent weld beads lying closer to each other. For lower overlap values, a larger and shallower melt pool forms across adjacent beads, resulting in a lower degree of fusion across the material deposits, and hence higher effective heights. As the bead overlap increases, the distance between the centres of adjacent bead decreases and this results in the formation of deeper melt pools. Further, more thermal energy is imparted to the substrate and feedstock during the partial re-melting that occurs as the laser source moves in a zigzag pattern. More feedstock gets fused in these deeper melt pools, and hence less of it is present above substrate level.

<figure><img src="image_320.png" /><figcaption>Fig. 2: A visual representation of how the deposition pattern evolves with varying bead overlap, from 10% to 30% to 50%</figcaption></figure>

For the single-layer deposits, bead height was seen to decrease significantly as R<sub>cent</sub> increases from 10% to 50%, as summarised in Table 1 below. The nominal height of 0.25mm for the single weld beads deposited at the start of the study reduced by 20% for the 10%-overlap single-layer geometry, while it dropped by around 50% for the 50%-overlap geometry, indicating a significant change in the definition of the melt pool and amount of feedstock fused to the substrate.

<table><caption>Table 1: Average dimensions of weld bead height for both single- and multi-layer geometries</caption><thead><tr><th rowspan="2">Bead overlap (%)</th><th colspan="2">Average bead height/mm</th><th colspan="2">Deviation from nominal height (%)</th></tr><tr><th>For single-layer deposits</th><th>For 10-layer 3D stacks</th><th>For single-layer deposits</th><th>For 10-layer 3D stacks</th></tr></thead><tbody><tr><td>10</td><td>0.20</td><td>0.16</td><td>20</td><td>36</td></tr><tr><td>30</td><td>0.14</td><td>0.14</td><td>44</td><td>44</td></tr><tr><td>50</td><td>0.13</td><td>0.11</td><td>48</td><td>56</td></tr></tbody></table>

For the multi-layer geometries, the average bead height decreased similarly with an increase in overlap, but this decrease was more significant than for the single-layer deposits. As layers are stacked in the DED process, the zigzag movement of the laser beam above the previously deposited layer gives rise to partial re-melting of this layer, creating a deeper melt pool and further absorption of the metallic powder into the said layer. This causes stacked layers to fuse to a greater extent
deposited surface, the average roughness of the profile through a given cross-section, R<sub>a</sub>, and the maximum peak-to-valley height of the same given profile, R<sub>t</sub>, were also measured using the microscope.

As in Fig. 2 below, no open and/or closed pores were visibly present in the deposits, indicating that the deposition process did not generate important defects in the printed parts. The increasing bead overlap resulted in adjacent weld beads lying closer to each other. For lower overlap values, a larger and shallower melt pool forms across adjacent beads, resulting in a lower degree of fusion across the material deposits, and hence higher effective heights. As the bead overlap increases, the distance between the centres of adjacent bead decreases and this results in the formation of deeper melt pools. Further, more thermal energy is imparted to the substrate and feedstock during the partial re-melting that occurs as the laser source moves in a zigzag pattern. More feedstock gets fused in these deeper melt pools, and hence less of it is present above substrate level.

<figure><img src="image_326.png" /><figcaption>Fig. 2: A visual representation of how the deposition pattern evolves with varying bead overlap, from 10% to 30% to 50%</figcaption></figure>

For the single-layer deposits, bead height was seen to decrease significantly as R<sub>cent</sub> increases from 10% to 50%, as summarised in Table 1 below. The nominal height of 0.25mm for the single weld beads deposited at the start of the study reduced by 20% for the 10%-overlap single-layer geometry, while it dropped by around 50% for the 50%-overlap geometry, indicating a significant change in the definition of the melt pool and amount of feedstock fused to the substrate.

<table><caption>Table 1: Average dimensions of weld bead height for both single- and multi-layer geometries</caption><thead><tr><th rowspan="2">Bead overlap (%)</th><th colspan="2">Average bead height/mm</th><th colspan="2">Deviation from nominal height (%)</th></tr><tr><th>For single-layer deposits</th><th>For 10-layer 3D stacks</th><th>For single-layer deposits</th><th>For 10-layer 3D stacks</th></tr></thead><tbody><tr><td>10</td><td>0.20</td><td>0.16</td><td>20</td><td>36</td></tr><tr><td>30</td><td>0.14</td><td>0.14</td><td>44</td><td>44</td></tr><tr><td>50</td><td>0.13</td><td>0.11</td><td>48</td><td>56</td></tr></tbody></table>

For the multi-layer geometries, the average bead height decreased similarly with an increase in overlap, but this decrease was more significant than for the single-layer deposits. As layers are stacked in the DED process, the zigzag movement of the laser beam above the previously deposited layer gives rise to partial re-melting of this layer, creating a deeper melt pool and further absorption of the metallic powder into the said layer. This causes stacked layers to fuse to a greater extent
with one another as the laser moves in the positive Z-direction, and results in a much lower average bead height than expected, as compared to the nominal value of 0.25mm.

Increasing bead overlap also leads to the formation of deeper melt pools and both these factors combine to create a more significant decrease in average bead height for the multi-layered geometries than for the single layers, as seen from the values in Table 1. In the case of 30% bead overlap however, no difference was observed in the height deviation between single- and multi-layered geometries, which seems to indicate a good and uniform extent of dilution between the surface of the substrate and the first deposited layer, as well as between the stacked layers themselves. Both the single- and multi-layered samples deposited with a 30% bead overlap have been retained for further analysis of the microstructure to confirm these findings.

A similar decreasing trend was seen in the average values of R<sub>a</sub> and R<sub>t</sub> for increasing bead overlap in both single- and multi-layer geometries, as summarised in Table 2 below, indicating a smoother and more uniform surface finish with lower peak-to-valley heights across a given profile. For lower overlap values, the melt pool forms predominantly on the previously deposited weld bead and less so on the adjacent one. As the bead overlap increases, the melt pool forms over a larger surface area, the powdered feedstock gets fused more uniformly, and this results in a lower, smoother geometry with markedly lower roughness.

<table><caption>Table 2: Average dimensions of R<sub>a</sub> and R<sub>t</sub> for both single- and multi-layer geometries</caption><thead><tr><th rowspan="2">Bead overlap (%)</th><th colspan="2">Average R<sub>a</sub>/µm</th><th colspan="2">Average R<sub>t</sub>/µm</th></tr><tr><th>For single-layer deposits</th><th>For 10-layer deposits</th><th>For single-layer deposits</th><th>For 10-layer deposits</th></tr></thead><tbody><tr><td>10</td><td>17.17</td><td>14.58</td><td>122.52</td><td>114.21</td></tr><tr><td>30</td><td>13.92</td><td>10.31</td><td>99.24</td><td>91.85</td></tr><tr><td>50</td><td>10.95</td><td>7.73</td><td>87.78</td><td>77.17</td></tr></tbody></table>

In order to qualify the rate of material deposition, the roughness profiles and bearing ratio curves were analysed for the different bead overlaps in both single- and multi-layer deposits. The bearing ratio curve, also known as the Abbott-Firestone curve is a qualitative measure of the surface texture of an object, where the curve is calculated from a roughness profile by drawing lines parallel to the datum and measuring the extent of the line that lies within the profile [8].

As seen in Figs. 3-5 below, the rate of material deposition for the single-layer geometries decreased faster with increasing bead overlap, with a noticeably sharper decrease from 30 to 50%, as compared to 10 to 30%. The average profile roughness was seen to reduce by over 35% while the average peak-to-valley height values dropped by almost 30%.

As bead overlap increases, adjacent weld beads lie closer to each other, and the melt pools formed are also deeper as described previously. More feedstock gets fused at the surface of the substrate, so that the average peak-to-valley height reduces, and more material is contained below the material line drawn to estimate the bearing ratio. The steeper gradient above the 50%-mark in the bearing ratio curve for the 50% bead overlap indicates a fewer number of peaks in a deposition pattern that is highly uniform and mostly planar due to the adjacent beads lying closer. Less material is contained in the series of peaks lying above the average material surface, and hence a sharp decline in the rate of material deposition is seen.
with one another as the laser moves in the positive Z-direction, and results in a much lower average bead height than expected, as compared to the nominal value of 0.25mm.

