<figure><img src="image_199.png" /><figcaption>Figure 5. Reconstructions of the fracture surfaces via optical microscope. a) ± 45° Tensile specimen. b) 67° k Tensile specimen.</figcaption></figure>

<table><caption>Table 6. Comparison of the main tensile properties measured on MEX and LPBF elements.</caption><thead><tr><th></th><th>MEX (± 45°)</th><th>MEX (67° k)</th><th>L-PBF</th></tr></thead><tbody><tr><th>Elongation at break</th><td>46.6 ± 4.5 %</td><td>55.6 ± 5.4 %</td><td>57 %</td></tr><tr><th>UTS</th><td>439.5 ± 36 MPa</td><td>441.0 ± 30 MPa</td><td>Up to 700 MPa</td></tr></tbody></table>

*Hardness test.* In Table 7, the average values and the standard deviation of the HV₁₀₀ for both infill patterns were reported. It is possible to observe that there is not an appreciable difference in the values, this is probably related to the dimension of the indentation and the part structure. Specifically, the indentation is usually placed on a single strand of material, it is very difficult that the indentation involves two strand side by side, thus define a hardness that is quite similar to the monolithic one and characterized by relative low standard deviation along the entire surface.

<table><caption>Table 7. Comparison of the average hardness values measured on MEX and LPBF elements.</caption><thead><tr><th></th><th>MEX (± 45°)</th><th>MEX (67° k)</th><th>LPBF</th></tr></thead><tbody><tr><th>HV<sub>1000</sub> / HV</th><td>124±6 HV</td><td>131±3 HV</td><td>230 HV</td></tr></tbody></table>

## Conclusion

Based on the optimized strategy defined by literature for LPBF process, the present work investigated whether, with the appropriate adaptations, the concept of an infill pattern characterized by a continuous rotation of the layers improves the structure of the parts 3D printed with metal-MEX technology. Specifically, the rotation of each layer of 67° was compared with the ±45° deposition strategy, which is the most common strategy used in MEX processes.

Cylindrical and tensile samples were manufactured modifying only the deposition strategy, via infill pattern, and the main differences in terms of porosity, tensile strength and hardness were investigated for evaluating the effectiveness of the LPBF optimal scanning strategy (67°k). The tested hypothesis consisted in the possibility of interrupting the porosity envelopes derived from the utilization of the ±45° strategy.

The relevant outcomes of the present research are summarized as follow:

* The investigated deposition strategies produced specimens with comparable total porosities; however, the porous network is significantly influenced by the deposition strategy, obtaining a periodic and semi-continuous network in the case of ±45° strategy and a fragmented network in the case of 67°k strategy.
<figure><img src="image_203.png" /><figcaption>Figure 5. Reconstructions of the fracture surfaces via optical microscope. a) ± 45° Tensile specimen. b) 67° k Tensile specimen.</figcaption></figure>

<table><caption>Table 6. Comparison of the main tensile properties measured on MEX and LPBF elements.</caption><thead><tr><th></th><th>MEX (± 45°)</th><th>MEX (67° k)</th><th>L-PBF</th></tr></thead><tbody><tr><th>Elongation at break</th><td>46.6 ± 4.5 %</td><td>55.6 ± 5.4 %</td><td>57 %</td></tr><tr><th>UTS</th><td>439.5 ± 36 MPa</td><td>441.0 ± 30 MPa</td><td>Up to 700 MPa</td></tr></tbody></table>

*Hardness test.* In Table 7, the average values and the standard deviation of the HV₁₀₀ for both infill patterns were reported. It is possible to observe that there is not an appreciable difference in the values, this is probably related to the dimension of the indentation and the part structure. Specifically, the indentation is usually placed on a single strand of material, it is very difficult that the indentation involves two strand side by side, thus define a hardness that is quite similar to the monolithic one and characterized by relative low standard deviation along the entire surface.

<table><caption>Table 7. Comparison of the average hardness values measured on MEX and LPBF elements.</caption><thead><tr><th></th><th>MEX (± 45°)</th><th>MEX (67° k)</th><th>LPBF</th></tr></thead><tbody><tr><th>HV<sub>1000</sub> / HV</th><td>124±6 HV</td><td>131±3 HV</td><td>230 HV</td></tr></tbody></table>

## Conclusion

Based on the optimized strategy defined by literature for LPBF process, the present work investigated whether, with the appropriate adaptations, the concept of an infill pattern characterized by a continuous rotation of the layers improves the structure of the parts 3D printed with metal-MEX technology. Specifically, the rotation of each layer of 67° was compared with the ±45° deposition strategy, which is the most common strategy used in MEX processes.

Cylindrical and tensile samples were manufactured modifying only the deposition strategy, via infill pattern, and the main differences in terms of porosity, tensile strength and hardness were investigated for evaluating the effectiveness of the LPBF optimal scanning strategy (67°k). The tested hypothesis consisted in the possibility of interrupting the porosity envelopes derived from the utilization of the ±45° strategy.

The relevant outcomes of the present research are summarized as follow:

* The investigated deposition strategies produced specimens with comparable total porosities; however, the porous network is significantly influenced by the deposition strategy, obtaining a periodic and semi-continuous network in the case of ±45° strategy and a fragmented network in the case of 67°k strategy.
* The differences in the porous network morphology showed negligible effects on hardness and maximum load obtained during the tensile test.

* A major effect was observed on elongation at break, where 67°k specimens withstood greater deformation before the breakage. This suggested a higher ductility derived from the optimal deposition strategy and, thus, beneficial results for applications, such as the medical ones, requiring greater predictability of fracture behavior. Additional specific investigations are required to confirm the hypothesis.

### Acknowledgments

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

### References

[1] Gong, H.; Snelling, D.; Kardel, K.; Carrano, A. Comparison of Stainless Steel 316L Parts Made by FDM- and SLM-Based Additive Manufacturing Processes. *JOM* 2019, *71*, 880–885.
https://doi.org/10.1007/S11837-018-3207-3/TABLES/2

[2] Kluczyński, J.; Jasik, K.; Łuszczek, J.; Sarzyński, B.; Grzelak, K.; Dražan, T.; Joska, Z.; Szachogluchowicz, I.; Płatek, P.; Małek, M. A Comparative Investigation of Properties of Metallic Parts Additively Manufactured through MEX and PBF-LB/M Technologies. *Mater. 2023, Vol. 16, Page 5200* 2023, *16*, 5200. https://doi.org/10.3390/MA16145200

[3] Kurose, T.; Abe, Y.; Santos, M.V.A.; Kanaya, Y.; Ishigami, A.; Tanaka, S.; Ito, H. Influence of the Layer Directions on the Properties of 316L Stainless Steel Parts Fabricated through Fused Deposition of Metals. *Materials (Basel)*. 2020, *13*. https://doi.org/10.3390/ma13112493

[4] Ahmed, N.; Barsoum, I.; Haidemenopoulos, G.; Al-Rub, R.K.A. Process Parameter Selection and Optimization of Laser Powder Bed Fusion for 316L Stainless Steel: A Review. *J. Manuf. Process*. 2022, *75*, 415–434. https://doi.org/10.1016/j.jmapro.2021.12.064

[5] Jia, H.; Sun, H.; Wang, H.; Wu, Y.; Wang, H. Scanning Strategy in Selective Laser Melting (SLM): A Review. *Int. J. Adv. Manuf. Technol.* 2021, *113*, 2413–2435.
https://doi.org/10.1007/s00170-021-06810-3

[6] Robinson, J.H.; Robert, I.; Ashton, T.; Jones, E.; Fox, P.; Sutcliffe, C. The Effect of Hatch Angle Rotation on Parts Manufactured Using Selective Laser Melting. https://doi.org/10.1108/RPJ-06-2017-0111

[7] Carrozza, A.; Lorenzi, S.; Carugo, F.; Fest-Santini, S.; Santini, M.; Marchese, G.; Barbieri, G.; Cognini, F.; Cabrini, M.; Pastore, T. A Comparative Analysis between Material Extrusion and Other Additive Manufacturing Techniques: Defects, Microstructure and Corrosion Behavior in Nickel Alloy 625. *Mater. Des.* 2023, *225*, 111545.
https://doi.org/10.1016/J.MATDES.2022.111545

[8] Singh, R. Welding Corrosion Resistant Alloys – Stainless Steel. *Appl. Weld. Eng.* 2012, 191–214. https://doi.org/10.1016/B978-0-12-391916-8.00018-2

[9] Yang, K.; Ren, Y. Nickel-Free Austenitic Stainless Steels for Medical Applications. *Sci. Technol. Adv. Mater.* 2010, *11*, 014105. https://doi.org/10.1088/1468-6996/11/1/014105
* The differences in the porous network morphology showed negligible effects on hardness and maximum load obtained during the tensile test.

* A major effect was observed on elongation at break, where 67°k specimens withstood greater deformation before the breakage. This suggested a higher ductility derived from the optimal deposition strategy and, thus, beneficial results for applications, such as the medical ones, requiring greater predictability of fracture behavior. Additional specific investigations are required to confirm the hypothesis.

## Acknowledgments

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

## References

[1] Gong, H.; Snelling, D.; Kardel, K.; Carrano, A. Comparison of Stainless Steel 316L Parts Made by FDM- and SLM-Based Additive Manufacturing Processes. *JOM* 2019, *71*, 880–885.
https://doi.org/10.1007/S11837-018-3207-3/TABLES/2

[2] Kluczyński, J.; Jasik, K.; Łuszczek, J.; Sarzyński, B.; Grzelak, K.; Dražan, T.; Joska, Z.; Szachogluchowicz, I.; Płatek, P.; Małek, M. A Comparative Investigation of Properties of Metallic Parts Additively Manufactured through MEX and PBF-LB/M Technologies. *Mater. 2023, Vol. 16, Page 5200* 2023, *16*, 5200. https://doi.org/10.3390/MA16145200

[3] Kurose, T.; Abe, Y.; Santos, M.V.A.; Kanaya, Y.; Ishigami, A.; Tanaka, S.; Ito, H. Influence of the Layer Directions on the Properties of 316L Stainless Steel Parts Fabricated through Fused Deposition of Metals. *Materials (Basel)*. 2020, *13*. https://doi.org/10.3390/ma13112493

[4] Ahmed, N.; Barsoum, I.; Haidemenopoulos, G.; Al-Rub, R.K.A. Process Parameter Selection and Optimization of Laser Powder Bed Fusion for 316L Stainless Steel: A Review. *J. Manuf. Process*. 2022, *75*, 415–434. https://doi.org/10.1016/j.jmapro.2021.12.064

[5] Jia, H.; Sun, H.; Wang, H.; Wu, Y.; Wang, H. Scanning Strategy in Selective Laser Melting (SLM): A Review. *Int. J. Adv. Manuf. Technol.* 2021, *113*, 2413–2435.
https://doi.org/10.1007/s00170-021-06810-3

[6] Robinson, J.H.; Robert, I.; Ashton, T.; Jones, E.; Fox, P.; Sutcliffe, C. The Effect of Hatch Angle Rotation on Parts Manufactured Using Selective Laser Melting. https://doi.org/10.1108/RPJ-06-2017-0111

[7] Carrozza, A.; Lorenzi, S.; Carugo, F.; Fest-Santini, S.; Santini, M.; Marchese, G.; Barbieri, G.; Cognini, F.; Cabrini, M.; Pastore, T. A Comparative Analysis between Material Extrusion and Other Additive Manufacturing Techniques: Defects, Microstructure and Corrosion Behavior in Nickel Alloy 625. *Mater. Des.* 2023, *225*, 111545.
https://doi.org/10.1016/J.MATDES.2022.111545

[8] Singh, R. Welding Corrosion Resistant Alloys – Stainless Steel. *Appl. Weld. Eng.* 2012, 191–214. https://doi.org/10.1016/B978-0-12-391916-8.00018-2

[9] Yang, K.; Ren, Y. Nickel-Free Austenitic Stainless Steels for Medical Applications. *Sci. Technol. Adv. Mater.* 2010, *11*, 014105. https://doi.org/10.1088/1468-6996/11/1/014105
[10] Chua, K.; Khan, I.; Malhotra, R.; Zhu, D. Additive Manufacturing and 3D Printing of Metallic Biomaterials. *Eng. Regen.* 2021, 2, 288-299. https://doi.org/10.1016/J.ENGREG.2021.11.002

[11] Quarto, M.; Carminati, M.; D'Urso, G. Density and Shrinkage Evaluation of AISI 316L Parts Printed via FDM Process. *Mater. Manuf. Process.* 2021, 36, 1535-1543. https://doi.org/10.1080/10426914.2021.1905830

[12] D'Andrea, D. Additive Manufacturing of AISI 316L Stainless Steel: A Review. *Met. 2023*, *Vol. 13*, Page 1370 2023, 13, 1370. https://doi.org/10.3390/MET13081370

[13] Suryawanshi, J.; Prashanth, K.G.; Ramamurty, U. Mechanical Behavior of Selective Laser Melted 316L Stainless Steel. *Mater. Sci. Eng. A* 2017, 696, 113-121. https://doi.org/10.1016/J.MSEA.2017.04.058

[14] Gatões, D.; Alves, R.; Alves, B.; Vieira, M.T. Selective Laser Melting and Mechanical Properties of Stainless Steels. *Materials (Basel)*. 2022, 15. https://doi.org/10.3390/ma15217575

[15] Xu, M.; Guo, H.; Wang, Y.; Hou, Y.; Dong, Z.; Zhang, L. Mechanical Properties and Microstructural Characteristics of 316L Stainless Steel Fabricated by Laser Powder Bed Fusion and Binder Jetting. *J. Mater. Res. Technol.* 2023, 24, 4427-4439. https://doi.org/10.1016/J.JMRT.2023.04.069

[16] Huang, G.; Wei, K.; Deng, J.; Liu, M.; Zeng, X. High-Power Laser Powder Bed Fusion of 316L Stainless Steel: Defects, Microstructure, and Mechanical Properties. *J. Manuf. Process.* 2022, 83, 235-245. https://doi.org/10.1016/J.JMAPRO.2022.08.066

[17] ASTM International. ASTM F3122-14 - Standard Guide for Evaluating Mechanical Properties of Metal Materials Made via Additive Manufacturing Processes 2014.

