affect the microstructure of materials, these models can permit control of the manufacturing process, enhance efficiency, and reduce costs. This mereotopological approach, therefore, stands as a groundbreaking method in the field of additive manufacturing, offering a comprehensive way to understand and manipulate the complex interplay of various factors that determine the quality and properties of the final product.

## References

[1] Korkmaz, M.E., Waqar, S., Garcia-Collado, A., Gupta, M.K., Krolczyk, G.M. (2022). A technical overview of metallic parts in hybrid additive manufacturing industry. Journal of Materials Research and Technology, 18, 384-395. https://doi.org/10.1016/j.jmrt.2022.08.074

[2] Huang, S.H., Liu, P., Mokasdar, A., Hou, L. (2013). Additive manufacturing and its societal impact: a literature review. International Journal of Advanced Manufacturing Technology, 67(5), 1191-1203.

[3] Queguineur, A., Rückert, G., Cortial, F., Hascoët, J. (2018). Evaluation of wire arc additive manufacturing for large-sized components in naval applications. Welding World, 62(2), 259-266. https://doi.org/10.1007/s40194-017-0533-7

[4] Vora, J., Parmar, H., Chaudhari, R., Khanna, S., Doshi, M., Patel, V. (2022). Experimental investigations on mechanical properties of multi-layered structure fabricated by GMAW-based WAAM of SS316L. Journal of Materials Research and Technology, 20, 2748-2757. https://doi.org/10.1016/j.jmrt.2022.08.074

[5] Wang, L., Xue, J., Wang, Q. (2019). Correlation between arc mode, microstructure, and mechanical properties during wire arc additive manufacturing of 316L stainless steel. Materials Science and Engineering: A, 751, 183-190. https://doi.org/10.1016/j.msea.2019.02.078

[6] Rosli, N.A., Alkahari, M.R., Abdollah, M.F.B., Maidin, S., Ramli, F.R., Herawan, S.G. (2021). Review on effect of heat input for wire arc additive manufacturing process. Journal of Materials Research and Technology, 11, 2127-2145. https://doi.org/10.1016/j.jmrt.2021.04.067

[7] Yadollahi, A., Shamsaei, N., Thompson, S.M., Seely, D.W. (2015). Effects of process time interval and heat treatment on the mechanical and microstructural properties of direct laser deposited 316L stainless steel. Materials Science and Engineering: A, 644, 171-183. https://doi.org/10.1016/j.msea.2015.07.056

[8] PS, G., S, J., DT, S. (2023). Influence of heat input on microstructure and mechanical behaviour of austenitic stainless steel 316L processed in wire and arc additive manufacturing. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 237(2), 149-161. https://doi.org/10.1177/09544089221100214

[9] Aldalur, E., Suarez, A., Veiga, F. (2022). Thermal expansion behaviour of Invar 36 alloy parts fabricated by wire-arc additive manufacturing. Journal of Materials Research and Technology, 19, 3634-3645.

[10] Chaudhari, R., Parmar, H., Vora, J., Patel, V.K. (2022). Parametric Study and Investigations of Bead Geometries of GMAW-Based Wire-Arc Additive Manufacturing of 316L Stainless Steels. Metals, 12, 1232.

[11] Wang, C., Liu, T.G., Zhu, P., Lu, Y.H., Shoji, T. (2020). Study on microstructure and tensile properties of 316L stainless steel fabricated by CMT wire and arc additive manufacturing. Materials Science and Engineering: A, 796. https://doi.org/10.1016/j.msea.2020.140006

[12] Elmer, J.W., Fisher, K., Gibbs, G., Sengthay, J., Urabe, D. (2022). Post-build thermomechanical processing of wire arc additively manufactured stainless steel for improved
affect the microstructure of materials, these models can permit control of the manufacturing process, enhance efficiency, and reduce costs. This mereotopological approach, therefore, stands as a groundbreaking method in the field of additive manufacturing, offering a comprehensive way to understand and manipulate the complex interplay of various factors that determine the quality and properties of the final product.

## References

[1] Korkmaz, M.E., Waqar, S., Garcia-Collado, A., Gupta, M.K., Krolczyk, G.M. (2022). A technical overview of metallic parts in hybrid additive manufacturing industry. Journal of Materials Research and Technology, 18, 384-395. https://doi.org/10.1016/j.jmrt.2022.08.074

[2] Huang, S.H., Liu, P., Mokasdar, A., Hou, L. (2013). Additive manufacturing and its societal impact: a literature review. International Journal of Advanced Manufacturing Technology, 67(5), 1191-1203.

[3] Queguineur, A., Rückert, G., Cortial, F., Hascoët, J. (2018). Evaluation of wire arc additive manufacturing for large-sized components in naval applications. Welding World, 62(2), 259-266. https://doi.org/10.1007/s40194-017-0533-7

[4] Vora, J., Parmar, H., Chaudhari, R., Khanna, S., Doshi, M., Patel, V. (2022). Experimental investigations on mechanical properties of multi-layered structure fabricated by GMAW-based WAAM of SS316L. Journal of Materials Research and Technology, 20, 2748-2757. https://doi.org/10.1016/j.jmrt.2022.08.074

[5] Wang, L., Xue, J., Wang, Q. (2019). Correlation between arc mode, microstructure, and mechanical properties during wire arc additive manufacturing of 316L stainless steel. Materials Science and Engineering: A, 751, 183-190. https://doi.org/10.1016/j.msea.2019.02.078

[6] Rosli, N.A., Alkahari, M.R., Abdollah, M.F.B., Maidin, S., Ramli, F.R., Herawan, S.G. (2021). Review on effect of heat input for wire arc additive manufacturing process. Journal of Materials Research and Technology, 11, 2127-2145. https://doi.org/10.1016/j.jmrt.2021.04.067

[7] Yadollahi, A., Shamsaei, N., Thompson, S.M., Seely, D.W. (2015). Effects of process time interval and heat treatment on the mechanical and microstructural properties of direct laser deposited 316L stainless steel. Materials Science and Engineering: A, 644, 171-183. https://doi.org/10.1016/j.msea.2015.07.056

[8] PS, G., S, J., DT, S. (2023). Influence of heat input on microstructure and mechanical behaviour of austenitic stainless steel 316L processed in wire and arc additive manufacturing. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 237(2), 149-161. https://doi.org/10.1177/09544089221100214

[9] Aldalur, E., Suarez, A., Veiga, F. (2022). Thermal expansion behaviour of Invar 36 alloy parts fabricated by wire-arc additive manufacturing. Journal of Materials Research and Technology, 19, 3634-3645.

[10] Chaudhari, R., Parmar, H., Vora, J., Patel, V.K. (2022). Parametric Study and Investigations of Bead Geometries of GMAW-Based Wire-Arc Additive Manufacturing of 316L Stainless Steels. Metals, 12, 1232.

[11] Wang, C., Liu, T.G., Zhu, P., Lu, Y.H., Shoji, T. (2020). Study on microstructure and tensile properties of 316L stainless steel fabricated by CMT wire and arc additive manufacturing. Materials Science and Engineering: A, 796. https://doi.org/10.1016/j.msea.2020.140006

[12] Elmer, J.W., Fisher, K., Gibbs, G., Sengthay, J., Urabe, D. (2022). Post-build thermomechanical processing of wire arc additively manufactured stainless steel for improved
mechanical properties and reduction of crystallographic texture. Additive Manufacturing, 50.
https://doi.org/10.1016/j.addma.2021.102573

[13] Singh, S., Jinoop, A.N., Kumar, T.G.T., Palani, I.A., Paul, C.P., Prashanth, K.G. (2021). Effect of Interlayer Delay on the Microstructure and Mechanical Properties of Wire Arc Additive Manufactured Wall Structures. Materials (Basel), 14(15), 4187.
https://doi.org/10.3390/ma14154187

[14] Belotti, L.P., van Dommelen, J.A.W., Geers, M.G.D., Ya, W., Hoefnagels, J.P.M. (2024). Influence of the printing strategy on the microstructure and mechanical properties of thick-walled wire arc additive manufactured stainless steels. Journal of Materials Processing Technology, 324.

[15] Baier, D., Wolf, F., Weckenmann, T. et al. Thermal process monitoring and control for a near-net-shape Wire and Arc Additive Manufacturing. Prod. Eng. Res. Devel. 16, 811–822 (2022).

[16] Guang Y., Fangbin D., Siyu Z., Bin W., Lanyun Q., Jianshen Z., Influence of shielding gas nitrogen content on the microstructure and mechanical properties of Cu-reinforced maraging steel fabricated by wire arc additive manufacturing. Materials Science and Engineering: A, 832, 2022, https://doi.org/10.1016/j.msea.2021.142463
mechanical properties and reduction of crystallographic texture. Additive Manufacturing, 50.
https://doi.org/10.1016/j.addma.2021.102573

[13] Singh, S., Jinoop, A.N., Kumar, T.G.T., Palani, I.A., Paul, C.P., Prashanth, K.G. (2021). Effect of Interlayer Delay on the Microstructure and Mechanical Properties of Wire Arc Additive Manufactured Wall Structures. Materials (Basel), 14(15), 4187.
https://doi.org/10.3390/ma14154187

[14] Belotti, L.P., van Dommelen, J.A.W., Geers, M.G.D., Ya, W., Hoefnagels, J.P.M. (2024). Influence of the printing strategy on the microstructure and mechanical properties of thick-walled wire arc additive manufactured stainless steels. Journal of Materials Processing Technology, 324.

[15] Baier, D., Wolf, F., Weckenmann, T. et al. Thermal process monitoring and control for a near-net-shape Wire and Arc Additive Manufacturing. Prod. Eng. Res. Devel. 16, 811–822 (2022).

[16] Guang Y., Fangbin D., Siyu Z., Bin W., Lanyun Q., Jianshen Z., Influence of shielding gas nitrogen content on the microstructure and mechanical properties of Cu-reinforced maraging steel fabricated by wire arc additive manufacturing. Materials Science and Engineering: A, 832, 2022, https://doi.org/10.1016/j.msea.2021.142463
# An efficient steady-state thermal model for predicting the lack-of-fusion porosity during laser powder bed fusion process

JIA Yabo<sup>1,2,a*</sup>, SAADLAOUI Yassine<sup>3,b</sup>, and BERGHEAU Jean-Michel<sup>3,c</sup>

<sup>1</sup>Univ. Polytechnique Hauts-de-France, CNRS, UMR 8201 - LAMIH - Laboratoire d'Automatique de Mécanique et d'Informatique industrielles et Humaines, F-59313, Valenciennes, France

<sup>2</sup>INSA Hauts-de-France, F-59313, Valenciennes, France

<sup>3</sup>Univ. Lyon, ECL-ENISE, CNRS, UMR 5513 - LTDS, F-42023 Saint-Etienne, France

<sup>a</sup>yabo.jia@uphf.fr, <sup>b</sup>yassine.saadlaoui@enise.fr, <sup>c</sup>jean-michel.bergheau@enise.fr

**Keywords:** Steady-State Model, Lack-Of-Fusion, Laser Powder Bed Fusion

**Abstract.** The lack-of-fusion porosity due to the incomplete melting of the powder should be avoided during laser powder bed fusion (LPBF) process. The porosity will reduce the performance of mechanical properties. In certain manufacturing processes, the thermal and metallurgical quasi-steady-state can be reached quickly. In this article, a steady-state model, incorporating new dedicated boundary conditions, is proposed and applied in the multi-track simulation of LPBF to control porosity. Various processing parameters, such as hatching space and cooling time inter-pass can be investigated. The transient step-by-step simulation is also employed as the numerical reference. Numerical comparisons demonstrate that the proposed steady-state model achieves a relative good agreement with the numerical reference while significantly reducing computing time (2-3 minutes).

**Introduction**

The lack-of-fusion porosity due to the incomplete melting of the powder should be avoided during laser powder bed fusion (LPBF) process. The part porosity will reduce the performance of mechanical properties. Traditionally, controlling the presence of such defects involves experimental trial-and-error tests to optimize fabrication parameters. However, conducting experimental studies for testing a large number of parameters can be extremely costly.

To mitigate the presence of such defects, various numerical models have been employed to simulate Laser Powder Bed Fusion (LPBF), including geometrical models [1] and analytical models [2]. However, these models lack predictability due to the absence of certain physical properties. In contrast, some multiphysics models [4] exhibit predictability, but their computational cost is prohibitive for multi-track simulations. A transient step-by-step heat-transfer simulation offers a good compromise. Nevertheless, its computational cost remains too high for multi-track and multi-layer simulations. Recently, JIA et al. [5] provides a comprehensive review of different modeling strategies of multiphysics and multi-scales applied in powder-based metallic additive manufacturing processes.

In certain manufacturing conditions (constant scanning speed, constant energy input, etc.), the thermal quasi-steady-state can be reached very quickly. Therefore, a steady-state thermal model based on new dedicated boundary conditions has been proposed and applied in the simulation of LPBF [3], which is much more computational efficient than transient step-by-step simulation. Meanwhile, this proposed steady-state model possibly considers temperature-dependent material properties, Gaussian distribution of heat source, convective and radiative heat losses, powder-compact transition, enthalpy due to solid-liquid transition, and effect of heat convection in the melt pool, while allowing fast and accurate predictions of the melt pool size and steady-state temperature distribution. Single-track LPBF simulations were conducted for two widely used

<figure><img src="image_334.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
# An efficient steady-state thermal model for predicting the lack-of-fusion porosity during laser powder bed fusion process

JIA Yabo<sup>1,2,a*</sup>, SAADLAOUI Yassine<sup>3,b</sup>, and BERGHEAU Jean-Michel<sup>3,c</sup>

<sup>1</sup>Univ. Polytechnique Hauts-de-France, CNRS, UMR 8201 - LAMIH - Laboratoire d'Automatique de Mécanique et d'Informatique industrielles et Humaines, F-59313, Valenciennes, France

<sup>2</sup>INSA Hauts-de-France, F-59313, Valenciennes, France

<sup>3</sup>Univ. Lyon, ECL-ENISE, CNRS, UMR 5513 – LTDS, F-42023 Saint-Etienne, France

<sup>a</sup>yabo.jia@uphf.fr, <sup>b</sup>yassine.saadlaoui@enise.fr, <sup>c</sup>jean-michel.bergheau@enise.fr

**Keywords:** Steady-State Model, Lack-Of-Fusion, Laser Powder Bed Fusion

**Abstract.** The lack-of-fusion porosity due to the incomplete melting of the powder should be avoided during laser powder bed fusion (LPBF) process. The porosity will reduce the performance of mechanical properties. In certain manufacturing processes, the thermal and metallurgical quasi-steady-state can be reached quickly. In this article, a steady-state model, incorporating new dedicated boundary conditions, is proposed and applied in the multi-track simulation of LPBF to control porosity. Various processing parameters, such as hatching space and cooling time inter-pass can be investigated. The transient step-by-step simulation is also employed as the numerical reference. Numerical comparisons demonstrate that the proposed steady-state model achieves a relative good agreement with the numerical reference while significantly reducing computing time (2-3 minutes).

**Introduction**

The lack-of-fusion porosity due to the incomplete melting of the powder should be avoided during laser powder bed fusion (LPBF) process. The part porosity will reduce the performance of mechanical properties. Traditionally, controlling the presence of such defects involves experimental trial-and-error tests to optimize fabrication parameters. However, conducting experimental studies for testing a large number of parameters can be extremely costly.

To mitigate the presence of such defects, various numerical models have been employed to simulate Laser Powder Bed Fusion (LPBF), including geometrical models [1] and analytical models [2]. However, these models lack predictability due to the absence of certain physical properties. In contrast, some multiphysics models [4] exhibit predictability, but their computational cost is prohibitive for multi-track simulations. A transient step-by-step heat-transfer simulation offers a good compromise. Nevertheless, its computational cost remains too high for multi-track and multi-layer simulations. Recently, JIA et al. [5] provides a comprehensive review of different modeling strategies of multiphysics and multi-scales applied in powder-based metallic additive manufacturing processes.

In certain manufacturing conditions (constant scanning speed, constant energy input, etc.), the thermal quasi-steady-state can be reached very quickly. Therefore, a steady-state thermal model based on new dedicated boundary conditions has been proposed and applied in the simulation of LPBF [3], which is much more computational efficient than transient step-by-step simulation. Meanwhile, this proposed steady-state model possibly considers temperature-dependent material properties, Gaussian distribution of heat source, convective and radiative heat losses, powder-compact transition, enthalpy due to solid-liquid transition, and effect of heat convection in the melt pool, while allowing fast and accurate predictions of the melt pool size and steady-state temperature distribution. Single-track LPBF simulations were conducted for two widely used

<figure><img src="image_340.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
materials, namely AlSi10Mg and Inconel 718. The melt pool sizes simulated by the proposed model were compared with those obtained from a transient model as well as experimental results, specifically the melt pool morphology. The relative error observed for all the numerical and experimental validation tests was less than 5%.

In the current contribution, the single-track steady-state thermal model is extended to multi-tracks simulation. The proposed multi-track steady-state thermal model will be used to investigate the presence of lack-of-fusion with different scanning speed, powder layer thickness and hatch spacing, which allows reduce the number of expensive trial and error experimental tests.

## Numerical models

The proposed multi-tracks model is based on single-track steady-state model presented in [4], which has been successfully applied to LPBF simulations with two most used materials (AlSi10Mg and Inconel 718). A perfect correlation has been observed compared with the experimental results. Below is a concise introduction to the single-track steady-state model based on new dedicated boundary conditions.

The steady-state heat transfer problem is governed by a convection-diffusion equation (refer with: Eq. 1), with the underlying assumption of infinite length in the direction of the moving heat source.

$$ \rho CW \cdot \text{grad}T = \text{div}(\lambda \text{grad}T) + q \quad (1) $$

where $\lambda = \sum_{state} p_i \lambda_i$ with $p_i$, $\lambda_i$ corresponding to the proportion and thermal conductivity of each state i (powder or compact). The state transformation (powder to bulk) is controlled by the temperature, and this transformation is invertible once the temperature exceeds the melting temperature. C represents the heat capacity and q is heat source term used to describe the interaction between powder bed and laser beam. A constant density value at room temperature is employed in the current study since no mass conservation equation is solved. The latent heat effect related to solid-liquid transformation can be modeled by apparent heat capacity method [6].

The powder bed is modeled as a continuum medium with thermal properties differing from the compact state ($\lambda_c(T)$) [8]. The thermal conductivity of powder bed ($\lambda_p(T)$) is defined as follows:

$$ \lambda_p(T) = \begin{cases} \lambda_c(T)(1-\varphi)^n & \text{for } T = 293K \\ \frac{\lambda_c(T_m - \lambda_p(293K))}{T_m - 293K}(T - 293K) + \lambda_p(293K) & \text{for } 293K < T < T_m \\ \lambda_c(T) & \text{for } T > T_m \end{cases} $$

here, $\varphi$ represents the porosity of the powder bed, and n is an empirical parameter. T (in Kelvin) corresponds to the temperature of the powder, with $T_m$ being the melting temperature.

To conduct a steady-state heat transfer simulation with a short geometry, novel dedicated boundary conditions are introduced for the steady-state model [4]. These conditions are derived from an analytically inspired solution of the 1D heat transfer equation outside the mesh. A Fourier-type condition is imposed on the downstream and upstream surfaces (2D element) to emulate the heat exchange between the considered geometry and the volume beyond it. These new boundary conditions are presented as follows:

* Upstream surface: $\lambda \frac{dT}{dn} = \lambda(\beta - \alpha)(T_0 - T(up))$

* Downstream surface: $\lambda \frac{dT}{dn} = \lambda(\alpha + \beta)(T_0 - T(down))$
materials, namely AlSi10Mg and Inconel 718. The melt pool sizes simulated by the proposed model were compared with those obtained from a transient model as well as experimental results, specifically the melt pool morphology. The relative error observed for all the numerical and experimental validation tests was less than 5%.

In the current contribution, the single-track steady-state thermal model is extended to multi-tracks simulation. The proposed multi-track steady-state thermal model will be used to investigate the presence of lack-of-fusion with different scanning speed, powder layer thickness and hatch spacing, which allows reduce the number of expensive trial and error experimental tests.

## Numerical models

The proposed multi-tracks model is based on single-track steady-state model presented in [4], which has been successfully applied to LPBF simulations with two most used materials (AlSi10Mg and Inconel 718). A perfect correlation has been observed compared with the experimental results. Below is a concise introduction to the single-track steady-state model based on new dedicated boundary conditions.

The steady-state heat transfer problem is governed by a convection-diffusion equation (refer with: Eq. 1), with the underlying assumption of infinite length in the direction of the moving heat source.

$$ \rho CW \cdot \mathrm{grad}T = \mathrm{div}(\lambda \mathrm{grad}T) + q \quad (1) $$

where $\lambda = \sum_{state} p_i \lambda_i$ with $p_i$, $\lambda_i$ corresponding to the proportion and thermal conductivity of each state i (powder or compact). The state transformation (powder to bulk) is controlled by the temperature, and this transformation is invertible once the temperature exceeds the melting temperature. C represents the heat capacity and q is heat source term used to describe the interaction between powder bed and laser beam. A constant density value at room temperature is employed in the current study since no mass conservation equation is solved. The latent heat effect related to solid-liquid transformation can be modeled by apparent heat capacity method [6].

The powder bed is modeled as a continuum medium with thermal properties differing from the compact state ($\lambda_c(T)$) [8]. The thermal conductivity of powder bed ($\lambda_p(T)$) is defined as follows:

$$ \lambda_p(T) = \begin{cases} \lambda_c(T)(1-\varphi)^n & \text{for } T = 293K \\ \frac{\lambda_c(T_m - \lambda_p(293K))}{T_m - 293K}(T - 293K) + \lambda_p(293K) & \text{for } 293K < T < T_m \\ \lambda_c(T) & \text{for } T > T_m \end{cases} $$

here, $\varphi$ represents the porosity of the powder bed, and n is an empirical parameter. T (in Kelvin) corresponds to the temperature of the powder, with $T_m$ being the melting temperature.

To conduct a steady-state heat transfer simulation with a short geometry, novel dedicated boundary conditions are introduced for the steady-state model [4]. These conditions are derived from an analytically inspired solution of the 1D heat transfer equation outside the mesh. A Fourier-type condition is imposed on the downstream and upstream surfaces (2D element) to emulate the heat exchange between the considered geometry and the volume beyond it. These new boundary conditions are presented as follows:

* Upstream surface: $\lambda \frac{dT}{dn} = \lambda(\beta - \alpha)(T_0 - T(up))$

* Downstream surface: $\lambda \frac{dT}{dn} = \lambda(\alpha + \beta)(T_0 - T(down))$
where $\alpha = -\frac{\rho CW}{2\lambda}$ and $\beta = \frac{\sqrt{4K\lambda+(\rho CW)^2}}{2\lambda} = \sqrt{\alpha^2 + \frac{K}{\lambda}}$ with $K = H \frac{P}{S}$. H is the heat exchange coefficient (simply equal to $h_c$). P is the perimeter, and S is the surface of upstream surface. $T_0$ is the temperature ambient, and $T(up)/T(down)$ is the temperature at upstream/downstream surface. More information can be found in [4].