Increasing bead overlap also leads to the formation of deeper melt pools and both these factors combine to create a more significant decrease in average bead height for the multi-layered geometries than for the single layers, as seen from the values in Table 1. In the case of 30% bead overlap however, no difference was observed in the height deviation between single- and multi-layered geometries, which seems to indicate a good and uniform extent of dilution between the surface of the substrate and the first deposited layer, as well as between the stacked layers themselves. Both the single- and multi-layered samples deposited with a 30% bead overlap have been retained for further analysis of the microstructure to confirm these findings.

A similar decreasing trend was seen in the average values of R<sub>a</sub> and R<sub>t</sub> for increasing bead overlap in both single- and multi-layer geometries, as summarised in Table 2 below, indicating a smoother and more uniform surface finish with lower peak-to-valley heights across a given profile. For lower overlap values, the melt pool forms predominantly on the previously deposited weld bead and less so on the adjacent one. As the bead overlap increases, the melt pool forms over a larger surface area, the powdered feedstock gets fused more uniformly, and this results in a lower, smoother geometry with markedly lower roughness.

<table><caption>Table 2: Average dimensions of R<sub>a</sub> and R<sub>t</sub> for both single- and multi-layer geometries</caption><thead><tr><th rowspan="2">Bead overlap (%)</th><th colspan="2">Average R<sub>a</sub>/µm</th><th colspan="2">Average R<sub>t</sub>/µm</th></tr><tr><th>For single-layer deposits</th><th>For 10-layer deposits</th><th>For single-layer deposits</th><th>For 10-layer deposits</th></tr></thead><tbody><tr><td>10</td><td>17.17</td><td>14.58</td><td>122.52</td><td>114.21</td></tr><tr><td>30</td><td>13.92</td><td>10.31</td><td>99.24</td><td>91.85</td></tr><tr><td>50</td><td>10.95</td><td>7.73</td><td>87.78</td><td>77.17</td></tr></tbody></table>

In order to qualify the rate of material deposition, the roughness profiles and bearing ratio curves were analysed for the different bead overlaps in both single- and multi-layer deposits. The bearing ratio curve, also known as the Abbott-Firestone curve is a qualitative measure of the surface texture of an object, where the curve is calculated from a roughness profile by drawing lines parallel to the datum and measuring the extent of the line that lies within the profile [8].

As seen in Figs. 3-5 below, the rate of material deposition for the single-layer geometries decreased faster with increasing bead overlap, with a noticeably sharper decrease from 30 to 50%, as compared to 10 to 30%. The average profile roughness was seen to reduce by over 35% while the average peak-to-valley height values dropped by almost 30%.

As bead overlap increases, adjacent weld beads lie closer to each other, and the melt pools formed are also deeper as described previously. More feedstock gets fused at the surface of the substrate, so that the average peak-to-valley height reduces, and more material is contained below the material line drawn to estimate the bearing ratio. The steeper gradient above the 50%-mark in the bearing ratio curve for the 50% bead overlap indicates a fewer number of peaks in a deposition pattern that is highly uniform and mostly planar due to the adjacent beads lying closer. Less material is contained in the series of peaks lying above the average material surface, and hence a sharp decline in the rate of material deposition is seen.
<figure><img src="image_321.png" /><figcaption>Figs. 3-5: Bearing ratio curves for single-layer deposits, showing steeper declines in the rate of material deposition with increasing bead overlap (top to bottom)</figcaption></figure>
<figure><img src="image_327.png" /><figcaption>Figs. 3-5: Bearing ratio curves for single-layer deposits, showing steeper declines in the rate of material deposition with increasing bead overlap (top to bottom)</figcaption></figure>
As bead overlap increased from 10 to 50% in the multi-layer deposits, the average profile roughness was seen to reduce by over 45% while the average peak-to-valley height values dropped by over 30%. This trend was similar to that observed for the single-layer deposits where the melt pool is both deeper and more uniform across closer adjacent weld beads.

A more pronounced drop in the rate of material deposition was observed as in Figs. 6-8 below. This is due to the additional movement of the laser beam in Z as it fuses successive layers, causing repeated melting and solidification cycles as the stack rises. This leads to partial re-melting between the stacked layers, forming deeper melt pools and resulting in even lower average peak-to-valley heights across the top surface of multi-layered deposits as compared to single-layer surfaces. The average value of profile roughness for a stack with 10% bead overlap was 15% lower than that for a single layer, and this rose to almost 30% for the 50% bead overlap.

<figure><img src="image_322.png" /></figure>

<figure><img src="image_323.png" /></figure>
As bead overlap increased from 10 to 50% in the multi-layer deposits, the average profile roughness was seen to reduce by over 45% while the average peak-to-valley height values dropped by over 30%. This trend was similar to that observed for the single-layer deposits where the melt pool is both deeper and more uniform across closer adjacent weld beads.

A more pronounced drop in the rate of material deposition was observed as in Figs. 6-8 below. This is due to the additional movement of the laser beam in Z as it fuses successive layers, causing repeated melting and solidification cycles as the stack rises. This leads to partial re-melting between the stacked layers, forming deeper melt pools and resulting in even lower average peak-to-valley heights across the top surface of multi-layered deposits as compared to single-layer surfaces. The average value of profile roughness for a stack with 10% bead overlap was 15% lower than that for a single layer, and this rose to almost 30% for the 50% bead overlap.

<figure><img src="image_328.png" /></figure>

<figure><img src="image_329.png" /></figure>
<figure><img src="image_324.png" /><figcaption>Figs. 6-8: Bearing ratio curves for multi-layer deposits, illustrating steeper declines than for single-layers as the bead overlap increases (top to bottom)</figcaption></figure>

**Conclusions and perspectives for further work**

From the set of experiments performed in this study on the influence of deposition strategy on bead geometry and surface roughness in powder-feed DED, the following conclusions can be drawn:

* better control of the toolpath generator makes it more robust and suitable for obtaining the desired bead geometry and reducing surface roughness,

* the horizontal zigzag deposition strategy was suitable for depositing geometrically accurate and high-quality single- and multi-layer geometries,

* increasing bead overlap from 10 to 50% resulted in lower average bead height for both the single- and multi-layer geometries, in direct correlation with the formation of deeper, narrower melt pools across adjacent weld beads,

* the 30% bead overlap seems optimal for the DED deposition process since it results in the same average bead height for both single- and multi-layered geometries, indicating a uniform extent of dilution,

* an increase in bead overlap also reduced the roughness characteristics for both types of deposit in line with the changed melt pool definition. Material deposition decreased at a faster rate as shown by the bearing ratio curves, indicating a higher proportion of fused feedstock below the average deposition line, and hence a better, smoother surface finish.

In view of these results, further analysis will be carried out on the deposited samples to characterise the resulting extent of porosity. For the multi-layer deposits, cuts will be made in the samples to observe the cross-section and measure the porosity within and between the successive layers, as a means of characterising the influence of the deposition strategy on defects occurring in the printed parts. The extent of dilution resulting from the varying bead overlap will also be studied to confirm the suitability of using 30% bead overlap for further material deposition.

Moreover, most commercially available toolpath generators are not comprehensive with regards to the deposition strategy and process parameters, emphasis usually being placed only on adjacent weld bead overlap rather than including the interaction between the contour curves and
<figure><img src="image_330.png" /><figcaption>Figs. 6-8: Bearing ratio curves for multi-layer deposits, illustrating steeper declines than for single-layers as the bead overlap increases (top to bottom)</figcaption></figure>

**Conclusions and perspectives for further work**

From the set of experiments performed in this study on the influence of deposition strategy on bead geometry and surface roughness in powder-feed DED, the following conclusions can be drawn:

* better control of the toolpath generator makes it more robust and suitable for obtaining the desired bead geometry and reducing surface roughness,

* the horizontal zigzag deposition strategy was suitable for depositing geometrically accurate and high-quality single- and multi-layer geometries,

* increasing bead overlap from 10 to 50% resulted in lower average bead height for both the single- and multi-layer geometries, in direct correlation with the formation of deeper, narrower melt pools across adjacent weld beads,

* the 30% bead overlap seems optimal for the DED deposition process since it results in the same average bead height for both single- and multi-layered geometries, indicating a uniform extent of dilution,

* an increase in bead overlap also reduced the roughness characteristics for both types of deposit in line with the changed melt pool definition. Material deposition decreased at a faster rate as shown by the bearing ratio curves, indicating a higher proportion of fused feedstock below the average deposition line, and hence a better, smoother surface finish.

In view of these results, further analysis will be carried out on the deposited samples to characterise the resulting extent of porosity. For the multi-layer deposits, cuts will be made in the samples to observe the cross-section and measure the porosity within and between the successive layers, as a means of characterising the influence of the deposition strategy on defects occurring in the printed parts. The extent of dilution resulting from the varying bead overlap will also be studied to confirm the suitability of using 30% bead overlap for further material deposition.