[18] International Organization for Standardization. UNI EN ISO 6892-1:2020 - Metallic Materials - Tensile Testing - Part 1: Method of Test at Room Temperature. 2020.

[19] International Organization for Standardization. UNI EN ISO 6507-1:2018 - Metallic Materials - Vickers Hardness Test - Part 1: Test Method. 2018
[10] Chua, K.; Khan, I.; Malhotra, R.; Zhu, D. Additive Manufacturing and 3D Printing of Metallic Biomaterials. *Eng. Regen.* 2021, 2, 288-299. https://doi.org/10.1016/J.ENGREG.2021.11.002

[11] Quarto, M.; Carminati, M.; D'Urso, G. Density and Shrinkage Evaluation of AISI 316L Parts Printed via FDM Process. *Mater. Manuf. Process.* 2021, 36, 1535-1543. https://doi.org/10.1080/10426914.2021.1905830

[12] D'Andrea, D. Additive Manufacturing of AISI 316L Stainless Steel: A Review. *Met. 2023*, *Vol. 13*, Page 1370 2023, 13, 1370. https://doi.org/10.3390/MET13081370

[13] Suryawanshi, J.; Prashanth, K.G.; Ramamurty, U. Mechanical Behavior of Selective Laser Melted 316L Stainless Steel. *Mater. Sci. Eng. A* 2017, 696, 113-121. https://doi.org/10.1016/J.MSEA.2017.04.058

[14] Gatões, D.; Alves, R.; Alves, B.; Vieira, M.T. Selective Laser Melting and Mechanical Properties of Stainless Steels. *Materials (Basel)*. 2022, 15. https://doi.org/10.3390/ma15217575

[15] Xu, M.; Guo, H.; Wang, Y.; Hou, Y.; Dong, Z.; Zhang, L. Mechanical Properties and Microstructural Characteristics of 316L Stainless Steel Fabricated by Laser Powder Bed Fusion and Binder Jetting. *J. Mater. Res. Technol.* 2023, 24, 4427-4439. https://doi.org/10.1016/J.JMRT.2023.04.069

[16] Huang, G.; Wei, K.; Deng, J.; Liu, M.; Zeng, X. High-Power Laser Powder Bed Fusion of 316L Stainless Steel: Defects, Microstructure, and Mechanical Properties. *J. Manuf. Process.* 2022, 83, 235-245. https://doi.org/10.1016/J.JMAPRO.2022.08.066

[17] ASTM International. ASTM F3122-14 - Standard Guide for Evaluating Mechanical Properties of Metal Materials Made via Additive Manufacturing Processes 2014.

[18] International Organization for Standardization. UNI EN ISO 6892-1:2020 - Metallic Materials - Tensile Testing - Part 1: Method of Test at Room Temperature. 2020.

[19] International Organization for Standardization. UNI EN ISO 6507-1:2018 - Metallic Materials - Vickers Hardness Test - Part 1: Test Method. 2018
# Influence of thermal treatment on surface roughness, microstructural, and mechanical properties of 3D printed ABS

NGUYEN Khanh Q.¹,ᵃ*, VUILLAUME Pascal Y.¹,ᵇ, HU Lei¹,ᶜ, VACHON Andro²,ᵈ, DIOUF-LEWIS Audrey¹,ᵉ, MARCOUX Pier-Luc¹,ᵉ, GAGNÉ Lambert¹,ᵉ, ROBERT Mathieu³,ᵇ and ELKOUN Saïd³,ᵢ

¹COALIA, Thetford Mines, QC, Canada G6G 1N1

²Cégep de Thetford, Thetford Mines, QC, Canada G6G 1N1

³University of Sherbrooke, Sherbrooke, QC, Canada J1K 2R1

ᵃqknguyen@coalia.ca, ᵇpvuillaume@coalia.ca, ᶜlhu@coalia.ca, ᵈavachon@cegepthetford.ca,

ᵉadiouflewis@coalia.ca, ᶜplmarcoux@coalia.ca, ᵉlambertgagne@outlook.com,

ʰmathieu.robert2@usherbrooke.ca, ᵉsaid.elkoun@usherbrooke.ca

**Keywords:** 3D Printing, FDM Technique, Radiant Heating System, Acrylonitrile Butadiene Styrene (ABS), Microstructural and Mechanical Properties

**Abstract.** Additive manufacturing (AM) has been widely used for rapid prototyping (RP) techniques due to its low cost and customizability. This technique allows for rapid and competitive price production compared to conventional manufacturing. However, pieces fabricated with the AM technique usually possess poor mechanical and surface properties compared to the injection molding technique. Based on COALIA's recent patent, a thermal treatment approach was used to improve the performance of printed parts. In this study, acrylonitrile-butadiene-styrene (ABS) was printed using fused deposition modeling (FDM) with and without heat treatment at 35 mm/s of printing speed. The physical and mechanical properties of printed ABS parts were then investigated. Tensile tests were performed to investigate the tensile strength, elastic modulus, and elongation at break. After tensile tests, X-ray microtomography was conducted to evaluate the surface morphologies. An optical profilometer analysis was also used to measure the surface roughness.

**Introduction**

In recent years, 3D printing also known as additive manufacturing (AM) has gained interest in various manufacturing industries due to its low cost, rapid production, and customizability [1-3]. AM technology has been developing dramatically in industrial applications such as automotive and aircraft interiors [4]. This technology allows for the creation of products by building layers of materials directly from 3D computer-aided design (CAD) software without needing a die or mold compared to conventional manufacturing [5]. In addition, AM technology can produce parts with a lattice structure leading to lower the weight of an aircraft and increasing its fuel efficiency [6,7]. There are numerous 3D printing technologies available to print a wide range of materials including polymers, metals, liquid resin, ceramics, and combinations of these [3,8]. However, fused deposition modeling (FDM) is the most widely used technique to extrude thermoplastic polymer filament for both functional and non-functional industrial applications [9,10]. Recently, the FDM technique has popular in 3D printing technology as rapid prototyping, ease of use, and low-cost technique when compared to other techniques. Moreover, this technique can produce complex geometries. In general, the principle of the FDM technique consists of heating and extruding filaments through a nozzle. Specifically, the continuous thermoplastic filaments are pushed toward the extrusion head to precisely regulate the feeding and retracting of filaments. A heating element

<figure><img src="image_200.png" /></figure>
# Influence of thermal treatment on surface roughness, microstructural, and mechanical properties of 3D printed ABS

NGUYEN Khanh Q.¹,ᵃ*, VUILLAUME Pascal Y.¹,ᵇ, HU Lei¹,ᶜ, VACHON Andro²,ᵈ, DIOUF-LEWIS Audrey¹,ᵉ, MARCOUX Pier-Luc¹,ᵉ, GAGNÉ Lambert¹,ᵉ, ROBERT Mathieu³,ᵇ and ELKOUN Saïd³,ᵢ

¹COALIA, Thetford Mines, QC, Canada G6G 1N1

²Cégep de Thetford, Thetford Mines, QC, Canada G6G 1N1

³University of Sherbrooke, Sherbrooke, QC, Canada J1K 2R1

ᵃqknguyen@coalia.ca, ᵇpvuillaume@coalia.ca, ᶜlhu@coalia.ca, ᵈavachon@cegepthetford.ca, ᶜadiouflewis@coalia.ca, ᵉplmarcoux@coalia.ca, ᵉlambertgagne@outlook.com,

ᵇmathieu.robert2@usherbrooke.ca, ᵈsaid.elkoun@usherbrooke.ca

**Keywords:** 3D Printing, FDM Technique, Radiant Heating System, Acrylonitrile Butadiene Styrene (ABS), Microstructural and Mechanical Properties

**Abstract.** Additive manufacturing (AM) has been widely used for rapid prototyping (RP) techniques due to its low cost and customizability. This technique allows for rapid and competitive price production compared to conventional manufacturing. However, pieces fabricated with the AM technique usually possess poor mechanical and surface properties compared to the injection molding technique. Based on COALIA's recent patent, a thermal treatment approach was used to improve the performance of printed parts. In this study, acrylonitrile-butadiene-styrene (ABS) was printed using fused deposition modeling (FDM) with and without heat treatment at 35 mm/s of printing speed. The physical and mechanical properties of printed ABS parts were then investigated. Tensile tests were performed to investigate the tensile strength, elastic modulus, and elongation at break. After tensile tests, X-ray microtomography was conducted to evaluate the surface morphologies. An optical profilometer analysis was also used to measure the surface roughness.

**Introduction**

In recent years, 3D printing also known as additive manufacturing (AM) has gained interest in various manufacturing industries due to its low cost, rapid production, and customizability [1-3]. AM technology has been developing dramatically in industrial applications such as automotive and aircraft interiors [4]. This technology allows for the creation of products by building layers of materials directly from 3D computer-aided design (CAD) software without needing a die or mold compared to conventional manufacturing [5]. In addition, AM technology can produce parts with a lattice structure leading to lower the weight of an aircraft and increasing its fuel efficiency [6,7]. There are numerous 3D printing technologies available to print a wide range of materials including polymers, metals, liquid resin, ceramics, and combinations of these [3,8]. However, fused deposition modeling (FDM) is the most widely used technique to extrude thermoplastic polymer filament for both functional and non-functional industrial applications [9,10]. Recently, the FDM technique has popular in 3D printing technology as rapid prototyping, ease of use, and low-cost technique when compared to other techniques. Moreover, this technique can produce complex geometries. In general, the principle of the FDM technique consists of heating and extruding filaments through a nozzle. Specifically, the continuous thermoplastic filaments are pushed toward the extrusion head to precisely regulate the feeding and retracting of filaments. A heating element

<figure><img src="image_204.png" /></figure>
and a nozzle are then used to heat and extrude the filament layer by layer. Finally, each printed layer is fused to produce 3D parts in a platform as shown in Fig. 1a.

Thermoplastic polymers are the main materials for the FDM technique, including polylactic acid (PLA), polycarbonate (PC), acrylonitrile butadiene styrene (ABS), polyethylene terephthalate (PET), and nylon. ABS is, however, the most widely used due to its low cost, high-impact strength, high durability, and interesting applications [11]. In contrast, shrinkage and warping are the main problems of ABS during the printing process. These drawbacks of ABS can be attributed to its high printing temperature and high coefficient of thermal expansion (CTE) [3, 12, 13]. Furthermore, some limitations of materials printed with the FDM technique are poor interlayer strength, inferior mechanical properties, high porosity, and voids [14]. Many previous studies revealed that changing printing parameters such as raster angle, nozzle size and temperature, printing speed, bed temperature, and layer height could enhance the mechanical properties of printed ABS parts [15]. In particular, a good interlayer is key to obtaining superior mechanical properties. Recently, several studies have concluded that heat treatment during and after the printing process can improve the quality of the final printed parts. Singh and al. (2019) [10] indicated that heat treatment during the printing process could enhance the mechanical and surface properties of printed ABS parts. A heat treatment approach was conducted in an air-convection electric oven by controlling different temperatures of 105, 115, and 125°C. It can be concluded that heat treatment of ABS parts above its glass transition temperature ($T_g$) enhanced material reflow. The results showed that surface roughness was decreased by increasing the heat temperature treatment. In addition, better tensile and flexural strength was observed at an annealing temperature of 125°C. Hart et al. (2018) [16] reported that the post-process heat treatment improved fracture roughness between printed layers. As a result, the interlayer fracture toughness of printed ABS parts with heat treatment increased to 2700% compared to printed parts without heat treatment. In addition, polymer reptation and mobility at the interface were also investigated using X-ray tomography to more explain the toughening mechanisms of treated ABS parts. Sharing the same point of view, Rane et al. (2020) [17] also revealed that post-thermal treatment increased 89% the tensile strength of printed ABS parts. However, the dimensional inaccuracy of parts with post-thermal treatment is the main limitation due to undesirable residual stresses from the print process.

In this study, a radiant heating system was developed based on our previous studies to evaluate the effect of thermal treatment during printing on interlayer strength, mechanical, and microstructure properties of printed ABS parts [18-20]. The preliminary investigations were conducted with different thermal treatments (220, 240, 260, 270, and 280°C). Tensile tests were then performed to investigate the tensile strength, modulus, and elongation at break of printed ABS parts with and without thermal treatment. Various analyses such as optical microscopy and optical profilometer were also conducted to evaluate the surface morphologies and roughness.

# Experimental methods

## Materials

Amorphous thermoplastic acrylonitrile-butadiene-styrene (ABS M30 Ivory) filaments (Stratasys Inc., MN, USA) with a nominal diameter of 1.75 mm, a density of 1.05 g/cm³, and a glass transition temperature of 105°C were used to print 3D parts of specimens.