The convective ($Q_c$) and radiative ($Q_r$) losses at free surface are modeled by Newton's law:

$$Q_c = h_c(T - T_0)$$

$$Q_r = \sigma\varepsilon(T^4 - T_0^4)$$

where $h_c$, $\sigma$ and $\varepsilon$ are the coefficient exchange with air, Stefan-Boltzmann constant, and surface emissivity, respectively. T and $T_0$ are in Kelvin.

All the development and simulations are performed in SYSWELD™ [7].

## Numerical example

In this section, a numerical example is prepared to highlight the computational efficiency of proposed multi-track steady-state model. In the multi-track simulations, both transient step-by-step and proposed steady-state models are employed, and the transient step-by-step simulation is considered as the numerical reference. Since the quality of numerical results predicted by SYSWELD™ have been verified and confirmed by lots of researchers, no further validations tests will be conducted in the current studies.

In the current study, the material properties of 316L are utilized, and the properties in both powder and bulk states are presented in Table 1. The material properties of the bulk state are sourced from the SYSWELD database [7]. It's important to note that the density of the powder state is considered equal to that of the bulk state, as the volume shrinkage due to the transformation from powder state to bulk state is not taken into account. The thermal conductivity of powder bed is calculated from aforementioned equation with $\varphi=0.7$ and $n=4$. Numerical experience indicates that the thermal conductivity difference between the two states should not be excessively large. Otherwise, convergence issues may arise, or an extremely small time step might be required. The specific values of powder bed are the same as the bulk state.

<table><caption>Table 1. material properties of 316L</caption><thead><tr><th>State</th><th>Properties</th><th>20°C</th><th>400°C</th><th>900°C</th><th>1400°C</th><th>1800°C</th></tr></thead><tbody><tr><td rowspan="3">powder</td><td>Density (kg/mm³)</td><td colspan="5">7.72 * 10<sup>-6</sup></td></tr><tr><td>Thermal conductivity (W/mm.K)</td><td>0.00357</td><td>0.0104</td><td>0.01941</td><td>0.02842</td><td>0.02842</td></tr><tr><td>Specific heat (J/kg.K)</td><td>494.1</td><td>578.0</td><td>635.5</td><td>799.8</td><td>799.8</td></tr><tr><td rowspan="3">bulk</td><td>Density (kg/mm³)</td><td colspan="5">7.72 * 10<sup>-6</sup></td></tr><tr><td>Thermal conductivity (W/mm.K)</td><td>0.01488</td><td>0.02056</td><td>0.02769</td><td>0.02842</td><td>0.02842</td></tr><tr><td>Specific heat (J/kg.K)</td><td>494.1</td><td>578.0</td><td>635.5</td><td>799.8</td><td>799.8</td></tr></tbody></table>
where $\alpha = -\frac{\rho CW}{2\lambda}$ and $\beta = \frac{\sqrt{4K\lambda+(\rho CW)^2}}{2\lambda} = \sqrt{\alpha^2 + \frac{K}{\lambda}}$ with $K = H\frac{P}{S}$. H is the heat exchange coefficient (simply equal to $h_c$). P is the perimeter, and S is the surface of upstream surface. $T_0$ is the temperature ambient, and $T(up)/T(down)$ is the temperature at upstream/downstream surface. More information can be found in [4].

The convective ($Q_c$) and radiative ($Q_r$) losses at free surface are modeled by Newton's law:

$$Q_c = h_c(T - T_0)$$

$$Q_r = \sigma\varepsilon(T^4 - T_0^4)$$

where $h_c$, $\sigma$ and $\varepsilon$ are the coefficient exchange with air, Stefan-Boltzmann constant, and surface emissivity, respectively. T and $T_0$ are in Kelvin.

All the development and simulations are performed in SYSWELD™ [7].

## Numerical example

In this section, a numerical example is prepared to highlight the computational efficiency of proposed multi-track steady-state model. In the multi-track simulations, both transient step-by-step and proposed steady-state models are employed, and the transient step-by-step simulation is considered as the numerical reference. Since the quality of numerical results predicted by SYSWELD™ have been verified and confirmed by lots of researchers, no further validations tests will be conducted in the current studies.

In the current study, the material properties of 316L are utilized, and the properties in both powder and bulk states are presented in Table 1. The material properties of the bulk state are sourced from the SYSWELD database [7]. It's important to note that the density of the powder state is considered equal to that of the bulk state, as the volume shrinkage due to the transformation from powder state to bulk state is not taken into account. The thermal conductivity of powder bed is calculated from aforementioned equation with $\varphi=0.7$ and $n=4$. Numerical experience indicates that the thermal conductivity difference between the two states should not be excessively large. Otherwise, convergence issues may arise, or an extremely small time step might be required. The specific values of powder bed are the same as the bulk state.

<table><caption>Table 1. material properties of 316L</caption><thead><tr><th>State</th><th>Properties</th><th>20°C</th><th>400°C</th><th>900°C</th><th>1400°C</th><th>1800°C</th></tr></thead><tbody><tr><td rowspan="3">powder</td><td>Density (kg/mm³)</td><td colspan="5">7.72 * 10<sup>-6</sup></td></tr><tr><td>Thermal conductivity (W/mm.K)</td><td>0.00357</td><td>0.0104</td><td>0.01941</td><td>0.02842</td><td>0.02842</td></tr><tr><td>Specific heat (J/kg.K)</td><td>494.1</td><td>578.0</td><td>635.5</td><td>799.8</td><td>799.8</td></tr><tr><td rowspan="3">bulk</td><td>Density (kg/mm³)</td><td colspan="5">7.72 * 10<sup>-6</sup></td></tr><tr><td>Thermal conductivity (W/mm.K)</td><td>0.01488</td><td>0.02056</td><td>0.02769</td><td>0.02842</td><td>0.02842</td></tr><tr><td>Specific heat (J/kg.K)</td><td>494.1</td><td>578.0</td><td>635.5</td><td>799.8</td><td>799.8</td></tr></tbody></table>
Figure 1 presents the geometry, mesh and process information, and the powder bed is of 40 µm. The model width (W), depth (D), length (L) are 2mm, 1mm, 1.5mm respectively. The simulation consists of 7 tracks with hatch spacing of 200 µm, and scanning speed is set of 100 mm/s. The cooling time between two successive track is of 0.005s. The 3D mesh is generated by translation of 2D mesh (Figure 1-(C)), and all the layers in scanning direction are equal to 10 µm. This model contains about 433k nodes and 462k 3d elements.

<figure><img src="image_335.png" /><figcaption>Fig.1, the geometry and mesh information for transient simulation</figcaption></figure>

The geometry of steady state model is shown in figure 2, and the 3D mesh topology is created with same as manner as the transient model. Compared to transient model, the steady-state model has the same width and depth, while the length is only of 0.6mm. Solving steady-state problem with convection-diffusion equation and a short geometry can greatly reduce the CPU time. The position of heat source is at center of its trajectory.

<figure><img src="image_336.png" /><figcaption>Fig.2, the geometry for steady-state simulation</figcaption></figure>

A 2D heat source is applied to simulate interaction between laser and powder bed (see reference [3]). The heat source is described by the following equation:
Figure 1 presents the geometry, mesh and process information, and the powder bed is of 40 µm. The model width (W), depth (D), length (L) are 2mm, 1mm, 1.5mm respectively. The simulation consists of 7 tracks with hatch spacing of 200 µm, and scanning speed is set of 100 mm/s. The cooling time between two successive track is of 0.005s. The 3D mesh is generated by translation of 2D mesh (Figure 1-(C)), and all the layers in scanning direction are equal to 10 µm. This model contains about 433k nodes and 462k 3d elements.

<figure><img src="image_341.png" /><figcaption>Fig.1, the geometry and mesh information for transient simulation</figcaption></figure>

The geometry of steady state model is shown in figure 2, and the 3D mesh topology is created with same as manner as the transient model. Compared to transient model, the steady-state model has the same width and depth, while the length is only of 0.6mm. Solving steady-state problem with convection-diffusion equation and a short geometry can greatly reduce the CPU time. The position of heat source is at center of its trajectory.

<figure><img src="image_342.png" /><figcaption>Fig.2, the geometry for steady-state simulation</figcaption></figure>

A 2D heat source is applied to simulate interaction between laser and powder bed (see reference [3]). The heat source is described by the following equation:
$$Q = \frac{P}{\pi R_0^2} \exp \left( \frac{-R^2}{R_0^2} \right)$$

where R represent the relative position between the gauss point and center of heat source. R₀ is the radius of laser beam. P is effective energy absorbed by powder bed. In the current study, R₀ is 50 µm, and P is 40 W.

Table 2 presents a comparison of simulated melt pool size. The melt pool size stabilizes rapidly after track 1 for the steady-state simulation, which means that only the precedent deposited track will affect the prediction of actual track. However, the melt pool size given by transient model increase, which can be explain by the fact of heat cumulation. In comparison, the steady-state model provides a good correlation for the first track, but discrepancies become more significant with subsequent depositions. This growing difference is attributed to the omission of heat cumulation effects in the steady-state model. The steady-state model is about 150 time more efficient than transient model in terms of CPU time, which requires only 2 minutes in place of 5 hours.

<table><caption>Table 2. comparison of simulated melt pool size</caption><thead><tr><th>Dimension</th><th>Type of simulation</th><th>Track 1</th><th>Track 2</th><th>Track 3</th><th>Track 4</th><th>Track 5</th><th>Track 6</th><th>Track 7</th></tr></thead><tbody><tr><td rowspan="2">Width (μm)</td><td>Steady-state</td><td>217</td><td>204</td><td>204</td><td>204</td><td>204</td><td>204</td><td>204</td></tr><tr><td>Transient</td><td>210</td><td>213</td><td>218</td><td>221</td><td>222</td><td>223</td><td>224</td></tr><tr><td rowspan="2">Depth (μm)</td><td>Steady-state</td><td>84</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td></tr><tr><td>Transient</td><td>81</td><td>86</td><td>88</td><td>92</td><td>93</td><td>96</td><td>99</td></tr><tr><td rowspan="2">Length (μm)</td><td>Steady-state</td><td>268</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td></tr><tr><td>Transient</td><td>270</td><td>282</td><td>286</td><td>294</td><td>310</td><td>310</td><td>310</td></tr></tbody></table>

Figure 3 provide the comparison of temperature distribution given by transient and steady-state model. The steady-state model cannot consider the effect of heat accumulation in the multi-track simulations, which lead to under-estimation of melt pool size. The figure 3 confirms again this conclusion.
$$Q = \frac{P}{\pi R_0^2} \exp \left( \frac{-R^2}{R_0^2} \right)$$

where R represent the relative position between the gauss point and center of heat source. R₀ is the radius of laser beam. P is effective energy absorbed by powder bed. In the current study, R₀ is 50 µm, and P is 40 W.

Table 2 presents a comparison of simulated melt pool size. The melt pool size stabilizes rapidly after track 1 for the steady-state simulation, which means that only the precedent deposited track will affect the prediction of actual track. However, the melt pool size given by transient model increase, which can be explain by the fact of heat cumulation. In comparison, the steady-state model provides a good correlation for the first track, but discrepancies become more significant with subsequent depositions. This growing difference is attributed to the omission of heat cumulation effects in the steady-state model. The steady-state model is about 150 time more efficient than transient model in terms of CPU time, which requires only 2 minutes in place of 5 hours.

<table><caption>Table 2. comparison of simulated melt pool size</caption><thead><tr><th>Dimension</th><th>Type of simulation</th><th>Track 1</th><th>Track 2</th><th>Track 3</th><th>Track 4</th><th>Track 5</th><th>Track 6</th><th>Track 7</th></tr></thead><tbody><tr><td rowspan="2">Width (μm)</td><td>Steady-state</td><td>217</td><td>204</td><td>204</td><td>204</td><td>204</td><td>204</td><td>204</td></tr><tr><td>Transient</td><td>210</td><td>213</td><td>218</td><td>221</td><td>222</td><td>223</td><td>224</td></tr><tr><td rowspan="2">Depth (μm)</td><td>Steady-state</td><td>84</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td></tr><tr><td>Transient</td><td>81</td><td>86</td><td>88</td><td>92</td><td>93</td><td>96</td><td>99</td></tr><tr><td rowspan="2">Length (μm)</td><td>Steady-state</td><td>268</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td></tr><tr><td>Transient</td><td>270</td><td>282</td><td>286</td><td>294</td><td>310</td><td>310</td><td>310</td></tr></tbody></table>

Figure 3 provide the comparison of temperature distribution given by transient and steady-state model. The steady-state model cannot consider the effect of heat accumulation in the multi-track simulations, which lead to under-estimation of melt pool size. The figure 3 confirms again this conclusion.
<figure><img src="image_337.png" /><figcaption>Fig.3, temperature distribution given by transient and steady-state model</figcaption></figure>

Figure 4 shows the comparison of melting profiles given by these two models, and the temperature presented here is the maximal temperature at each node during the process. We can remark that the melt pool enlarges progressively in the transient model because of heat cumulation. However, the melt pool of each track predicted by steady-state model is almost the same. The lack of fusion can be observed in both cases. Furthermore, since the steady-state model tends to slightly underestimate the melt pool size, the parameters suggested by the steady-state model provide more reliability.

<figure><img src="image_338.png" /><figcaption>Fig.4, comparison of melting profile given by transient model (left) and steady-state model (right)</figcaption></figure>

To avoid the presence of porosity, the steady-state model is employed to optimize different hatch spacing. Therefore, the hatch spacing of 200 µm, 190 µm, 180 µm, 170 µm are tested, and figure 5 shows the simulated fusion profiles. The comparisons shows that a hatch spacing of 170 µm should be adopted.
<figure><img src="image_343.png" /><figcaption>Fig.3, temperature distribution given by transient and steady-state model</figcaption></figure>

Figure 4 shows the comparison of melting profiles given by these two models, and the temperature presented here is the maximal temperature at each node during the process. We can remark that the melt pool enlarges progressively in the transient model because of heat cumulation. However, the melt pool of each track predicted by steady-state model is almost the same. The lack of fusion can be observed in both cases. Furthermore, since the steady-state model tends to slightly underestimate the melt pool size, the parameters suggested by the steady-state model provide more reliability.

<figure><img src="image_344.png" /><figcaption>Fig.4, comparison of melting profile given by transient model (left) and steady-state model (right)</figcaption></figure>

To avoid the presence of porosity, the steady-state model is employed to optimize different hatch spacing. Therefore, the hatch spacing of 200 µm, 190 µm, 180 µm, 170 µm are tested, and figure 5 shows the simulated fusion profiles. The comparisons shows that a hatch spacing of 170 µm should be adopted.
<figure><img src="image_339.png" /><figcaption>Fig. 5, fusion profile with different hatch spacings: (A) 200 µm, (B) 190 µm, (C) 180 µm, (D) 170 µm</figcaption></figure>

**Conclusion**

This article presents the first tentative of applying steady-state model to multi-track simulation. The numerical results are compared against the transient model, and a relative error less than 18% has been observed in the comparison of simulated melting pool size. Moreover, the steady-state model highlights its extremely high computational efficiency, which can be beneficial for optimizing the fabrication parameters to avoid the presence of porosity. To improve the prediction of steady-state model, the next step is to incorporate the heat cumulation effect. Moreover, it would be also interesting to consider the phase transformation (perlite, austinite, etc.) for the multi-track simulations.

**References**

[1] TANG, Ming, PISTORIUS, P. Chris, et BEUTH, Jack L. Prediction of lack-of-fusion porosity for powder bed fusion. Additive Manufacturing, 2017, vol. 14, p. 39-48.
http://dx.doi.org/10.1016/j.addma.2016.12.001

[2] NING, Jinqiang, WANG, Wenjia, ZAMORANO, Bruno, et al. Analytical modeling of lack-of-fusion porosity in metal additive manufacturing. Applied Physics A, 2019, vol. 125, p. 1-11.

[3] JIA, Yabo, SAADLAOUI, Yassine, ROUX, Jean-Christophe, et al. Steady-state thermal model based on new dedicated boundary conditions-application in the simulation of laser powder bed fusion process. Applied Mathematical Modelling, 2022, vol. 112, p. 749-766.
https://doi.org/10.1016/j.apm.2022.08.013

[4] JIA, Yabo, SAADLAOUI, Yassine, FEULVARCH, Eric, BERGHEAU, Jean-Michel. An efficient local moving thermal-fluid framework for accelerating heat and mass transfer simulation during welding and additive manufacturing processes. Computer Methods in Applied Mechanics and Engineering, 2023, vol. 419, p. 116673. https://doi.org/10.1016/j.cma.2023.116673

[5] JIA, Yabo, NACEUR, Hakim, SAADLAOUI, Yassine, DUBAR, Laurent, BERGHEAU, Jean-Michel. A comprehensive comparison of modeling strategies and simulation techniques applied in
<figure><img src="image_345.png" /><figcaption>Fig. 5, fusion profile with different hatch spacings: (A) 200 µm, (B) 190 µm, (C) 180 µm, (D) 170 µm</figcaption></figure>

**Conclusion**

This article presents the first tentative of applying steady-state model to multi-track simulation. The numerical results are compared against the transient model, and a relative error less than 18% has been observed in the comparison of simulated melting pool size. Moreover, the steady-state model highlights its extremely high computational efficiency, which can be beneficial for optimizing the fabrication parameters to avoid the presence of porosity. To improve the prediction of steady-state model, the next step is to incorporate the heat cumulation effect. Moreover, it would be also interesting to consider the phase transformation (perlite, austinite, etc.) for the multi-track simulations.

**References**

[1] TANG, Ming, PISTORIUS, P. Chris, et BEUTH, Jack L. Prediction of lack-of-fusion porosity for powder bed fusion. Additive Manufacturing, 2017, vol. 14, p. 39-48.
http://dx.doi.org/10.1016/j.addma.2016.12.001

[2] NING, Jinqiang, WANG, Wenjia, ZAMORANO, Bruno, et al. Analytical modeling of lack-of-fusion porosity in metal additive manufacturing. Applied Physics A, 2019, vol. 125, p. 1-11.

[3] JIA, Yabo, SAADLAOUI, Yassine, ROUX, Jean-Christophe, et al. Steady-state thermal model based on new dedicated boundary conditions-application in the simulation of laser powder bed fusion process. Applied Mathematical Modelling, 2022, vol. 112, p. 749-766.
https://doi.org/10.1016/j.apm.2022.08.013

[4] JIA, Yabo, SAADLAOUI, Yassine, FEULVARCH, Eric, BERGHEAU, Jean-Michel. An efficient local moving thermal-fluid framework for accelerating heat and mass transfer simulation during welding and additive manufacturing processes. Computer Methods in Applied Mechanics and Engineering, 2023, vol. 419, p. 116673. https://doi.org/10.1016/j.cma.2023.116673

[5] JIA, Yabo, NACEUR, Hakim, SAADLAOUI, Yassine, DUBAR, Laurent, BERGHEAU, Jean-Michel. A comprehensive comparison of modeling strategies and simulation techniques applied in
powder-based metallic additive manufacturing processes. Journal of Manufacturing Processes, 2024, vol 110, p. 1-29. https://doi.org/10.1016/j.jmapro.2023.12.048

[6] BONACINA, Comini, COMINI, G., FASANO, A., et al. Numerical solution of phase-change problems. International Journal of Heat and Mass Transfer, 1973, vol. 16, no 10, p. 1825-1832.

[7] Software SYSWELD version 2023, ESI-Group, Lyon, France, 2023.

[8] Loong-Ee Loh, Chee-Kai Chua, Wai-Yee Yeong, Jie Song, Mahta Mapar, Swee-Leong Sing, Zhong-Hong Liu, Dan-Qing Zhang. Numerical investigation and an effective modelling on the Selective Laser Melting (SLM) process with aluminium alloy 6061. International Journal of Heat and Mass Transfer, 2015, vol 80, p.288-300, https://doi.org/10.1016/j.ijheatmasstransfer.2014.09.014
powder-based metallic additive manufacturing processes. Journal of Manufacturing Processes, 2024, vol 110, p. 1-29. https://doi.org/10.1016/j.jmapro.2023.12.048

[6] BONACINA, Comini, COMINI, G., FASANO, A., et al. Numerical solution of phase-change problems. International Journal of Heat and Mass Transfer, 1973, vol. 16, no 10, p. 1825-1832.

[7] Software SYSWELD version 2023, ESI-Group, Lyon, France, 2023.

[8] Loong-Ee Loh, Chee-Kai Chua, Wai-Yee Yeong, Jie Song, Mahta Mapar, Swee-Leong Sing, Zhong-Hong Liu, Dan-Qing Zhang. Numerical investigation and an effective modelling on the Selective Laser Melting (SLM) process with aluminium alloy 6061. International Journal of Heat and Mass Transfer, 2015, vol 80, p.288-300, https://doi.org/10.1016/j.ijheatmasstransfer.2014.09.014
# Differential thermal analysis to assist the design of corrosion-resistant high entropy alloys for laser powder bed fusion

SEIDOU Abdul Herrim<fnref n="1" />,ᵃ*, BLONDIAU Catherine<fnref n="1" />,ᵇ, DEDRY Olivier<fnref n="1" />,ᶜ,
OÑATE Angelo<fnref n="2" />,ᵈ, TUNINETTI Víctor<fnref n="3" />,ᵉ,
TCHOUFANG TCHUINDJANG Jérôme<fnref n="1" />,ᵉ, and MERTENS Anne<fnref n="1" />,,g

**Keywords:** High Entropy Alloys, Alloy Design, DTA, Solidification Path, Microstructure

**Abstract.** In this study, Al, Cr, Fe, Mn, and Ni are selected and pure elemental powders were used to prepare several medium entropy alloys (MEAs) and high entropy alloys (HEAs). Differential Thermal Analysis (DTA) is used as a tool for pre-screening of the compositions suitable to design corrosion-resistant alloys for Laser Powder Bed Fusion (LPBF). The advantage of DTA lies in the precise temperature control and in the small quantity of powder necessary to perform the test in near-equilibrium conditions. The powder mixtures were heated up to 1550°C, fully melted, and then cooled down to room temperature at 5°C/min. The results of DTA are used as reference to understand the complex microstructures obtained using LPBF. Microstructure analysis of DTA samples by combining Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) helped to confirm the phase prediction theories. Most of the samples showed a heterogeneous structure with Ni-Al rich B2 phase, Fe-Cr rich BCC and FCC phases. The spinodal decomposition of the BCC phase was also observed in the equimolar AlCrFeMnNi sample. The Valence Electron Concentration (VEC) theory was verified and the partitioning of the elements between the phases was investigated.