Moreover, most commercially available toolpath generators are not comprehensive with regards to the deposition strategy and process parameters, emphasis usually being placed only on adjacent weld bead overlap rather than including the interaction between the contour curves and
infill pattern. This increases the probability of gap formation in the deposition pattern, and hence the rate of porosity in the part. When building multi-layer geometries, porosity-related defects will accumulate and eventually lead to irreparable construction defects and a drop in density, as well as mechanical properties [2,5].

In future studies, we propose to investigate the influence of contours delimiting the deposition region, as well as the overlap between the end portion of the weld beads and this contour, and the overlap between the contour and first deposited bead. By definition, these overlaps are not independent of the bead overlap, and the interaction between them is expected to be significant with respect to the resulting bead geometry, surface finish, and extent of porosity.

## References

[1] Optomec, 2019. How 3D Metal Printing Saves Time and Lowers Costs: DED for Repair of Industrial Components [pdf] https://optomec.com/wp-content/uploads/2019/03/DEDWebinar-slides.pdf

[2] Ribeiro, K.S.B., Mariani, F.E., Coelho, R.T., 2020. A Study of Different Deposition Strategies in Direct Energy Deposition (DED) Processes. Procedia Manufacturing 48, 663-670. https://doi.org/10.1016/j.promfg.2020.05.158

[3] Sames, W. J., Medina, F., Peter, W. H., Babu, S. S., Dehoff, R. R., 2014. Effect of Process Control and Powder Quality on Inconel 718 Produced Using Electron Beam Melting. in Superalloy 718 and Derivatives, Pittsburgh, PA.

[4] Sames, W.J., List, F.A., Pannala, S., Dehoff, R.R., Babu, S.S., 2016. The metallurgy and processing science of metal additive manufacturing. International Materials Reviews 61, 315-360. https://doi.org/10.1080/09506608.2015.1116649

[5] Flores, J., Garmendia, I., Pujana, J., 2019. Toolpath generation for the manufacture of metallic components by means of the laser metal deposition technique. Int J Adv Manufacturing 101, 2111-2120. https://doi.org/10.1007/s00170-018-3124-1

[6] Parimi, L., L., Ravi, G. A., Clark, D., Attallah, M.M., 2014. Microstructural and texture development in direct laser fabricated IN718. Materials Characterization 89:102-111

[7] Calleja, A., Tabernero, I., Fernández, A., Celaya, A., Lamikiz, A., López de Lacalle, L.N., 2014. Improvement of strategies and parameters for multi-axis laser cladding operations, Optics and Lasers in Engineering, Vol. 56, pp. 113-120, ISSN 0143-8166, https://doi.org/10.1016/j.optlaseng.2013.12.017

[8] Johnson, K. L., 1985. Contact Mechanics. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139171731
infill pattern. This increases the probability of gap formation in the deposition pattern, and hence the rate of porosity in the part. When building multi-layer geometries, porosity-related defects will accumulate and eventually lead to irreparable construction defects and a drop in density, as well as mechanical properties [2,5].

In future studies, we propose to investigate the influence of contours delimiting the deposition region, as well as the overlap between the end portion of the weld beads and this contour, and the overlap between the contour and first deposited bead. By definition, these overlaps are not independent of the bead overlap, and the interaction between them is expected to be significant with respect to the resulting bead geometry, surface finish, and extent of porosity.

## References

[1] Optomec, 2019. How 3D Metal Printing Saves Time and Lowers Costs: DED for Repair of Industrial Components [pdf] https://optomec.com/wp-content/uploads/2019/03/DEDWebinar-slides.pdf

[2] Ribeiro, K.S.B., Mariani, F.E., Coelho, R.T., 2020. A Study of Different Deposition Strategies in Direct Energy Deposition (DED) Processes. Procedia Manufacturing 48, 663-670. https://doi.org/10.1016/j.promfg.2020.05.158

[3] Sames, W. J., Medina, F., Peter, W. H., Babu, S. S., Dehoff, R. R., 2014. Effect of Process Control and Powder Quality on Inconel 718 Produced Using Electron Beam Melting. in Superalloy 718 and Derivatives, Pittsburgh, PA.

[4] Sames, W.J., List, F.A., Pannala, S., Dehoff, R.R., Babu, S.S., 2016. The metallurgy and processing science of metal additive manufacturing. International Materials Reviews 61, 315-360. https://doi.org/10.1080/09506608.2015.1116649

[5] Flores, J., Garmendia, I., Pujana, J., 2019. Toolpath generation for the manufacture of metallic components by means of the laser metal deposition technique. Int J Adv Manufacturing 101, 2111-2120. https://doi.org/10.1007/s00170-018-3124-1

[6] Parimi, L., L., Ravi, G. A., Clark, D., Attallah, M.M., 2014. Microstructural and texture development in direct laser fabricated IN718. Materials Characterization 89:102-111

[7] Calleja, A., Tabernero, I., Fernández, A., Celaya, A., Lamikiz, A., López de Lacalle, L.N., 2014. Improvement of strategies and parameters for multi-axis laser cladding operations, Optics and Lasers in Engineering, Vol. 56, pp. 113-120, ISSN 0143-8166, https://doi.org/10.1016/j.optlaseng.2013.12.017

[8] Johnson, K. L., 1985. Contact Mechanics. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139171731
# Multiscale Analysis of 316L stainless steel microstructures for WAAM manufacturing tool prediction

KROMER Robin¹,ª* and ARVIEU Corinne¹

¹ I2M, UMR CNRS 5295, Université de Bordeaux, Arts et Métiers, Bordeaux INP, INRAe
USC1368, France

ª robin.kromer@u-bordeaux.fr

**Keywords:** WAAM, Stainless Steel, Microstructure, SDAS

**Abstract.** This study proposes a new approach on a multiscale analysis of 316L stainless steel microstructures to enhance the predictability and homogeneity of microstructure for the Wire Arc Additive Manufacturing (WAAM) process. Despite the promise in the fabrication of large-scale metallic components, achieving consistent microstructure and mechanical properties remains a challenge with WAAM. This research investigates the solidification behaviors, grain morphology, and mechanical characteristics of 316L stainless steel, aiming to develop a predictive framework for WAAM process optimization. By employing a systematic approach to the fabrication and subsequent analysis of 316L stainless steel walls, the study reveals critical insights into thermal gradients, solidification rates, and their impacts on microstructural features. The findings are anticipated to inform improved fabrication strategies, leading to enhanced mechanical properties and reliability in WAAM-manufactured components based on mereotopology philosophical framework. A spatio-temporal of the different tracks could be defined based on trajectory analysis.

**Introduction**

Wire Arc Additive Manufacturing (WAAM) is emerging as a revolutionary technology in the field of fabrication, promising to transform the production of large metallic structures [1]. As an additive manufacturing (AM) process, WAAM builds parts layer by layer, using an electric arc as the heat source and wire as the feed material. This method is not a novelty. It represents a significant leap forward in manufacturing large-scale parts, offering high deposition rates and lower costs compared to traditional manufacturing methods [2]. Specifically, when it comes to fabricating components from stainless steels, known for their robust mechanical properties and exceptional corrosion resistance, WAAM is particularly effective [3]. However, the metallurgy of stainless steel significantly contributes to its mechanical properties, such as resilience, resistance, and tenacity [4].