## Printing parameters

Before printing, ABS filaments were dried at 80°C for 8 hours in an oven and they were then kept in a conditioned room at 75°C to remove moisture. The FDM Prusa Mk3s+ printer was used to print ABS specimens. Type IV specimens were printed along Z-X axes with dimensions according to ASTM D638 [21] as shown in Fig. 1b.
and a nozzle are then used to heat and extrude the filament layer by layer. Finally, each printed layer is fused to produce 3D parts in a platform as shown in Fig. 1a.

Thermoplastic polymers are the main materials for the FDM technique, including polylactic acid (PLA), polycarbonate (PC), acrylonitrile butadiene styrene (ABS), polyethylene terephthalate (PET), and nylon. ABS is, however, the most widely used due to its low cost, high-impact strength, high durability, and interesting applications [11]. In contrast, shrinkage and warping are the main problems of ABS during the printing process. These drawbacks of ABS can be attributed to its high printing temperature and high coefficient of thermal expansion (CTE) [3, 12, 13]. Furthermore, some limitations of materials printed with the FDM technique are poor interlayer strength, inferior mechanical properties, high porosity, and voids [14]. Many previous studies revealed that changing printing parameters such as raster angle, nozzle size and temperature, printing speed, bed temperature, and layer height could enhance the mechanical properties of printed ABS parts [15]. In particular, a good interlayer is key to obtaining superior mechanical properties. Recently, several studies have concluded that heat treatment during and after the printing process can improve the quality of the final printed parts. Singh and al. (2019) [10] indicated that heat treatment during the printing process could enhance the mechanical and surface properties of printed ABS parts. A heat treatment approach was conducted in an air-convection electric oven by controlling different temperatures of 105, 115, and 125°C. It can be concluded that heat treatment of ABS parts above its glass transition temperature ($T_g$) enhanced material reflow. The results showed that surface roughness was decreased by increasing the heat temperature treatment. In addition, better tensile and flexural strength was observed at an annealing temperature of 125°C. Hart et al. (2018) [16] reported that the post-process heat treatment improved fracture roughness between printed layers. As a result, the interlayer fracture toughness of printed ABS parts with heat treatment increased to 2700% compared to printed parts without heat treatment. In addition, polymer reptation and mobility at the interface were also investigated using X-ray tomography to more explain the toughening mechanisms of treated ABS parts. Sharing the same point of view, Rane et al. (2020) [17] also revealed that post-thermal treatment increased 89% the tensile strength of printed ABS parts. However, the dimensional inaccuracy of parts with post-thermal treatment is the main limitation due to undesirable residual stresses from the print process.

In this study, a radiant heating system was developed based on our previous studies to evaluate the effect of thermal treatment during printing on interlayer strength, mechanical, and microstructure properties of printed ABS parts [18-20]. The preliminary investigations were conducted with different thermal treatments (220, 240, 260, 270, and 280°C). Tensile tests were then performed to investigate the tensile strength, modulus, and elongation at break of printed ABS parts with and without thermal treatment. Various analyses such as optical microscopy and optical profilometer were also conducted to evaluate the surface morphologies and roughness.

# Experimental methods

## Materials

Amorphous thermoplastic acrylonitrile-butadiene-styrene (ABS M30 Ivory) filaments (Stratasys Inc., MN, USA) with a nominal diameter of 1.75 mm, a density of 1.05 g/cm³, and a glass transition temperature of 105°C were used to print 3D parts of specimens.

## Printing parameters

Before printing, ABS filaments were dried at 80°C for 8 hours in an oven and they were then kept in a conditioned room at 75°C to remove moisture. The FDM Prusa Mk3s+ printer was used to print ABS specimens. Type IV specimens were printed along Z-X axes with dimensions according to ASTM D638 [21] as shown in Fig. 1b.
<figure><img src="image_201.png" /><figcaption>Fig. 1. a) Principle of the fused deposition modeling (FDM) technique with a radiant heating (adapted from [3]) and b) Printed ABS specimens</figcaption></figure>

In this study, a radiant heating system coupled with the Prusa Mk3s+ printer (Fig. 1a) was used to evaluate the effect of thermal treatment on mechanical and microstructure properties of printed ABS parts. The printing process consists of five main stages (i) 1st heating materials, (ii) 1st extruding a portion of heated materials, (iii) creating a layer of deposition, (iv) 2nd heating a portion of the deposition layer by the radiant system, and (v) 2nd extruding a portion of heated materials to create a new layer of deposition. The nozzle and radiant heating temperatures were 295°C and 240°C, respectively, with a printing speed of 35 mm/s. The printing parameters were selected based on our previous studies [18,19] as shown in Table 1. The terms "treated" and "untreated" specimens in this study refer to ABS specimens printed with and without a radiant heating system, respectively.

## Characterization methods

### Tensile tests

The tensile test, using dog-bone specimens (Fig. 1b), was performed on a universal testing machine (Zwick/Roell Z030) at 23°C and 50% relative humidity. A load cell of 30 kN and a rate of 5 mm/min were applied to evaluate the tensile strength of ABS printed with and without a radiant system. After five replicates, the stress-strain curves of treated and untreated ABS were then recorded. The average of tensile strength, modulus, elongation at break, and standard deviation were reported.
<figure><img src="image_205.png" /><figcaption>Fig. 1. a) Principle of the fused deposition modeling (FDM) technique with a radiant heating (adapted from [3]) and b) Printed ABS specimens</figcaption></figure>

In this study, a radiant heating system coupled with the Prusa Mk3s+ printer (Fig. 1a) was used to evaluate the effect of thermal treatment on mechanical and microstructure properties of printed ABS parts. The printing process consists of five main stages (i) 1st heating materials, (ii) 1st extruding a portion of heated materials, (iii) creating a layer of deposition, (iv) 2nd heating a portion of the deposition layer by the radiant system, and (v) 2nd extruding a portion of heated materials to create a new layer of deposition. The nozzle and radiant heating temperatures were 295°C and 240°C, respectively, with a printing speed of 35 mm/s. The printing parameters were selected based on our previous studies [18,19] as shown in Table 1. The terms "treated" and "untreated" specimens in this study refer to ABS specimens printed with and without a radiant heating system, respectively.

## Characterization methods

### Tensile tests

The tensile test, using dog-bone specimens (Fig. 1b), was performed on a universal testing machine (Zwick/Roell Z030) at 23°C and 50% relative humidity. A load cell of 30 kN and a rate of 5 mm/min were applied to evaluate the tensile strength of ABS printed with and without a radiant system. After five replicates, the stress-strain curves of treated and untreated ABS were then recorded. The average of tensile strength, modulus, elongation at break, and standard deviation were reported.
<table><caption>Table 1. Printing parameters used to untreated and treated ABS parts.</caption><thead><tr><td>Parameters</td><td>Values</td></tr></thead><tbody><tr><td>Nozzle diameter</td><td>0.6 [mm]</td></tr><tr><td>Nozzle temperature</td><td>295 [°C]</td></tr><tr><td>Bed temperature</td><td>100 [°C]</td></tr><tr><td>Radiant heating temperature</td><td>240 [°C] (only for treated sample)</td></tr><tr><td>Extrusion factor</td><td>0.98</td></tr><tr><td>Layer thickness</td><td>0.2 [mm]</td></tr><tr><td>Infill line direction</td><td>Z-direction (0°C)</td></tr><tr><td>Infill density</td><td>100 [%]</td></tr><tr><td>Printing speed</td><td>35 [mm.s<sup>-1</sup>]</td></tr></tbody></table>

### X-ray microtomography (μ-CT)

X-ray microtomography (μ-CT), a non-destructive imaging, is used to investigate the porosity and distribution of voids in materials. In the current study, the μ-CT analysis was conducted using a ZEISS Xradia 520 Versa 3D X-ray microscope at a voltage of 60 kV. The fractured samples after tensile tests were scanned to provide 2D radiographs. The image reconstruction Dragonfly software with tomographic data was applied for 3D visualization and analysis. Three replicates were performed to calculate the porosity of ABS printed with and without a radiant system.

### Optical profilometry (OP) analysis

The effect of thermal treatment during printing on the surface roughness of printed ABS parts was investigated using an optical profilometer (Polytec micro view®+). In this study, fractured samples after tensile tests received 5 nm of a palladium-gold coating before analysis to enhance the reflectivity of samples. A non-contact optical method, white light interferometry, was used to measure surface height and profile on 3D structures. Three replicates were performed to investigate the surface roughness of untreated and treated samples.

## Results and discussions

### Mechanical properties

Figure 2 shows the typical stress-strain curves of untreated and treated printed ABS samples after five replicates. The results indicate a brittle fracture behavior for both untreated and treated samples. In addition, Figs. 1A, 2A, and 3A present the average tensile strength, elastic modulus, and elongation at break along with standard deviations of untreated and treated samples, respectively. It should be noted that preliminary tests with different thermal treatments of 220, 240, 260, 270, and 280°C were performed to determine the optimal thermal treatment of 240°C. These results were similar to our previous study [18] and can be found in supplementary data (Appendix A). As a result, the average tensile strength, elastic modulus, and elongation at break of the untreated sample were 19.0 MPa, 1921 MPa, and 1.0%, respectively. Contrariwise, the average tensile strength, elastic modulus, and elongation at break of the treated sample increased up to 30.7 MPa, 2028 MPa, and 2.1%, respectively, due to the thermal treatment. In other words, the radiant heating system can improve 62% in tensile strength, 6% in elastic modulus, and 110% in elongation at break. This observation manifested that radiant heating increased the mobility and thermal energy of polymer chains. Layers fused once achieved enough thermal energy, resulting in enhancing mechanical properties. It should be noted that the optimal heat treatment was 240°C. Contrariwise, higher heat treatments (260, 270, and 280°C) could degrade the interlayer surface, leading to reduced mechanical properties (Appendix A).
<table><caption>Table 1. Printing parameters used to untreated and treated ABS parts.</caption><thead><tr><td>Parameters</td><td>Values</td></tr></thead><tbody><tr><td>Nozzle diameter</td><td>0.6 [mm]</td></tr><tr><td>Nozzle temperature</td><td>295 [°C]</td></tr><tr><td>Bed temperature</td><td>100 [°C]</td></tr><tr><td>Radiant heating temperature</td><td>240 [°C] (only for treated sample)</td></tr><tr><td>Extrusion factor</td><td>0.98</td></tr><tr><td>Layer thickness</td><td>0.2 [mm]</td></tr><tr><td>Infill line direction</td><td>Z-direction (0°C)</td></tr><tr><td>Infill density</td><td>100 [%]</td></tr><tr><td>Printing speed</td><td>35 [mm.s<sup>-1</sup>]</td></tr></tbody></table>

### X-ray microtomography (μ-CT)

X-ray microtomography (μ-CT), a non-destructive imaging, is used to investigate the porosity and distribution of voids in materials. In the current study, the μ-CT analysis was conducted using a ZEISS Xradia 520 Versa 3D X-ray microscope at a voltage of 60 kV. The fractured samples after tensile tests were scanned to provide 2D radiographs. The image reconstruction Dragonfly software with tomographic data was applied for 3D visualization and analysis. Three replicates were performed to calculate the porosity of ABS printed with and without a radiant system.

### Optical profilometry (OP) analysis

The effect of thermal treatment during printing on the surface roughness of printed ABS parts was investigated using an optical profilometer (Polytec micro view®+). In this study, fractured samples after tensile tests received 5 nm of a palladium-gold coating before analysis to enhance the reflectivity of samples. A non-contact optical method, white light interferometry, was used to measure surface height and profile on 3D structures. Three replicates were performed to investigate the surface roughness of untreated and treated samples.

## Results and discussions

### Mechanical properties

Figure 2 shows the typical stress-strain curves of untreated and treated printed ABS samples after five replicates. The results indicate a brittle fracture behavior for both untreated and treated samples. In addition, Figs. 1A, 2A, and 3A present the average tensile strength, elastic modulus, and elongation at break along with standard deviations of untreated and treated samples, respectively. It should be noted that preliminary tests with different thermal treatments of 220, 240, 260, 270, and 280°C were performed to determine the optimal thermal treatment of 240°C. These results were similar to our previous study [18] and can be found in supplementary data (Appendix A). As a result, the average tensile strength, elastic modulus, and elongation at break of the untreated sample were 19.0 MPa, 1921 MPa, and 1.0%, respectively. Contrariwise, the average tensile strength, elastic modulus, and elongation at break of the treated sample increased up to 30.7 MPa, 2028 MPa, and 2.1%, respectively, due to the thermal treatment. In other words, the radiant heating system can improve 62% in tensile strength, 6% in elastic modulus, and 110% in elongation at break. This observation manifested that radiant heating increased the mobility and thermal energy of polymer chains. Layers fused once achieved enough thermal energy, resulting in enhancing mechanical properties. It should be noted that the optimal heat treatment was 240°C. Contrariwise, higher heat treatments (260, 270, and 280°C) could degrade the interlayer surface, leading to reduced mechanical properties (Appendix A).
<figure><img src="image_202.png" /><figcaption>Fig. 2. Stress-strain curves of a) untreated and b) treated ABS samples</figcaption></figure>

## Microstructural properties

μ-CT analysis was conducted on fractured untreated and treated ABS samples after tensile tests to investigate the degree of porosity. As illustrated, Fig. 3 presents a typical μ-CT image for both untreated and treated samples. The reconstructed volume data was generated using Dragonfly software coupled with tomographic data. The results indicate that porosity was found to be 3% and 1.6% in untreated and treated samples, respectively. According to the findings, the heat treatment could reduce porosity contents leading to enhanced mechanical strength of the treated sample.