**Introduction**

Additive manufacturing (AM) technologies, such as Laser Powder Bed Fusion (LPBF), allow the creation of intricate parts with features that pose challenges for conventional methods. Notably, LPBF has revolutionized the manufacturing of components traditionally crafted through injection molding or die-casting, marking a departure from conventional approaches [1,2]. In comparison to standard casting and forging techniques, the melt-pool generated in LPBF is submitted to exceptionally high cooling rates in a range from 10³ to 10⁶ K/s [3]. This rapid cooling induces an out-of-equilibrium solidification, promoting grain refinement and potentially leading to the formation of novel sub-crystal or amorphous phases [4]. LPBF offers a better design flexibility and functional optimization of high-value products at small production volumes, which are not feasible through traditional manufacturing processes [5]. LPBF processes often operate with fixed parameters for specific applications, limiting the responsive control. The choice of input materials and printing parameters can lead to variations in the properties of the final components. A poor powder quality can cause defects such as pores, cracks, inclusions, residual stresses, suboptimal roughness. Therefore, understanding the intricate relationships between material properties,

<figure><img src="image_340.png" /></figure>

<footnote n="1">¹ Metallic Materials Science (MMS), A&M Dept, University of Liège, Liège, 4000, Belgium</footnote>

<footnote n="2">²Dept of Materials Engineering, Universidad de Concepción, Edmundo Larenas 315, Concepción, Chile</footnote>

<footnote n="3">³Dept of Mechanical Engineering, Universidad de La Frontera, Francisco Salazar 01145, Temuco, Chile</footnote>

<footnote>ᵃahb.seidou@uliege.be, ᵇcatherine.blondiau@student.uliege.be, ᶜolivier.dedry@uliege.be,</footnote>

<footnote>ᵈaonates@udec.cl, ᶜVictor.tuninetti@ufrontera.cl, ṭj.tchuindjang@uliege.be,</footnote>

<footnote>ganne.mertens@uliege.be</footnote>
# Differential thermal analysis to assist the design of corrosion-resistant high entropy alloys for laser powder bed fusion

SEIDOU Abdul Herrim<fnref n="1" />, a*, BLONDIAU Catherine<fnref n="1" />, b, DEDRY Olivier<fnref n="1" />, c,
OÑATE Angelo<fnref n="2" />, d, TUNINETTI Víctor<fnref n="3" />, e,
TCHOUFANG TCHUINDJANG Jérôme<fnref n="1" />, f, and MERTENS Anne<fnref n="1" />, g

**Keywords:** High Entropy Alloys, Alloy Design, DTA, Solidification Path, Microstructure

**Abstract.** In this study, Al, Cr, Fe, Mn, and Ni are selected and pure elemental powders were used to prepare several medium entropy alloys (MEAs) and high entropy alloys (HEAs). Differential Thermal Analysis (DTA) is used as a tool for pre-screening of the compositions suitable to design corrosion-resistant alloys for Laser Powder Bed Fusion (LPBF). The advantage of DTA lies in the precise temperature control and in the small quantity of powder necessary to perform the test in near-equilibrium conditions. The powder mixtures were heated up to 1550°C, fully melted, and then cooled down to room temperature at 5°C/min. The results of DTA are used as reference to understand the complex microstructures obtained using LPBF. Microstructure analysis of DTA samples by combining Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) helped to confirm the phase prediction theories. Most of the samples showed a heterogeneous structure with Ni-Al rich B2 phase, Fe-Cr rich BCC and FCC phases. The spinodal decomposition of the BCC phase was also observed in the equimolar AlCrFeMnNi sample. The Valence Electron Concentration (VEC) theory was verified and the partitioning of the elements between the phases was investigated.

**Introduction**

Additive manufacturing (AM) technologies, such as Laser Powder Bed Fusion (LPBF), allow the creation of intricate parts with features that pose challenges for conventional methods. Notably, LPBF has revolutionized the manufacturing of components traditionally crafted through injection molding or die-casting, marking a departure from conventional approaches [1,2]. In comparison to standard casting and forging techniques, the melt-pool generated in LPBF is submitted to exceptionally high cooling rates in a range from $10^3$ to $10^6$ K/s [3]. This rapid cooling induces an out-of-equilibrium solidification, promoting grain refinement and potentially leading to the formation of novel sub-crystal or amorphous phases [4]. LPBF offers a better design flexibility and functional optimization of high-value products at small production volumes, which are not feasible through traditional manufacturing processes [5]. LPBF processes often operate with fixed parameters for specific applications, limiting the responsive control. The choice of input materials and printing parameters can lead to variations in the properties of the final components. A poor powder quality can cause defects such as pores, cracks, inclusions, residual stresses, suboptimal roughness. Therefore, understanding the intricate relationships between material properties,

<figure><img src="image_346.png" /></figure>

<footnote n="1">¹ Metallic Materials Science (MMS), A&M Dept, University of Liège, Liège, 4000, Belgium</footnote>

<footnote n="2">²Dept of Materials Engineering, Universidad de Concepción, Edmundo Larenas 315, Concepción, Chile</footnote>

<footnote n="3">³Dept of Mechanical Engineering, Universidad de La Frontera, Francisco Salazar 01145, Temuco, Chile</footnote>

<footnote>aahb.seidou@uliege.be, bcatherine.blondiau@student.uliege.be, colivier.dedry@uliege.be,</footnote>

<footnote>daonates@udec.cl, eVictor.tuninetti@ufrontera.cl, fj.tchuindjang@uliege.be,</footnote>

<footnote>ganne.mertens@uliege.be</footnote>
processing performance, and end component properties is necessary to develop alloys that fulfill the requirements [6,7].

High entropy alloys (HEAs) were defined originally as alloys that contain more than five principal elements with the concentration of each between 5% and 35%. This concept of HEAs has been lately extended to alloys with four principal elements [8,9]. Due to the high mixing entropy effect, the crystal lattice structure of most HEAs tends to be either body-centered cubic (BCC) or face-centered cubic (FCC). BCC-structured HEAs typically exhibit high strength but low ductility, whereas FCC-structured HEAs showcase high ductility, high wear, and corrosion resistance, with relatively low strength [10,11]. Consequently, a judicious selection of alloying elements is vital to achieve the required properties in HEAs.

Conventional casting, with cooling rates between 10 and 20 K/s, usually causes significant phase separation during the fabrication process of HEAs, where a post-treatment process is usually required to further adjust the microstructure for desired properties [10]. The rapid solidification of LPBF can restrict compositional segregation and intermetallics formation in the as-built parts, contributing to the strengthening effect through grain refinement [12].

In recent efforts to predict phases in HEAs, theories have been developed based on thermodynamic and structural parameters, including entropy of mixing ($\Delta S_{mix}$), atomic size differences ($\delta$), melting points ($T_m$), enthalpy of mixing ($\Delta H_{mix}$), valence electron concentration ($VEC$), and $\Omega$ that represents the scale ratio of $\Delta S_{mix}$ to $\Delta H_{mix}$. These parameters play crucial roles in determining the stability and composition of solid-solution phases in HEA systems [8,13]. The electron concentration is defined as the average number of itinerant electrons per atom ($e/a$) and the $VEC$ encompasses all electrons, including the d-electrons accommodated in the valence band [14,15]. For a multi-component alloy, both $e/a$ and $VEC$ can be mathematically expressed by Eq. 1 and Eq. 2:

$$e/a = \sum_{i=1}^{n} c_{i}(e/a)_{i} \qquad (1)$$

$$VEC = \sum_{i=1}^{n} c_{i}(VEC)_{i} \qquad (2)$$

Here, $c_i$ represents the concentration of the individual element in the alloy, $(e/a)_i$ and $(VEC)_i$ denote the $e/a$ and $VEC$ for the respective element. A stable, solid-solution phase forms in an HEA system when two parameters meet the conditions $\Omega > 1.1$ and $\delta < 6.6\%$ [14,16]. Specifically, when $VEC > 8$, FCC solid-solution phase is established. In the range of $6.87 \le VEC < 8$, both FCC and BCC phases coexist. However, when $VEC < 6.87$, only a single BCC phase is observed [11].

Regarding the corrosion resistance properties, alloys with higher Al and lower Cr contents tend to develop porous oxide films on the surface, failing to provide effective barriers against Cl⁻ permeation. Moreover, the high Al content promotes the growth of the BCC phase, characterized by enrichment in Al and depletion of Cr, leading to increased elemental segregation. This, in turn, influences the corrosion behavior, with the Cr-depleted BCC phase demonstrating weaker corrosion resistance compared to the FCC phase [17]. The control of the microstructure is thus fundamental in developing materials with specific properties.

Thermal analysis methods may be used to investigate the thermodynamic phenomena during the phase transformations. Differential Thermal Analysis (DTA) in particular employs the temperature difference between an inert reference sample and the test sample, during a thermal ramp conducted in a furnace with a fixed cooling rate. However, commercial DTA equipment faces limitations, notably in sample size, hindering adequate nucleation statistics. Consequently, while DTA has been employed in solidification experiments, its use in metal solidification studies is less frequent due to these inherent challenges [18,19]. However, in order to avoid the use of large amount of material to investigate the thermodynamic phenomena and perform preliminary microstructural characterization, DTA represents a rapid and cost-effective solution. Due to the small amount of material needed for DTA, mixture of powders represents an efficient choice to
processing performance, and end component properties is necessary to develop alloys that fulfill the requirements [6,7].

High entropy alloys (HEAs) were defined originally as alloys that contain more than five principal elements with the concentration of each between 5% and 35%. This concept of HEAs has been lately extended to alloys with four principal elements [8,9]. Due to the high mixing entropy effect, the crystal lattice structure of most HEAs tends to be either body-centered cubic (BCC) or face-centered cubic (FCC). BCC-structured HEAs typically exhibit high strength but low ductility, whereas FCC-structured HEAs showcase high ductility, high wear, and corrosion resistance, with relatively low strength [10,11]. Consequently, a judicious selection of alloying elements is vital to achieve the required properties in HEAs.

Conventional casting, with cooling rates between 10 and 20 K/s, usually causes significant phase separation during the fabrication process of HEAs, where a post-treatment process is usually required to further adjust the microstructure for desired properties [10]. The rapid solidification of LPBF can restrict compositional segregation and intermetallics formation in the as-built parts, contributing to the strengthening effect through grain refinement [12].

In recent efforts to predict phases in HEAs, theories have been developed based on thermodynamic and structural parameters, including entropy of mixing (ΔS<sub>mix</sub>), atomic size differences (δ), melting points (T<sub>m</sub>), enthalpy of mixing (ΔH<sub>mix</sub>), valence electron concentration (VEC), and Ω that represents the scale ratio of ΔS<sub>mix</sub> to ΔH<sub>mix</sub>. These parameters play crucial roles in determining the stability and composition of solid-solution phases in HEA systems [8,13]. The electron concentration is defined as the average number of itinerant electrons per atom (e/a) and the VEC encompasses all electrons, including the d-electrons accommodated in the valence band [14,15]. For a multi-component alloy, both e/a and VEC can be mathematically expressed by Eq. 1 and Eq. 2:

$$e/a = \sum_{i=1}^{n} c_i(e/a)_i \qquad (1)$$

$$VEC = \sum_{i=1}^{n} c_i(VEC)_i \qquad (2)$$

Here, c<sub>i</sub> represents the concentration of the individual element in the alloy, (e/a)<sub>i</sub> and (VEC)<sub>i</sub> denote the e/a and VEC for the respective element. A stable, solid-solution phase forms in an HEA system when two parameters meet the conditions Ω > 1.1 and δ < 6.6% [14,16]. Specifically, when VEC > 8, FCC solid-solution phase is established. In the range of 6.87 ≤ VEC < 8, both FCC and BCC phases coexist. However, when VEC < 6.87, only a single BCC phase is observed [11].

Regarding the corrosion resistance properties, alloys with higher Al and lower Cr contents tend to develop porous oxide films on the surface, failing to provide effective barriers against Cl<sup>-</sup> permeation. Moreover, the high Al content promotes the growth of the BCC phase, characterized by enrichment in Al and depletion of Cr, leading to increased elemental segregation. This, in turn, influences the corrosion behavior, with the Cr-depleted BCC phase demonstrating weaker corrosion resistance compared to the FCC phase [17]. The control of the microstructure is thus fundamental in developing materials with specific properties.

Thermal analysis methods may be used to investigate the thermodynamic phenomena during the phase transformations. Differential Thermal Analysis (DTA) in particular employs the temperature difference between an inert reference sample and the test sample, during a thermal ramp conducted in a furnace with a fixed cooling rate. However, commercial DTA equipment faces limitations, notably in sample size, hindering adequate nucleation statistics. Consequently, while DTA has been employed in solidification experiments, its use in metal solidification studies is less frequent due to these inherent challenges [18,19]. However, in order to avoid the use of large amount of material to investigate the thermodynamic phenomena and perform preliminary microstructural characterization, DTA represents a rapid and cost-effective solution. Due to the small amount of material needed for DTA, mixture of powders represents an efficient choice to
prepare several alloys compositions. For HEAs design through AM technologies, this method is useful to select the elements by highlighting their influence on the microstructure and on the near-equilibrium solidification phenomena that will serve as reference to understand the as-built AM microstructure.

## Materials and methods

Elemental powders, supplied by abcr GmbH (Germany), were employed in the fabrication of the test samples. Multiple mixtures were prepared manually, delineating two distinct groups: the CrFeMnNi-based medium entropy alloys (MEAs) and the AlCrFeMnNi-based HEAs. The nominal composition of the powder blends is outlined in Table 1 and Table 2.

Thermal analysis was conducted using the NETZSCH STA 449C Jupiter DTA. Approximately 700 mg of powder mixture were used for each DTA test. To facilitate near-equilibrium solidification and enable cross comparisons of the results, the heating and cooling rates were consistently maintained at 5°C/min. The samples underwent heating up to 1550°C within Al₂O₃ crucibles in an argon gas-purged chamber to minimize oxygen levels. An essential baseline measurement involved an empty crucible subjected to the same temperature profile, eliminating any artificial signals originating from the sample holder.

<table><caption>Table 1. Compositions and thermodynamic parameters of CrFeMnNi-based MEAs.</caption><thead><tr><th>% at.</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th><th>δ</th><th>ΔH<sub>mix</sub></th><th>ΔS<sub>mix</sub></th><th>Ω</th><th>VEC</th></tr></thead><tbody><tr><td>CrFeMnNi</td><td>25</td><td>25</td><td>25</td><td>25</td><td>3.57</td><td>-4.00</td><td>11.53</td><td>5.21</td><td>7.75</td></tr><tr><td>Cr<sub>2</sub>Fe<sub>2</sub>MnNi</td><td>33.33</td><td>33.33</td><td>16.67</td><td>16.67</td><td>3.10</td><td>-2.88</td><td>11.05</td><td>7.16</td><td>7.50</td></tr><tr><td>CrFe<sub>2</sub>MnNi<sub>2</sub></td><td>16.67</td><td>33.33</td><td>16.67</td><td>33.33</td><td>3.12</td><td>-4.22</td><td>11.05</td><td>4.70</td><td>8.17</td></tr></tbody></table>

<table><caption>Table 2. Compositions and thermodynamic parameters of AlCrFeMnNi-based HEAs.</caption><thead><tr><th>% at.</th><th>Al</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th><th>δ</th><th>ΔH<sub>mix</sub></th><th>ΔS<sub>mix</sub></th><th>Ω</th><th>VEC</th></tr></thead><tbody><tr><td>AlCrFeMnNi</td><td>20</td><td>20</td><td>20</td><td>20</td><td>20</td><td>5.82</td><td>-12.48</td><td>13.38</td><td>1.75</td><td>6.80</td></tr><tr><td>AlCrFe<sub>2</sub>MnNi<sub>2</sub></td><td>14.29</td><td>14.29</td><td>28.57</td><td>14.29</td><td>28.57</td><td>5.41</td><td>-10.86</td><td>12.89</td><td>1.98</td><td>7.43</td></tr><tr><td>AlCrFe<sub>2</sub>Ni<sub>2</sub></td><td>16.67</td><td>16.67</td><td>33.33</td><td>-</td><td>33.33</td><td>5.47</td><td>-11.11</td><td>13.37</td><td>2.07</td><td>7.50</td></tr></tbody></table>

The samples underwent polishing for microstructural observation under an optical microscope (OM) or a scanning electron microscope (SEM) Tescan Clara Ultra-High Resolution UHR. Cr₂Fe₂MnNi and CrFeMnNi samples are etched with 3% Nital to improve the detection of phases during quantification with Stream analysis software. Additionally, semi-quantitative energy dispersive X-ray spectroscopy (EDX) point and line scan analyses were performed across notable microstructural features. Phase and crystallographic information were obtained through electron backscatter diffraction (EBSD).

## Results

The DTA cooling curves of CrFeMnNi-based MEAs are shown in Fig. 1a. Reactions that occur during the cooling are characterized by exothermic peaks. For clarity, the derivative curves used
prepare several alloys compositions. For HEAs design through AM technologies, this method is useful to select the elements by highlighting their influence on the microstructure and on the near-equilibrium solidification phenomena that will serve as reference to understand the as-built AM microstructure.

## Materials and methods

Elemental powders, supplied by abcr GmbH (Germany), were employed in the fabrication of the test samples. Multiple mixtures were prepared manually, delineating two distinct groups: the CrFeMnNi-based medium entropy alloys (MEAs) and the AlCrFeMnNi-based HEAs. The nominal composition of the powder blends is outlined in Table 1 and Table 2.

Thermal analysis was conducted using the NETZSCH STA 449C Jupiter DTA. Approximately 700 mg of powder mixture were used for each DTA test. To facilitate near-equilibrium solidification and enable cross comparisons of the results, the heating and cooling rates were consistently maintained at 5°C/min. The samples underwent heating up to 1550°C within Al₂O₃ crucibles in an argon gas-purged chamber to minimize oxygen levels. An essential baseline measurement involved an empty crucible subjected to the same temperature profile, eliminating any artificial signals originating from the sample holder.

<table><caption>Table 1. Compositions and thermodynamic parameters of CrFeMnNi-based MEAs.</caption><thead><tr><th>% at.</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th><th>δ</th><th>ΔH<sub>mix</sub></th><th>ΔS<sub>mix</sub></th><th>Ω</th><th>VEC</th></tr></thead><tbody><tr><td>CrFeMnNi</td><td>25</td><td>25</td><td>25</td><td>25</td><td>3.57</td><td>-4.00</td><td>11.53</td><td>5.21</td><td>7.75</td></tr><tr><td>Cr<sub>2</sub>Fe<sub>2</sub>MnNi</td><td>33.33</td><td>33.33</td><td>16.67</td><td>16.67</td><td>3.10</td><td>-2.88</td><td>11.05</td><td>7.16</td><td>7.50</td></tr><tr><td>CrFe<sub>2</sub>MnNi<sub>2</sub></td><td>16.67</td><td>33.33</td><td>16.67</td><td>33.33</td><td>3.12</td><td>-4.22</td><td>11.05</td><td>4.70</td><td>8.17</td></tr></tbody></table>

<table><caption>Table 2. Compositions and thermodynamic parameters of AlCrFeMnNi-based HEAs.</caption><thead><tr><th>% at.</th><th>Al</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th><th>δ</th><th>ΔH<sub>mix</sub></th><th>ΔS<sub>mix</sub></th><th>Ω</th><th>VEC</th></tr></thead><tbody><tr><td>AlCrFeMnNi</td><td>20</td><td>20</td><td>20</td><td>20</td><td>20</td><td>5.82</td><td>-12.48</td><td>13.38</td><td>1.75</td><td>6.80</td></tr><tr><td>AlCrFe<sub>2</sub>MnNi<sub>2</sub></td><td>14.29</td><td>14.29</td><td>28.57</td><td>14.29</td><td>28.57</td><td>5.41</td><td>-10.86</td><td>12.89</td><td>1.98</td><td>7.43</td></tr><tr><td>AlCrFe<sub>2</sub>Ni<sub>2</sub></td><td>16.67</td><td>16.67</td><td>33.33</td><td>-</td><td>33.33</td><td>5.47</td><td>-11.11</td><td>13.37</td><td>2.07</td><td>7.50</td></tr></tbody></table>

The samples underwent polishing for microstructural observation under an optical microscope (OM) or a scanning electron microscope (SEM) Tescan Clara Ultra-High Resolution UHR. Cr₂Fe₂MnNi and CrFeMnNi samples are etched with 3% Nital to improve the detection of phases during quantification with Stream analysis software. Additionally, semi-quantitative energy dispersive X-ray spectroscopy (EDX) point and line scan analyses were performed across notable microstructural features. Phase and crystallographic information were obtained through electron backscatter diffraction (EBSD).

## Results

The DTA cooling curves of CrFeMnNi-based MEAs are shown in Fig. 1a. Reactions that occur during the cooling are characterized by exothermic peaks. For clarity, the derivative curves used
to determine the corresponding peak temperatures, but also to set both the beginning and the end of any given phase transformation, as reported in previous works, are not shown in this figure [20,21].

The CrFe₂MnNi₂ curve is characterized by a single peak at 1350°C (Fig. 1a). The OM and SEM micrographs, combined with EDX measurements, reveal the presence of one single FCC phase with composition close to the nominal composition of the alloy (Table 1). A single-phase structure is obtained, and the VEC value of 8.17 (Table 1) confirms the prediction theory. According to the same theory, CrFeMnNi and Cr₂Fe₂MnNi, that present respectively a VEC values of 7.75 and 7.50 (Table 1), should present a dual-phase structure associating FCC and BCC phases.

<figure><img src="image_341.png" /><figcaption>Fig. 1. (a) DTA cooling combined plots for alloys CrFeMnNi, Cr₂Fe₂MnNi and CrFe₂MnNi₂, (b) optical and scanning electron micrographs of alloy CrFeMnNi, (c) quantification of the phases in alloy CrFeMnNi using stream analysis.</figcaption></figure>

Observing the micrographs of CrFeMnNi (Fig. 1b), two phases can be identified. The DTA cooling curve, in Fig. 1a, reveals two different peaks in a range of temperature between 1360°C and 1260°C (Fig. 1a). The first phase to form was the Fe-Cr rich FCC matrix, followed by the BCC phase that is rich in Mn and Ni (Table 3). The FCC phase forms due to the dendritic solidification. The remained liquid in the interdendritic region solidifies to form the BCC phase around 1300°C. CrFeMnNi and Cr₂Fe₂MnNi are very similar in terms of microstructure and distribution of elements between the phases (Table 4). The solidification temperature of Cr₂Fe₂MnNi is higher, in a range of temperature between 1380°C and 1280°C, as shown in Fig.
to determine the corresponding peak temperatures, but also to set both the beginning and the end of any given phase transformation, as reported in previous works, are not shown in this figure [20,21].

The CrFe₂MnNi₂ curve is characterized by a single peak at 1350°C (Fig. 1a). The OM and SEM micrographs, combined with EDX measurements, reveal the presence of one single FCC phase with composition close to the nominal composition of the alloy (Table 1). A single-phase structure is obtained, and the VEC value of 8.17 (Table 1) confirms the prediction theory. According to the same theory, CrFeMnNi and Cr₂Fe₂MnNi, that present respectively a VEC values of 7.75 and 7.50 (Table 1), should present a dual-phase structure associating FCC and BCC phases.