At the heart of WAAM technology is the electric arc, typically using the CMT technology from Fronius firm, which melts the metal wire feedstock [5]. As the molten metal is deposited, it solidifies to form a successive layer of the desired 3D object [6]. This process is repeated, stacking layers upon layers until the part is complete. As the metal is deposited, it undergoes rapid melting and solidification cycles, leading to non-equilibrium microstructures that can affect the mechanical properties of the final product [7]. For stainless steel components, in particular, maintaining the balance between austenite and ferrite phases in the microstructure is crucial to ensure the desired combination of strength, ductility, and corrosion resistance [8]. To address these challenges, researchers and practitioners are focusing on understanding the underlying physical metallurgy mechanisms of the WAAM process [9]. This involves studying the effects of process parameters such as heat input, cooling rate, and material feed rate on the microstructure and mechanical properties of the manufactured parts. For instance, the cooling rate during deposition significantly influences the formation and morphology of microstructures, including austenite and ferrite [10]. By optimizing these parameters, it is possible to control the microstructure and, consequently, the

<figure><img src="image_325.png" /></figure>
# Multiscale Analysis of 316L stainless steel microstructures for WAAM manufacturing tool prediction

KROMER Robin¹,ª* and ARVIEU Corinne¹

¹ I2M, UMR CNRS 5295, Université de Bordeaux, Arts et Métiers, Bordeaux INP, INRAe
USC1368, France

ª robin.kromer@u-bordeaux.fr

**Keywords:** WAAM, Stainless Steel, Microstructure, SDAS

**Abstract.** This study proposes a new approach on a multiscale analysis of 316L stainless steel microstructures to enhance the predictability and homogeneity of microstructure for the Wire Arc Additive Manufacturing (WAAM) process. Despite the promise in the fabrication of large-scale metallic components, achieving consistent microstructure and mechanical properties remains a challenge with WAAM. This research investigates the solidification behaviors, grain morphology, and mechanical characteristics of 316L stainless steel, aiming to develop a predictive framework for WAAM process optimization. By employing a systematic approach to the fabrication and subsequent analysis of 316L stainless steel walls, the study reveals critical insights into thermal gradients, solidification rates, and their impacts on microstructural features. The findings are anticipated to inform improved fabrication strategies, leading to enhanced mechanical properties and reliability in WAAM-manufactured components based on mereotopology philosophical framework. A spatio-temporal of the different tracks could be defined based on trajectory analysis.

**Introduction**

Wire Arc Additive Manufacturing (WAAM) is emerging as a revolutionary technology in the field of fabrication, promising to transform the production of large metallic structures [1]. As an additive manufacturing (AM) process, WAAM builds parts layer by layer, using an electric arc as the heat source and wire as the feed material. This method is not a novelty. It represents a significant leap forward in manufacturing large-scale parts, offering high deposition rates and lower costs compared to traditional manufacturing methods [2]. Specifically, when it comes to fabricating components from stainless steels, known for their robust mechanical properties and exceptional corrosion resistance, WAAM is particularly effective [3]. However, the metallurgy of stainless steel significantly contributes to its mechanical properties, such as resilience, resistance, and tenacity [4].

At the heart of WAAM technology is the electric arc, typically using the CMT technology from Fronius firm, which melts the metal wire feedstock [5]. As the molten metal is deposited, it solidifies to form a successive layer of the desired 3D object [6]. This process is repeated, stacking layers upon layers until the part is complete. As the metal is deposited, it undergoes rapid melting and solidification cycles, leading to non-equilibrium microstructures that can affect the mechanical properties of the final product [7]. For stainless steel components, in particular, maintaining the balance between austenite and ferrite phases in the microstructure is crucial to ensure the desired combination of strength, ductility, and corrosion resistance [8]. To address these challenges, researchers and practitioners are focusing on understanding the underlying physical metallurgy mechanisms of the WAAM process [9]. This involves studying the effects of process parameters such as heat input, cooling rate, and material feed rate on the microstructure and mechanical properties of the manufactured parts. For instance, the cooling rate during deposition significantly influences the formation and morphology of microstructures, including austenite and ferrite [10]. By optimizing these parameters, it is possible to control the microstructure and, consequently, the

<figure><img src="image_331.png" /></figure>
mechanical properties of WAAM parts [11]. Another area of focus is the development of specific WAAM techniques to enhance the quality of stainless-steel parts. Techniques such as in-situ rolling combined with WAAM have shown promise in reducing anisotropy in the microstructure, thereby improving the uniformity of mechanical properties throughout the part. In vitro heat treatments, on the other hand, can help in relieving residual stresses and refining the microstructure, leading to better performance of the final component. Despite the progress made, achieving high-quality WAAM stainless steel parts remains a challenge due to the complexity of controlling the microstructure. The WAAM process leads to considerable anisotropy in both microstructure and mechanical properties, as the deposited layers cool and solidify at different rates [13]. This anisotropy can manifest in the form of residual stresses and distortion, affecting the dimensional accuracy and structural integrity of the components. Therefore, further research is needed to develop strategies to minimize these effects and produce components with uniform properties [14]. One promising strategy is the use of advanced monitoring and control systems during the WAAM process. For instance, defects in WAAM stainless steel parts, such as porosity, lack of fusion, or inclusions, can significantly compromise the integrity and performance of the components. These defects are typically attributed to sub-optimal process parameters or material feedstock quality. Advanced monitoring and control during the WAAM process can help in detecting and mitigating these defects early in the manufacturing process. Techniques such as real-time temperature monitoring and adaptive control systems adjust parameters on-the-fly to maintain optimal conditions, reducing the likelihood of defects [15].

To address the gap in understanding the influence of thermal management on microstructural evolution in WAAM processes, this study introduces a novel analysis of 316L stainless steel microstructures. Unlike existing works, which predominantly focus on qualitative descriptions, our approach leverages advanced quantitative methods to elucidate the relationship between process parameters and the formation of highly oriented columnar grains. This multidimensional analysis extends beyond mere equipment validation, offering new insights into optimizing heat input, cooling rates, and material feed rates for improved WAAM outcomes. The objectives are:

* To conduct an analysis of the microstructures. The study will focus on understanding the formation of large, highly oriented columnar grains dominated by epitaxial growth at the interface due to remelting

* To seeks to optimize the WAAM process strategy. This includes adjusting heat input, cooling rates and material feed rates. The research will develop a model to evaluate microstructure based on dwell time and interface between beads,

A spatio-temporal perspective is employed to use the information at a microscopic level linked to thermal gradient and solidification rate at different beads and their interaction. The research aims to demonstrate how these factors influence the microstructure from the bottom to the top location of the deposits, leading to finer microstructure in the upper zones and the gradual transition of the solidification mode.

## Material, process and method description

### Material

316L is an austenitic stainless-steel alloy known for its superior corrosion resistance and excellent mechanical properties at both room and high temperatures. The alloy typically contains 16-18% chromium, 10-14% nickel, and 2-3% molybdenum, along with small amounts of silicon, manganese, and carbon. The lower carbon content reduces susceptibility to sensitization when heated in the carbide precipitation range, which is especially beneficial in applications where the material is subjected to welding. The solidification of 316L during the process is complex and crucial in microstructure and, consequently, the mechanical properties of the produced part. The rapid melting and solidification cycles inherent in the WAAM process led to non-equilibrium microstructures typically characterized by fine dendritic structures and segregation of alloying
mechanical properties of WAAM parts [11]. Another area of focus is the development of specific WAAM techniques to enhance the quality of stainless-steel parts. Techniques such as in-situ rolling combined with WAAM have shown promise in reducing anisotropy in the microstructure, thereby improving the uniformity of mechanical properties throughout the part. In vitro heat treatments, on the other hand, can help in relieving residual stresses and refining the microstructure, leading to better performance of the final component. Despite the progress made, achieving high-quality WAAM stainless steel parts remains a challenge due to the complexity of controlling the microstructure. The WAAM process leads to considerable anisotropy in both microstructure and mechanical properties, as the deposited layers cool and solidify at different rates [13]. This anisotropy can manifest in the form of residual stresses and distortion, affecting the dimensional accuracy and structural integrity of the components. Therefore, further research is needed to develop strategies to minimize these effects and produce components with uniform properties [14]. One promising strategy is the use of advanced monitoring and control systems during the WAAM process. For instance, defects in WAAM stainless steel parts, such as porosity, lack of fusion, or inclusions, can significantly compromise the integrity and performance of the components. These defects are typically attributed to sub-optimal process parameters or material feedstock quality. Advanced monitoring and control during the WAAM process can help in detecting and mitigating these defects early in the manufacturing process. Techniques such as real-time temperature monitoring and adaptive control systems adjust parameters on-the-fly to maintain optimal conditions, reducing the likelihood of defects [15].

To address the gap in understanding the influence of thermal management on microstructural evolution in WAAM processes, this study introduces a novel analysis of 316L stainless steel microstructures. Unlike existing works, which predominantly focus on qualitative descriptions, our approach leverages advanced quantitative methods to elucidate the relationship between process parameters and the formation of highly oriented columnar grains. This multidimensional analysis extends beyond mere equipment validation, offering new insights into optimizing heat input, cooling rates, and material feed rates for improved WAAM outcomes. The objectives are:

* To conduct an analysis of the microstructures. The study will focus on understanding the formation of large, highly oriented columnar grains dominated by epitaxial growth at the interface due to remelting

* To seeks to optimize the WAAM process strategy. This includes adjusting heat input, cooling rates and material feed rates. The research will develop a model to evaluate microstructure based on dwell time and interface between beads,

A spatio-temporal perspective is employed to use the information at a microscopic level linked to thermal gradient and solidification rate at different beads and their interaction. The research aims to demonstrate how these factors influence the microstructure from the bottom to the top location of the deposits, leading to finer microstructure in the upper zones and the gradual transition of the solidification mode.