<figure><img src="image_203.png" /><figcaption>Fig. 3. μ-CT images of a) untreated and b) treated ABS samples</figcaption></figure>

## Surface roughness properties

The surface roughness plays a key role in assessing the material's performance. Surface irregularities may result in fractures or material deterioration. In general, area and profile roughness are the two main parameters evaluating the surface roughness of materials. The roughness average (S<sub>a</sub>) and the root mean square (S<sub>q</sub>) are used to define area roughness. On the other hand, the roughness average (R<sub>a</sub>) and the root mean square (R<sub>q</sub>) determine the profile roughness. Figure 4 shows the area roughness of untreated and treated ABS samples. Moreover, the profile roughness of the untreated and treated ABS samples was calculated by drawing two-dimensional lines I-I and II-II, respectively. In the current study, the surface data was generated using a Gaussian filter which area and profile roughness were determined according to ISO 25178 [22] and ISO 21920 [23] as shown in Table 2. The results revealed that both the area and profile roughness of treated samples were lower than those of untreated ones. The heat treatment, therefore, helps the surface of samples less rough than untreated ones.
<figure><img src="image_206.png" /><figcaption>Fig. 2. Stress-strain curves of a) untreated and b) treated ABS samples</figcaption></figure>

## Microstructural properties

μ-CT analysis was conducted on fractured untreated and treated ABS samples after tensile tests to investigate the degree of porosity. As illustrated, Fig. 3 presents a typical μ-CT image for both untreated and treated samples. The reconstructed volume data was generated using Dragonfly software coupled with tomographic data. The results indicate that porosity was found to be 3% and 1.6% in untreated and treated samples, respectively. According to the findings, the heat treatment could reduce porosity contents leading to enhanced mechanical strength of the treated sample.

<figure><img src="image_207.png" /><figcaption>Fig. 3. μ-CT images of a) untreated and b) treated ABS samples</figcaption></figure>

## Surface roughness properties

The surface roughness plays a key role in assessing the material's performance. Surface irregularities may result in fractures or material deterioration. In general, area and profile roughness are the two main parameters evaluating the surface roughness of materials. The roughness average (S<sub>a</sub>) and the root mean square (S<sub>q</sub>) are used to define area roughness. On the other hand, the roughness average (R<sub>a</sub>) and the root mean square (R<sub>q</sub>) determine the profile roughness. Figure 4 shows the area roughness of untreated and treated ABS samples. Moreover, the profile roughness of the untreated and treated ABS samples was calculated by drawing two-dimensional lines I-I and II-II, respectively. In the current study, the surface data was generated using a Gaussian filter which area and profile roughness were determined according to ISO 25178 [22] and ISO 21920 [23] as shown in Table 2. The results revealed that both the area and profile roughness of treated samples were lower than those of untreated ones. The heat treatment, therefore, helps the surface of samples less rough than untreated ones.
<table><caption>Table 2. Surface roughness values of untreated and treated ABS samples.</caption><thead><tr><td>Surface roughness</td><td>Untreated sample</td><td>Treated sample</td></tr></thead><tbody><tr><td>Roughness average (S<sub>a</sub>) - Area</td><td>6.01 [µm]</td><td>4.98 [µm]</td></tr><tr><td>Root mean square (S<sub>q</sub>) - Area</td><td>9.14 [µm]</td><td>7.42 [µm]</td></tr><tr><td>Roughness average (R<sub>a</sub>) - Profile</td><td>2.21 [µm]</td><td>1.78 [µm]</td></tr><tr><td>Root mean square (R<sub>q</sub>) - Profile</td><td>2.87 [µm]</td><td>2.39 [µm]</td></tr></tbody></table>

<figure><img src="image_204.png" /><figcaption>Fig. 4. Surface roughness of a) untreated and b) treated ABS samples</figcaption></figure>

## Conclusions

Acrylonitrile butadiene styrene (ABS) is the most widely used in the fused deposition modeling (FDM) technique due to its low cost, high-impact strength, high durability, and interesting applications. Contrariwise, shrinkage and warping are the main problems of ABS during the printing process. In this study, thermal treatment using a radiant heating system was investigated to evaluate the microstructural, mechanical properties, and surface roughness of printed ABS parts. The thermal treatment at 240°C and printing speed at 35 mm/s were chosen as optimal printing parameters. The following conclusions may be drawn:

1. The mechanical properties of printed ABS parts were enhanced by thermal treatment at 240°C using a radiant heating system. Compared to untreated ones, treated samples showed
<table><caption>Table 2. Surface roughness values of untreated and treated ABS samples.</caption><thead><tr><td>Surface roughness</td><td>Untreated sample</td><td>Treated sample</td></tr></thead><tbody><tr><td>Roughness average (S<sub>a</sub>) - Area</td><td>6.01 [µm]</td><td>4.98 [µm]</td></tr><tr><td>Root mean square (S<sub>q</sub>) - Area</td><td>9.14 [µm]</td><td>7.42 [µm]</td></tr><tr><td>Roughness average (R<sub>a</sub>) - Profile</td><td>2.21 [µm]</td><td>1.78 [µm]</td></tr><tr><td>Root mean square (R<sub>q</sub>) - Profile</td><td>2.87 [µm]</td><td>2.39 [µm]</td></tr></tbody></table>

<figure><img src="image_208.png" /><figcaption>Fig. 4. Surface roughness of a) untreated and b) treated ABS samples</figcaption></figure>

## Conclusions

Acrylonitrile butadiene styrene (ABS) is the most widely used in the fused deposition modeling (FDM) technique due to its low cost, high-impact strength, high durability, and interesting applications. Contrariwise, shrinkage and warping are the main problems of ABS during the printing process. In this study, thermal treatment using a radiant heating system was investigated to evaluate the microstructural, mechanical properties, and surface roughness of printed ABS parts. The thermal treatment at 240°C and printing speed at 35 mm/s were chosen as optimal printing parameters. The following conclusions may be drawn:

1. The mechanical properties of printed ABS parts were enhanced by thermal treatment at 240°C using a radiant heating system. Compared to untreated ones, treated samples showed
increases in tensile strength of 62%, elastic modulus of 6%, and elongation at break of 110%.

2. The heat treatment could reduce porosity contents leading to enhanced mechanical strength of the treated sample. The μ-CT analysis revealed that porosity was found to be 3% and 1.6% in untreated and treated samples, respectively.

3. The heat treatment could enhance the surface roughness of printed ABS samples. Optical profilometry (OP) analysis showed that untreated samples were rougher than treated ones.

4. The finding of this study showed that thermal treatment could enhance the mechanical and microstructural properties of printed ABS parts. Further research on the FDM technique and other advanced analyses should be conducted to better understand the performance of printed parts.

**Acknowledgments**

The authors gratefully acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the PRIMA Quebec. Also, the authors would like to thank COALIA and University of Sherbrooke for the support of this study.

**Appendix A (supplementary data)**

<figure><img src="image_205.png" /><figcaption>Fig. 2A
Elastic modulus of printed ABS samples at different treated temperatures</figcaption></figure>

<figure><img src="image_206.png" /></figure>
increases in tensile strength of 62%, elastic modulus of 6%, and elongation at break of 110%.

2. The heat treatment could reduce porosity contents leading to enhanced mechanical strength of the treated sample. The μ-CT analysis revealed that porosity was found to be 3% and 1.6% in untreated and treated samples, respectively.

3. The heat treatment could enhance the surface roughness of printed ABS samples. Optical profilometry (OP) analysis showed that untreated samples were rougher than treated ones.

4. The finding of this study showed that thermal treatment could enhance the mechanical and microstructural properties of printed ABS parts. Further research on the FDM technique and other advanced analyses should be conducted to better understand the performance of printed parts.

**Acknowledgments**

The authors gratefully acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the PRIMA Quebec. Also, the authors would like to thank COALIA and University of Sherbrooke for the support of this study.

**Appendix A (supplementary data)**

<figure><img src="image_209.png" /><figcaption>Fig. 2A
Elastic modulus of printed ABS samples at different treated temperatures</figcaption></figure>

<figure><img src="image_210.png" /></figure>
<figure><img src="image_207.png" /><figcaption>Fig. 3A
Elongation at break of printed ABS samples at different treated temperatures</figcaption></figure>

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[19] Nguyen, Q. K., Vuillaume, P. Y., Hu, L., Vachon, A., Marcoux, P. L., Robert, M., & Elkoun, S. “Effect of in Situ Thermal Treatment on Interlayer Adhesion of 3d Printed Polyetherimide (Pei) Parts Produced by Fused Deposition Modeling (Fdm)”. Available at SSRN 4654854. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4654854

[20] Nguyen, K. Q., Vuillaume, P. Y., Robert, M., & Elkoun, S. (2022, December). “AFM Analysis of 3D Printing PEI for Automotive Applications”. *In Conference on Mechanical, Automotive and Materials Engineering* (pp. 123-133). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3672-4_10

[21] ASTM D638, “Standard Test Methods for Tensile Properties of Plastics.” *ASTM International*, West Conshohocken, PA, USA, 2014

[22] ISO 25178, “Geometrical product specifications (GPS) — Surface texture.” *ISO* (the International Organization for Standardization), 2021.

[23] ISO 21920, “Geometrical product specifications (GPS) — Surface texture - Profile methods.” *ISO* (the International Organization for Standardization), 2021.
fused filament fabricated parts”. *The International Journal of Advanced Manufacturing Technology*, 102, 1521-1536. https://doi.org/10.1007/s00170-018-03276-8

[11] Dawoud, M., Taha, I., & Ebeid, S. J. (2016). “Mechanical behaviour of ABS: An experimental study using FDM and injection moulding techniques”. *Journal of manufacturing Processes*, 21, 39-45. https://doi.org/10.1016/j.jmapro.2015.11.002

[12] Weng, Z., Wang, J., Senthil, T., & Wu, L. (2016). “Mechanical and thermal properties of ABS/montmorillonite nanocomposites for fused deposition modeling 3D printing”. *Materials & Design*, 102, 276-283. https://doi.org/10.1016/j.matdes.2016.04.045

[13] Choi, Y. H., Kim, C. M., Jeong, H. S., & Youn, J. H. (2016). “Influence of bed temperature on heat shrinkage shape error in FDM additive manufacturing of the ABS-engineering plastic”. *World Journal of Engineering and Technology*, 4(3), 186-192. Doi: 10.4236/wjet.2016.43D022

[14] Sood, A. K., Ohdar, R. K., & Mahapatra, S. S. (2010). “Parametric appraisal of mechanical property of fused deposition modelling processed parts”. *Materials & Design*, 31(1), 287-295. https://doi.org/10.1016/j.matdes.2009.06.016

[15] Rodríguez-Panes, A., Claver, J., & Camacho, A. M. (2018). “The influence of manufacturing parameters on the mechanical behaviour of PLA and ABS pieces manufactured by FDM: A comparative analysis”. *Materials*, 11(8), 1333. https://doi.org/10.3390/ma11081333

[16] Hart, K. R., Dunn, R. M., Sietins, J. M., Mock, C. M. H., Mackay, M. E., & Wetzel, E. D. (2018). “Increased fracture toughness of additively manufactured amorphous thermoplastics via thermal annealing”. *Polymer*, 144, 192-204. https://doi.org/10.1016/j.polymer.2018.04.024

[17] Rane, R., Kulkarni, A., Prajapati, H., Taylor, R., Jain, A., & Chen, V. (2020). “Post-process effects of isothermal annealing and initially applied static uniaxial loading on the ultimate tensile strength of fused filament fabrication parts”. *Materials*, 13(2), 352. https://doi.org/10.3390/ma13020352

[18] A. Vachon, P.-Y. Vuillaume, L. Deschamps, L. Hu, A. Diouf Lewis, and P.-L. Marcoux, “COALIA Patent CA3177826, Printhead and additive manufacturing methods.” Canada, 2023. [Online]. Available: https://www.ic.gc.ca/opicipo/cpd/eng/patent/3177826/summary.html?query=3177826&type=basic_search

[19] Nguyen, Q. K., Vuillaume, P. Y., Hu, L., Vachon, A., Marcoux, P. L., Robert, M., & Elkoun, S. “Effect of in Situ Thermal Treatment on Interlayer Adhesion of 3d Printed Polyetherimide (Pei) Parts Produced by Fused Deposition Modeling (Fdm)”. Available at SSRN 4654854. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4654854

[20] Nguyen, K. Q., Vuillaume, P. Y., Robert, M., & Elkoun, S. (2022, December). “AFM Analysis of 3D Printing PEI for Automotive Applications”. *In Conference on Mechanical, Automotive and Materials Engineering* (pp. 123-133). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3672-4_10

[21] ASTM D638, “Standard Test Methods for Tensile Properties of Plastics.” *ASTM International*, West Conshohocken, PA, USA, 2014

[22] ISO 25178, “Geometrical product specifications (GPS) — Surface texture.” *ISO* (the International Organization for Standardization), 2021.