<figure><img src="image_347.png" /><figcaption>Fig. 1. (a) DTA cooling combined plots for alloys CrFeMnNi, Cr₂Fe₂MnNi and CrFe₂MnNi₂, (b) optical and scanning electron micrographs of alloy CrFeMnNi, (c) quantification of the phases in alloy CrFeMnNi using stream analysis.</figcaption></figure>

Observing the micrographs of CrFeMnNi (Fig. 1b), two phases can be identified. The DTA cooling curve, in Fig. 1a, reveals two different peaks in a range of temperature between 1360°C and 1260°C (Fig. 1a). The first phase to form was the Fe-Cr rich FCC matrix, followed by the BCC phase that is rich in Mn and Ni (Table 3). The FCC phase forms due to the dendritic solidification. The remained liquid in the interdendritic region solidifies to form the BCC phase around 1300°C. CrFeMnNi and Cr₂Fe₂MnNi are very similar in terms of microstructure and distribution of elements between the phases (Table 4). The solidification temperature of Cr₂Fe₂MnNi is higher, in a range of temperature between 1380°C and 1280°C, as shown in Fig.
1a. The main difference lies in the volume fraction of the two phases. Using Stream analysis software on the optical micrographs, as shown in the left-hand side of Fig. 1c, the phases present in the microstructures were quantified. As an example of such analysis, the FCC matrix is highlighted in red on the right-hand side of Fig. 1c. In the Cr₂Fe₂MnNi sample, the percentage of FCC phase was 59.7% (40.3% of BCC). As Fe and Cr is increased, the percentage of BCC phase increases and the temperature at which the solidification starts is also increased, passing from 1360°C for the equimolar sample CrFeMnNi to 1380°C for Cr₂Fe₂MnNi (Fig. 1a).

<table><caption>Table 3. EDX semi-quantitative analysis of CrFeMnNi.</caption><thead><tr><th>% at.</th><th>Chromium</th><th>Iron</th><th>Manganese</th><th>Nickel</th></tr></thead><tbody><tr><td>BCC</td><td>46.2 ± 2.0</td><td>30.3 ± 1.0</td><td>17.5 ± 0.4</td><td>6.0 ± 1.4</td></tr><tr><td>FCC</td><td>25.0 ± 1.4</td><td>29.5 ± 2.5</td><td>26.0 ± 2.0</td><td>19.5 ± 1.7</td></tr></tbody></table>

<table><caption>Table 4. EDX semi-quantitative analysis of Cr₂Fe₂MnNi.</caption><thead><tr><th>% at.</th><th>Chromium</th><th>Iron</th><th>Manganese</th><th>Nickel</th></tr></thead><tbody><tr><td>BCC</td><td>47.0 ± 0.7</td><td>33.16 ± 0.5</td><td>10.9 ± 0.4</td><td>8.9 ± 0.5</td></tr><tr><td>FCC</td><td>28.4 ± 1.0</td><td>34.83 ± 1.5</td><td>14.8 ± 1.0</td><td>22.0 ± 1.5</td></tr></tbody></table>

For the AlCrFeMnNi-based HEAs, the DTA curves are shown in Fig. 2a. All DTA cooling curves present several peaks at high temperature, indicating the formation of complex microstructures. Indeed, almost all samples exhibit dual-phase microstructures, as shown in Fig. 3 and Fig. 4.

According to the VEC theory, the equimolar sample AlCrFeMnNi should consist in a single BCC solid solution. However, the AlCrFeMnNi sample presents a transformation that starts at 1340°C and ends at 1240°C. No further transformation occurs at lower temperatures. The detailed microstructures of this sample are illustrated in Fig. 2b and Fig. 2c. Typical dendritic and interdendritic structures were observed. The compositions of the phases in the different regions were evaluated using EDX and the results are shown in Table 5. In accordance with prior works [22,23], the dendritic region is characterized by a mixed FCC + B2 matrix rich in Cr and Fe, in which cuboid precipitates rich in Al and Ni are embedded. The interdendritic region is characterized by a BCC matrix rich in Al and Ni in which Cr-Fe rich rod-shaped B2 precipitates are embedded. The precipitates are known to form through a spinodal decomposition [22,23].
1a. The main difference lies in the volume fraction of the two phases. Using Stream analysis software on the optical micrographs, as shown in the left-hand side of Fig. 1c, the phases present in the microstructures were quantified. As an example of such analysis, the FCC matrix is highlighted in red on the right-hand side of Fig. 1c. In the Cr₂Fe₂MnNi sample, the percentage of FCC phase was 59.7% (40.3% of BCC). As Fe and Cr is increased, the percentage of BCC phase increases and the temperature at which the solidification starts is also increased, passing from 1360°C for the equimolar sample CrFeMnNi to 1380°C for Cr₂Fe₂MnNi (Fig. 1a).

<table><caption>Table 3. EDX semi-quantitative analysis of CrFeMnNi.</caption><thead><tr><th>% at.</th><th>Chromium</th><th>Iron</th><th>Manganese</th><th>Nickel</th></tr></thead><tbody><tr><td>BCC</td><td>46.2 ± 2.0</td><td>30.3 ± 1.0</td><td>17.5 ± 0.4</td><td>6.0 ± 1.4</td></tr><tr><td>FCC</td><td>25.0 ± 1.4</td><td>29.5 ± 2.5</td><td>26.0 ± 2.0</td><td>19.5 ± 1.7</td></tr></tbody></table>

<table><caption>Table 4. EDX semi-quantitative analysis of Cr₂Fe₂MnNi.</caption><thead><tr><th>% at.</th><th>Chromium</th><th>Iron</th><th>Manganese</th><th>Nickel</th></tr></thead><tbody><tr><td>BCC</td><td>47.0 ± 0.7</td><td>33.16 ± 0.5</td><td>10.9 ± 0.4</td><td>8.9 ± 0.5</td></tr><tr><td>FCC</td><td>28.4 ± 1.0</td><td>34.83 ± 1.5</td><td>14.8 ± 1.0</td><td>22.0 ± 1.5</td></tr></tbody></table>

For the AlCrFeMnNi-based HEAs, the DTA curves are shown in Fig. 2a. All DTA cooling curves present several peaks at high temperature, indicating the formation of complex microstructures. Indeed, almost all samples exhibit dual-phase microstructures, as shown in Fig. 3 and Fig. 4.

According to the VEC theory, the equimolar sample AlCrFeMnNi should consist in a single BCC solid solution. However, the AlCrFeMnNi sample presents a transformation that starts at 1340°C and ends at 1240°C. No further transformation occurs at lower temperatures. The detailed microstructures of this sample are illustrated in Fig. 2b and Fig. 2c. Typical dendritic and interdendritic structures were observed. The compositions of the phases in the different regions were evaluated using EDX and the results are shown in Table 5. In accordance with prior works [22,23], the dendritic region is characterized by a mixed FCC + B2 matrix rich in Cr and Fe, in which cuboid precipitates rich in Al and Ni are embedded. The interdendritic region is characterized by a BCC matrix rich in Al and Ni in which Cr-Fe rich rod-shaped B2 precipitates are embedded. The precipitates are known to form through a spinodal decomposition [22,23].
<figure><img src="image_342.png" /><figcaption>Fig. 2. (a) DTA cooling combined plots for alloys AlCrFeMnNi, AlCrFe₂MnNi₂ and AlCrFe₂Ni₂, (b) optical micrograph of alloy AlCrFeMnNi, (c) scanning electron micrograph of alloy AlCrFeMnNi.</figcaption></figure>

<table><caption>Table 5. EDX semi-quantitative analysis of AlCrFeMnNi.</caption><thead><tr><th>% at.</th><th>Al</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th></tr></thead><tbody><tr><td>Precipitates</td><td>29.4 ± 4.0</td><td>6.5 ± 3.0</td><td>9.0 ± 4.3</td><td>16.0 ± 1.5</td><td>39.1 ± 7.9</td></tr><tr><td>Matrix</td><td>7.7 ± 1.1</td><td>34.6 ± 1.8</td><td>30.0 ± 0.9</td><td>24.7 ± 0.5</td><td>3.1 ± 1.6</td></tr></tbody></table>

With increased amounts of Fe and Ni, the AlCrFe₂MnNi₂ sample presents a lower solidification temperature in a range between 1280°C and 1190°C (Fig. 2a). At 1210°C, the remaining liquid, which reaches the eutectic composition went through the eutectic transformation. Two different areas can be observed in the microstructure and the typical eutectic lamellar structure is present in some areas of the microstructure (Fig. 3a) [24]. This is consistent with the peaks revealed by the DTA cooling curve (Fig. 2a). Observing the SEM micrographs in Fig. 3a, the presence of the dual-phase microstructure confirms the prediction by the VEC theory for a value of 7.43 (Table 2). Compared to the equimolar AlCrFeMnNi sample, no dendritic and interdendritic regions were observed (Fig. 3a). EBSD analysis shows the presence of FCC and BCC/B2 phases (Fig. 3b). The results of EDX analysis presented in Table 6 show that the microstructure is composed by an FCC matrix rich in Fe and Cr while the BCC/B2 phase is rich with Al and Ni. The microstructure analysis suggests that a solid-state transformation corresponding to the spinodal decomposition of BCC phase into an ordered (B2) and disordered phases (BCC) occurs. The semi-quantitative
<figure><img src="image_348.png" /><figcaption>Fig. 2. (a) DTA cooling combined plots for alloys AlCrFeMnNi, AlCrFe₂MnNi₂ and AlCrFe₂Ni₂, (b) optical micrograph of alloy AlCrFeMnNi, (c) scanning electron micrograph of alloy AlCrFeMnNi.</figcaption></figure>

<table><caption>Table 5. EDX semi-quantitative analysis of AlCrFeMnNi.</caption><thead><tr><th>% at.</th><th>Al</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th></tr></thead><tbody><tr><td>Precipitates</td><td>29.4 ± 4.0</td><td>6.5 ± 3.0</td><td>9.0 ± 4.3</td><td>16.0 ± 1.5</td><td>39.1 ± 7.9</td></tr><tr><td>Matrix</td><td>7.7 ± 1.1</td><td>34.6 ± 1.8</td><td>30.0 ± 0.9</td><td>24.7 ± 0.5</td><td>3.1 ± 1.6</td></tr></tbody></table>

With increased amounts of Fe and Ni, the AlCrFe₂MnNi₂ sample presents a lower solidification temperature in a range between 1280°C and 1190°C (Fig. 2a). At 1210°C, the remaining liquid, which reaches the eutectic composition went through the eutectic transformation. Two different areas can be observed in the microstructure and the typical eutectic lamellar structure is present in some areas of the microstructure (Fig. 3a) [24]. This is consistent with the peaks revealed by the DTA cooling curve (Fig. 2a). Observing the SEM micrographs in Fig. 3a, the presence of the dual-phase microstructure confirms the prediction by the VEC theory for a value of 7.43 (Table 2). Compared to the equimolar AlCrFeMnNi sample, no dendritic and interdendritic regions were observed (Fig. 3a). EBSD analysis shows the presence of FCC and BCC/B2 phases (Fig. 3b). The results of EDX analysis presented in Table 6 show that the microstructure is composed by an FCC matrix rich in Fe and Cr while the BCC/B2 phase is rich with Al and Ni. The microstructure analysis suggests that a solid-state transformation corresponding to the spinodal decomposition of BCC phase into an ordered (B2) and disordered phases (BCC) occurs. The semi-quantitative
chemical analysis using EDX reveals that the elemental distribution varies between the two phases. The B2 phase is depleted in Cr and Fe compared to the BCC phase. On the other hand, Mn is nearly uniformly distributed in all the three phases present into the microstructure (Table 6).

<figure><img src="image_343.png" /><figcaption>Fig. 3. (a) scanning electron micrographs of AlCrFe₂MnNi₂, (b) EBSD analysis of AlCrFe₂MnNi₂: pattern quality at left, and phase map at right.</figcaption></figure>

<table><caption>Table 6. EDX semi-quantitative analysis of AlCrFe₂MnNi₂.</caption><thead><tr><th>% at.</th><th>Al</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th></tr></thead><tbody><tr><td>FCC</td><td>4.7 ± 0.4</td><td>18.6 ± 0.9</td><td>39.6 ± 0.6</td><td>14.2 ± 0.3</td><td>22.9 ± 0.8</td></tr><tr><td>B2</td><td>26.0 ± 0.6</td><td>2.1 ± 0.2</td><td>7.9 ± 0.4</td><td>15.9 ± 0.5</td><td>48.1 ± 0.5</td></tr><tr><td>BCC</td><td>20.3 ± 5.6</td><td>10.5 ± 7.8</td><td>16.8 ± 9.0</td><td>14.0 ± 1.5</td><td>38.4 ± 9.8</td></tr></tbody></table>

<figure><img src="image_344.png" /><figcaption>Fig. 4. (a) scanning electron micrographs of AlCrFe₂Ni₂, (b) EBSD analysis of AlCrFe₂Ni₂: pattern quality at left, and phase map at right.</figcaption></figure>

When the Mn is removed from the mixture, in AlCrFe₂Ni₂, the solidification temperature is increased up to a range between 1340°C and 1260°C (Fig. 2a). The solidification starts with the formation of the FCC phase. Around 1285°C, the remaining liquid with the eutectic composition forms the typical lamellar structure that characterize the eutectic transformation of some HEAs (Fig. 4a) [24]. The EBSD analysis revealed the presence of a dual-phase mixture composed of FCC and BCC/B2 phases (Fig. 4b). The same partitioning of elements as in AlCrFe₂MnNi₂ phases were found, the FCC phase is rich in Fe and Cr while BCC/B2 phase is rich in Al and Ni.
chemical analysis using EDX reveals that the elemental distribution varies between the two phases. The B2 phase is depleted in Cr and Fe compared to the BCC phase. On the other hand, Mn is nearly uniformly distributed in all the three phases present into the microstructure (Table 6).

<figure><img src="image_349.png" /><figcaption>Fig. 3. (a) scanning electron micrographs of AlCrFe₂MnNi₂, (b) EBSD analysis of AlCrFe₂MnNi₂: pattern quality at left, and phase map at right.</figcaption></figure>

<table><caption>Table 6. EDX semi-quantitative analysis of AlCrFe₂MnNi₂.</caption><thead><tr><th>% at.</th><th>Al</th><th>Cr</th><th>Fe</th><th>Mn</th><th>Ni</th></tr></thead><tbody><tr><td>FCC</td><td>4.7 ± 0.4</td><td>18.6 ± 0.9</td><td>39.6 ± 0.6</td><td>14.2 ± 0.3</td><td>22.9 ± 0.8</td></tr><tr><td>B2</td><td>26.0 ± 0.6</td><td>2.1 ± 0.2</td><td>7.9 ± 0.4</td><td>15.9 ± 0.5</td><td>48.1 ± 0.5</td></tr><tr><td>BCC</td><td>20.3 ± 5.6</td><td>10.5 ± 7.8</td><td>16.8 ± 9.0</td><td>14.0 ± 1.5</td><td>38.4 ± 9.8</td></tr></tbody></table>

<figure><img src="image_350.png" /><figcaption>Fig. 4. (a) scanning electron micrographs of AlCrFe₂Ni₂, (b) EBSD analysis of AlCrFe₂Ni₂: pattern quality at left, and phase map at right.</figcaption></figure>

When the Mn is removed from the mixture, in AlCrFe₂Ni₂, the solidification temperature is increased up to a range between 1340°C and 1260°C (Fig. 2a). The solidification starts with the formation of the FCC phase. Around 1285°C, the remaining liquid with the eutectic composition forms the typical lamellar structure that characterize the eutectic transformation of some HEAs (Fig. 4a) [24]. The EBSD analysis revealed the presence of a dual-phase mixture composed of FCC and BCC/B2 phases (Fig. 4b). The same partitioning of elements as in AlCrFe₂MnNi₂ phases were found, the FCC phase is rich in Fe and Cr while BCC/B2 phase is rich in Al and Ni.
Discussion

As mentioned in the introduction, the properties of HEAs depends on the microstructure [10,11]. The VEC, that represents an important parameter for phase structures, is directly correlated to the FCC/BCC volume fraction. It is known that for high VEC, the atomic bonding forces are higher, so the atoms tend to rearrange into the FCC structure with a higher packing density, as observed in CrFe₂MnNi₂ sample. On the contrary, low VEC leads to lower atomic bonding forces and atoms tend to arrange into the BCC structure with lower atomic packing density [13].

In CrFeMnNi and Cr₂Fe₂MnNi samples, the microstructures present a dual-phase of FCC and BCC (Fig. 1b), which is consistent with the prediction of a dual-phase microstructure for VEC in a range between 6.8 and 8 (Table 1). The same is true for AlCrFe₂MnNi₂ and AlCrFe₂Ni₂ samples just after the end of the solidification (Table 2). The prediction theory is correctly validated. This result is significant for the field of alloy design, as it provides a foundational basis for leveraging the theory to systematically optimize microstructures according to the desired material properties.

After the solidification, at lower temperature, a solid-state transformation can occur. In fact, when Al acting as a BCC stabilizer, is added to form an equimolar AlCrFeMnNi, the microstructure should consist of a single BCC phase according to the parametric prediction theory. This theory is validated for the solidification, when solid-state transformations are not considered, because in this alloy the formation of B2 precipitates, as shown in Fig. 2c, was favored. According to the results obtained for equimolar AlCrFeMnNi in several papers [22,23], the first solid to form is BCC in both dendritic and interdendritic regions, and decreasing the temperature, the spinodal decomposition occurs. In this specific case, the spinodal decomposition is different in both regions due to the different average composition of saturated BCC phase in the two regions [22]. The solid-state transformations that occur during the cooling are not considered by the VEC theory. But it still represents a useful tool for the explanation of the HEAs microstructures, especially to predict the phases that form during the solidification. Recent works, based on the optimization of the VEC parameters, employing machine learning were able to predict a BCC+FCC+IM structure of the equimolar composition AlCrFeMnNi [25].

It is important to notice that, in near-equilibrium solidification, elements such as Mn do not actively contribute to the phase separation by preferential partitioning in AlCrFeMnNi-based HEAs. In order to design an alloy with high corrosion resistance, the distribution of the elements in both FCC and BCC phase should be taken into account. Al and Ni tend to be highly present in BCC/B2 phase while Fe and Cr are mainly present in FCC phase (Table 5 and Table 6). Knowing that the FCC phase is the preferred one for the aim of enhanced corrosion resistance, the addition of high BCC stabilizer such as Al should be carefully monitored, also due to the tendency of BCC phase containing Al to undergo further solid-state transformations and form precipitates which would reduce the corrosion resistance.

As the material response is in close relationship to the microstructure, the use of DTA to investigate and to understand the phase transformations can be a useful tool for the selection of elements and range of composition to develop an alloy that fulfill the requirements. In the case of HEAs, where the research is wide opened, DTA represents a fast experimental and cost-effective method to develop an alloy with corrosion resistance. Understanding phenomena that occur under near-equilibrium conditions, such as the significant partitioning of elements, is crucial when examining the microstructure that may arise, for instance, in the Heat Affected Zone (HAZ) of a sample produced through LPBF. Due to the high energy of the laser during the short interaction times and the high solidification rates of the melt pool, the resulting microstructure may also be strongly textured. In order to control the microstructure or to propose heat treatments to restore more homogeneous and stable microstructures, investigating the phase transformations that occur during the heating of the LPBF-printed samples through a reverse DTA is a valid option [20,21]. In addition, using the reverse DTA, the solidification path in out-of-equilibrium conditions offer
Discussion

As mentioned in the introduction, the properties of HEAs depends on the microstructure [10,11]. The VEC, that represents an important parameter for phase structures, is directly correlated to the FCC/BCC volume fraction. It is known that for high VEC, the atomic bonding forces are higher, so the atoms tend to rearrange into the FCC structure with a higher packing density, as observed in CrFe₂MnNi₂ sample. On the contrary, low VEC leads to lower atomic bonding forces and atoms tend to arrange into the BCC structure with lower atomic packing density [13].

In CrFeMnNi and Cr₂Fe₂MnNi samples, the microstructures present a dual-phase of FCC and BCC (Fig. 1b), which is consistent with the prediction of a dual-phase microstructure for VEC in a range between 6.8 and 8 (Table 1). The same is true for AlCrFe₂MnNi₂ and AlCrFe₂Ni₂ samples just after the end of the solidification (Table 2). The prediction theory is correctly validated. This result is significant for the field of alloy design, as it provides a foundational basis for leveraging the theory to systematically optimize microstructures according to the desired material properties.

After the solidification, at lower temperature, a solid-state transformation can occur. In fact, when Al acting as a BCC stabilizer, is added to form an equimolar AlCrFeMnNi, the microstructure should consist of a single BCC phase according to the parametric prediction theory. This theory is validated for the solidification, when solid-state transformations are not considered, because in this alloy the formation of B2 precipitates, as shown in Fig. 2c, was favored. According to the results obtained for equimolar AlCrFeMnNi in several papers [22,23], the first solid to form is BCC in both dendritic and interdendritic regions, and decreasing the temperature, the spinodal decomposition occurs. In this specific case, the spinodal decomposition is different in both regions due to the different average composition of saturated BCC phase in the two regions [22]. The solid-state transformations that occur during the cooling are not considered by the VEC theory. But it still represents a useful tool for the explanation of the HEAs microstructures, especially to predict the phases that form during the solidification. Recent works, based on the optimization of the VEC parameters, employing machine learning were able to predict a BCC+FCC+IM structure of the equimolar composition AlCrFeMnNi [25].

It is important to notice that, in near-equilibrium solidification, elements such as Mn do not actively contribute to the phase separation by preferential partitioning in AlCrFeMnNi-based HEAs. In order to design an alloy with high corrosion resistance, the distribution of the elements in both FCC and BCC phase should be taken into account. Al and Ni tend to be highly present in BCC/B2 phase while Fe and Cr are mainly present in FCC phase (Table 5 and Table 6). Knowing that the FCC phase is the preferred one for the aim of enhanced corrosion resistance, the addition of high BCC stabilizer such as Al should be carefully monitored, also due to the tendency of BCC phase containing Al to undergo further solid-state transformations and form precipitates which would reduce the corrosion resistance.

As the material response is in close relationship to the microstructure, the use of DTA to investigate and to understand the phase transformations can be a useful tool for the selection of elements and range of composition to develop an alloy that fulfill the requirements. In the case of HEAs, where the research is wide opened, DTA represents a fast experimental and cost-effective method to develop an alloy with corrosion resistance. Understanding phenomena that occur under near-equilibrium conditions, such as the significant partitioning of elements, is crucial when examining the microstructure that may arise, for instance, in the Heat Affected Zone (HAZ) of a sample produced through LPBF. Due to the high energy of the laser during the short interaction times and the high solidification rates of the melt pool, the resulting microstructure may also be strongly textured. In order to control the microstructure or to propose heat treatments to restore more homogeneous and stable microstructures, investigating the phase transformations that occur during the heating of the LPBF-printed samples through a reverse DTA is a valid option [20,21]. In addition, using the reverse DTA, the solidification path in out-of-equilibrium conditions offer
by LPBF can be fully understood. Those results are fundamental to determine the heat treatments temperatures to reach the microstructure that would fulfill the requirements.

As the next step of this work, LPBF samples will be printed with optimized parameters (laser power and scanning speed). The evolution of the microstructure from near-equilibrium to the out-of-equilibrium conditions brought by the ultra-fast cooling in LPBF, will be investigated.

## Summary

The VEC theory is a useful tool to predict the first phases that form during the solidification in HEAs as either FCC, BCC, or mixture of both phases, but does not consider solid-state transformations. In particular, in Cr, Fe, Mn, Ni system, the addition of Al promotes the formation of BCC phase followed by its spinodal decomposition. In AlCrFeMnNi-based HEAs, FCC phase is rich in Fe and Cr while BCC phase is Al-Ni rich, and Mn is equally distributed in all phases. In absence of Al, the Mn tend to partition mainly in FCC phase as observed in CrFeMnNi and Cr₂Fe₂MnNi.