## Material, process and method description

### Material

316L is an austenitic stainless-steel alloy known for its superior corrosion resistance and excellent mechanical properties at both room and high temperatures. The alloy typically contains 16-18% chromium, 10-14% nickel, and 2-3% molybdenum, along with small amounts of silicon, manganese, and carbon. The lower carbon content reduces susceptibility to sensitization when heated in the carbide precipitation range, which is especially beneficial in applications where the material is subjected to welding. The solidification of 316L during the process is complex and crucial in microstructure and, consequently, the mechanical properties of the produced part. The rapid melting and solidification cycles inherent in the WAAM process led to non-equilibrium microstructures typically characterized by fine dendritic structures and segregation of alloying
elements. At the interface of beads, composition can be different than in the beads itself. As the molten 316L solidifies, it typically forms a microstructure (Figure 1). The primary dendrites are often austenitic, with secondary phases possibly precipitating between the dendrites depending on the solidification conditions and the chemical composition of the alloy. The Primary Dendritic Arm Space (PDAS) and the Secondary Dendritic Arm Space (SDAS) are measured to enable gradient and solidification rate evaluation. The cooling rate significantly affects the dendrite arm spacing, with higher cooling rates leading to finer dendritic structures.

<figure><img src="image_326.png" /><figcaption>Figure 1: Beads interface with dendritic microstructure under the bead interface</figcaption></figure>

## WAAM

### Process Parameters and Setup

The core of the WAAM setup is the arc welding system using CMT from Fronius (model 320i). The technology provides a low heat transfer and minimize the heat input, optimize arc stability, and provide good material deposition rate. A wire feeder is used to deliver the metal wire feedstock at a controlled rate into the arc, where it melts and gets deposited. The wire feed rate and the torch speed are set together to provide a specific weld track characterized by its width and height. To facilitate the layer-by-layer deposition, WAAM utilizes a gantry system that moves the welding torch along the predetermined path. The path speed is set to 15 mm/s. A 304L stainless steel support is used to support the workpiece (20mm thick plate Arc energy or heat input is a critical parameter as it influences the melt pool size, cooling rate, and consequently the microstructure of the deposit. It is determined by the voltage and current settings in the welding system. They are set to 227A and 17.5V. A balance needs to be struck between wire feed rate and path speed for sufficient deposition and build efficiency. The wire feed rate was set to 12 m/min. The shielding gas protects the molten metal from atmospheric contamination. For 316L, a mixture of argon with additions of carbon dioxide is commonly used (Ferroline gas- 92% Argon and 8% CO2).

The samples are composed of three zones: one, three and four beads. The idea is to get different configurations between the interactions. It was one track. An interpass time of 60 seconds for each track (complete layer) was set. The first observed area is to get information for a single bead wall where there are no side effects. Then the second and third configurations are used to define the connectivity effects in heat transfer and microstructures. For the bead A, the cross-section 1,2 and 3 is then a unique and enable to compare the different interaction on the microstructure. It is true also for B, C and D respectively.
elements. At the interface of beads, composition can be different than in the beads itself. As the molten 316L solidifies, it typically forms a microstructure (Figure 1). The primary dendrites are often austenitic, with secondary phases possibly precipitating between the dendrites depending on the solidification conditions and the chemical composition of the alloy. The Primary Dendritic Arm Space (PDAS) and the Secondary Dendritic Arm Space (SDAS) are measured to enable gradient and solidification rate evaluation. The cooling rate significantly affects the dendrite arm spacing, with higher cooling rates leading to finer dendritic structures.

<figure><img src="image_332.png" /><figcaption>Figure 1: Beads interface with dendritic microstructure under the bead interface</figcaption></figure>

## WAAM

### Process Parameters and Setup

The core of the WAAM setup is the arc welding system using CMT from Fronius (model 320i). The technology provides a low heat transfer and minimize the heat input, optimize arc stability, and provide good material deposition rate. A wire feeder is used to deliver the metal wire feedstock at a controlled rate into the arc, where it melts and gets deposited. The wire feed rate and the torch speed are set together to provide a specific weld track characterized by its width and height. To facilitate the layer-by-layer deposition, WAAM utilizes a gantry system that moves the welding torch along the predetermined path. The path speed is set to 15 mm/s. A 304L stainless steel support is used to support the workpiece (20mm thick plate Arc energy or heat input is a critical parameter as it influences the melt pool size, cooling rate, and consequently the microstructure of the deposit. It is determined by the voltage and current settings in the welding system. They are set to 227A and 17.5V. A balance needs to be struck between wire feed rate and path speed for sufficient deposition and build efficiency. The wire feed rate was set to 12 m/min. The shielding gas protects the molten metal from atmospheric contamination. For 316L, a mixture of argon with additions of carbon dioxide is commonly used (Ferroline gas- 92% Argon and 8% CO2).

The samples are composed of three zones: one, three and four beads. The idea is to get different configurations between the interactions. It was one track. An interpass time of 60 seconds for each track (complete layer) was set. The first observed area is to get information for a single bead wall where there are no side effects. Then the second and third configurations are used to define the connectivity effects in heat transfer and microstructures. For the bead A, the cross-section 1,2 and 3 is then a unique and enable to compare the different interaction on the microstructure. It is true also for B, C and D respectively.
<figure><img src="image_327.png" /><figcaption>Figure 2: Configuration of the sample with tracks movement – red lines are the configuration for cross section observations</figcaption></figure>

Sample Preparation:

Samples are typically extracted from various locations along the build direction across welding cross-sections of the path to capture the variance in microstructure between beads. The selected specimens are sectioned. Samples are meticulously prepared through mechanical polishing and chemical etching, a process essential for revealing microstructural features with high resolution for subsequent analysis. Chemical etching is performed to reveal the microstructural features (regal solution). An overview of the entire specimen cross-section is obtained by taking a series of micrographs, which are then stitched together to form a comprehensive image of the area (Figure 3). Python library is used to compute the interface length between different beads. The complete analysis is then calculated based on the total length that are localized together.

<figure><img src="image_328.png" /><figcaption>Figure 3: A/ Cross section 2 and B Coordinates of the interface of beads that are classified has the number 5</figcaption></figure>

The microstructural analysis was conducted on a series of representative micrographs obtained from cross-sections of the alloy samples. SDAS were evaluated based on their visibility and structural integrity within the micrograph frame (Figure4). Measurements were limited to dendrite arms fully contained within the image to prevent partial measurement biases. The selection excluded any arms intersected by the image border. They are associated to the beads interface. A minimum of 12 secondary arms were analyzed per micrograph and per beads to ensure a statistically significant assessment of the SDAS, capturing the heterogeneity of the microstructural distribution. A grid of systematically oriented lines was digitally superimposed over the dendritic microstructure using image analysis tool in python. The center-to-center distance between consecutive secondary dendrite arms intersected by these lines was recorded. The variation of black and white enables to get the number and the check the consistency.
<figure><img src="image_333.png" /><figcaption>Figure 2: Configuration of the sample with tracks movement – red lines are the configuration for cross section observations</figcaption></figure>

Sample Preparation:

Samples are typically extracted from various locations along the build direction across welding cross-sections of the path to capture the variance in microstructure between beads. The selected specimens are sectioned. Samples are meticulously prepared through mechanical polishing and chemical etching, a process essential for revealing microstructural features with high resolution for subsequent analysis. Chemical etching is performed to reveal the microstructural features (regal solution). An overview of the entire specimen cross-section is obtained by taking a series of micrographs, which are then stitched together to form a comprehensive image of the area (Figure 3). Python library is used to compute the interface length between different beads. The complete analysis is then calculated based on the total length that are localized together.