[23] ISO 21920, “Geometrical product specifications (GPS) — Surface texture - Profile methods.” *ISO* (the International Organization for Standardization), 2021.
# Impact of heat treatment cycles on work hardening characteristics in selective laser melted Ti6Al4V ELI alloy

SUKRE Mahendra¹,²,ᵃ and MEENA Anil¹,ᵇ*

¹Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai-600036, India

²Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India

ᵃmdsukre@sggs.ac.in, ᵇanilm@iitm.ac.in

**Keywords:** Selective Laser Melting (SLM), Heat Treatment, Ti6Al4V (ELI), Work Hardening Behaviour

**Abstract.** The present study investigates the influence of heat treatment cycles on the work hardening behaviour of a selective laser-melted (SLM) Ti6Al4V ELI alloy. To modify the microstructure and assess its impact on work hardening behaviour, the SLM Ti6Al4V ELI alloy is subjected to a series of heat treatment cycles, including solution treatment, quenching, and ageing. The microstructural evolution is characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD). Tensile tests are conducted to investigate the work hardening behaviour, including yield strength, ultimate tensile strength, and strain hardening. The findings show that heat treatment cycles have a significant impact on the microstructure and work hardening behavior of the SLM Ti6Al4V ELI alloy. Solution treatment and quenching produce a refined α' martensitic structure by homogenizing the microstructure and dissolving the secondary phases. Subsequent ageing treatments induce the precipitation of secondary phases, such as α and β phases, within the α' martensite matrix.

**Introduction**

Titanium alloys are widely used in the aerospace, automotive, medical, and chemical industries because of their important properties, which include biocompatibility, excellent corrosion resistance, and high specific strength [1,2]. Titanium alloys are primarily used in the aerospace industry to reduce weight and fuel consumption. Ti6Al4V is the most commonly used titanium alloy in industry [3]. However, due to the costs of mineral extraction, refinement, and manufacturing, product costs are high. As with mineral extraction costs, refinement costs are unavoidable [4]. As a result, industries require cost-effective solutions to reduce titanium alloy product manufacturing costs by reducing machining costs. Additive manufacturing (AM) is a low-cost solution for industries that reduces material costs (due to the reuse of powder) and tooling costs. It can also create complex components with the desired mechanical properties [5].

The most common AM process for metal AM manufacturing is selective laser melting (SLM). Manufacturing defects such as lack of fusion and porosity [6] can occur in titanium alloy SLM processes, affecting the mechanical performance of the additively manufactured component by increasing the stress concentration in the material. However, the material's microstructure plays an important role in determining its mechanical properties. The microstructure is determined by process parameters (laser power, layer thickness, and speed), design parameters (shape and size), and machine parameters (heated bed temperature) [5,7]. The direction of heat transfer causes columnar grains to form during selective laser melting. Because the crystals' growth direction is aligned with the maximum temperature gradient, the grains grow toward the molten pool in the z-direction. Furthermore, rapid cooling rates in small molten pools result in the formation of a metastable martensitic microstructure [8,9]. A fine microstructure can increase component

<figure><img src="image_208.png" /></figure>
# Impact of heat treatment cycles on work hardening characteristics in selective laser melted Ti6Al4V ELI alloy

SUKRE Mahendra¹,²,ᵃ and MEENA Anil¹,ᵇ*

¹Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai-600036, India

²Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India

ᵃmdsukre@sggs.ac.in, ᵇanilm@iitm.ac.in

**Keywords:** Selective Laser Melting (SLM), Heat Treatment, Ti6Al4V (ELI), Work Hardening Behaviour

**Abstract.** The present study investigates the influence of heat treatment cycles on the work hardening behaviour of a selective laser-melted (SLM) Ti6Al4V ELI alloy. To modify the microstructure and assess its impact on work hardening behaviour, the SLM Ti6Al4V ELI alloy is subjected to a series of heat treatment cycles, including solution treatment, quenching, and ageing. The microstructural evolution is characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD). Tensile tests are conducted to investigate the work hardening behaviour, including yield strength, ultimate tensile strength, and strain hardening. The findings show that heat treatment cycles have a significant impact on the microstructure and work hardening behavior of the SLM Ti6Al4V ELI alloy. Solution treatment and quenching produce a refined α' martensitic structure by homogenizing the microstructure and dissolving the secondary phases. Subsequent ageing treatments induce the precipitation of secondary phases, such as α and β phases, within the α' martensite matrix.

**Introduction**

Titanium alloys are widely used in the aerospace, automotive, medical, and chemical industries because of their important properties, which include biocompatibility, excellent corrosion resistance, and high specific strength [1,2]. Titanium alloys are primarily used in the aerospace industry to reduce weight and fuel consumption. Ti6Al4V is the most commonly used titanium alloy in industry [3]. However, due to the costs of mineral extraction, refinement, and manufacturing, product costs are high. As with mineral extraction costs, refinement costs are unavoidable [4]. As a result, industries require cost-effective solutions to reduce titanium alloy product manufacturing costs by reducing machining costs. Additive manufacturing (AM) is a low-cost solution for industries that reduces material costs (due to the reuse of powder) and tooling costs. It can also create complex components with the desired mechanical properties [5].

The most common AM process for metal AM manufacturing is selective laser melting (SLM). Manufacturing defects such as lack of fusion and porosity [6] can occur in titanium alloy SLM processes, affecting the mechanical performance of the additively manufactured component by increasing the stress concentration in the material. However, the material's microstructure plays an important role in determining its mechanical properties. The microstructure is determined by process parameters (laser power, layer thickness, and speed), design parameters (shape and size), and machine parameters (heated bed temperature) [5,7]. The direction of heat transfer causes columnar grains to form during selective laser melting. Because the crystals' growth direction is aligned with the maximum temperature gradient, the grains grow toward the molten pool in the z-direction. Furthermore, rapid cooling rates in small molten pools result in the formation of a metastable martensitic microstructure [8,9]. A fine microstructure can increase component

<figure><img src="image_212.png" /></figure>
strength while decreasing ductility and work hardening. The main challenge in the AM component is balancing the material's strength, ductility, and work hardening. The heat treatment technique can help improve the mechanical properties and performance of Ti6Al4V components. However, traditional heat treatment is ineffective in achieving the desired mechanical properties [10]. Previous research suggests that work-hardening occurs due to kinematic hardening caused by the mechanical contrast between the α and α' phases in electron beam-melted Ti6Al4V [11]. Stress-relieved samples exhibited a fine acicular martensitic α' phase due to rapid solidification caused by LPBF [12].

This study investigated the impact of heat treatment on the microstructure and work hardening behavior of SLMed Ti6Al4V ELI below the trans temperature. Two heat treatment cycles were used: HT-730 and HT-940-540. Tensile tests were performed on both as-received and heat-treated samples to determine the material's work-hardening parameter. The study proposed a straightforward method for linking heat treatment, work hardening, and SLMed Ti6Al4V ELI alloys.

## Experimental procedure

All samples were created using the SLM machine in the presence of argon inert gas. The feedstock material used was Ti6Al4V ELI powders. The powder size ranged from 10 µm to 60 µm, with a 31 µm average. For sample production, the default manufacturing parameters were used. The rectangular samples were created vertically along the Z-axis. Three samples were made for each process condition. Following the printing process, the samples were stress-relieved under specific conditions: 800°C for 2 hours, followed by furnace cooling to 300°C and air cooling to room temperature. Using wire-EDM, the samples were cut from build plates. As shown in Table 1, heat treatments were used to modify the microstructure of stress-relieved samples. Both stress relief and heat treatment took place in an inert gas environment. Micro tensile testing was performed on sub-sized rectangular samples with a gauge length of 6 mm, as defined by ASTM E8/E8M. A micro-tensile testing system with a load cell capacity of 2 kN was used, with a constant strain rate of 0.001 s⁻¹ applied during testing. Additionally, rectangular samples measuring 5 × 10 × 10 mm³ were produced for metallography. Standard metallographic sample preparation procedures, such as sectioning, grinding, and polishing, were used. Kroll's reagent was used as an etchant. The microstructure was studied using a digital microscope (Olympus GX53 Inverted) and a scanning electron microscope (INSPECT F50 FE-SEM). The Bruker D8 Discover powder XRD machine was used for the X-ray diffraction (XRD) analysis.

<table><caption>Table 1: Heat treatment cycle performed on the SLM Ti6Al4V ELI</caption><thead><tr><td>Terminology</td><td>Heat treatment condition</td></tr></thead><tbody><tr><td>As received (AR)</td><td>Stress relieved</td></tr><tr><td>HT-730</td><td>Heating at 730 °C for 2h and AC*</td></tr><tr><td>HT-940-550</td><td>Heating at 940 °C for 1h and WQ* + Aging at 550 °C for 4h and AC*</td></tr></tbody></table>

## Results and Discussions

The optical microscopic image of the AR and heat-treated sample, shown in Fig. 1, depicts the material's microstructure. However, as a proper process section, 3D printing parameters significantly reduce pore amount and size [13]. As shown in Fig. 1, the sample has an equiaxed microstructure. The columnar grain will be visible in the build direction's longitudinal section. All microstructural features are difficult to detect on an optical microscopic image. The grain boundaries visible in the heat-treated samples, on the other hand, are indicated with dashed lines in Fig. 1 (a), (b), and (c). The acicular microstructure of the AR samples allows for the detection

<footnote>* AC- Air cooling, WQ- Water quenching</footnote>
strength while decreasing ductility and work hardening. The main challenge in the AM component is balancing the material's strength, ductility, and work hardening. The heat treatment technique can help improve the mechanical properties and performance of Ti6Al4V components. However, traditional heat treatment is ineffective in achieving the desired mechanical properties [10]. Previous research suggests that work-hardening occurs due to kinematic hardening caused by the mechanical contrast between the α and α' phases in electron beam-melted Ti6Al4V [11]. Stress-relieved samples exhibited a fine acicular martensitic α' phase due to rapid solidification caused by LPBF [12].

This study investigated the impact of heat treatment on the microstructure and work hardening behavior of SLMed Ti6Al4V ELI below the trans temperature. Two heat treatment cycles were used: HT-730 and HT-940-540. Tensile tests were performed on both as-received and heat-treated samples to determine the material's work-hardening parameter. The study proposed a straightforward method for linking heat treatment, work hardening, and SLMed Ti6Al4V ELI alloys.

## Experimental procedure

All samples were created using the SLM machine in the presence of argon inert gas. The feedstock material used was Ti6Al4V ELI powders. The powder size ranged from 10 µm to 60 µm, with a 31 µm average. For sample production, the default manufacturing parameters were used. The rectangular samples were created vertically along the Z-axis. Three samples were made for each process condition. Following the printing process, the samples were stress-relieved under specific conditions: 800°C for 2 hours, followed by furnace cooling to 300°C and air cooling to room temperature. Using wire-EDM, the samples were cut from build plates. As shown in Table 1, heat treatments were used to modify the microstructure of stress-relieved samples. Both stress relief and heat treatment took place in an inert gas environment. Micro tensile testing was performed on sub-sized rectangular samples with a gauge length of 6 mm, as defined by ASTM E8/E8M. A micro-tensile testing system with a load cell capacity of 2 kN was used, with a constant strain rate of 0.001 s⁻¹ applied during testing. Additionally, rectangular samples measuring 5 × 10 × 10 mm³ were produced for metallography. Standard metallographic sample preparation procedures, such as sectioning, grinding, and polishing, were used. Kroll's reagent was used as an etchant. The microstructure was studied using a digital microscope (Olympus GX53 Inverted) and a scanning electron microscope (INSPECT F50 FE-SEM). The Bruker D8 Discover powder XRD machine was used for the X-ray diffraction (XRD) analysis.

<table><caption>Table 1: Heat treatment cycle performed on the SLM Ti6Al4V ELI</caption><thead><tr><td>Terminology</td><td>Heat treatment condition</td></tr></thead><tbody><tr><td>As received (AR)</td><td>Stress relieved</td></tr><tr><td>HT-730</td><td>Heating at 730 °C for 2h and AC*</td></tr><tr><td>HT-940-550</td><td>Heating at 940 °C for 1h and WQ* + Aging at 550 °C for 4h and AC*</td></tr></tbody></table>

## Results and Discussions

The optical microscopic image of the AR and heat-treated sample, shown in Fig. 1, depicts the material's microstructure. However, as a proper process section, 3D printing parameters significantly reduce pore amount and size [13]. As shown in Fig. 1, the sample has an equiaxed microstructure. The columnar grain will be visible in the build direction's longitudinal section. All microstructural features are difficult to detect on an optical microscopic image. The grain boundaries visible in the heat-treated samples, on the other hand, are indicated with dashed lines in Fig. 1 (a), (b), and (c). The acicular microstructure of the AR samples allows for the detection

<footnote>* AC- Air cooling, WQ- Water quenching</footnote>
of grain boundaries. SEM and XRD analyses have been performed to extract detailed microstructure information, and phase identification. The phase and grain size are consistent in all directions. As a result, SEM and XRD tests are performed on the transverse section of the built direction in all samples.

Figure 2 depicts the XRD pattern of AR and heat-treated samples. The pattern of AR samples shows a high amount of martensitic phase and a very small amount of peak, which corresponds to the microstructure feature of the SEM of AR samples in Fig. 2. Some studies indicate the absence of the β peak in the as-built sample. This distinction was caused by the stress-relieving step that followed the printing process. The martensitic α' phase decomposed into α+β phases during the HT 730 heat treatment process, as indicated by the β peaks. However, the HT-940-550 specimen showed minimal presence of the β phase.