DTA analysis represents a rapid and cost-effective test to investigate transformations that occur during heating and cooling in near-equilibrium conditions. These results can then be used as reference to understand the mechanisms and phenomena that occur in out-of-equilibrium conditions and to guide the design of new alloys and/or thermal treatments.

## References

[1] I. Ribeiro, F. Matos, C. Jacinto, H. Salman, G. Cardeal, H. Carvalho, R. Godina, P. Peças, Framework for life cycle sustainability assessment of additive manufacturing, Sustainability, 12 (2020) 929. https://doi.org/10.3390/su12030929

[2] T.T. Wohlers, Wohlers report 2010: additive manufacturing state of the industry annual worldwide progress report, Wohlers Associates, 2010.

[3] F. Bartolomeu, M. Gasik, F.S. Silva, G. Miranda, Mechanical Properties of Ti6Al4V Fabricated by Laser Powder Bed Fusion: A Review Focused on the Processing and Microstructural Parameters Influence on the Final Properties, Metals 12 (2022) 986. https://doi.org/10.3390/met12060986

[4] M.N. Dogu, E. McCarthy, R. McCann, V. Mahato, A. Caputo, I.U. Ahad, D. Brabazon, Digitisation of metal AM for part microstructure and property control, Int. J. Mater. Form. 15, 30 (2022). https://doi.org/10.1007/s12289-022-01686-4

[5] W.E. King, A.T. Anderson, R.M. Ferencz, N.E. Hodge, C. Kamath, S.A. Khairallah, A.M. Rubenchik, Laser powder bed fusion additive manufacturing of metals: physics, computational, and materials challenges, Appl. Phys. Rev. 2. (2015). https://doi.org/10.1063/1.4937809

[6] S. Chowdhury, N. Yadaiah, C. Prakash, S. Ramakrishna, S. Dixit, L.R. Gupta, D. Buddhi, Laser powder bed fusion: a state-of-the-art review of the technology, materials, properties & defects, and numerical modelling, J. Mater. Res. Technol. 20 (2022) 2109-2172

[7] R. Pal, A. Basak, Linking powder properties, printing parameters, post-processing methods, and fatigue properties in additive manufacturing of AlSi10Mg, Alloys 1 (2022) 149-179.

[8] J.W. Yeh, S.K. Chen, S.J. Lin, J.Y. Gan, T.S. Chin, T.T. Shun, S.H. Tsau, S.Y. Chang, Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes, Adv. Eng. Mater. 6 (2004) 299-303. https://doi.org/10.1002/adem.200300567

[9] B. Cantor, I.T.H. Chang, P. Knight, A.J.B. Vincent, Microstructural development in equiatomic multicomponent alloys, Mater. Sci. Eng. 375-377 (2004) 213-218.

[10] Y. Zhang, T.T. Zuo, Z. Tang, M.C. Gao, K.A. Dahmen, P.K. Liaw, Z.P. Lu, Microstructures and properties of high-entropy alloys, Prog. Mater Sci. 61 (2014) 1-93.
by LPBF can be fully understood. Those results are fundamental to determine the heat treatments temperatures to reach the microstructure that would fulfill the requirements.

As the next step of this work, LPBF samples will be printed with optimized parameters (laser power and scanning speed). The evolution of the microstructure from near-equilibrium to the out-of-equilibrium conditions brought by the ultra-fast cooling in LPBF, will be investigated.

## Summary

The VEC theory is a useful tool to predict the first phases that form during the solidification in HEAs as either FCC, BCC, or mixture of both phases, but does not consider solid-state transformations. In particular, in Cr, Fe, Mn, Ni system, the addition of Al promotes the formation of BCC phase followed by its spinodal decomposition. In AlCrFeMnNi-based HEAs, FCC phase is rich in Fe and Cr while BCC phase is Al-Ni rich, and Mn is equally distributed in all phases. In absence of Al, the Mn tend to partition mainly in FCC phase as observed in CrFeMnNi and Cr₂Fe₂MnNi.

DTA analysis represents a rapid and cost-effective test to investigate transformations that occur during heating and cooling in near-equilibrium conditions. These results can then be used as reference to understand the mechanisms and phenomena that occur in out-of-equilibrium conditions and to guide the design of new alloys and/or thermal treatments.

## References

[1] I. Ribeiro, F. Matos, C. Jacinto, H. Salman, G. Cardeal, H. Carvalho, R. Godina, P. Peças, Framework for life cycle sustainability assessment of additive manufacturing, Sustainability, 12 (2020) 929. https://doi.org/10.3390/su12030929

[2] T.T. Wohlers, Wohlers report 2010: additive manufacturing state of the industry annual worldwide progress report, Wohlers Associates, 2010.

[3] F. Bartolomeu, M. Gasik, F.S. Silva, G. Miranda, Mechanical Properties of Ti6Al4V Fabricated by Laser Powder Bed Fusion: A Review Focused on the Processing and Microstructural Parameters Influence on the Final Properties, Metals 12 (2022) 986. https://doi.org/10.3390/met12060986

[4] M.N. Dogu, E. McCarthy, R. McCann, V. Mahato, A. Caputo, I.U. Ahad, D. Brabazon, Digitisation of metal AM for part microstructure and property control, Int. J. Mater. Form. 15, 30 (2022). https://doi.org/10.1007/s12289-022-01686-4

[5] W.E. King, A.T. Anderson, R.M. Ferencz, N.E. Hodge, C. Kamath, S.A. Khairallah, A.M. Rubenchik, Laser powder bed fusion additive manufacturing of metals: physics, computational, and materials challenges, Appl. Phys. Rev. 2. (2015). https://doi.org/10.1063/1.4937809

[6] S. Chowdhury, N. Yadaiah, C. Prakash, S. Ramakrishna, S. Dixit, L.R. Gupta, D. Buddhi, Laser powder bed fusion: a state-of-the-art review of the technology, materials, properties & defects, and numerical modelling, J. Mater. Res. Technol. 20 (2022) 2109-2172

[7] R. Pal, A. Basak, Linking powder properties, printing parameters, post-processing methods, and fatigue properties in additive manufacturing of AlSi10Mg, Alloys 1 (2022) 149-179.

[8] J.W. Yeh, S.K. Chen, S.J. Lin, J.Y. Gan, T.S. Chin, T.T. Shun, S.H. Tsau, S.Y. Chang, Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes, Adv. Eng. Mater. 6 (2004) 299-303. https://doi.org/10.1002/adem.200300567

[9] B. Cantor, I.T.H. Chang, P. Knight, A.J.B. Vincent, Microstructural development in equiatomic multicomponent alloys, Mater. Sci. Eng. 375-377 (2004) 213-218.

[10] Y. Zhang, T.T. Zuo, Z. Tang, M.C. Gao, K.A. Dahmen, P.K. Liaw, Z.P. Lu, Microstructures and properties of high-entropy alloys, Prog. Mater Sci. 61 (2014) 1-93.
[11] S. Guo, C. Ng, J. Lu, C.T. Liu, Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys, J Appl Phys. 109. (2011).

[12] D.B. Miracle, O.N. Senkov, A critical review of high entropy alloys and related concepts, Acta Mater. 122 (2017) 448–511. https://doi.org/10.1016/j.actamat.2016.08.081

[13] W. Zhang, A. Chabok, B.J. Kooi, Y. Pei, Y., Additive manufactured high entropy alloys: A review of the microstructure and properties, Mater. Des., 220 (2022) 110875. https://doi.org/10.1016/j.matdes.2022.110875

[14] X. Yang, Y. Zhang, Prediction of high-entropy stabilized solid-solution in multi-component alloys, Mater. Chem. Phys. 132 (2012) 233–238.

[15] B.S. Li, Y.P. Wang, M.X. Ren, C. Yang, H.Z. Fu, Effects of Mn, Ti and V on the microstructure and properties of AlCrFeCoNiCu high entropy alloy, Mater. Sci. Eng. 498 (2008) 482-486. https://doi.org/10.1016/j.msea.2008.08.025

[16] A. Inoue, B. Shen, A. Takeuchi, Developments and applications of bulk glassy alloys in late transition metal base system, Mater. Trans. 47 (2006) 1275-1285.

[17] Y. Shi, B. Yang, P.K. Liaw, Corrosion-resistant high-entropy alloys: A review, Metals 7 (2017). https://doi.org/10.3390/met7020043.

[18] J. Tamminen, Thermal analysis for investigation of solidification mechanisms in metals and alloys, Dept. of Structural Chemistry, University of Stockholm, Sweden. (1988).

[19] D.M. Stefanescu, Thermal analysis-theory and applications in metalcasting, Int. J. Met. 9. (2015). https://doi.org/10.1007/BF03355598

[20] T. Maurizi Enrici, O. Dedry, F. Boschini, J.T. Tchuindjang, A. Mertens, Microstructural and Thermal Characterization of 316L + WC Composite Coatings Obtained by Laser Cladding, Adv. Eng. Mater. 22. (2020). https://doi.org/10.1002/adem.202000291

[21] T. Maurizi Enrici, A. Mertens, M. Sinnaeve, J.T. Tchuindjang, Elucidation of the solidification sequence of a complex graphitic HSS alloy under a combined approach of DTA and EBSD analyses, J. Therm. Anal. Calorim. 141 (2020) 1075–1089. https://doi.org/10.1007/s10973-019-09093-9

[22] A. Munitz, L. Meshi, M.J. Kaufman, Heat treatments' effects on the microstructure and mechanical properties of an equiatomic Al-Cr-Fe-Mn-Ni high entropy alloy, Mater. Sci. Eng. 689 (2017) 384–394. https://doi.org/10.1016/j.msea.2017.02.072

[23] E. Ananiadis, K. Lentzaris, E. Georgatis, C. Mathiou, A. Poulia, A.E. Karantzalis, AlNiCrFeMn equiatomic high entropy alloy: a further insight in its microstructural evolution, mechanical and surface degradation response, Met. Mater. Int. 26 (2020) 793–811.

[24] Y. Lu, Y. Dong, S. Guo, L. Jiang, H. Kang, T. Wang, B. Wen, Z. Wang, J. Jie, Z. Cao, H. Ruan, T. Li, A promising new class of high-temperature alloys: Eutectic high-entropy alloys, Sci. Rep. 4. (2014). https://doi.org/10.1038/srep06200

[25] A. Oñate, J.P. Sanhueza, D. Zegpi, V. Tuninetti, J. Ramirez, C. Medina, M. Melendrez, D. Rojas, Supervised machine learning-based multi-class phase prediction in high-entropy alloys using robust databases, J. Alloys Compd. 962 (2023) 171224. https://doi.org/10.1016/j.jallcom.2023.171224
[11] S. Guo, C. Ng, J. Lu, C.T. Liu, Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys, J Appl Phys. 109. (2011).

[12] D.B. Miracle, O.N. Senkov, A critical review of high entropy alloys and related concepts, Acta Mater. 122 (2017) 448–511. https://doi.org/10.1016/j.actamat.2016.08.081

[13] W. Zhang, A. Chabok, B.J. Kooi, Y. Pei, Y., Additive manufactured high entropy alloys: A review of the microstructure and properties, Mater. Des., 220 (2022) 110875. https://doi.org/10.1016/j.matdes.2022.110875

[14] X. Yang, Y. Zhang, Prediction of high-entropy stabilized solid-solution in multi-component alloys, Mater. Chem. Phys. 132 (2012) 233–238.

[15] B.S. Li, Y.P. Wang, M.X. Ren, C. Yang, H.Z. Fu, Effects of Mn, Ti and V on the microstructure and properties of AlCrFeCoNiCu high entropy alloy, Mater. Sci. Eng. 498 (2008) 482-486. https://doi.org/10.1016/j.msea.2008.08.025

[16] A. Inoue, B. Shen, A. Takeuchi, Developments and applications of bulk glassy alloys in late transition metal base system, Mater. Trans. 47 (2006) 1275-1285.

[17] Y. Shi, B. Yang, P.K. Liaw, Corrosion-resistant high-entropy alloys: A review, Metals 7 (2017). https://doi.org/10.3390/met7020043.

[18] J. Tamminen, Thermal analysis for investigation of solidification mechanisms in metals and alloys, Dept. of Structural Chemistry, University of Stockholm, Sweden. (1988).

[19] D.M. Stefanescu, Thermal analysis-theory and applications in metalcasting, Int. J. Met. 9. (2015). https://doi.org/10.1007/BF03355598

[20] T. Maurizi Enrici, O. Dedry, F. Boschini, J.T. Tchuindjang, A. Mertens, Microstructural and Thermal Characterization of 316L + WC Composite Coatings Obtained by Laser Cladding, Adv. Eng. Mater. 22. (2020). https://doi.org/10.1002/adem.202000291

[21] T. Maurizi Enrici, A. Mertens, M. Sinnaeve, J.T. Tchuindjang, Elucidation of the solidification sequence of a complex graphitic HSS alloy under a combined approach of DTA and EBSD analyses, J. Therm. Anal. Calorim. 141 (2020) 1075–1089. https://doi.org/10.1007/s10973-019-09093-9

[22] A. Munitz, L. Meshi, M.J. Kaufman, Heat treatments' effects on the microstructure and mechanical properties of an equiatomic Al-Cr-Fe-Mn-Ni high entropy alloy, Mater. Sci. Eng. 689 (2017) 384–394. https://doi.org/10.1016/j.msea.2017.02.072

[23] E. Ananiadis, K. Lentzaris, E. Georgatis, C. Mathiou, A. Poulia, A.E. Karantzalis, AlNiCrFeMn equiatomic high entropy alloy: a further insight in its microstructural evolution, mechanical and surface degradation response, Met. Mater. Int. 26 (2020) 793–811.

[24] Y. Lu, Y. Dong, S. Guo, L. Jiang, H. Kang, T. Wang, B. Wen, Z. Wang, J. Jie, Z. Cao, H. Ruan, T. Li, A promising new class of high-temperature alloys: Eutectic high-entropy alloys, Sci. Rep. 4. (2014). https://doi.org/10.1038/srep06200

[25] A. Oñate, J.P. Sanhueza, D. Zegpi, V. Tuninetti, J. Ramirez, C. Medina, M. Melendrez, D. Rojas, Supervised machine learning-based multi-class phase prediction in high-entropy alloys using robust databases, J. Alloys Compd. 962 (2023) 171224. https://doi.org/10.1016/j.jallcom.2023.171224
# Local reinforcement of titanium sheet by means of GTAW droplet deposition for threaded connections

VANHOVE Hans<fnref n="1" />,ᵃ*, KURTOV Oleksandr<fnref n="2" />,ᵇ, DEJANS Arnout<fnref n="1" />,ᶜ, OZDEN Ecem<fnref n="1" />,ᵈ and DUFLOU Joost R.<fnref n="1" />,ᵉ

**Keywords:** Titanium, Droplet Deposition, Joining

**Abstract.** Making threaded connections to thin metal sheets requires locally thickening of the sheet in order to provide enough thread length for a structurally sound connection. Shaped Metal Deposition processes like Gas Tungsten Arc Welding (GTAW) allow to locally build-up material in order to provide thickness for a sufficient length of thread engagement. This publication describes the research towards local thickening of a titanium sheet by means of pulsed Tungsten Inert Gas (TIG) droplet deposition, aimed at creating threaded holes for thin shelled bone fracture fixation plates. The influence of current, weld time and amount of filler material on droplet diameter and height is studied.

**Introduction**

The introduction of Incremental Sheet Forming processes allows for a cost-effective access to customized sheet metal products. Although these processes excel at creating a wide variety of shell geometries without the need for extensive machine or tooling requirements, they are not able to provide local thickening of the sheet for structural purposes. Merklein et. al.[1] provided a useful overview of ways to obtain thickness variations of the blank prior to forming through the use of tailored welded or rolled blanks or patchwork blanks (by attaching a secondary sheet layer). The succeeding forming step however often induces excessive stressing of the interface between patch and sheet or at the circumference of the thickened zone leading to delamination or fracture. Ambrogio et. al. [2] demonstrated the potential of using selective laser sintering prior to incremental forming for increasing the dimensional accuracy, thickness distribution and achievable shape complexity of a stainless steel part. Bambach et al. [3] have discussed the use of Laser Metal Deposition (LMD) on a pre-formed cup made of EN AW 6082, to increase its stiffness with only limited weight increase. LMD allows for a wide geometry variation both of the substrate and added volume. A review on hybrid process chains, combining sheet forming and additive manufacturing (AM) was published by Hafenecker et.al. [4]. They acknowledge the potential for stiffness control and the possible synergy of combining AM prior to forming and vice versa. However they also identified the lack of knowledge on how material flow is altered by the additive structures, the changes in material properties as a function of the selected process sequence and thermal distortions of formed parts and changing residual stresses due to AM processes.

The requirements for thickening of metal sheets for threaded connections are less complex. In contrast to cladding entire areas or building up stiffening ribs, threaded connections often only need a very limited area of increased thickness around the hole and are typically performed after the forming operation of the part.

<figure><img src="image_345.png" /></figure>

<footnote n="1">¹Department of Mechanical Engineering, Katholieke Universiteit Leuven / Flanders Make, Celestijnenlaan 300B, B-3001 Leuven, Belgium</footnote>

<footnote n="2">²Katholieke Universiteit Leuven, Jan Pieter De Nayerlaan 5, B-2860 Sint-Katlijne-Waver, Belgium</footnote>

<footnote>ᵃhans.vanhove@kuleuven.be, ᵇoleksandr.kurtov@kuleuven.be, ᶜArnout.Dejans@kuleuven.be</footnote>

<footnote>ᵈEcem.ozden@kuleuven.be, ᵉJoost.duflou@kuleuven.be</footnote>
# Local reinforcement of titanium sheet by means of GTAW droplet deposition for threaded connections

VANHOVE Hans<fnref n="1" />,ª*, KURTOV Oleksandr<fnref n="2" />,ᵇ, DEJANS Arnout<fnref n="1" />,ᶜ, OZDEN Ecem<fnref n="1" />,ᵈ and DUFLOU Joost R.<fnref n="1" />,ᵉ

**Keywords:** Titanium, Droplet Deposition, Joining

**Abstract.** Making threaded connections to thin metal sheets requires locally thickening of the sheet in order to provide enough thread length for a structurally sound connection. Shaped Metal Deposition processes like Gas Tungsten Arc Welding (GTAW) allow to locally build-up material in order to provide thickness for a sufficient length of thread engagement. This publication describes the research towards local thickening of a titanium sheet by means of pulsed Tungsten Inert Gas (TIG) droplet deposition, aimed at creating threaded holes for thin shelled bone fracture fixation plates. The influence of current, weld time and amount of filler material on droplet diameter and height is studied.

**Introduction**

The introduction of Incremental Sheet Forming processes allows for a cost-effective access to customized sheet metal products. Although these processes excel at creating a wide variety of shell geometries without the need for extensive machine or tooling requirements, they are not able to provide local thickening of the sheet for structural purposes. Merklein et. al.[1] provided a useful overview of ways to obtain thickness variations of the blank prior to forming through the use of tailored welded or rolled blanks or patchwork blanks (by attaching a secondary sheet layer). The succeeding forming step however often induces excessive stressing of the interface between patch and sheet or at the circumference of the thickened zone leading to delamination or fracture. Ambrogio et. al. [2] demonstrated the potential of using selective laser sintering prior to incremental forming for increasing the dimensional accuracy, thickness distribution and achievable shape complexity of a stainless steel part. Bambach et al. [3] have discussed the use of Laser Metal Deposition (LMD) on a pre-formed cup made of EN AW 6082, to increase its stiffness with only limited weight increase. LMD allows for a wide geometry variation both of the substrate and added volume. A review on hybrid process chains, combining sheet forming and additive manufacturing (AM) was published by Hafenecker et.al. [4]. They acknowledge the potential for stiffness control and the possible synergy of combining AM prior to forming and vice versa. However they also identified the lack of knowledge on how material flow is altered by the additive structures, the changes in material properties as a function of the selected process sequence and thermal distortions of formed parts and changing residual stresses due to AM processes.

The requirements for thickening of metal sheets for threaded connections are less complex. In contrast to cladding entire areas or building up stiffening ribs, threaded connections often only need a very limited area of increased thickness around the hole and are typically performed after the forming operation of the part.

<figure><img src="image_351.png" /></figure>

<footnote n="1">¹Department of Mechanical Engineering, Katholieke Universiteit Leuven / Flanders Make, Celestijnenlaan 300B, B-3001 Leuven, Belgium</footnote>

<footnote n="2">²Katholieke Universiteit Leuven, Jan Pieter De Nayerlaan 5, B-2860 Sint-Katlijne-Waver, Belgium</footnote>

<footnote>ªhans.vanhove@kuleuven.be, ᵇoleksandr.kurtov@kuleuven.be, ᶜArnout.Dejans@kuleuven.be</footnote>

<footnote>ᵈEcem.ozden@kuleuven.be, ᶜJoost.duflou@kuleuven.be</footnote>
The application envisaged in this article consists of threaded connections to accommodate osteosynthesis locking screws in thin walled medical fracture fixation plates [5]. These plates, with a thickness of 0.5-1.5mm, especially require local bulking of the sheet in order to provide enough thread length for the head of the locking screw [6] Fig 1(a). Popular means of providing thread length are attaching a pre-made threaded fastener, such as a rivet nut, clinch nut, weld nut or cage nut [7]. These processes, however, require a foreign body to be connected to the sheet and are thus less suitable for medical applications. Friction drilling and tapping of the created bushing has been studied as a process to create threaded holes without addition of external material, but it is challenging to perform in titanium due to its low thermal conductivity [6, 7] Fig1(b).

<figure><img src="image_346.png" /><figcaption>Figure 1. Medical implant fixation through locking screws (based on) [8]: (a) implant with insufficient thread length, (b) thickening through friction drilling, (c) thickening through droplet deposition</figcaption></figure>

The use of additive manufacturing techniques to deposit single droplets on pre-formed titanium implants, enables sufficient adhesion and thread length whilst thermal deformation is kept to a minimum. Given the pre-shaped freeform geometry of the substrate, metal deposition techniques are advisable. This publication describes the use of Shaped Metal Deposition (SMD) techniques using Gas Tungsten Arc Welding (GTAW) with wire feedstock for droplet deposition. As one of the Wire Arc Additive Manufacturing (WAAM) techniques, it finds its roots in the 1920s patent by Baker [9]. It has the benefit of a stable arc, high deposition rates, low splashing, reduced material waste and limited cost. The main disadvantage is the low deposition resolution. [10]. The major process parameters identified to influence the deposited geometry are: volume of infeed material (diameter of wire x wire speed), welding current and welding speed [11-12]. Most studies have been performed on single or multiple beads. Analysis on the deposition in single spots is lacking.

This paper will investigate the influence of current, wire amount and welding time on the diameter and height of a single spot for GTAW additive manufacturing of Titanium Grade 2.

## Material and methods

The workpiece material selected is Titanium grade 2 with a thickness of 1mm. This grade is extensively employed in medical applications owing to its superior strength-to-weight ratio, biocompatibility, absence of magnetic attraction, and widespread accessibility.