<figure><img src="image_334.png" /><figcaption>Figure 3: A/ Cross section 2 and B Coordinates of the interface of beads that are classified has the number 5</figcaption></figure>

The microstructural analysis was conducted on a series of representative micrographs obtained from cross-sections of the alloy samples. SDAS were evaluated based on their visibility and structural integrity within the micrograph frame (Figure4). Measurements were limited to dendrite arms fully contained within the image to prevent partial measurement biases. The selection excluded any arms intersected by the image border. They are associated to the beads interface. A minimum of 12 secondary arms were analyzed per micrograph and per beads to ensure a statistically significant assessment of the SDAS, capturing the heterogeneity of the microstructural distribution. A grid of systematically oriented lines was digitally superimposed over the dendritic microstructure using image analysis tool in python. The center-to-center distance between consecutive secondary dendrite arms intersected by these lines was recorded. The variation of black and white enables to get the number and the check the consistency.
<figure><img src="image_329.png" /><figcaption>Figure 4: SDAS measurement based on micrography</figcaption></figure>

### Spatio-Temporal Analysis:

WAAM's heat dissipation modes transition from primarily conduction-based near the substrate to a blend of radiation and convection within the thin wall section as building progresses. This evolution significantly influences the cooling rate and solidification rate, impacting microstructure. Also, the overlapping of beads in WAAM is a common scenario, especially in multi-layer, multi-bead deposition. This overlapping significantly influences the local cooling rates and thermal gradients, leading to variations in microstructural features such as PDAS and SDAS. In the revised section titled 'Microstructural Evolution in WAAM,' we streamline our discussion to focus on the critical impact of dwell time and interface interaction between beads on grain orientation and size. This concise analysis underscores our contribution to refining WAAM process strategies through precise control over thermal gradients, directly influencing microstructural attributes and mechanical properties. On one hand, dwell time plays a critical role in this context, impacting the cooling rates and solidification rates, and thereby the microstructure. The transition from conduction-based to a blend of radiation and convection heat dissipation modes as building progresses not only influences cooling rates but also has a profound impact on the solidification rate and resulting microstructure. Dwell time directly impacts this transition. A longer dwell time allows for more heat to escape through conduction, potentially leading to a more uniform temperature distribution across the part. This uniformity can result in a more consistent microstructure. The cooling rate, which is directly influenced by the dwell time, plays a crucial role in determining the spacing of dendritic arms. A slower cooling rate, associated with shorter dwell times, tends to produce larger PDAS and SDAS, leading to a coarser microstructure. Conversely, longer dwell times can promote faster cooling rates, resulting in finer PDAS and SDAS and a more refined microstructure. The protocol enables to get on the different cross-sections and different configuration of dwell time as such there is two tracks (odd and even directions) (Figure 5).

<figure><img src="image_330.png" /><figcaption>Figure 5: Dwell time for each configuration in the case of four beads (configuration 3)</figcaption></figure>
<figure><img src="image_335.png" /><figcaption>Figure 4: SDAS measurement based on micrography</figcaption></figure>

### Spatio-Temporal Analysis:

WAAM's heat dissipation modes transition from primarily conduction-based near the substrate to a blend of radiation and convection within the thin wall section as building progresses. This evolution significantly influences the cooling rate and solidification rate, impacting microstructure. Also, the overlapping of beads in WAAM is a common scenario, especially in multi-layer, multi-bead deposition. This overlapping significantly influences the local cooling rates and thermal gradients, leading to variations in microstructural features such as PDAS and SDAS. In the revised section titled 'Microstructural Evolution in WAAM,' we streamline our discussion to focus on the critical impact of dwell time and interface interaction between beads on grain orientation and size. This concise analysis underscores our contribution to refining WAAM process strategies through precise control over thermal gradients, directly influencing microstructural attributes and mechanical properties. On one hand, dwell time plays a critical role in this context, impacting the cooling rates and solidification rates, and thereby the microstructure. The transition from conduction-based to a blend of radiation and convection heat dissipation modes as building progresses not only influences cooling rates but also has a profound impact on the solidification rate and resulting microstructure. Dwell time directly impacts this transition. A longer dwell time allows for more heat to escape through conduction, potentially leading to a more uniform temperature distribution across the part. This uniformity can result in a more consistent microstructure. The cooling rate, which is directly influenced by the dwell time, plays a crucial role in determining the spacing of dendritic arms. A slower cooling rate, associated with shorter dwell times, tends to produce larger PDAS and SDAS, leading to a coarser microstructure. Conversely, longer dwell times can promote faster cooling rates, resulting in finer PDAS and SDAS and a more refined microstructure. The protocol enables to get on the different cross-sections and different configuration of dwell time as such there is two tracks (odd and even directions) (Figure 5).

<figure><img src="image_336.png" /><figcaption>Figure 5: Dwell time for each configuration in the case of four beads (configuration 3)</figcaption></figure>
On the other hand, in WAAM, each layer's cooling rate can be affected by its position in the sequence of deposition. Typically, even layers and odd layers exhibit different cooling behaviors due to the alternating deposition paths and the resulting variations in heat accumulation and dissipation. The differences in cooling rates between even and odd layers can lead to significant variations in microstructure across the build. To account for the differences in cooling rates between even and odd layers, a layer-specific cooling rate model can be developed and the interface of heat interaction need to be defined. This model would take into consideration the heat transfer mechanisms active in each layer, influenced by the layer's position and the history of heat input from previous layers. It means that the different interface length is computed based on micrography. Figure 6 present the interaction that have the bead 2. Interpass distance are define based on TOM method for continuous material deposition. So, the bead C remelt bead B (3→2 & 4→2), bead B remelt underneath bead B (5→2) and bead A remelt bead B (6→2). They are then classified based on the number of interactions. So, the heat from an adjacent bead can affect the thermal profile of its neighbors, leading to a more complex and less predictable cooling behavior. There is multi-dendrite structure direction near the interface due to multi-interaction. This interaction needs to be understood and modeled to predict its effect on microstructure.

<figure><img src="image_331.png" /><figcaption>Figure 6: Heat flux for the bead 2 over time and classification of the beads based on heat flux number of interactions</figcaption></figure>

## Results

*Microstructural analysis for one and third walls*

The microstructural analysis focused on two specific sections: Section A1 (bead A and cross section 1) and the left line of Section A2 (bead A and cross section 2). These sections were chosen due to their differing thermal histories and positions within the WAAM build. A1 consists of a single bead wall, whereas A2 interacts with bead B. Figure 7 highlights the SDAS measured across these sections, providing a snapshot of the microstructural variations within the WAAM wall. The consistent SDAS measurements in Section A1 suggest a uniform cooling rate, highlighting the efficacy of maintaining stable process parameters (Figure 7.A). This uniformity can be attributed to consistent manufacturing parameters and the location of the section within the build, likely away from more complex geometric features or overlapping bead regions. For the left line of Section A2, SDAS measurements indicated a higher degree of variability. This variability could be due to the complex interplay of thermal histories in this section, influenced by overlapping beads and varying bead geometries. As this section is likely closer to the wall's edge or features more bead overlaps, the resultant thermal gradients would be more variable, leading to diverse cooling rates and, consequently, a more heterogeneous microstructure. An intriguing aspect of the study, as presented in Figure 7.B, was the examination of SDAS variability based on the parity of the layers in Section A1 and the left line of Section A2. This analysis aimed to understand how the alternating deposition strategy (even and odd layers) impacts the microstructure. On one hand, in even-numbered layers, the SDAS values tended to be more uniform, suggesting a more consistent cooling rate. This uniformity might be due to the repetitive nature of the deposition process, where even layers experience similar thermal histories. On the other hand, the odd-numbered layers exhibited more variation in SDAS values. This finding suggests that the cooling rates in these
On the other hand, in WAAM, each layer's cooling rate can be affected by its position in the sequence of deposition. Typically, even layers and odd layers exhibit different cooling behaviors due to the alternating deposition paths and the resulting variations in heat accumulation and dissipation. The differences in cooling rates between even and odd layers can lead to significant variations in microstructure across the build. To account for the differences in cooling rates between even and odd layers, a layer-specific cooling rate model can be developed and the interface of heat interaction need to be defined. This model would take into consideration the heat transfer mechanisms active in each layer, influenced by the layer's position and the history of heat input from previous layers. It means that the different interface length is computed based on micrography. Figure 6 present the interaction that have the bead 2. Interpass distance are define based on TOM method for continuous material deposition. So, the bead C remelt bead B (3→2 & 4→2), bead B remelt underneath bead B (5→2) and bead A remelt bead B (6→2). They are then classified based on the number of interactions. So, the heat from an adjacent bead can affect the thermal profile of its neighbors, leading to a more complex and less predictable cooling behavior. There is multi-dendrite structure direction near the interface due to multi-interaction. This interaction needs to be understood and modeled to predict its effect on microstructure.