Table 2 compares the tensile properties of different Ti6Al4V (ELI) samples, including SLMed, AR, and heat-treated samples. The AR sample has high ultimate tensile strength (UTS) and yield strength (YS), but low elongation (EL) due to the presence of fine acicular α' martensite, a well-studied feature. However, the HT-730 heat treatment significantly improves elongation. This improvement could be attributed to an increase in the average width of the α lathe, as shown in Fig. 3 (b). The presence of transformed β grains also helps to increase ductility.

HT-730 specimens have slightly lower ultimate tensile and yield strengths (UTS) due to larger α laths and β intercrystalline grains. Although the HT-940-550 specimen has coarser α laths, which improve UTS and YS, there is a noticeable decrease in ductility. As shown in Fig. 3 (c), this is due to the presence of extremely fine and dispersed α+β grains interspersed among the coarse α laths. These grains impede lath plastic deformation, resulting in relatively high UTS and YS values. As a result, the HT-940-540 treatment offers a balanced combination of high tensile strength and ductility, making it an ideal solution for heat treatment in SLMed Ti6Al4V ELI.

<figure><img src="image_209.png" /><figcaption>Fig. 1: Microstructure image of Ti6Al4V ELI using optical microscopic image of (a) as received sample (b) HT-730 (c) HT-940-550 (α phase indicated by the light shade and β phase indicated by dark shade).</figcaption></figure>
of grain boundaries. SEM and XRD analyses have been performed to extract detailed microstructure information, and phase identification. The phase and grain size are consistent in all directions. As a result, SEM and XRD tests are performed on the transverse section of the built direction in all samples.

Figure 2 depicts the XRD pattern of AR and heat-treated samples. The pattern of AR samples shows a high amount of martensitic phase and a very small amount of peak, which corresponds to the microstructure feature of the SEM of AR samples in Fig. 2. Some studies indicate the absence of the β peak in the as-built sample. This distinction was caused by the stress-relieving step that followed the printing process. The martensitic α' phase decomposed into α+β phases during the HT 730 heat treatment process, as indicated by the β peaks. However, the HT-940-550 specimen showed minimal presence of the β phase.

Table 2 compares the tensile properties of different Ti6Al4V (ELI) samples, including SLMed, AR, and heat-treated samples. The AR sample has high ultimate tensile strength (UTS) and yield strength (YS), but low elongation (EL) due to the presence of fine acicular α' martensite, a well-studied feature. However, the HT-730 heat treatment significantly improves elongation. This improvement could be attributed to an increase in the average width of the α lathe, as shown in Fig. 3 (b). The presence of transformed β grains also helps to increase ductility.

HT-730 specimens have slightly lower ultimate tensile and yield strengths (UTS) due to larger α laths and β intercrystalline grains. Although the HT-940-550 specimen has coarser α laths, which improve UTS and YS, there is a noticeable decrease in ductility. As shown in Fig. 3 (c), this is due to the presence of extremely fine and dispersed α+β grains interspersed among the coarse α laths. These grains impede lath plastic deformation, resulting in relatively high UTS and YS values. As a result, the HT-940-540 treatment offers a balanced combination of high tensile strength and ductility, making it an ideal solution for heat treatment in SLMed Ti6Al4V ELI.

<figure><img src="image_213.png" /><figcaption>Fig. 1: Microstructure image of Ti6Al4V ELI using optical microscopic image of (a) as received sample (b) HT-730 (c) HT-940-550 (α phase indicated by the light shade and β phase indicated by dark shade).</figcaption></figure>
<figure><img src="image_210.png" /><figcaption>Fig. 2: X-ray diffraction of (a) as received and heat-treated samples shows the different phases (b) magnified view of X-ray diffraction.</figcaption></figure>

<figure><img src="image_211.png" /><figcaption>Fig. 3: Microstructure image of Ti6Al4V ELI using scanning electron microscopy image of (a) as received sample (b) HT-730 (c) HT-940-550 (a phase indicated by the dark shade and β phase indicated by light shade).</figcaption></figure>
<figure><img src="image_214.png" /><figcaption>Fig. 2: X-ray diffraction of (a) as received and heat-treated samples shows the different phases (b) magnified view of X-ray diffraction.</figcaption></figure>

<figure><img src="image_215.png" /><figcaption>Fig. 3: Microstructure image of Ti6Al4V ELI using scanning electron microscopy image of (a) as received sample (b) HT-730 (c) HT-940-550 (a phase indicated by the dark shade and β phase indicated by light shade).</figcaption></figure>
<figure><img src="image_212.png" /><figcaption>Fig. 4: Work hardening rate plotted as a function of true strain.</figcaption></figure>

<table><caption>Table 2: Mechanical properties of SLM Ti6Al4V ELI with AR and heat-treated samples</caption><thead><tr><th></th><th>YS (MPa)</th><th>UTS (MPa)</th><th>% Elongation</th><th>Hardening capacity</th></tr></thead><tbody><tr><td>As received (AR)</td><td>1026 ± 20</td><td>1110 ± 3</td><td>14</td><td>0.082</td></tr><tr><td>HT-730</td><td>1016 ± 6</td><td>1086 ± 5</td><td>16</td><td>0.069</td></tr><tr><td>HT-940-550</td><td>1091 ± 12</td><td>1197 ± 11</td><td>13</td><td>0.098</td></tr></tbody></table>

The rate at which the flow stress changes with strain is referred to as the work hardening rate. The work hardening rate in AR and heat-treated conditions is calculated in this study. The mathematical equation for the work hardening rate is as follows:

$$ \frac{d\sigma}{d\varepsilon} = \frac{\sigma_{i+1} - \sigma_i}{\varepsilon_{i+1} - \varepsilon_i} \qquad (1) $$

The work hardening rate is depicted in Fig. 4 by taking the derivative of the tensile true-stress-true-strain curve under heat-treated conditions using the formula shown in eq. (1). All of the samples studied show a consistent pattern of work hardening rate. The work hardening rate initially decreased significantly before stabilizing at a constant value under quasi-static conditions. A more gradual shift in the work hardening rate, as shown in Fig. 4, is advantageous in achieving an improved balance between the material's ductility and strength. Furthermore, a decrease in the work hardening rate indicates a decrease in the material's work hardening capacity. The work hardening rate curve is divided into three stages: a strong stage, a stable stage, and a weak stage. The hardening rate in the HT-940-550 condition is higher than in the AR and HT-730 conditions, which can be attributed to finely dispersed α particles in the β phase. The work hardening rate is closely related to the retained α' martensite volume fraction of the material. There is a linear relationship between yield strength and work hardening rate, as well as an inverse relationship with material ductility.

## Conclusions

The AR has an average acicular α' martensitic structure, which improves the YS and UTS of the material. This acicular α' martensite decreased after heat treatment. The average width of the α lathe of Ti6Al4V ELI is critical in determining the material's work-hardening capacity. Because
<figure><img src="image_216.png" /><figcaption>Fig. 4: Work hardening rate plotted as a function of true strain.</figcaption></figure>

<table><caption>Table 2: Mechanical properties of SLM Ti6Al4V ELI with AR and heat-treated samples</caption><thead><tr><th></th><th>YS (MPa)</th><th>UTS (MPa)</th><th>% Elongation</th><th>Hardening capacity</th></tr></thead><tbody><tr><td>As received (AR)</td><td>1026 ± 20</td><td>1110 ± 3</td><td>14</td><td>0.082</td></tr><tr><td>HT-730</td><td>1016 ± 6</td><td>1086 ± 5</td><td>16</td><td>0.069</td></tr><tr><td>HT-940-550</td><td>1091 ± 12</td><td>1197 ± 11</td><td>13</td><td>0.098</td></tr></tbody></table>

The rate at which the flow stress changes with strain is referred to as the work hardening rate. The work hardening rate in AR and heat-treated conditions is calculated in this study. The mathematical equation for the work hardening rate is as follows:

$$ \frac{d\sigma}{d\varepsilon} = \frac{\sigma_{i+1} - \sigma_i}{\varepsilon_{i+1} - \varepsilon_i} \qquad (1) $$

The work hardening rate is depicted in Fig. 4 by taking the derivative of the tensile true-stress-true-strain curve under heat-treated conditions using the formula shown in eq. (1). All of the samples studied show a consistent pattern of work hardening rate. The work hardening rate initially decreased significantly before stabilizing at a constant value under quasi-static conditions. A more gradual shift in the work hardening rate, as shown in Fig. 4, is advantageous in achieving an improved balance between the material's ductility and strength. Furthermore, a decrease in the work hardening rate indicates a decrease in the material's work hardening capacity. The work hardening rate curve is divided into three stages: a strong stage, a stable stage, and a weak stage. The hardening rate in the HT-940-550 condition is higher than in the AR and HT-730 conditions, which can be attributed to finely dispersed α particles in the β phase. The work hardening rate is closely related to the retained α' martensite volume fraction of the material. There is a linear relationship between yield strength and work hardening rate, as well as an inverse relationship with material ductility.

## Conclusions

The AR has an average acicular α' martensitic structure, which improves the YS and UTS of the material. This acicular α' martensite decreased after heat treatment. The average width of the α lathe of Ti6Al4V ELI is critical in determining the material's work-hardening capacity. Because
of the coarsening of laths, HT-730 treatments can significantly improve the ductility of SLMed Ti6Al4V ELI, resulting in a decrease in the material's hardening capability. Because of the presence of α particles inside the β phase, HT-940-550 has the highest UTS and YS when compared to other heat treatment cycle. The work hardening rate of Ti6Al4V ELI decreased significantly as true strain increased. Heat treatment has a significant impact on the work hardening rate, with HT-940-550 exhibiting the highest work hardening rate.

## References

[1] Y. Okazaki and A. Ishino, Microstructures and Mechanical Properties of Laser-Sintered Commercially Pure Ti and Ti-6Al-4V Alloy for Dental Applications, Materials 13 (2020) 1-18. https://doi.org/10.3390/ma13030609

[2] J. Lu, H. Lu, X. Xu, J. Yao, J. Cai, and K. Luo, *Int J Mach Tools Manuf* 148 (2020) 103475. https://doi.org/10.1016/j.ijmachtools.2019.103475

[3] B. Dutta, F.H. Froes: *Additive Manufacturing of Titanium Alloys: State of the Art, Challenges and Opportunities*, vol. 1, 1st edn., Elsevier, Oxford, 2016.

[4] F. Froes, M.N. Gungor, M. Ashraf Imam, Cost-affordable titanium: the component fabrication perspective, JOM 59 (2007) 28-31. https://doi.org/10.1007/s11837-007-0074-8

[5] J. Zhang, B. Song, Q. Wei, D. Bourell, Y. Shi, A review of selective laser melting of aluminum alloys: Processing, microstructure, property and developing trends, *Journal of Materials Science & Technology* 35.2 (2019) 270-284. https://doi.org/10.1016/j.jmst.2018.09.004

[6] S. Cao, Z. Chen, C.V.S. Lim, K. Yang, Q. Jia, T. Jarvis, D. Tomus, X. Wu, Defect, microstructure, and mechanical property of Ti-6Al-4V alloy fabricated by high-power selective laser melting, *JOM* 69.12 (2017) 2684-2692. https://doi.org/10.1007/s11837-017-2581-6

[7] Ó. Teixeira, F.J.G. Silva, E. Atzeni, Residual stresses and heat treatments of Inconel 718 parts manufactured via metal laser beam powder bed fusion: an overview, *The International Journal of Advanced Manufacturing Technology* 113.11-12 (2021) 3139-3162. https://doi.org/10.1007/s00170-021-06835-8

[8] D. Chen, P. Wang, R. Pan, C. Zha, J. Fan, D. Liang, Y. Zhao, Characteristics of metal specimens formed by selective laser melting: a state-of-the-art review, *Journal of Materials Engineering and Performance* 30 (2021) 7073-7100. https://doi.org/10.1007/s11665-020-05323-6

[9] N.T. Aboulkhair, M. Simonelli, L. Parry, I. Ashcroft, C. Tuck, R. Hague, 3D printing of Aluminium alloys: Additive Manufacturing of Aluminium alloys using selective laser melting, *Progress in materials science* 106 (2019) 100578. https://doi.org/10.1016/j.pmatsci.2019.100578

[10] B. Vrancken, L. Thijs, J.P. Kruth, J. Van Humbeeck, Heat treatment of Ti6Al4V produced by Selective Laser Melting: Microstructure and mechanical properties, *Journal of Alloys and Compounds* 541 (2012) 177-185. https://doi.org/10.1016/j.jallcom.2012.07.022

[11] C. de Formanoir, A. Brulard, S. Vivès, G. Martin, F. Prima, S. Michotte, E. Rivière, A. Dolimont, and S. Godet, A strategy to improve the work-hardening behavior of Ti-6Al-4V parts produced by additive manufacturing, *Materials Research Letters* 5.3 (2017) 201-208. https://doi.org/10.1080/21663831.2016.1245681

[12] A. Mahmud, T. Huynh, L. Zhou, H. Hyer, A. Mehta, D.D. Imholte, N.E. Woolstenhulme, D.M. Wachs, and Y. Sohn, Mechanical behavior assessment of Ti-6Al-4V ELI alloy produced by laser powder bed fusion, *Metals* 11.11 (2021) 1671. https://doi.org/10.3390/met11111671

[13] H. Yu, F. Li, Z. Wang, and X. Zeng, Fatigue performances of selective laser melted Ti-6Al-4V alloy: Influence of surface finishing, hot isostatic pressing and heat treatments, *International Journal of Fatigue* 120 (2019) 175-183. https://doi.org/10.1016/j.ijfatigue.2018.11.019

[14] Rodney. Boyer, Gerhard. Welsch, and E.W. Collings: *Materials Properties Handbook: Titanium Alloys*, ASM International, 1994.
of the coarsening of laths, HT-730 treatments can significantly improve the ductility of SLMed Ti6Al4V ELI, resulting in a decrease in the material's hardening capability. Because of the presence of α particles inside the β phase, HT-940-550 has the highest UTS and YS when compared to other heat treatment cycle. The work hardening rate of Ti6Al4V ELI decreased significantly as true strain increased. Heat treatment has a significant impact on the work hardening rate, with HT-940-550 exhibiting the highest work hardening rate.