The thermal conductivity of titanium is notably lower than that of steel, conveying both advantages and disadvantages to the welding process. The required heat for welding becomes more concentrated and dissipates less to the surrounding base material. However, the reduced conductivity results in a pronounced gradient in material properties. The low coefficient of thermal expansion in titanium reduces thermal stresses following the welding process [13].
The application envisaged in this article consists of threaded connections to accommodate osteosynthesis locking screws in thin walled medical fracture fixation plates [5]. These plates, with a thickness of 0.5-1.5mm, especially require local bulking of the sheet in order to provide enough thread length for the head of the locking screw [6] Fig 1(a). Popular means of providing thread length are attaching a pre-made threaded fastener, such as a rivet nut, clinch nut, weld nut or cage nut [7]. These processes, however, require a foreign body to be connected to the sheet and are thus less suitable for medical applications. Friction drilling and tapping of the created bushing has been studied as a process to create threaded holes without addition of external material, but it is challenging to perform in titanium due to its low thermal conductivity [6, 7] Fig1(b).

<figure><img src="image_352.png" /><figcaption>Figure 1. Medical implant fixation through locking screws (based on) [8]: (a) implant with insufficient thread length, (b) thickening through friction drilling, (c) thickening through droplet deposition</figcaption></figure>

The use of additive manufacturing techniques to deposit single droplets on pre-formed titanium implants, enables sufficient adhesion and thread length whilst thermal deformation is kept to a minimum. Given the pre-shaped freeform geometry of the substrate, metal deposition techniques are advisable. This publication describes the use of Shaped Metal Deposition (SMD) techniques using Gas Tungsten Arc Welding (GTAW) with wire feedstock for droplet deposition. As one of the Wire Arc Additive Manufacturing (WAAM) techniques, it finds its roots in the 1920s patent by Baker [9]. It has the benefit of a stable arc, high deposition rates, low splashing, reduced material waste and limited cost. The main disadvantage is the low deposition resolution. [10]. The major process parameters identified to influence the deposited geometry are: volume of infeed material (diameter of wire x wire speed), welding current and welding speed [11-12]. Most studies have been performed on single or multiple beads. Analysis on the deposition in single spots is lacking.

This paper will investigate the influence of current, wire amount and welding time on the diameter and height of a single spot for GTAW additive manufacturing of Titanium Grade 2.

## Material and methods

The workpiece material selected is Titanium grade 2 with a thickness of 1mm. This grade is extensively employed in medical applications owing to its superior strength-to-weight ratio, biocompatibility, absence of magnetic attraction, and widespread accessibility.

The thermal conductivity of titanium is notably lower than that of steel, conveying both advantages and disadvantages to the welding process. The required heat for welding becomes more concentrated and dissipates less to the surrounding base material. However, the reduced conductivity results in a pronounced gradient in material properties. The low coefficient of thermal expansion in titanium reduces thermal stresses following the welding process [13].
<table><caption>Table 1. Properties of Titanium Grade 2.</caption><tr><td>Density [Kg/m³]</td><td>4510</td></tr><tr><td>Youngs modulus [GPa]</td><td>100 - 105</td></tr><tr><td>Yield strength [MPa]</td><td>276 - 400</td></tr><tr><td>Hardness, Vickers [HV]</td><td>145 - 165</td></tr><tr><td>Thermal Conductivity [W/(mK)]</td><td>16 - 18</td></tr><tr><td>Melting Point [°C]</td><td>1670</td></tr></table>

An unalloyed filler wire material ERTI-1 of 1.6mm diameter is chosen for its ductility and lower yield strength, enabling a better forming or cutting of thread for the locking screw fixation

<table><caption>Table 2. Chemical composition of Ti2 and filler material ERTi-1</caption><thead><tr><th></th><th>wt % C</th><th>wt % N</th><th>wt % Fe</th><th>wt % O</th><th>wt % H</th></tr></thead><tbody><tr><td>Ti 2</td><td>0,08</td><td>0,03</td><td>0,030</td><td>0,025</td><td>0,015</td></tr><tr><td>ERTi-1</td><td>0,03</td><td>0,012</td><td>0,08</td><td>0,03 - 0,10</td><td>0,005</td></tr></tbody></table>

Titanium becomes highly reactive with oxygen, hydrogen, and nitrogen when exposed to temperatures above 500 °C. These elements can be absorbed, migrating to interstitial sites and causing brittleness. To prevent this embrittlement, the use of shielding and backing gas during the welding process with titanium is required. The titanium must be protected until it cools below 260°C. The weld is shielded by a 8 l/min pure argon flow supplied through the TIG torch with a mounted gas lens to ensure a laminar flow (Fig 2.).

The back of the sheet is protected with the same argon as backing gas, flowing at a rate of 11/min in a chamber at the backside of the sheet (Fig 2).

<figure><img src="image_347.png" /><figcaption>Figure 2. a) Picture of TIG welding platform and b) schematic of Tig source current profile</figcaption></figure>

A pulsed arc power supply from MacGregor was used as power source which is able to generate an arc for a time interval of 0-999ms second, preceded and followed by a respective programmable upslope (0-99ms) and downslope(0-999ms) with a resolution of 1ms. The current can be set between 0 and 99A in steps of 0.1A. The arc current has the option for a pulsed profile. (Fig 2b).

A purpose built feeder system, which is synchronized with the source, enables a controlled amount of filler material during the arc duration. The filler wire is fed into the arc through a guiding system parallel to the workpiece and retracted before the downslope is started (Fig 3.).
<table><caption>Table 1. Properties of Titanium Grade 2.</caption><tr><td>Density [Kg/m³]</td><td>4510</td></tr><tr><td>Youngs modulus [GPa]</td><td>100 - 105</td></tr><tr><td>Yield strength [MPa]</td><td>276 - 400</td></tr><tr><td>Hardness, Vickers [HV]</td><td>145 - 165</td></tr><tr><td>Thermal Conductivity [W/(mK)]</td><td>16 - 18</td></tr><tr><td>Melting Point [°C]</td><td>1670</td></tr></table>

An unalloyed filler wire material ERTI-1 of 1.6mm diameter is chosen for its ductility and lower yield strength, enabling a better forming or cutting of thread for the locking screw fixation

<table><caption>Table 2. Chemical composition of Ti2 and filler material ERTi-1</caption><thead><tr><th></th><th>wt % C</th><th>wt % N</th><th>wt % Fe</th><th>wt % O</th><th>wt % H</th></tr></thead><tbody><tr><td>Ti 2</td><td>0,08</td><td>0,03</td><td>0,030</td><td>0,025</td><td>0,015</td></tr><tr><td>ERTi-1</td><td>0,03</td><td>0,012</td><td>0,08</td><td>0,03 - 0,10</td><td>0,005</td></tr></tbody></table>

Titanium becomes highly reactive with oxygen, hydrogen, and nitrogen when exposed to temperatures above 500 °C. These elements can be absorbed, migrating to interstitial sites and causing brittleness. To prevent this embrittlement, the use of shielding and backing gas during the welding process with titanium is required. The titanium must be protected until it cools below 260°C. The weld is shielded by a 8 l/min pure argon flow supplied through the TIG torch with a mounted gas lens to ensure a laminar flow (Fig 2.).

The back of the sheet is protected with the same argon as backing gas, flowing at a rate of 11/min in a chamber at the backside of the sheet (Fig 2).

<figure><img src="image_353.png" /><figcaption>Figure 2. a) Picture of TIG welding platform and b) schematic of Tig source current profile</figcaption></figure>

A pulsed arc power supply from MacGregor was used as power source which is able to generate an arc for a time interval of 0-999ms second, preceded and followed by a respective programmable upslope (0-99ms) and downslope(0-999ms) with a resolution of 1ms. The current can be set between 0 and 99A in steps of 0.1A. The arc current has the option for a pulsed profile. (Fig 2b).

A purpose built feeder system, which is synchronized with the source, enables a controlled amount of filler material during the arc duration. The filler wire is fed into the arc through a guiding system parallel to the workpiece and retracted before the downslope is started (Fig 3.).
<figure><img src="image_348.png" /><figcaption>Figure 3. Design of the feeder system for filler wire</figcaption></figure>

The stand-off distance of the tungsten electrode and the workpiece was kept constant at 3mm by means of the XZ stage. The electrode was ground to a 30° tip angle.

The droplet dimensions are measured by slicing the droplet through the center using EDM and measuring the diameter by optically choosing the intersection between the blank sheet and the start of the thickened zone and the height as the length between the original blank sheet end the top of the thickened zone (Fig 4). The measurements were repeated 3 times.

<figure><img src="image_349.png" /><figcaption>Figure 4. Overview of droplet dimensions</figcaption></figure>

This study focusses on the influence of the welding current (I), welding time (t<sub>w</sub>) and length of wire fed into the arc (L<sub>w</sub>) on the height and the diameter of the droplet through a factorial analysis. Minitab 18 was used to perform the factorial analysis. The investigated variables and their ranges are presented in Table 3, accompanied by a summary of the fixed parameters. Every parameter set is executed six times in random order. Between two tests the sheets are verified to be below 30°C.

<table><caption>Table 3. Overview of process parameters</caption><thead><tr><th>Variables</th><th>low</th><th>high</th></tr></thead><tbody><tr><td>Current (I)</td><td>60 A</td><td>99 A</td></tr><tr><td>Weld time (t<sub>w</sub>)</td><td>600 ms</td><td>999 ms</td></tr><tr><td>Length of filler wire (L<sub>w</sub>)</td><td>0 mm</td><td>6 mm</td></tr><tr><td colspan="3"><strong>Fixed parameters</strong></td></tr><tr><td>Upslope time</td><td>99 ms</td><td>Argon flow rate torch</td><td>8 l/min</td></tr><tr><td>Downslope</td><td>999 ms</td><td>Argon flow rate backing</td><td>1 l/min</td></tr><tr><td>Filler wire diameter</td><td>1,6 mm</td><td>Temperature baseplate</td><td>&lt;30°C</td></tr><tr><td>Stand-off distance</td><td>3 mm</td><td>Plate thickness</td><td>1 mm</td></tr><tr><td>Base material</td><td>Ti-2</td><td>Filler material</td><td>ERTi-1</td></tr><tr><td>Electrode angle</td><td>30°</td><td></td><td></td></tr></tbody></table>

## Results and discussion

*Droplet Diameter:* The analysis results showed a significant influence of current and weld time as single factor on the droplet diameter (Fig 5). It can be seen that the diameter increase significantly
<figure><img src="image_354.png" /><figcaption>Figure 3. Design of the feeder system for filler wire</figcaption></figure>

The stand-off distance of the tungsten electrode and the workpiece was kept constant at 3mm by means of the XZ stage. The electrode was ground to a 30° tip angle.

The droplet dimensions are measured by slicing the droplet through the center using EDM and measuring the diameter by optically choosing the intersection between the blank sheet and the start of the thickened zone and the height as the length between the original blank sheet end the top of the thickened zone (Fig 4). The measurements were repeated 3 times.

<figure><img src="image_355.png" /><figcaption>Figure 4. Overview of droplet dimensions</figcaption></figure>

This study focusses on the influence of the welding current (I), welding time (t<sub>w</sub>) and length of wire fed into the arc (L<sub>w</sub>) on the height and the diameter of the droplet through a factorial analysis. Minitab 18 was used to perform the factorial analysis. The investigated variables and their ranges are presented in Table 3, accompanied by a summary of the fixed parameters. Every parameter set is executed six times in random order. Between two tests the sheets are verified to be below 30°C.

<table><caption>Table 3. Overview of process parameters</caption><thead><tr><th>Variables</th><th>low</th><th>high</th></tr></thead><tbody><tr><td>Current (I)</td><td>60 A</td><td>99 A</td></tr><tr><td>Weld time (t<sub>w</sub>)</td><td>600 ms</td><td>999 ms</td></tr><tr><td>Length of filler wire (L<sub>w</sub>)</td><td>0 mm</td><td>6 mm</td></tr><tr><td colspan="3"><strong>Fixed parameters</strong></td></tr><tr><td>Upslope time</td><td>99 ms</td><td>Argon flow rate torch</td><td>8 l/min</td></tr><tr><td>Downslope</td><td>999 ms</td><td>Argon flow rate backing</td><td>1 l/min</td></tr><tr><td>Filler wire diameter</td><td>1,6 mm</td><td>Temperature baseplate</td><td>&lt;30°C</td></tr><tr><td>Stand-off distance</td><td>3 mm</td><td>Plate thickness</td><td>1 mm</td></tr><tr><td>Base material</td><td>Ti-2</td><td>Filler material</td><td>ERTi-1</td></tr><tr><td>Electrode angle</td><td>30°</td><td></td><td></td></tr></tbody></table>

## Results and discussion

*Droplet Diameter:* The analysis results showed a significant influence of current and weld time as single factor on the droplet diameter (Fig 5). It can be seen that the diameter increase significantly
when the current or the weld time increases, with the current having the highest effect. An increase in current will provide a higher heat flux to the melt, which becomes less viscous and can thus spread to a wider diameter. A longer weld time allows the heated or molten zone to spread to a larger diameter. The wire length did not have a significant influence on the droplet diameter.

<figure><img src="image_350.png" /><figcaption>Figure 5. Minitab main effect plots for the weld diameter</figcaption></figure>

The interactions current/weld time and current/wire amount also show a significant effect on the diameter. From the current x weld time interaction it is clear that the diameter is more sensitive to the current when the weld time is short. A high weld time and high current yield the largest diameter. The current x wire length interaction is less sensitive, where no wire input shows a slightly higher effect than the 6mm input.

<figure><img src="image_351.png" /><figcaption>Figure 6. Minitab interaction plot for the diameter</figcaption></figure>

*Droplet height*: all three variables have a significant influence on the droplet height. The weld time stands out as the major contributing factor. While an increase of the weld time interval or wire length lead to a raised the droplet height, the current has the reverse effect. This can be explained by the fact that a longer weld time will allow a larger volume to melt and thus be attracted towards the center of the droplet. This also clarifies why even without wire infeed there is a significant droplet formed after welding. Furthermore, an increased wire infeed means more material to build up a higher droplet. Finally a higher current will raise the melt temperature and lower the viscosity allowing the melt to spread out more.
when the current or the weld time increases, with the current having the highest effect. An increase in current will provide a higher heat flux to the melt, which becomes less viscous and can thus spread to a wider diameter. A longer weld time allows the heated or molten zone to spread to a larger diameter. The wire length did not have a significant influence on the droplet diameter.

<figure><img src="image_356.png" /><figcaption>Figure 5. Minitab main effect plots for the weld diameter</figcaption></figure>

The interactions current/weld time and current/wire amount also show a significant effect on the diameter. From the current x weld time interaction it is clear that the diameter is more sensitive to the current when the weld time is short. A high weld time and high current yield the largest diameter. The current x wire length interaction is less sensitive, where no wire input shows a slightly higher effect than the 6mm input.

<figure><img src="image_357.png" /><figcaption>Figure 6. Minitab interaction plot for the diameter</figcaption></figure>

*Droplet height:* all three variables have a significant influence on the droplet height. The weld time stands out as the major contributing factor. While an increase of the weld time interval or wire length lead to a raised the droplet height, the current has the reverse effect. This can be explained by the fact that a longer weld time will allow a larger volume to melt and thus be attracted towards the center of the droplet. This also clarifies why even without wire infeed there is a significant droplet formed after welding. Furthermore, an increased wire infeed means more material to build up a higher droplet. Finally a higher current will raise the melt temperature and lower the viscosity allowing the melt to spread out more.
<figure><img src="image_352.png" /><figcaption>Figure 7. Minitab main effect plots for the weld height</figcaption></figure>

The current x weld time interaction is notably more significant than the others. At a low time interval a raised current leads to an increased droplet height while this relation reverses at a high time interval. This leads to believe that the low weld time is insufficient for the droplet to spread wide before solidification.

Finally, it can be concluded from the weld time x wire length interaction plot, that an increase in weld time has a higher influence on the droplet height when the wire feed is higher.

<figure><img src="image_353.png" /><figcaption>Figure 8. Minitab interaction plot for the weld height</figcaption></figure>

*Response surface optimization:* For a the implants envisioned as application for this research a droplet height of 0.55mm is set forward to achieve the required thread length. A droplet diameter of 5.5mm suffices for cutting or forming the threads for commercial locking screws while still small enough to limit thermal deformation of the sheet.

Applying Minitab's response optimizer tool to achieve the target values for droplet height and diameter yields the following variable values: a current of 86.5A, weld time of 834ms, and a wire amount of 6mm. The results of the optimizer are depicted in Figure 8. Note that the optimal variable value for wire feed is 6mm which is the maximum tested, which means extra experiments with higher feed length are necessary but were not possible at the time of testing.
<figure><img src="image_358.png" /><figcaption>Figure 7. Minitab main effect plots for the weld height</figcaption></figure>

The current x weld time interaction is notably more significant than the others. At a low time interval a raised current leads to an increased droplet height while this relation reverses at a high time interval. This leads to believe that the low weld time is insufficient for the droplet to spread wide before solidification.

Finally, it can be concluded from the weld time x wire length interaction plot, that an increase in weld time has a higher influence on the droplet height when the wire feed is higher.

<figure><img src="image_359.png" /><figcaption>Figure 8. Minitab interaction plot for the weld height</figcaption></figure>

*Response surface optimization:* For a the implants envisioned as application for this research a droplet height of 0.55mm is set forward to achieve the required thread length. A droplet diameter of 5.5mm suffices for cutting or forming the threads for commercial locking screws while still small enough to limit thermal deformation of the sheet.

Applying Minitab's response optimizer tool to achieve the target values for droplet height and diameter yields the following variable values: a current of 86.5A, weld time of 834ms, and a wire amount of 6mm. The results of the optimizer are depicted in Figure 8

Note that the optimal variable value for wire feed is 6mm which is the maximum tested, which means extra experiments with higher feed length are necessary but were not possible at the time of testing.
<figure><img src="image_354.png" /><figcaption>Figure 8. Results optimizer tool for a droplet diameter of 5.5mm and height of 0.5mm</figcaption></figure>

## Conclusion and future work

In this paper the influence of current, weld time and amount of wire feed on the diameter and height of the resulting droplet were studied for Ti grade 2.

- While a raised current from 60 to 99A resulted in an extended droplet diameter (2 to 7mm) due to the increased melt pool temperature it showed an inverse correlation to the height (0.54 to 0.39mm) for similar reason.

- An increased weld time (600 to 999ms) lead to both an increased droplet diameter (3 to 6mm) and height (0.16 to 0.67 mm).

- The amount of filler material added only has a significant and positive effect on the droplet height. Future work encompasses the extension to wire lengths exceeding 6mm, drilling the holes for thread and cutting/forming the thread. Finally the mechanical strength of the threaded connection will be tested and accompanying microstructural analysis will be performed.

## Acknowledgement

This paper was based on the thesis work of Oskar Vandenbroecke. The research on droplet deposition for medical implants is performed in the framework of the KUL, STIFF project.

## References

[1] M. Merklein, M. Johannes, M. Lechner, A. Kuppert, A review on tailored blanks Production, applications and evaluation, Journal of Materials Processing Technology, vol 214 issue 2 (2014) 151-164. https://doi.org/10.1016/j.jmatprotec.2013.08.015

[2] G. Ambrogio, F. Gagliardi, M. Muzzupappa, L. Filice, Additive-incremental forming hybrid manufacturing technique to improve customised part performance, Journal of Manufacturing Processes, 37 (2019) 386-391. https://doi.org/10.1016/j.jmapro.2018.12.008

[3] M. Bambach, A. Sviridov, A. Weisheit, Stiffness management of sheet metal parts using laser metal deposition, AIP Conf. Proc.; 1896 (1) (2017). https://doi.org/10.1063/1.5008094
<figure><img src="image_360.png" /><figcaption>Figure 8. Results optimizer tool for a droplet diameter of 5.5mm and height of 0.5mm</figcaption></figure>

## Conclusion and future work

In this paper the influence of current, weld time and amount of wire feed on the diameter and height of the resulting droplet were studied for Ti grade 2.

- While a raised current from 60 to 99A resulted in an extended droplet diameter (2 to 7mm) due to the increased melt pool temperature it showed an inverse correlation to the height (0.54 to 0.39mm) for similar reason.

- An increased weld time (600 to 999ms) lead to both an increased droplet diameter (3 to 6mm) and height (0.16 to 0.67 mm).

- The amount of filler material added only has a significant and positive effect on the droplet height. Future work encompasses the extension to wire lengths exceeding 6mm, drilling the holes for thread and cutting/forming the thread. Finally the mechanical strength of the threaded connection will be tested and accompanying microstructural analysis will be performed.

## Acknowledgement

This paper was based on the thesis work of Oskar Vandenbroecke. The research on droplet deposition for medical implants is performed in the framework of the KUL, STIFF project.

## References

[1] M. Merklein, M. Johannes, M. Lechner, A. Kuppert, A review on tailored blanks Production, applications and evaluation, Journal of Materials Processing Technology, vol 214 issue 2 (2014) 151-164. https://doi.org/10.1016/j.jmatprotec.2013.08.015

[2] G. Ambrogio, F. Gagliardi, M. Muzzupappa, L. Filice, Additive-incremental forming hybrid manufacturing technique to improve customised part performance, Journal of Manufacturing Processes, 37 (2019) 386-391. https://doi.org/10.1016/j.jmapro.2018.12.008

[3] M. Bambach, A. Sviridov, A. Weisheit, Stiffness management of sheet metal parts using laser metal deposition, AIP Conf. Proc.; 1896 (1) (2017). https://doi.org/10.1063/1.5008094
[4] J. Hafenecker, D. Bartels, C.-Maria Kuball, M. Kreß, R. Rothfelder, M. Schmidt, M. Merklein, Hybrid process chains combining metal additive manufacturing and forming – A review, CIRP Journal of Manufacturing Science and Technology.46 (2023) 98-115. https://doi.org/10.1016/j.cirpj.2023.08.002

[5] S. Vancleef, M. Wesseling, J.R. Duflou, S. Nijs, I. Jonkers, J. Vander Sloten, Thin patient-specific clavicle fracture fixation plates can mechanically outperform commercial plates: An in silico approach, Journal of Orthopeadic Research. 40. no.7 (2021) 1695-17006

[6] H. Vanhove, E. Ozden, J.R. Duflou, An Experimental Study on Bushing Formation during Friction Drilling of Titanium Grade 2 for Medical Applications, J. Manuf. Mater. Process. 7 no1 (2023). https://doi.org/10.3390/jmmp7060220

[7] S. Dehgan, M. I. S. Ismail, M. K. A. Ariffin., and B. T. H. T. Baharudin, Experimental investigation on friction drilling of Titanium alloy, Engineering Solid Mechanics. 46, no. 12, (2018) 135-142.

[8] P. Szypryt, D. Forward, The use and abuse of locking plates, Orthopaedics and trauma. 23, no. 4, (2009) 281-290.

[9] R. Baker, Method of making decorative articles. U.S. Patent 1,533,300, USA (1925)

[10] O. Yilmaz, AA. Ugla, Shaped metal deposition technique in additive manufacturing: A review, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 230 no10 (2016) 1781-1798. doi:10.1177/0954405416640181

[11] N.P. Gokhale, P. Kala, V. Sharma, Thin-walled metal deposition with GTAW welding-based additive manufacturing process, J Braz. Soc. Mech. Sci. Eng. 41 (2019) 569.