<figure><img src="image_337.png" /><figcaption>Figure 6: Heat flux for the bead 2 over time and classification of the beads based on heat flux number of interactions</figcaption></figure>

## Results

*Microstructural analysis for one and third walls*

The microstructural analysis focused on two specific sections: Section A1 (bead A and cross section 1) and the left line of Section A2 (bead A and cross section 2). These sections were chosen due to their differing thermal histories and positions within the WAAM build. A1 consists of a single bead wall, whereas A2 interacts with bead B. Figure 7 highlights the SDAS measured across these sections, providing a snapshot of the microstructural variations within the WAAM wall. The consistent SDAS measurements in Section A1 suggest a uniform cooling rate, highlighting the efficacy of maintaining stable process parameters (Figure 7.A). This uniformity can be attributed to consistent manufacturing parameters and the location of the section within the build, likely away from more complex geometric features or overlapping bead regions. For the left line of Section A2, SDAS measurements indicated a higher degree of variability. This variability could be due to the complex interplay of thermal histories in this section, influenced by overlapping beads and varying bead geometries. As this section is likely closer to the wall's edge or features more bead overlaps, the resultant thermal gradients would be more variable, leading to diverse cooling rates and, consequently, a more heterogeneous microstructure. An intriguing aspect of the study, as presented in Figure 7.B, was the examination of SDAS variability based on the parity of the layers in Section A1 and the left line of Section A2. This analysis aimed to understand how the alternating deposition strategy (even and odd layers) impacts the microstructure. On one hand, in even-numbered layers, the SDAS values tended to be more uniform, suggesting a more consistent cooling rate. This uniformity might be due to the repetitive nature of the deposition process, where even layers experience similar thermal histories. On the other hand, the odd-numbered layers exhibited more variation in SDAS values. This finding suggests that the cooling rates in these
layers are less consistent, possibly due to variations in bead overlap or changes in the thermal environment as the build progresses. The odd layers are possibly more influenced by the cumulative heat input and varying bead geometries. Regions with finer SDAS are expected to exhibit higher strength and potentially better fatigue resistance due to their refined microstructure. In contrast, areas with coarser SDAS might have lower strength but potentially better ductility. This microstructural analysis underscores the complexity of predicting and controlling the microstructure in WAAM processes. The variability in SDAS across different sections and layers reflects the intricate interplay of thermal dynamics during the build and material anisotropy.

<figure><img src="image_332.png" /><figcaption>Figure 7: SDAS and Cooling rate at each layer for Bead A and cross section 1 and 2 for comparison (divided into odd and even layers)</figcaption></figure>

## Integration of Bead Interface, Dwell Time, and SDAS in WAAM

As new material is deposited, it interacts thermally with the previously deposited bead (remelting bead), affecting the overall heat dissipation dynamics. The surface area of this interface is directly proportional to the amount of the interaction and is a signature of the heat that was conducted away during the thermal ageing effects. In addition, dwell time, the waiting period between the deposition of successive layers or beads, allows for the reduction of residual heat in the part. Longer dwell times facilitate greater heat dissipation, leading to lower local temperatures at the time of subsequent bead deposition. This cooling effect influences the solidification rate of the new layer, directly impacting the formation of dendritic structures. Based on the dwell time analysis of each beads and section configurations, SDAS were measured following the micrography protocol. These quantified values of SDAS were then correlated with the dwell time and interface area to analyze their effects on microstructural characteristics. The number of beads was considered due to observed variations in SDAS from the bottom to the top. Odd and even number were also used. The regression meta model provides a Mean Predictive Average of 0.91(R²) of prediction (Bayesian model with Pycaret library tool). The accompanying feature importance plot (Figure 8) elucidates the relative significance of various parameters in the predictive model for 3D printing process optimization. Dwell time emerges as the most influential factor, with a variable importance value of approximately 0.61, underscoring its pivotal role in
layers are less consistent, possibly due to variations in bead overlap or changes in the thermal environment as the build progresses. The odd layers are possibly more influenced by the cumulative heat input and varying bead geometries. Regions with finer SDAS are expected to exhibit higher strength and potentially better fatigue resistance due to their refined microstructure. In contrast, areas with coarser SDAS might have lower strength but potentially better ductility. This microstructural analysis underscores the complexity of predicting and controlling the microstructure in WAAM processes. The variability in SDAS across different sections and layers reflects the intricate interplay of thermal dynamics during the build and material anisotropy.

<figure><img src="image_338.png" /><figcaption>Figure 7: SDAS and Cooling rate at each layer for Bead A and cross section 1 and 2 for comparison (divided into odd and even layers)</figcaption></figure>

## Integration of Bead Interface, Dwell Time, and SDAS in WAAM

As new material is deposited, it interacts thermally with the previously deposited bead (remelting bead), affecting the overall heat dissipation dynamics. The surface area of this interface is directly proportional to the amount of the interaction and is a signature of the heat that was conducted away during the thermal ageing effects. In addition, dwell time, the waiting period between the deposition of successive layers or beads, allows for the reduction of residual heat in the part. Longer dwell times facilitate greater heat dissipation, leading to lower local temperatures at the time of subsequent bead deposition. This cooling effect influences the solidification rate of the new layer, directly impacting the formation of dendritic structures. Based on the dwell time analysis of each beads and section configurations, SDAS were measured following the micrography protocol. These quantified values of SDAS were then correlated with the dwell time and interface area to analyze their effects on microstructural characteristics. The number of beads was considered due to observed variations in SDAS from the bottom to the top. Odd and even number were also used. The regression meta model provides a Mean Predictive Average of 0.91(R²) of prediction (Bayesian model with Pycaret library tool). The accompanying feature importance plot (Figure 8) elucidates the relative significance of various parameters in the predictive model for 3D printing process optimization. Dwell time emerges as the most influential factor, with a variable importance value of approximately 0.61, underscoring its pivotal role in
determining the microstructural characteristics of the printed material. This is in alignment with the thermal control hypothesis, where the dwell time governs the rate of heat dissipation, subsequently affecting the solidification dynamics and the evolution of the dendritic structures. The interface length, representing the contact area between successive layers, holds a variable importance of around 0.21. This parameter is indicative of the thermal interaction at the interface, which can influence the microstructure due to localized remelting and cooling cycles. The number of beads, though less significant with a variable importance close to 0.13, still contributes to the model's predictive capacity. This factor likely represents the cumulative effect of layering in the build process, influencing the overall thermal profile and gradient experienced by the material. Lastly, the variable labeled 'Order' exhibits minimal importance, suggesting that the sequence of deposition has a negligible direct impact on the microstructure as captured by the model.

<figure><img src="image_333.png" /><figcaption>Figure 8: Feature importance (%) based on metamodel generation</figcaption></figure>

## Discussion

The influence of thermal gradients and solidification rates on grain morphology is crucial because they dictate the cooling speed and, consequently, the microstructural features. For instance, faster cooling rates typically result in finer grains, enhancing the material's mechanical properties. This aspect is pivotal as it directly affects the mechanical properties of the printed material. The central argument here is that by controlling these parameters, it's possible to achieve a more predictable and uniform microstructure. Presented by Wang et al. [5], the arc energy transfer is the key point of the microstructure and mechanical behaviors. Heat input is directly linked to finer structure. Also, the interface lead to microstructure population variation such as it was presented by the model. Belotti et al. [14] presented that the solidification structure of the WAAM part contains γ-austenite and δ-ferrite, as empirically described by constitution diagrams. An EDX analysis needs to be performed to check also the impact on the composition based on different heat interaction configuration and confirm with a literature review.

Secondly, Guang et al [16] found that using shielding gases that result in higher heat input reduces the amount of retained austenite in the as-deposited microstructure. It has been demonstrated that the required tensile properties can be achieved by applying post-deposition heat treatment. However, it is suggested that direct aging in as deposited condition resulted of harmful intermetallic phases which embrittles the deposit. It means that a lot of environment effects can have influence on localized variation. Also heat treatments over the complete part is a key to eventually control microstructure homogeneity and it is shape dependent. It should be necessary to check their effects on the model prediction with a checking when it goes from four bead to three or two beads.
determining the microstructural characteristics of the printed material. This is in alignment with the thermal control hypothesis, where the dwell time governs the rate of heat dissipation, subsequently affecting the solidification dynamics and the evolution of the dendritic structures. The interface length, representing the contact area between successive layers, holds a variable importance of around 0.21. This parameter is indicative of the thermal interaction at the interface, which can influence the microstructure due to localized remelting and cooling cycles. The number of beads, though less significant with a variable importance close to 0.13, still contributes to the model's predictive capacity. This factor likely represents the cumulative effect of layering in the build process, influencing the overall thermal profile and gradient experienced by the material. Lastly, the variable labeled 'Order' exhibits minimal importance, suggesting that the sequence of deposition has a negligible direct impact on the microstructure as captured by the model.