## References

[1] Y. Okazaki and A. Ishino, Microstructures and Mechanical Properties of Laser-Sintered Commercially Pure Ti and Ti-6Al-4V Alloy for Dental Applications, Materials 13 (2020) 1-18. https://doi.org/10.3390/ma13030609

[2] J. Lu, H. Lu, X. Xu, J. Yao, J. Cai, and K. Luo, *Int J Mach Tools Manuf* 148 (2020) 103475. https://doi.org/10.1016/j.ijmachtools.2019.103475

[3] B. Dutta, F.H. Froes: *Additive Manufacturing of Titanium Alloys: State of the Art, Challenges and Opportunities*, vol. 1, 1st edn., Elsevier, Oxford, 2016.

[4] F. Froes, M.N. Gungor, M. Ashraf Imam, Cost-affordable titanium: the component fabrication perspective, JOM 59 (2007) 28-31. https://doi.org/10.1007/s11837-007-0074-8

[5] J. Zhang, B. Song, Q. Wei, D. Bourell, Y. Shi, A review of selective laser melting of aluminum alloys: Processing, microstructure, property and developing trends, *Journal of Materials Science & Technology* 35.2 (2019) 270-284. https://doi.org/10.1016/j.jmst.2018.09.004

[6] S. Cao, Z. Chen, C.V.S. Lim, K. Yang, Q. Jia, T. Jarvis, D. Tomus, X. Wu, Defect, microstructure, and mechanical property of Ti-6Al-4V alloy fabricated by high-power selective laser melting, *JOM* 69.12 (2017) 2684-2692. https://doi.org/10.1007/s11837-017-2581-6

[7] Ó. Teixeira, F.J.G. Silva, E. Atzeni, Residual stresses and heat treatments of Inconel 718 parts manufactured via metal laser beam powder bed fusion: an overview, *The International Journal of Advanced Manufacturing Technology* 113.11-12 (2021) 3139-3162. https://doi.org/10.1007/s00170-021-06835-8

[8] D. Chen, P. Wang, R. Pan, C. Zha, J. Fan, D. Liang, Y. Zhao, Characteristics of metal specimens formed by selective laser melting: a state-of-the-art review, *Journal of Materials Engineering and Performance* 30 (2021) 7073-7100. https://doi.org/10.1007/s11665-020-05323-6

[9] N.T. Aboulkhair, M. Simonelli, L. Parry, I. Ashcroft, C. Tuck, R. Hague, 3D printing of Aluminium alloys: Additive Manufacturing of Aluminium alloys using selective laser melting, *Progress in materials science* 106 (2019) 100578. https://doi.org/10.1016/j.pmatsci.2019.100578

[10] B. Vrancken, L. Thijs, J.P. Kruth, J. Van Humbeeck, Heat treatment of Ti6Al4V produced by Selective Laser Melting: Microstructure and mechanical properties, *Journal of Alloys and Compounds* 541 (2012) 177-185. https://doi.org/10.1016/j.jallcom.2012.07.022

[11] C. de Formanoir, A. Brulard, S. Vivès, G. Martin, F. Prima, S. Michotte, E. Rivière, A. Dolimont, and S. Godet, A strategy to improve the work-hardening behavior of Ti-6Al-4V parts produced by additive manufacturing, *Materials Research Letters* 5.3 (2017) 201-208. https://doi.org/10.1080/21663831.2016.1245681

[12] A. Mahmud, T. Huynh, L. Zhou, H. Hyer, A. Mehta, D.D. Imholte, N.E. Woolstenhulme, D.M. Wachs, and Y. Sohn, Mechanical behavior assessment of Ti-6Al-4V ELI alloy produced by laser powder bed fusion, *Metals* 11.11 (2021) 1671. https://doi.org/10.3390/met11111671

[13] H. Yu, F. Li, Z. Wang, and X. Zeng, Fatigue performances of selective laser melted Ti-6Al-4V alloy: Influence of surface finishing, hot isostatic pressing and heat treatments, *International Journal of Fatigue* 120 (2019) 175-183. https://doi.org/10.1016/j.ijfatigue.2018.11.019

[14] Rodney. Boyer, Gerhard. Welsch, and E.W. Collings: *Materials Properties Handbook: Titanium Alloys*, ASM International, 1994.
# Effect of SiC addition on processability of AISI S2 tool steel for laser powder bed fusion

SAGGIONETTO Enrico¹,ª, ROGER VILA Gerard¹,ᵇ, DEDRY Olivier¹,ᶜ, TCHOUFANG TCHUINDJANG Jérôme¹,ᵈ and MERTENS Anne¹,ᵉ,*

¹Metallic Materials Science (MMS) team, Aerospace and Mechanical Engineering Dpt., University of Liège, Belgium

ªenrico.saggionetto@uliege.be, ᵇgerard.rogervila@student.uliege.be, ᶜolivier.dedry@uliege.be, ᵈj.tchuindjang@uliege.be, ᶜanne.mertens@uliege.be

**Keywords:** Additive Manufacturing, Laser Powder Bed Fusion, Tool Steel, Melt Pool

**Abstract.** Laser Powder Bed Fusion (LPBF) of metallic alloys allows to achieve complex shape parts with innovative properties. However, the commercial availability of powder for LPBF is still limited, thus restraining the development of new alloys. To overcome this shortcoming, mixing different powders allows to tailor the chemical composition, although affecting the LPBF process. Indeed, to achieve a fully dense and defect-free part a proper melt pool must be generated during the LPBF process, in order to ensure good overlapping between each track and layer. Melt pools can be described as conductive or key-hole like, promoting the appearance of process-induced defects such as lack of fusion or key-hole porosities. Processing a mixture of several powders by changing the amount of one constituent can affect the type of melt pool generated during the process, thus shifting the process map. In this work, AISI S2 tool steel powders are enriched with 5 and 10% (in volume) of Silicon Carbide (SiC) and processed by LPBF. The effect of SiC on the processability is discussed for different volumetric energy density (E<sub>d</sub>). Defects within cross sections are characterized and quantified, as well as the melt pool depth and morphology.

**Introduction**

Over recent years, the development of new metallic alloys manufactured by Laser Powder Bed Fusion (LPBF) has been the object of increasing attention. Indeed, the ultra-fast heating and cooling rates involved during the LPBF process are responsible for the formation of strongly out-of-equilibrium microstructures, involving supersaturated solid solution as well as new metastable phases, thus offering new possibilities in terms of usage properties such as mechanical, corrosion and tribological properties. However, these conditions make difficult the processing of metal alloys such as high carbon tool steels, due to the tendency to the formation of cracks that makes the final part unusable [1]. On the other hand, the use of LPBF allows to produce complex shape parts implementing internal cooling channels, difficult to obtain by conventional manufacturing methods. Therefore, research is now focusing on the development by LPBF of tool steels with complex chemical composition in order to combine all the advantages of this technology.

However, the commercial availability of metal powders suitable for LPBF is still limited. Therefore, the processability of new metal alloys with specific chemical composition is restrained. One way to overcome this hurdle is the mixing of different powders to tailor the composition. Obtaining a proper powder mixture is often challenging because of the dependence of the processability on the rheological properties of the mixture, such as spreadability and packability [2]. To achieve a fully dense, defect-free part, proper melt pools must be generated during the LPBF process, in order to ensure good overlapping between each track and layer [3]. Melt pool quality can lead to the presence of defects and porosities such as lack of fusion, key-hole, balling and spattering. Melt pools are classified as conductive or key-hole [4]. A conductive melt pool is usually shallow, resulting in insufficient liquid phase for the powders to bond together and thus

<figure><img src="image_213.png" /></figure>
# Effect of SiC addition on processability of AISI S2 tool steel for laser powder bed fusion

SAGGIONETTO Enrico¹,ª, ROGER VILA Gerard¹,ᵇ, DEDRY Olivier¹,ᶜ, TCHOUFANG TCHUINDJANG Jérôme¹,ᵈ and MERTENS Anne¹,ᵉ,*

¹Metallic Materials Science (MMS) team, Aerospace and Mechanical Engineering Dpt., University of Liège, Belgium

ªenrico.saggionetto@uliege.be, ᵇgerard.rogervila@student.uliege.be, ᶜolivier.dedry@uliege.be, ᵈj.tchuindjang@uliege.be, ᶜanne.mertens@uliege.be

**Keywords:** Additive Manufacturing, Laser Powder Bed Fusion, Tool Steel, Melt Pool

**Abstract.** Laser Powder Bed Fusion (LPBF) of metallic alloys allows to achieve complex shape parts with innovative properties. However, the commercial availability of powder for LPBF is still limited, thus restraining the development of new alloys. To overcome this shortcoming, mixing different powders allows to tailor the chemical composition, although affecting the LPBF process. Indeed, to achieve a fully dense and defect-free part a proper melt pool must be generated during the LPBF process, in order to ensure good overlapping between each track and layer. Melt pools can be described as conductive or key-hole like, promoting the appearance of process-induced defects such as lack of fusion or key-hole porosities. Processing a mixture of several powders by changing the amount of one constituent can affect the type of melt pool generated during the process, thus shifting the process map. In this work, AISI S2 tool steel powders are enriched with 5 and 10% (in volume) of Silicon Carbide (SiC) and processed by LPBF. The effect of SiC on the processability is discussed for different volumetric energy density (E<sub>d</sub>). Defects within cross sections are characterized and quantified, as well as the melt pool depth and morphology.

**Introduction**

Over recent years, the development of new metallic alloys manufactured by Laser Powder Bed Fusion (LPBF) has been the object of increasing attention. Indeed, the ultra-fast heating and cooling rates involved during the LPBF process are responsible for the formation of strongly out-of-equilibrium microstructures, involving supersaturated solid solution as well as new metastable phases, thus offering new possibilities in terms of usage properties such as mechanical, corrosion and tribological properties. However, these conditions make difficult the processing of metal alloys such as high carbon tool steels, due to the tendency to the formation of cracks that makes the final part unusable [1]. On the other hand, the use of LPBF allows to produce complex shape parts implementing internal cooling channels, difficult to obtain by conventional manufacturing methods. Therefore, research is now focusing on the development by LPBF of tool steels with complex chemical composition in order to combine all the advantages of this technology.

However, the commercial availability of metal powders suitable for LPBF is still limited. Therefore, the processability of new metal alloys with specific chemical composition is restrained. One way to overcome this hurdle is the mixing of different powders to tailor the composition. Obtaining a proper powder mixture is often challenging because of the dependence of the processability on the rheological properties of the mixture, such as spreadability and packability [2]. To achieve a fully dense, defect-free part, proper melt pools must be generated during the LPBF process, in order to ensure good overlapping between each track and layer [3]. Melt pool quality can lead to the presence of defects and porosities such as lack of fusion, key-hole, balling and spattering. Melt pools are classified as conductive or key-hole [4]. A conductive melt pool is usually shallow, resulting in insufficient liquid phase for the powders to bond together and thus

<figure><img src="image_217.png" /></figure>
generating lack of fusion defects. On the contrary, unstable key-hole melt pools are responsible for the formation of key-hole porosities, due to excessive evaporation that causes entrapment of gas at the bottom of the melt pool. The type of melt pool is affected by the processing parameters, such as laser power, scan speed, layer thickness and hatch distance. Such parameters are encased within the volumetric energy density $E_d$. Usually, low $E_d$ as result of low power and high scan velocity leads to a conductive shallow melt pool. In contrast, high $E_d$ as result of high power and low scan velocity leads to the formation of unstable key-hole melt pools. The transition from conductive to key-hole is accompanied by a change in the morphology of the melt pool from semi-circular to key-hole like [5]. Close to the threshold for transition, stable key-hole melt pool generated by intermediate parameters allows to achieve defect-free parts.

Understanding the proper process parameters to ensure an optimal melt pool is crucial for the processability of new metal alloys for LPBF. The above-mentioned types of melt pool are affected by the interaction of the laser with both powder and solidified material underneath the powder layer. Indeed, properties such as absorption of the laser energy on powders, thermal conductivity of both powder and solidified material play a crucial role on the melt pool generated [6-9]. These properties depend on powder characteristics such as particle size distribution, morphology, roughness etc. The complexity of such phenomena increases when a mixture of different powders is processed. Different works reported the change in powder absorptivity when nanoparticles are added to the mixture, as well as a change in the temperature of melt pool due to a change in conductivity [5,10-13]. Therefore, the processing of different powder mixtures can change the melt pool features, thus affecting the processability of the material.

This work focuses in particular on the effect of SiC on the processability of the low alloy tool steel AISI S2, previously processed as reference material [14]. A restriction of the process map is observed with increasing amount of SiC, due to a change in the type and depth of melt pools generated during the process.