[12] S. Kapil, F. Legesse, P. Kulkarni, P. Joshi, A. Desai, K.P. Karunakaran, Hybrid-layered manufacturing using tungsten inert gas cladding. Prog Addit Manuf. 1 (2016) 79-91. https://doi.org/10.1007/s40964-016-0005-8

[13] TWI-global, „Weldability of materials-Titanium and Titanium alloys,” 2021. [Online]. Available: https://www.twi-global.com/technical-knowledge/job-knowledge/weldability-of-materials-titanium-and-titanium-alloys-024 [Geopend February 2021].
[4] J. Hafenecker, D. Bartels, C.-Maria Kuball, M. Kreß, R. Rothfelder, M. Schmidt, M. Merklein, Hybrid process chains combining metal additive manufacturing and forming – A review, CIRP Journal of Manufacturing Science and Technology.46 (2023) 98-115. https://doi.org/10.1016/j.cirpj.2023.08.002

[5] S. Vancleef, M. Wesseling, J.R. Duflou, S. Nijs, I. Jonkers, J. Vander Sloten, Thin patient-specific clavicle fracture fixation plates can mechanically outperform commercial plates: An in silico approach, Journal of Orthopeadic Research. 40. no.7 (2021) 1695-17006

[6] H. Vanhove, E. Ozden, J.R. Duflou, An Experimental Study on Bushing Formation during Friction Drilling of Titanium Grade 2 for Medical Applications, J. Manuf. Mater. Process. 7 no1 (2023). https://doi.org/10.3390/jmmp7060220

[7] S. Dehgan, M. I. S. Ismail, M. K. A. Ariffin., and B. T. H. T. Baharudin, Experimental investigation on friction drilling of Titanium alloy, Engineering Solid Mechanics. 46, no. 12, (2018) 135-142.

[8] P. Szypryt, D. Forward, The use and abuse of locking plates, Orthopaedics and trauma. 23, no. 4, (2009) 281-290.

[9] R. Baker, Method of making decorative articles. U.S. Patent 1,533,300, USA (1925)

[10] O. Yilmaz, AA. Ugla, Shaped metal deposition technique in additive manufacturing: A review, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 230 no10 (2016) 1781-1798. doi:10.1177/0954405416640181

[11] N.P. Gokhale, P. Kala, V. Sharma, Thin-walled metal deposition with GTAW welding-based additive manufacturing process, J Braz. Soc. Mech. Sci. Eng. 41 (2019) 569.

[12] S. Kapil, F. Legesse, P. Kulkarni, P. Joshi, A. Desai, K.P. Karunakaran, Hybrid-layered manufacturing using tungsten inert gas cladding. Prog Addit Manuf. 1 (2016) 79-91. https://doi.org/10.1007/s40964-016-0005-8

[13] TWI-global, „Weldability of materials-Titanium and Titanium alloys,” 2021. [Online]. Available: https://www.twi-global.com/technical-knowledge/job-knowledge/weldability-of-materials-titanium-and-titanium-alloys-024 [Geopend February 2021].
# Multi-sensor in process monitoring for WAAM: Detection of process instability in electrical signals

OUESLATI Sarra¹,ᵃ*, PAQUET Elodie¹,ᵇ, RITOU Mathieu¹,ᶜ, BELKADI Farouk¹,ᵈ and LE BOT Philippe²,ᵉ

¹Nantes Université, Ecole Centrale de Nantes, LS2N (Laboratory of Digital Sciences of Nantes, UMR CNRS 6004), F-44000 Nantes, France

²Nantes Université, IRT Jules Verne, F-44340 Bouguenais, France

ᵃsarra.oueslati@etu.univ-nantes.fr, ᵇelodie.paquet@univ-nantes.fr, ᶜmathieu.ritou@univ-nantes.fr, ᵈFarouk.Belkadi@ls2n.fr, ᵉphilippe.le-bot@irt-jules-verne.fr

**Keywords:** WAAM, Monitoring, Additive Manufacturing, Smart Manufacturing

**Abstract.** Wire Arc Additive Manufacturing (WAAM) is a promising process for producing medium to large scale metallic parts at a low cost and with a high deposition rate. However, the multitude of process parameters and physical phenomena involved makes it complex and hard to master. Therefore, monitoring the process becomes crucial for unraveling complexities and attaining a more profound comprehension of the intricacies inherent in WAAM, hence ensuring process stability. In order to produce a defect-free part, while keeping a stable process, the operating parameters must be carefully selected. Nonetheless, one of the significant hurdles in WAAM is the variability of the deposited layers height. The accumulation of these geometrical inaccuracies induces instabilities in the process which results into the appearing of defects on the deposited part. The aim of this study is to investigate the correlations between process instabilities and electrical signals obtained by a deposition monitoring system. A monitoring criterion is then extracted from experimental data. Correlation with instabilities will be confirmed using a thermal camera.

**Introduction**

Additive Manufacturing (AM) is the process of joining materials layer upon layer to make a 3D part. AM presents a long list of advantages among which the reduction of material waste and manufacturing cost. Compared with conventional subtractive manufacturing, namely machining, additive manufacturing offers a higher level of design complexity and an increased automation level from part design to fabrication [1]. Directed Energy Deposition (DED) is a category of AM established to manufacture metallic parts. It involves using a focused thermal energy (e.g. electron beam or electrical arc) to melt materials as they are being deposited [2].

Wire Arc Additive Manufacturing (WAAM) is a DED technology that offers a remarkable energy efficacy. WAAM employs the concept of arc welding combined with a wire feeding mechanism and a motion platform to produce parts. The feedstock wire undergoes melting through the heat energy of an electric arc created between the wire and a metallic substrate. Subsequently, the melted drops are deposited on the substrate and the formation of the deposition bead occurs through the cooling of the molten pool. Additionally, the process involves gas-shielding techniques to protect the molten metal from atmospheric contaminants [3]. Various energy sources are employed for generating input energy, including Cold Metal Transfer (CMT), a variant of Gas Metal Arc Welding (GMAW) developed by Fronius. It provides a precise method of material deposition through a mechanical retraction wire system, facilitating controlled droplet release in a short circuit. This results in low heat input, heightened stability, and minimal spatter [4].

<figure><img src="image_355.png" /></figure>
# Multi-sensor in process monitoring for WAAM: Detection of process instability in electrical signals

OUESLATI Sarra¹,ᵃ*, PAQUET Elodie¹,ᵇ, RITOU Mathieu¹,ᶜ, BELKADI Farouk¹,ᵈ and LE BOT Philippe²,ᵉ

¹Nantes Université, Ecole Centrale de Nantes, LS2N (Laboratory of Digital Sciences of Nantes, UMR CNRS 6004), F-44000 Nantes, France

²Nantes Université, IRT Jules Verne, F-44340 Bouguenais, France

ᵃsarra.oueslati@etu.univ-nantes.fr, ᵇelodie.paquet@univ-nantes.fr, ᶜmathieu.ritou@univ-nantes.fr, ᵈFarouk.Belkadi@ls2n.fr, ᵉphilippe.le-bot@irt-jules-verne.fr

**Keywords:** WAAM, Monitoring, Additive Manufacturing, Smart Manufacturing

**Abstract.** Wire Arc Additive Manufacturing (WAAM) is a promising process for producing medium to large scale metallic parts at a low cost and with a high deposition rate. However, the multitude of process parameters and physical phenomena involved makes it complex and hard to master. Therefore, monitoring the process becomes crucial for unraveling complexities and attaining a more profound comprehension of the intricacies inherent in WAAM, hence ensuring process stability. In order to produce a defect-free part, while keeping a stable process, the operating parameters must be carefully selected. Nonetheless, one of the significant hurdles in WAAM is the variability of the deposited layers height. The accumulation of these geometrical inaccuracies induces instabilities in the process which results into the appearing of defects on the deposited part. The aim of this study is to investigate the correlations between process instabilities and electrical signals obtained by a deposition monitoring system. A monitoring criterion is then extracted from experimental data. Correlation with instabilities will be confirmed using a thermal camera.

## Introduction

Additive Manufacturing (AM) is the process of joining materials layer upon layer to make a 3D part. AM presents a long list of advantages among which the reduction of material waste and manufacturing cost. Compared with conventional subtractive manufacturing, namely machining, additive manufacturing offers a higher level of design complexity and an increased automation level from part design to fabrication [1]. Directed Energy Deposition (DED) is a category of AM established to manufacture metallic parts. It involves using a focused thermal energy (e.g. electron beam or electrical arc) to melt materials as they are being deposited [2].

Wire Arc Additive Manufacturing (WAAM) is a DED technology that offers a remarkable energy efficacy. WAAM employs the concept of arc welding combined with a wire feeding mechanism and a motion platform to produce parts. The feedstock wire undergoes melting through the heat energy of an electric arc created between the wire and a metallic substrate. Subsequently, the melted drops are deposited on the substrate and the formation of the deposition bead occurs through the cooling of the molten pool. Additionally, the process involves gas-shielding techniques to protect the molten metal from atmospheric contaminants [3]. Various energy sources are employed for generating input energy, including Cold Metal Transfer (CMT), a variant of Gas Metal Arc Welding (GMAW) developed by Fronius. It provides a precise method of material deposition through a mechanical retraction wire system, facilitating controlled droplet release in a short circuit. This results in low heat input, heightened stability, and minimal spatter [4].

<figure><img src="image_361.png" /></figure>
In WAAM, the occurrence of defects on manufactured parts represents a significant challenge. Commonly observed defects include porosity, deformations due to residual stresses, surface cracks and spatter. These imperfections often stem from inappropriate process parameter configurations, excessive heat accumulation, or external environmental factors such as gas contamination or machine malfunctions [5]. Therefore, monitoring the process represents a first step toward a stable and defect free fabrication [6].

Monitoring WAAM processes can improve stability and part quality. Methods include temperature sensing, arc electrical signal sensing, visual sensing with cameras, and acoustic emission sensing [7]. Optical and InfraRed cameras are proven to be efficient in detecting geometrical inaccuracies [8] and surface defects [9] but collecting data can be challenging in harsh welding conditions due to sensitivity to lighting conditions. On another hand, welding sound signals were confirmed to be a good indicator of weld defects [10]. Yet it is very challenging to precisely discern these signals amid a noisy welding environment.

To produce part by WAAM, the designed CAD model undergoes layer slicing with constant estimated layer height, defining the torch toolpath. Setting key process parameters, including wire feed speed, torch travel speed, and gas flow rate, is also crucial. Unfortunately, despite optimized parameters, geometric inaccuracies remain inevitable. In some cases, the accumulation of these geometrical inaccuracies leads to the increase of the Contact Tube to Workpiece Distance (CTWD) causing the appearance of porosity. Conversely, an excessively short CTWD might cause collision with the work piece [11]. In both cases process instability is noticeable through the appearance of spatter. Researchers explored the stability of the process through optical measurement of the distance between the tip of the electrode and the top layer in Gas Tungsten based WAAM. They confirmed that the stability of the process is reflected by the measured distance and developed a closed loop control to maintain it constant, resulting in a spatter free part [12]. On another hand, monitoring arc stability in Gas Metal based WAAM was also explored and characterized for different metal transfer modes [13]. It was proven that arc electrical signals are very sensitive to CTWD variations, hence to process stability. Finally, many researchers underscore the need for increased focus on identifying criteria parameters that give insights into the welding process, including spatter [14]. This paper introduces a method for detecting process instability. Section 2 outlines the experimental procedure. The experimental results are presented in section 3. In section 4, the identification of instability in the signals leads to the proposal of a monitoring criterion, validated by thermal camera data.

## Experimental procedure and methodology

To conduct experiments, a Fronius TPS 500i© welding machine was used. The welding torch is mounted on a Staübli TX90© six-axis robot which serves as motion platform and allows the production of large-scale parts (Fig. 1).

<table><caption>Table 1: Process parameters</caption><thead><tr><td>Wire material</td><td>Wire diameter</td><td>Gas composition</td><td>Gas flowrate</td><td>Substrate thickness</td></tr></thead><tbody><tr><td>ER70S-6</td><td>1 [mm]</td><td>(M20 Ar+5-10%CO2)</td><td>20 [L/min]</td><td>6 [mm]</td></tr></tbody></table>

For all the experiments, the gas flow rate was fixed at 20L/min. A steel wire with a diameter of 1.0 mm and a 6 mm thick steel substrate were used. The substrate was clamped at four diagonal points to avoid the effect of residual stress and maintain consistent substrate flatness for all tests (Fig 1.). Table 1 lists the process parameters for all the experiments.
In WAAM, the occurrence of defects on manufactured parts represents a significant challenge. Commonly observed defects include porosity, deformations due to residual stresses, surface cracks and spatter. These imperfections often stem from inappropriate process parameter configurations, excessive heat accumulation, or external environmental factors such as gas contamination or machine malfunctions [5]. Therefore, monitoring the process represents a first step toward a stable and defect free fabrication [6].

Monitoring WAAM processes can improve stability and part quality. Methods include temperature sensing, arc electrical signal sensing, visual sensing with cameras, and acoustic emission sensing [7]. Optical and InfraRed cameras are proven to be efficient in detecting geometrical inaccuracies [8] and surface defects [9] but collecting data can be challenging in harsh welding conditions due to sensitivity to lighting conditions. On another hand, welding sound signals were confirmed to be a good indicator of weld defects [10]. Yet it is very challenging to precisely discern these signals amid a noisy welding environment.

To produce part by WAAM, the designed CAD model undergoes layer slicing with constant estimated layer height, defining the torch toolpath. Setting key process parameters, including wire feed speed, torch travel speed, and gas flow rate, is also crucial. Unfortunately, despite optimized parameters, geometric inaccuracies remain inevitable. In some cases, the accumulation of these geometrical inaccuracies leads to the increase of the Contact Tube to Workpiece Distance (CTWD) causing the appearance of porosity. Conversely, an excessively short CTWD might cause collision with the work piece [11]. In both cases process instability is noticeable through the appearance of spatter. Researchers explored the stability of the process through optical measurement of the distance between the tip of the electrode and the top layer in Gas Tungsten based WAAM. They confirmed that the stability of the process is reflected by the measured distance and developed a closed loop control to maintain it constant, resulting in a spatter free part [12]. On another hand, monitoring arc stability in Gas Metal based WAAM was also explored and characterized for different metal transfer modes [13]. It was proven that arc electrical signals are very sensitive to CTWD variations, hence to process stability. Finally, many researchers underscore the need for increased focus on identifying criteria parameters that give insights into the welding process, including spatter [14]. This paper introduces a method for detecting process instability. Section 2 outlines the experimental procedure. The experimental results are presented in section 3. In section 4, the identification of instability in the signals leads to the proposal of a monitoring criterion, validated by thermal camera data.

## Experimental procedure and methodology

To conduct experiments, a Fronius TPS 500i© welding machine was used. The welding torch is mounted on a Staübli TX90© six-axis robot which serves as motion platform and allows the production of large-scale parts (Fig. 1).

<table><caption>Table 1: Process parameters</caption><thead><tr><td>Wire material</td><td>Wire diameter</td><td>Gas composition</td><td>Gas flowrate</td><td>Substrate thickness</td></tr></thead><tbody><tr><td>ER70S-6</td><td>1 [mm]</td><td>(M20 Ar+5-10%CO2)</td><td>20 [L/min]</td><td>6 [mm]</td></tr></tbody></table>

For all the experiments, the gas flow rate was fixed at 20L/min. A steel wire with a diameter of 1.0 mm and a 6 mm thick steel substrate were used. The substrate was clamped at four diagonal points to avoid the effect of residual stress and maintain consistent substrate flatness for all tests (Fig 1.). Table 1 lists the process parameters for all the experiments.
<figure><img src="image_356.png" /><figcaption>Figure 1: Experimental setup</figcaption></figure>

Arc current was measured at the welding machine ground using a Pico TA167© current probe. As for arc voltage, it was recorded at the nearest possible points to minimize external interference and signal noise using GE8100 differential probe. Data acquisition was done using a National Instrument© card at a sampling rate of 10 kHz in order to capture all phenomena. Finally, an Optris© IR camera was mounted on the welding torch and used to acquire images to validate the detection of instabilities.

<figure><img src="image_357.png" /><figcaption>Figure 2: Schematic representation of WAAM</figcaption></figure>
<figure><img src="image_362.png" /><figcaption>Figure 1: Experimental setup</figcaption></figure>

Arc current was measured at the welding machine ground using a Pico TA167© current probe. As for arc voltage, it was recorded at the nearest possible points to minimize external interference and signal noise using GE8100 differential probe. Data acquisition was done using a National Instrument© card at a sampling rate of 10 kHz in order to capture all phenomena. Finally, an Optris© IR camera was mounted on the welding torch and used to acquire images to validate the detection of instabilities.

<figure><img src="image_363.png" /><figcaption>Figure 2: Schematic representation of WAAM</figcaption></figure>
<table><caption>Table 2: Experimental test conditions</caption><thead><tr><td>Wall nb</td><td>WFS [m/min]</td><td>Set_U [V]</td><td>Set_I [A]</td><td>TS [mm/s]</td><td>CTWD [mm]</td></tr></thead><tbody><tr><td>1</td><td>11</td><td>239</td><td>25,4</td><td>10</td><td>14</td></tr><tr><td>2</td><td>11</td><td>239</td><td>25,4</td><td>10</td><td>17</td></tr><tr><td>3</td><td>11</td><td>239</td><td>25,4</td><td>10</td><td>20</td></tr></tbody></table>

Three thin walls of 24 layers were deposited, as outlined in Table 2. Both Wire Feed Speed (WFS) and the Torch Travel Speed (TS) were the same for the three walls to maintain a constant material deposition rate throughout all experiments. The height increment dz (Fig. 2) was set based on previous experiments and kept constant throughout all the experiments. Manual adjustments of the Contact Tube to Workpiece Distance to the nominal designed values are made at layer 12 and layer 24 for all walls.

## Experimental results

Figure 3 exhibits the measured current and voltage signals during deposition. The waveforms of the chosen CMT-MIX synergy show a periodicity, of around 115 ms. It alternates cold CMT cycles (red rectangles) and pulsed hot arc sequences of 4 ms. This method combines the advantages of both CMT and Pulsed modes and ensures a stable, regular and rapid process with controlled heat input [15].

<figure><img src="image_358.png" /><figcaption>Figure 3: Waveforms of the typical voltage and current in CMT-MIX characteristic</figcaption></figure>

During deposition, the more layers are deposited, the more geometric errors accumulate. This is primarily attributed to the increasing flatness of each layer. The difference between the actual observed value of CTWD and the set value of CTWD increases progressively. For all walls, from the 7th layer onwards, process instabilities appear in the form of projections. Fig. 4 illustrates how these instabilities are also reflected in both current and voltage signals for layer 11 of artifact 3.
<table><caption>Table 2: Experimental test conditions</caption><thead><tr><td>Wall nb</td><td>WFS [m/min]</td><td>Set_U [V]</td><td>Set_I [A]</td><td>TS [mm/s]</td><td>CTWD [mm]</td></tr></thead><tbody><tr><td>1</td><td>11</td><td>239</td><td>25,4</td><td>10</td><td>14</td></tr><tr><td>2</td><td>11</td><td>239</td><td>25,4</td><td>10</td><td>17</td></tr><tr><td>3</td><td>11</td><td>239</td><td>25,4</td><td>10</td><td>20</td></tr></tbody></table>

Three thin walls of 24 layers were deposited, as outlined in Table 2. Both Wire Feed Speed (WFS) and the Torch Travel Speed (TS) were the same for the three walls to maintain a constant material deposition rate throughout all experiments. The height increment dz (Fig. 2) was set based on previous experiments and kept constant throughout all the experiments. Manual adjustments of the Contact Tube to Workpiece Distance to the nominal designed values are made at layer 12 and layer 24 for all walls.

## Experimental results

Figure 3 exhibits the measured current and voltage signals during deposition. The waveforms of the chosen CMT-MIX synergy show a periodicity, of around 115 ms. It alternates cold CMT cycles (red rectangles) and pulsed hot arc sequences of 4 ms. This method combines the advantages of both CMT and Pulsed modes and ensures a stable, regular and rapid process with controlled heat input [15].

<figure><img src="image_364.png" /><figcaption>Figure 3: Waveforms of the typical voltage and current in CMT-MIX characteristic</figcaption></figure>

During deposition, the more layers are deposited, the more geometric errors accumulate. This is primarily attributed to the increasing flatness of each layer. The difference between the actual observed value of CTWD and the set value of CTWD increases progressively. For all walls, from the 7th layer onwards, process instabilities appear in the form of projections. Fig.4 illustrates how these instabilities are also reflected in both current and voltage signals for layer 11 of artifact 3.
<figure><img src="image_359.png" /><figcaption>Figure 4 : Electrical signal before CTWD correction</figcaption></figure>

As soon as the CTWD is manually adjusted to its nominal value at layer 12, the deposition resumes a stable appearance, reflected by a significant reduction in spatter. Figure 5 shows the electrical signals of layer 12 of the same wall, just after manual adjustment to its nominal value of 20 mm. It can be seen that the electrical signals are more stable (stationarity and absence of erratic peaks). The same behavior is observed between the 13th and 24th layers for all three walls.

<figure><img src="image_360.png" /><figcaption>Figure 5 : Electrical signal after CTWD correction</figcaption></figure>

## Results and discussion

The experimental results show a clear correlation between the CTWD (Contact Tip to Workpiece Distance) and the stability of electrical signals: as the deviation between the nominal CTWD and the actual CTWD value increases due to the accumulation of geometric inaccuracies, the deposition becomes unstable. During the deposition, three different and interconnected variables are involved: The height increment (dz), the layer height (h) and the contact tube to workpiece distance (CTWD) as shown in figure 2. It is important to note that the layer height h is not constant [16].
<figure><img src="image_365.png" /><figcaption>Figure 4 : Electrical signal before CTWD correction</figcaption></figure>

As soon as the CTWD is manually adjusted to its nominal value at layer 12, the deposition resumes a stable appearance, reflected by a significant reduction in spatter. Figure 5 shows the electrical signals of layer 12 of the same wall, just after manual adjustment to its nominal value of 20 mm. It can be seen that the electrical signals are more stable (stationarity and absence of erratic peaks). The same behavior is observed between the 13th and 24th layers for all three walls.

<figure><img src="image_366.png" /><figcaption>Figure 5 : Electrical signal after CTWD correction</figcaption></figure>

## Results and discussion

The experimental results show a clear correlation between the CTWD (Contact Tip to Workpiece Distance) and the stability of electrical signals: as the deviation between the nominal CTWD and the actual CTWD value increases due to the accumulation of geometric inaccuracies, the deposition becomes unstable. During the deposition, three different and interconnected variables are involved: The height increment (dz), the layer height (h) and the contact tube to workpiece distance (CTWD) as shown in figure 2. It is important to note that the layer height h is not constant [16].
<figure><img src="image_361.png" /><figcaption>Figure 6: Mean current per layer for artefacts 1, 2 and 3</figcaption></figure>

As CTWD increases due to accumulation of geometrical inaccuracies, the Fronius generator autonomously adjusts the stick out (SO) value to maintain a constant arc length, consequently leading to a proportional increase in SO (Fig2.). The extended length of wire extruding from the contact tube results in an elevated wire resistance. On another hand, the welding machine performs corrective measures by manipulating currents. Figure 6 illustrates a decrease in average currents as the CTWD increases. Once the CTWD is corrected, the average currents rise again, as observed in layers 12 and 24, approaching values nearly equal to the set current (set_I = 239 A).