<figure><img src="image_339.png" /><figcaption>Figure 8: Feature importance (%) based on metamodel generation</figcaption></figure>

## Discussion

The influence of thermal gradients and solidification rates on grain morphology is crucial because they dictate the cooling speed and, consequently, the microstructural features. For instance, faster cooling rates typically result in finer grains, enhancing the material's mechanical properties. This aspect is pivotal as it directly affects the mechanical properties of the printed material. The central argument here is that by controlling these parameters, it's possible to achieve a more predictable and uniform microstructure. Presented by Wang et al. [5], the arc energy transfer is the key point of the microstructure and mechanical behaviors. Heat input is directly linked to finer structure. Also, the interface lead to microstructure population variation such as it was presented by the model. Belotti et al. [14] presented that the solidification structure of the WAAM part contains γ-austenite and δ-ferrite, as empirically described by constitution diagrams. An EDX analysis needs to be performed to check also the impact on the composition based on different heat interaction configuration and confirm with a literature review.

Secondly, Guang et al [16] found that using shielding gases that result in higher heat input reduces the amount of retained austenite in the as-deposited microstructure. It has been demonstrated that the required tensile properties can be achieved by applying post-deposition heat treatment. However, it is suggested that direct aging in as deposited condition resulted of harmful intermetallic phases which embrittles the deposit. It means that a lot of environment effects can have influence on localized variation. Also heat treatments over the complete part is a key to eventually control microstructure homogeneity and it is shape dependent. It should be necessary to check their effects on the model prediction with a checking when it goes from four bead to three or two beads.
Finally, employing a mereotopological approach allows for a nuanced analysis of the material's behavior, tracing the continuous and discrete interactions within the WAAM process. This aids in developing predictive models that accurately simulate the effects of varying process parameters on the microstructure. The key argument here is that mereotopology aids in a deeper and more nuanced understanding of the material's trajectory and behavior, taking into consideration the interface interaction. The model can be used to predict the microstructure composition of different beads based on trajectory analysis. The mereotopological equation:

$$ WAAM\_system := \exists \{ MoltenPool, WireFeed, Arc, Substrate \} \\ \quad \{ (MoltenPool \times WireFeed \land MoltenPool \times Substrate) \land \\ \quad ((Temperature \oplus CoolingRate) \land (FeedSpeed \otimes ArcStability)) \land \\ \quad F(FeedSpeed, CoolingRate) \} \tag{1} $$

It integrates key components like the molten pool, wire feed, arc, and substrate, each playing a vital role in the deposition process. The equation's structure reflects the complex dynamics of Wire Arc Additive Manufacturing (WAAM), where the behavior of each component influences the final product's quality. The interaction between the molten pool and wire feed (MoltenPool X WireFeed) is crucial in determining bead formation, directly influenced by factors such as dwell time – the duration the arc focuses on a specific area. Dwell time affects the heat input, influencing the cooling rate and ultimately the microstructure of the material. The overlap between the molten pool and the substrate (MoltenPool O Previous layer) represents the interface interaction between successive beads, a critical factor in ensuring mechanical strength and integrity of the final product. The behavioral operators in the equation, such as (Temperature ⊕ CoolingRate) and (FeedSpeed ⊗ ArcStability), denote the allowable variations in temperature and cooling rates, and the necessity of maintaining consistent feed speed and arc stability, respectively. These factors are fundamental in controlling the trajectory of the deposition process, impacting the microstructural properties of the manufactured material. Furthermore, the predictive function F(FeedSpeed, CoolingRate) in the equation represents the feedback mechanisms that link process parameters such as feed speed and cooling rates to the system's behavior over time. The development of predictive models, as evidenced by our findings, marks a significant step towards process control in WAAM. For example, by incorporating dwell time and interface measurements into our models, we can predict with high accuracy the resulting microstructure. Such predictive capability allows for real-time adjustments to process parameters, potentially saving significant resources and time.

## Conclusion

Our study culminates in a conclusion that succinctly encapsulates the significant advancements made in the predictive modeling of WAAM processes. By articulating the practical implications of our findings, we underscore the potential for these models to revolutionize the manufacturing of 316L stainless steel components, offering a pathway towards optimized, cost-effective production methods. The study led to the development of predictive models that utilize interface measurements, dwell time evaluation, and bead classification to optimize WAAM processes. It enables an advancement in optimizing WAAM processes. The interface and dwell time are associated with SDAS, which can then contribute to a more accurate and controlled method of manufacturing. This research highlights the importance of detailed microstructural analysis and its correlation with process parameters. Dwell time is a key point to enhance microstructure homogeneity. It is also important to optimize inter-path width to get homogeneous interface between beads. Heat accumulation also influence of the anisotropy. The development of predictive models based on the study's findings is a first step in process control advancement. The primary argument for these models is their potential to significantly reduce the trial and error typically associated with WAAM processes. By accurately predicting how changes in process parameters
Finally, employing a mereotopological approach allows for a nuanced analysis of the material's behavior, tracing the continuous and discrete interactions within the WAAM process. This aids in developing predictive models that accurately simulate the effects of varying process parameters on the microstructure. The key argument here is that mereotopology aids in a deeper and more nuanced understanding of the material's trajectory and behavior, taking into consideration the interface interaction. The model can be used to predict the microstructure composition of different beads based on trajectory analysis. The mereotopological equation:

$$ WAAM\_system := \exists \{ MoltenPool, WireFeed, Arc, Substrate \} \\ \quad \{ (MoltenPool \times WireFeed \land MoltenPool \times Substrate) \land \\ \quad ((Temperature \oplus CoolingRate) \land (FeedSpeed \otimes ArcStability)) \land \\ \quad F(FeedSpeed, CoolingRate) \} \tag{1} $$

It integrates key components like the molten pool, wire feed, arc, and substrate, each playing a vital role in the deposition process. The equation's structure reflects the complex dynamics of Wire Arc Additive Manufacturing (WAAM), where the behavior of each component influences the final product's quality. The interaction between the molten pool and wire feed (MoltenPool X WireFeed) is crucial in determining bead formation, directly influenced by factors such as dwell time – the duration the arc focuses on a specific area. Dwell time affects the heat input, influencing the cooling rate and ultimately the microstructure of the material. The overlap between the molten pool and the substrate (MoltenPool O Previous layer) represents the interface interaction between successive beads, a critical factor in ensuring mechanical strength and integrity of the final product. The behavioral operators in the equation, such as (Temperature ⊕ CoolingRate) and (FeedSpeed ⊗ ArcStability), denote the allowable variations in temperature and cooling rates, and the necessity of maintaining consistent feed speed and arc stability, respectively. These factors are fundamental in controlling the trajectory of the deposition process, impacting the microstructural properties of the manufactured material. Furthermore, the predictive function F(FeedSpeed, CoolingRate) in the equation represents the feedback mechanisms that link process parameters such as feed speed and cooling rates to the system's behavior over time. The development of predictive models, as evidenced by our findings, marks a significant step towards process control in WAAM. For example, by incorporating dwell time and interface measurements into our models, we can predict with high accuracy the resulting microstructure. Such predictive capability allows for real-time adjustments to process parameters, potentially saving significant resources and time.

## Conclusion

Our study culminates in a conclusion that succinctly encapsulates the significant advancements made in the predictive modeling of WAAM processes. By articulating the practical implications of our findings, we underscore the potential for these models to revolutionize the manufacturing of 316L stainless steel components, offering a pathway towards optimized, cost-effective production methods. The study led to the development of predictive models that utilize interface measurements, dwell time evaluation, and bead classification to optimize WAAM processes. It enables an advancement in optimizing WAAM processes. The interface and dwell time are associated with SDAS, which can then contribute to a more accurate and controlled method of manufacturing. This research highlights the importance of detailed microstructural analysis and its correlation with process parameters. Dwell time is a key point to enhance microstructure homogeneity. It is also important to optimize inter-path width to get homogeneous interface between beads. Heat accumulation also influence of the anisotropy. The development of predictive models based on the study's findings is a first step in process control advancement. The primary argument for these models is their potential to significantly reduce the trial and error typically associated with WAAM processes. By accurately predicting how changes in process parameters