On another hand, figure 7 illustrates an almost constant average voltage aligned with the set value (Set_U= 25.4 V) for all three walls. Recorded data for layers 5 and 7 show lower values due to issues encountered with the voltage probe during the deposition of these layers. This observation confirms that as the CTWD increases, leading to an increase in resistance, the Fronius welding machine responds maintaining a constant arc voltage, resulting in a decrease in currents. This correlation between CTWD adjustments and current changes underscores the real-time correction capability of the Fronius generator, ensuring stability in material deposition despite varying geometric conditions.

<figure><img src="image_362.png" /><figcaption>Figure 7: Mean voltage and peak count for artefact 1, 2 and 3</figcaption></figure>
<figure><img src="image_367.png" /><figcaption>Figure 6: Mean current per layer for artefacts 1, 2 and 3</figcaption></figure>

As CTWD increases due to accumulation of geometrical inaccuracies, the Fronius generator autonomously adjusts the stick out (SO) value to maintain a constant arc length, consequently leading to a proportional increase in SO (Fig2.). The extended length of wire extruding from the contact tube results in an elevated wire resistance. On another hand, the welding machine performs corrective measures by manipulating currents. Figure 6 illustrates a decrease in average currents as the CTWD increases. Once the CTWD is corrected, the average currents rise again, as observed in layers 12 and 24, approaching values nearly equal to the set current (set_I = 239 A).

On another hand, figure 7 illustrates an almost constant average voltage aligned with the set value (Set_U= 25.4 V) for all three walls. Recorded data for layers 5 and 7 show lower values due to issues encountered with the voltage probe during the deposition of these layers. This observation confirms that as the CTWD increases, leading to an increase in resistance, the Fronius welding machine responds maintaining a constant arc voltage, resulting in a decrease in currents. This correlation between CTWD adjustments and current changes underscores the real-time correction capability of the Fronius generator, ensuring stability in material deposition despite varying geometric conditions.

<figure><img src="image_368.png" /><figcaption>Figure 7: Mean voltage and peak count for artefact 1, 2 and 3</figcaption></figure>
Figure 4 shows that, for a large CTWD of 33 mm, the voltage signals exhibit instability with significant and random peaks. This behavior has also been observed for other unstable layers. To characterize these instabilities, a monitoring criterion is proposed based on the detection of peaks in the voltage signals. The detection and counting of peaks with amplitudes exceeding a fixed threshold $T_u$, as shown in Equation (1), allow quantifying the number of peaks surpassing a defined threshold $T_{pic}$, providing information about the process stability.

$$T_u = 1,5 \cdot Set\_U \qquad (1)$$

Figure 7 illustrates the increase in the number of peaks with the accumulation of layers, corresponding to the accumulation of geometric errors and the widening gap between the actual CTWD and nominal CTWD. When manually adjusting the CTWD at layer 12, a decrease in the number of peaks is observed. This trend repeats until the final adjustment of the CTWD at layer 24.

<figure><img src="image_363.png" /><figcaption>Figure 8: IR-images of a) unstable process, b) stable process</figcaption></figure>

To validate this result, infrared camera images showcase abundance of spatter in the deposition area prior to the correction of CTWD (see Figure 8.a). Conversely, a notable decrease in spatter is evident in the layer immediately following the CTWD correction (see Figure 8.b). These findings suggest the possibility to establish a threshold $T_p$ for the number of peaks, providing valuable insights into the stability of the process (Figure 7).

## Conclusion and perspectives

In summary, our study establishes a link between CTWD and electrical signals during WAAM. Peak detection analysis reveals a correlation between layer accumulation, geometrical errors, and CTWD increase. Manual CTWD correction reduces peaks, suggesting the potential for establishing a stability threshold. Infrared camera images validate our findings, demonstrating decreased spatter post-CTWD correction. This collective evidence supports the feasibility of defining a peak threshold, offering valuable insights into welding process stability.

The findings presented in this paper mark the initiation of data analysis for an extensive experimental campaign. Geometric and thermal data have been collected and pending analysis, and criteria for monitoring are yet to be formulated.

## Acknowledgments

The authors would like to acknowledge the funding and support of the PERFORM Program of IRT-Jules Verne
Figure 4 shows that, for a large CTWD of 33 mm, the voltage signals exhibit instability with significant and random peaks. This behavior has also been observed for other unstable layers. To characterize these instabilities, a monitoring criterion is proposed based on the detection of peaks in the voltage signals. The detection and counting of peaks with amplitudes exceeding a fixed threshold $T_u$, as shown in Equation (1), allow quantifying the number of peaks surpassing a defined threshold $T_{pic}$, providing information about the process stability.

$$T_u = 1,5 \cdot Set\_U \qquad (1)$$

Figure 7 illustrates the increase in the number of peaks with the accumulation of layers, corresponding to the accumulation of geometric errors and the widening gap between the actual CTWD and nominal CTWD. When manually adjusting the CTWD at layer 12, a decrease in the number of peaks is observed. This trend repeats until the final adjustment of the CTWD at layer 24.

<figure><img src="image_369.png" /><figcaption>Figure 8: IR-images of a) unstable process, b) stable process</figcaption></figure>

To validate this result, infrared camera images showcase abundance of spatter in the deposition area prior to the correction of CTWD (see Figure 8.a). Conversely, a notable decrease in spatter is evident in the layer immediately following the CTWD correction (see Figure 8.b). These findings suggest the possibility to establish a threshold $T_p$ for the number of peaks, providing valuable insights into the stability of the process (Figure 7).

## Conclusion and perspectives

In summary, our study establishes a link between CTWD and electrical signals during WAAM. Peak detection analysis reveals a correlation between layer accumulation, geometrical errors, and CTWD increase. Manual CTWD correction reduces peaks, suggesting the potential for establishing a stability threshold. Infrared camera images validate our findings, demonstrating decreased spatter post-CTWD correction. This collective evidence supports the feasibility of defining a peak threshold, offering valuable insights into welding process stability.

The findings presented in this paper mark the initiation of data analysis for an extensive experimental campaign. Geometric and thermal data have been collected and pending analysis, and criteria for monitoring are yet to be formulated.

## Acknowledgments

The authors would like to acknowledge the funding and support of the PERFORM Program of IRT-Jules Verne
## References

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[2] D.-G. Ahn, « Directed Energy Deposition (DED) Process: State of the Art », *Int. J. of Precis. Eng. and Manuf.-Green Tech.*, vol. 8, n° 2, p. 703-742, mars 2021. https://doi.org/10.1007/s40684-020-00302-7

[3] B. Tomar, S. Shiva, et N. Tameshwer, « A review on wire arc additive manufacturing: Processing parameters, defects, quality improvement and recent advances | Elsevier Enhanced Reader », *Materials Today Communications*, vol. 32, 2022. https://doi.org/10.1016/j.mtcomm.2022.103739

[4] S. Selvi, A. Vishvaksenan, et E. Rajasekar, « Cold metal transfer (CMT) technology - An overview | Elsevier Enhanced Reader », *Defence Technology*, p. 28-44. https://doi.org/10.1016/j.dt.2017.08.002

[5] Y. Li, C. Su, et J. Zhu, « Comprehensive review of wire arc additive manufacturing: Hardware system, physical process, monitoring, property characterization, application and future prospects », *Results in Engineering*, vol. 13, p. 100330, mars 2022. https://doi.org/10.1016/j.rineng.2021.100330

[6] H. Mu, F. He, L. Yuan, P. Commins, H. Wang, et Z. Pan, « Toward a smart wire arc additive manufacturing system: A review on current developments and a framework of digital twin », *Journal of Manufacturing Systems*, vol. 67, p. 174-189, avr. 2023. https://doi.org/10.1016/j.jmsy.2023.01.012

[7] C. Xia et al., « A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system », *Journal of Manufacturing Systems*, vol. 57, p. 31-45, oct. 2020. https://doi.org/10.1016/j.jmsy.2020.08.008

[8] A. Dellarre, N. Béraud, N. Tardif, F. Vignat, F. Villeneuve, et M. Limousin, « Qualify a NIR camera to detect thermal deviation during aluminum WAAM », *Int J Adv Manuf Technol*, vol. 127, n° 1, p. 625-634, juill. 2023. https://doi.org/10.1007/s00170-023-11587-8

[9] S. Tang, G. Wang, H. Zhang, et R. Wang, « An Online Surface Defects Detection System for AWAM Based on Deep Learning », University of Texas at Austin, 2017. https://doi.org/10.26153/16919

[10] K. Pal, S. Bhattacharya, et S. K. Pal, « Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding », *Journal of Materials Processing Technology*, vol. 210, n° 10, p. 1397-1410, juill. 2010. https://doi.org/10.1016/j.jmatprotec.2010.03.029

[11] L. V. Hölscher, T. Hassel, et H. J. Maier, « Detection of the contact tube to working distance in wire and arc additive manufacturing », *Int J Adv Manuf Technol*, vol. 120, n° 1, p. 989-999, mai 2022. https://doi.org/10.1007/s00170-022-08805-0

[12] J. Xiong, G. Liu, et Y. Pi, « Increasing stability in robotic GTA-based additive manufacturing through optical measurement and feedback control », *Robotics and Computer-Integrated Manufacturing*, vol. 59, p. 385-393, oct. 2019. https://doi.org/10.1016/j.rcim.2019.05.012
## References

[1] D. Ding, Z. Pan, D. Cuiuri, et H. Li, « Wire-feed additive manufacturing of metal components: technologies, developments and future interests », *Int J Adv Manuf Technol*, vol. 81, n° 1, p. 465-481, oct. 2015. https://doi.org/10.1007/s00170-015-7077-3

[2] D.-G. Ahn, « Directed Energy Deposition (DED) Process: State of the Art », *Int. J. of Precis. Eng. and Manuf.-Green Tech.*, vol. 8, n° 2, p. 703-742, mars 2021. https://doi.org/10.1007/s40684-020-00302-7

[3] B. Tomar, S. Shiva, et N. Tameshwer, « A review on wire arc additive manufacturing: Processing parameters, defects, quality improvement and recent advances | Elsevier Enhanced Reader », *Materials Today Communications*, vol. 32, 2022. https://doi.org/10.1016/j.mtcomm.2022.103739

[4] S. Selvi, A. Vishvaksenan, et E. Rajasekar, « Cold metal transfer (CMT) technology - An overview | Elsevier Enhanced Reader », *Defence Technology*, p. 28-44. https://doi.org/10.1016/j.dt.2017.08.002

[5] Y. Li, C. Su, et J. Zhu, « Comprehensive review of wire arc additive manufacturing: Hardware system, physical process, monitoring, property characterization, application and future prospects », *Results in Engineering*, vol. 13, p. 100330, mars 2022. https://doi.org/10.1016/j.rineng.2021.100330

[6] H. Mu, F. He, L. Yuan, P. Commins, H. Wang, et Z. Pan, « Toward a smart wire arc additive manufacturing system: A review on current developments and a framework of digital twin », *Journal of Manufacturing Systems*, vol. 67, p. 174-189, avr. 2023. https://doi.org/10.1016/j.jmsy.2023.01.012

[7] C. Xia et al., « A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system », *Journal of Manufacturing Systems*, vol. 57, p. 31-45, oct. 2020. https://doi.org/10.1016/j.jmsy.2020.08.008

[8] A. Dellarre, N. Béraud, N. Tardif, F. Vignat, F. Villeneuve, et M. Limousin, « Qualify a NIR camera to detect thermal deviation during aluminum WAAM », *Int J Adv Manuf Technol*, vol. 127, n° 1, p. 625-634, juill. 2023. https://doi.org/10.1007/s00170-023-11587-8

[9] S. Tang, G. Wang, H. Zhang, et R. Wang, « An Online Surface Defects Detection System for AWAM Based on Deep Learning », University of Texas at Austin, 2017. https://doi.org/10.26153/16919

[10] K. Pal, S. Bhattacharya, et S. K. Pal, « Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding », *Journal of Materials Processing Technology*, vol. 210, n° 10, p. 1397-1410, juill. 2010. https://doi.org/10.1016/j.jmatprotec.2010.03.029

[11] L. V. Hölscher, T. Hassel, et H. J. Maier, « Detection of the contact tube to working distance in wire and arc additive manufacturing », *Int J Adv Manuf Technol*, vol. 120, n° 1, p. 989-999, mai 2022. https://doi.org/10.1007/s00170-022-08805-0

[12] J. Xiong, G. Liu, et Y. Pi, « Increasing stability in robotic GTA-based additive manufacturing through optical measurement and feedback control », *Robotics and Computer-Integrated Manufacturing*, vol. 59, p. 385-393, oct. 2019. https://doi.org/10.1016/j.rcim.2019.05.012
[13] H. Li et J. Xiong, « Monitoring process stability in gas metal arc additive manufacturing based on arc sensing », *Measurement*, vol. 220, p. 113377, oct. 2023. https://doi.org/10.1016/j.measurement.2023.113377

[14] A. Bernard *et al.*, « Vision on metal additive manufacturing: Developments, challenges and future trends », *CIRP Journal of Manufacturing Science and Technology*, vol. 47, p. 18-58, déc. 2023. https://doi.org/10.1016/j.cirpj.2023.08.005

[15] E. F. da Silva, J. R. M, A. Scotti, et J. C. de Oliveira, « Power quality analysis of gas metal ARC welding process operating under different drop transfer modes », in *XI Brazilian Power Electronics Conference*, sept. 2011, p. 129-135. https://doi.org/10.1109/COBEP.2011.6085187

[16] U. Reisgen, S. Mann, L. Oster, P. Lozano, et R. Sharma, « Study on Workpiece and Welding Torch Height Control for Polydirectional WAAM by Means of Image Processing », in *2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)*, août 2019, p. 6-11. https://doi.org/10.1109/COASE.2019.8843076
[13] H. Li et al. « Monitoring process stability in gas metal arc additive manufacturing based on arc sensing », *Measurement*, vol. 220, p. 113377, oct. 2023. https://doi.org/10.1016/j.measurement.2023.113377

[14] A. Bernard *et al.*, « Vision on metal additive manufacturing: Developments, challenges and future trends », *CIRP Journal of Manufacturing Science and Technology*, vol. 47, p. 18-58, déc. 2023. https://doi.org/10.1016/j.cirpj.2023.08.005

[15] E. F. da Silva, J. R. M, A. Scotti, et J. C. de Oliveira, « Power quality analysis of gas metal ARC welding process operating under different drop transfer modes », in *XI Brazilian Power Electronics Conference*, sept. 2011, p. 129-135. https://doi.org/10.1109/COBEP.2011.6085187

[16] U. Reisgen, S. Mann, L. Oster, P. Lozano, et R. Sharma, « Study on Workpiece and Welding Torch Height Control for Polydirectional WAAM by Means of Image Processing », in *2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)*, août 2019, p. 6-11. https://doi.org/10.1109/COASE.2019.8843076
# Real-time in-situ thermal monitoring system and defect detection using deep learning applied to additive manufacturing

RHIM Safouene¹,ᵃ*, ALBAHLOUL Hala¹,²,ᵇ and ROUA Christophe¹,ᶜ

¹Cogit Composites, 9117 Rue des Vignerons 18390 Saint-Germain-du-Puy, France

²Université Paris Cité, Learning planet institute, 8 Bis rue Charles V, 75004 Paris, France

ᵃs.rhim@cogit-composites.com, ᵇh.albahloul@cogit-composites.com,

ᶜchristophe roua@cogit-composites.com

**Keywords:** Additive Manufacturing, Thermography, Artificial Intelligence, Computer Vision, Deep-Learning, Defect Detection, Fused Filament Fabrication

**Abstract.** Fused deposition modeling, a widely employed additive manufacturing method, has witnessed a significant trend towards printing advanced materials such as PEEK and PAEK in recent years. Research studies have demonstrated the significance of process thermal dynamics in influencing the mechanical and geometric properties of printed components. This paper introduces a real-time thermal monitoring system that comprehensively tracks the thermal history of the printed component. Additionally, a deep learning model is presented, capable of detecting defects during the printing process. The integration of this monitoring system in a closed-loop mode offers the advantage of real-time adjustments, facilitating an immediate enhancement in the quality of the printed parts based on the continuously measured thermal data and the identified defects. Beyond real-time improvements, the data output from the monitoring system holds immense potential for broader applications. It can be seamlessly integrated into simulation software, providing a valuable dataset that can be leveraged to predict the physical properties and the adhesion quality of the printed parts.

**Introduction**

Additive manufacturing, specifically the Fused Filament Fabrication (FFF) process, involves the conversion of 3D-designed components into tangible fabricated parts. The procedure starts with the creation of the part using 3D Computer-Aided Design (CAD) software. Subsequently, the design is transferred to a slicer, which generates the tool-path (G-Code). Finally, the machine executes the printing of the part based on the specified tool-path. The method consists of converting a solid filament into a semi-liquid substance through the application of a heated nozzle. Once the material reaches a molten state, it is deposited along a defined two-dimensional XY plane. The process advances layer by layer to construct a three-dimensional object.

Additive manufacturing has gained a lot of importance in the last years, especially for high performance materials (PEEK, PAEK,...) that are used in a lot of applications (aerospace, defence, medical, ...) as it showed the potential of fabricating multi-functional parts that are hard or sometimes impossible to be done using old technologies.

Like any other technology, achieving consistently high-quality 3D printed parts that satisfy industry requirements, whether in terms of mechanical properties or geometric aspects, poses inherent challenges. 3D printed components are susceptible to various defects, including under-extrusion, over-extrusion, spaghetti, and filament adhesion issues [1] as shown in Figure 1.

<figure><img src="image_364.png" /></figure>
# Real-time in-situ thermal monitoring system and defect detection using deep learning applied to additive manufacturing

RHIM Safouene¹,ᵃ*, ALBAHLOUL Hala¹,²,ᵇ and ROUA Christophe¹,ᶜ

¹Cogit Composites, 9117 Rue des Vignerons 18390 Saint-Germain-du-Puy, France

²Université Paris Cité, Learning planet institute, 8 Bis rue Charles V, 75004 Paris, France

ᵃs.rhim@cogit-composites.com, ᵇh.albahloul@cogit-composites.com,

ᶜchristophe roua@cogit-composites.com

**Keywords:** Additive Manufacturing, Thermography, Artificial Intelligence, Computer Vision, Deep-Learning, Defect Detection, Fused Filament Fabrication

**Abstract.** Fused deposition modeling, a widely employed additive manufacturing method, has witnessed a significant trend towards printing advanced materials such as PEEK and PAEK in recent years. Research studies have demonstrated the significance of process thermal dynamics in influencing the mechanical and geometric properties of printed components. This paper introduces a real-time thermal monitoring system that comprehensively tracks the thermal history of the printed component. Additionally, a deep learning model is presented, capable of detecting defects during the printing process. The integration of this monitoring system in a closed-loop mode offers the advantage of real-time adjustments, facilitating an immediate enhancement in the quality of the printed parts based on the continuously measured thermal data and the identified defects. Beyond real-time improvements, the data output from the monitoring system holds immense potential for broader applications. It can be seamlessly integrated into simulation software, providing a valuable dataset that can be leveraged to predict the physical properties and the adhesion quality of the printed parts.

## Introduction

Additive manufacturing, specifically the Fused Filament Fabrication (FFF) process, involves the conversion of 3D-designed components into tangible fabricated parts. The procedure starts with the creation of the part using 3D Computer-Aided Design (CAD) software. Subsequently, the design is transferred to a slicer, which generates the tool-path (G-Code). Finally, the machine executes the printing of the part based on the specified tool-path. The method consists of converting a solid filament into a semi-liquid substance through the application of a heated nozzle. Once the material reaches a molten state, it is deposited along a defined two-dimensional XY plane. The process advances layer by layer to construct a three-dimensional object.

Additive manufacturing has gained a lot of importance in the last years, especially for high performance materials (PEEK, PAEK,...) that are used in a lot of applications (aerospace, defence, medical, ...) as it showed the potential of fabricating multi-functional parts that are hard or sometimes impossible to be done using old technologies.

Like any other technology, achieving consistently high-quality 3D printed parts that satisfy industry requirements, whether in terms of mechanical properties or geometric aspects, poses inherent challenges. 3D printed components are susceptible to various defects, including under-extrusion, over-extrusion, spaghetti, and filament adhesion issues [1] as shown in Figure 1.

<figure><img src="image_370.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
<figure><img src="image_365.png" /><figcaption>Figure 1: Examples of defects in 3D printed parts</figcaption></figure>

At the moment the only possible solutions to qualify printed parts often involves the implementation of post-processing quality control systems either destructive or non-destructive, that are always very expensive. Otherwise, we can opt for visual inspection alone, which is more cost-effective but lacks a quantitative assessment of the defect and cannot penetrate the core of the part. As examples of non-destructive methods, laser scanning systems that can be utilized to perform detailed dimensional inspections and identify deviations from the intended geometry, X-ray and CT scanning methods to detect internal defects and assess the structural integrity of printed parts. Destructive testing methods includes tensile, compression, fatigue...

As a consequence, considerable research has been undertaken to grasp the critical parameters impacting the quality of printed parts. Simultaneously, efforts have been directed towards identifying defects in real-time or during post-processing through the application of image processing algorithms or the utilization of deep learning models. Various configurations have been tested, employing optical or thermal cameras positioned alongside printers to achieve a comprehensive perspective of parts, demonstrating positive outcomes in terms of quality control.

Single camera approach like camera coupled with computer vision techniques [2-5] or deep learning models [3,6-8] and fixed on the side view, can detect various macro extrusion AM defects (Blobs, Under-extrusion...), but this solution has its limitation since the printer head could obscure the view which make it difficult to always view the material as it is being deposited from the nozzle.

Methods that use algorithms for change detection or Siamese network [9] to calculate the similarity or the difference between a reference image and the real time image, can give high accurate results in addition to localize the defect. However, techniques related to similarity score, need one correct printed reference at least. This may be especially limiting for custom parts.

Techniques like multi-camera 3D structured light scanning and digital image correlation offer the potential to compare 3D reconstructions of printed parts with digital models to identify dimensional inaccuracies. It offers high accuracy measurement but it is applied either on finished parts or layer by layer so it requires pausing the printing [10,11].

Within the context of this paper, we present a comprehensive monitoring system that emphasizes the critical role of thermal measurement in the 3D printing process. Our approach extends beyond thermal monitoring by incorporating a sophisticated deep learning model, specifically trained to detect defects in real-time during the printing process. The goal of this monitoring system is to generate a digital shadow of the printed components, facilitating the inspection during the post-processing phase. Furthermore, the system can be integrated seamlessly in close-loop mode, facilitating continuous adjustments and optimizations during the printing process, thereby enhancing the overall quality and reliability of the final printed components.

This integrated approach positions our monitoring system as a robust solution for advancing the precision and quality assurance aspects of 3D printing technology.