## Materials and methods

Commercial gas atomized powders AISI S2 Tool Steel (0.49 C, 1.2 Si, 0.6 Mo, 0.6 Mn, Fe bal., wt. %) and Silicon Carbide (SiC) were used as raw material. The particle size distribution of the SiC powder is given as follows: D10 = 0.4 µm, D50 = 0.75 µm and D90 = 1.5 µm. More details concerning the AISI S2 tool steel powder may be found in our previous work [14]. SiC powder was added in 5 and 10% (in volume) to S2 tool steel powder.

Starting from the powders, cubes of 10x10x10 mm³ were produced with an Aconity3D Mini LPBF machine under argon atmosphere. A fixed power (P) of 200 W was combined with a scan velocity ($v_s$) from 500 to 2000 mm/s (Table 1). A laser spot size of 80 µm with a gaussian distribution, a layer thickness (t) of 30 µm and a hatch spacing (h) of 80 µm were used for all samples. The scanning strategy implied a rotation of 90° for each layer, with no contouring. No preheating of the substrate was applied. More details about the processing parameters can be found in our previous work [14].

The volumetric energy density ($E_d$) was used to correlate the results observed with the processing parameters (Eq. 1).

$$E_d = \frac{P}{t \cdot h \cdot v_s} \qquad (1)$$

Table 1 lists the samples studied in this work, together with their processing parameters. The codification of each sample considers the powder mixture used (0, 5, 10 for the mixture S2, S2 + 5% SiC, S2 + 10% SiC respectively) followed by the level of $E_d$ (1, 2, 3, 4). $E_d$ is classified based on the intensity from low to high, with numbers from 1 to 4 corresponding to an approximative value of $E_d$ of 40, 80, 115, 170 J/mm³ respectively. Sample 10.1 was not fully processed, as the
generating lack of fusion defects. On the contrary, unstable key-hole melt pools are responsible for the formation of key-hole porosities, due to excessive evaporation that causes entrapment of gas at the bottom of the melt pool. The type of melt pool is affected by the processing parameters, such as laser power, scan speed, layer thickness and hatch distance. Such parameters are encased within the volumetric energy density $E_d$. Usually, low $E_d$ as result of low power and high scan velocity leads to a conductive shallow melt pool. In contrast, high $E_d$ as result of high power and low scan velocity leads to the formation of unstable key-hole melt pools. The transition from conductive to key-hole is accompanied by a change in the morphology of the melt pool from semi-circular to key-hole like [5]. Close to the threshold for transition, stable key-hole melt pool generated by intermediate parameters allows to achieve defect-free parts.

Understanding the proper process parameters to ensure an optimal melt pool is crucial for the processability of new metal alloys for LPBF. The above-mentioned types of melt pool are affected by the interaction of the laser with both powder and solidified material underneath the powder layer. Indeed, properties such as absorption of the laser energy on powders, thermal conductivity of both powder and solidified material play a crucial role on the melt pool generated [6-9]. These properties depend on powder characteristics such as particle size distribution, morphology, roughness etc. The complexity of such phenomena increases when a mixture of different powders is processed. Different works reported the change in powder absorptivity when nanoparticles are added to the mixture, as well as a change in the temperature of melt pool due to a change in conductivity [5,10-13]. Therefore, the processing of different powder mixtures can change the melt pool features, thus affecting the processability of the material.

This work focuses in particular on the effect of SiC on the processability of the low alloy tool steel AISI S2, previously processed as reference material [14]. A restriction of the process map is observed with increasing amount of SiC, due to a change in the type and depth of melt pools generated during the process.

## Materials and methods

Commercial gas atomized powders AISI S2 Tool Steel (0.49 C, 1.2 Si, 0.6 Mo, 0.6 Mn, Fe bal., wt. %) and Silicon Carbide (SiC) were used as raw material. The particle size distribution of the SiC powder is given as follows: D10 = 0.4 µm, D50 = 0.75 µm and D90 = 1.5 µm. More details concerning the AISI S2 tool steel powder may be found in our previous work [14]. SiC powder was added in 5 and 10% (in volume) to S2 tool steel powder.

Starting from the powders, cubes of 10x10x10 mm³ were produced with an Aconity3D Mini LPBF machine under argon atmosphere. A fixed power (P) of 200 W was combined with a scan velocity ($v_s$) from 500 to 2000 mm/s (Table 1). A laser spot size of 80 µm with a gaussian distribution, a layer thickness (t) of 30 µm and a hatch spacing (h) of 80 µm were used for all samples. The scanning strategy implied a rotation of 90° for each layer, with no contouring. No preheating of the substrate was applied. More details about the processing parameters can be found in our previous work [14].

The volumetric energy density ($E_d$) was used to correlate the results observed with the processing parameters (Eq. 1).

$$E_d = \frac{P}{t \cdot h \cdot v_s} \qquad (1)$$

Table 1 lists the samples studied in this work, together with their processing parameters. The codification of each sample considers the powder mixture used (0, 5, 10 for the mixture S2, S2 + 5% SiC, S2 + 10% SiC respectively) followed by the level of $E_d$ (1, 2, 3, 4). $E_d$ is classified based on the intensity from low to high, with numbers from 1 to 4 corresponding to an approximative value of $E_d$ of 40, 80, 115, 170 J/mm³ respectively. Sample 10.1 was not fully processed, as the
printing was stopped after few layers due to the appearance of an irregular surface. Therefore, sample 10.1 was not considered for further analysis.

<table><caption>Tab. 1. Processing parameters and volumetric energy density (Ed) for each sample.</caption><thead><tr><th>Powder mixture</th><th>Sample</th><th>P [W]</th><th>v<sub>s</sub> [mm/s]</th><th>E<sub>d</sub> [J/mm<sup>3</sup>]</th></tr></thead><tbody><tr><td rowspan="4">S2</td><td>0.1</td><td>200</td><td>2000</td><td>42</td></tr><tr><td>0.2</td><td>200</td><td>1000</td><td>83</td></tr><tr><td>0.3</td><td>200</td><td>750</td><td>111</td></tr><tr><td>0.4</td><td>200</td><td>500</td><td>167</td></tr><tr><td rowspan="4">S2 + 5% SiC</td><td>5.1</td><td>200</td><td>2000</td><td>42</td></tr><tr><td>5.2</td><td>200</td><td>1000</td><td>83</td></tr><tr><td>5.3</td><td>200</td><td>750</td><td>111</td></tr><tr><td>5.4</td><td>200</td><td>500</td><td>167</td></tr><tr><td rowspan="4">S2 + 10% SiC</td><td>10.1</td><td>200</td><td>2000</td><td>42</td></tr><tr><td>10.2</td><td>200</td><td>1000</td><td>83</td></tr><tr><td>10.3</td><td>200</td><td>700</td><td>119</td></tr><tr><td>10.4</td><td>200</td><td>500</td><td>167</td></tr></tbody></table>

All as-built samples were cut by Electro Discharge Machining perpendicularly to the laser scan direction of the last layer to obtain the cross section. After cutting, samples were hot mounted with an electrically conductive Bakelite, then ground and polished down to 1 µm. Defects within the cross sections as well as melt pool dimensions were analyzed through an optical microscope Olympus BX60. Nital 3% was used to etch the cross sections.

## Results

Fig. 1 shows an overview of the cross section of each sample, where the main defects present are highlighted and quantified through the percentage of porosity $ε$. Samples obtained with S2 powder exhibits lack of fusion defects for low $E_d$ (sample 0.1) and key-hole porosities for high $E_d$ (sample 0.4). At the intermediates values of $E_d$ of 80 and 115 J/mm³ (sample 0.2, 0.3) only small gas porosities are spotted. With the addition of SiC, the amount of lack of fusion defects significantly increases for low $E_d$ of 40 J/mm³, passing from being 1.4% in sample 0.1 to 19% in sample 5.1. Similarly, for $E_d$ of 80 J/mm³, sample 0.2 has 0.04% porosity, being mainly small gas porosities, whereas sample 5.2 has 0.2% porosity, associated to lack of fusion defects. The amount of porosity further increases up to 1% in sample 10.2. For $E_d$ of 115 J/mm³ only small gas porosities are spotted in all samples. At the highest value of $E_d$, key-hole porosities are visible in all samples (0.4, 5.4, 10.4), with an amount remaining within the same order of magnitude between 0.4 – 0.7%. It is important to mention that with 10% SiC all samples exhibited cracks.
printing was stopped after few layers due to the appearance of an irregular surface. Therefore, sample 10.1 was not considered for further analysis.

<table><caption>Tab. 1. Processing parameters and volumetric energy density (Ed) for each sample.</caption><thead><tr><th>Powder mixture</th><th>Sample</th><th>P [W]</th><th>v<sub>s</sub> [mm/s]</th><th>E<sub>d</sub> [J/mm<sup>3</sup>]</th></tr></thead><tbody><tr><td rowspan="4">S2</td><td>0.1</td><td>200</td><td>2000</td><td>42</td></tr><tr><td>0.2</td><td>200</td><td>1000</td><td>83</td></tr><tr><td>0.3</td><td>200</td><td>750</td><td>111</td></tr><tr><td>0.4</td><td>200</td><td>500</td><td>167</td></tr><tr><td rowspan="4">S2 + 5% SiC</td><td>5.1</td><td>200</td><td>2000</td><td>42</td></tr><tr><td>5.2</td><td>200</td><td>1000</td><td>83</td></tr><tr><td>5.3</td><td>200</td><td>750</td><td>111</td></tr><tr><td>5.4</td><td>200</td><td>500</td><td>167</td></tr><tr><td rowspan="4">S2 + 10% SiC</td><td>10.1</td><td>200</td><td>2000</td><td>42</td></tr><tr><td>10.2</td><td>200</td><td>1000</td><td>83</td></tr><tr><td>10.3</td><td>200</td><td>700</td><td>119</td></tr><tr><td>10.4</td><td>200</td><td>500</td><td>167</td></tr></tbody></table>

All as-built samples were cut by Electro Discharge Machining perpendicularly to the laser scan direction of the last layer to obtain the cross section. After cutting, samples were hot mounted with an electrically conductive Bakelite, then ground and polished down to 1 µm. Defects within the cross sections as well as melt pool dimensions were analyzed through an optical microscope Olympus BX60. Nital 3% was used to etch the cross sections.

## Results

Fig. 1 shows an overview of the cross section of each sample, where the main defects present are highlighted and quantified through the percentage of porosity $ε$. Samples obtained with S2 powder exhibits lack of fusion defects for low $E_d$ (sample 0.1) and key-hole porosities for high $E_d$ (sample 0.4). At the intermediates values of $E_d$ of 80 and 115 J/mm³ (sample 0.2, 0.3) only small gas porosities are spotted. With the addition of SiC, the amount of lack of fusion defects significantly increases for low $E_d$ of 40 J/mm³, passing from being 1.4% in sample 0.1 to 19% in sample 5.1. Similarly, for $E_d$ of 80 J/mm³, sample 0.2 has 0.04% porosity, being mainly small gas porosities, whereas sample 5.2 has 0.2% porosity, associated to lack of fusion defects. The amount of porosity further increases up to 1% in sample 10.2. For $E_d$ of 115 J/mm³ only small gas porosities are spotted in all samples. At the highest value of $E_d$, key-hole porosities are visible in all samples (0.4, 5.4, 10.4), with an amount remaining within the same order of magnitude between 0.4 – 0.7%. It is important to mention that with 10% SiC all samples exhibited cracks.
<figure><img src="image_214.png" /><figcaption>Fig. 1. Cross section overviews of each sample, varying the amount of SiC and $E_d$. Defects are highlighted in terms of type (lack of fusion or key-hole) and amount (percentage of porosity $\varepsilon$). Samples 0.3, 5.2 and 5.3 exhibits imperfections due to the polishing conditions.</figcaption></figure>

Fig. 2 shows an overview of the top layer of each sample, where melt pools are highlighted. Shallow melt pools are visible at the lowest value of $E_d$, with a semi-circular shape and poor overlapping between each track. The irregularity of each track and layer increases passing from sample 0.1 to 5.1. Increasing $E_d$, in the range 80 – 170 J/mm³ the melt pool morphology changes to key-hole like, except for sample 10.2 where the melt pool morphology is still semi-circular. Key-hole defects are visible at the bottom of the melt pool of samples 0.4, 5.4, 10.4 generated at the highest $E_d$ of 170 J/mm³.

<figure><img src="image_215.png" /><figcaption>Fig. 2. Top layer overviews of each sample, varying the amount of SiC and $E_d$. Melt pools morphologies are highlighted.</figcaption></figure>
<figure><img src="image_218.png" /><figcaption>Fig. 1. Cross section overviews of each sample, varying the amount of SiC and $E_d$. Defects are highlighted in terms of type (lack of fusion or key-hole) and amount (percentage of porosity $ε$). Samples 0.3, 5.2 and 5.3 exhibits imperfections due to the polishing conditions.</figcaption></figure>

Fig. 2 shows an overview of the top layer of each sample, where melt pools are highlighted. Shallow melt pools are visible at the lowest value of $E_d$, with a semi-circular shape and poor overlapping between each track. The irregularity of each track and layer increases passing from sample 0.1 to 5.1. Increasing $E_d$, in the range 80 – 170 J/mm³ the melt pool morphology changes to key-hole like, except for sample 10.2 where the melt pool morphology is still semi-circular. Key-hole defects are visible at the bottom of the melt pool of samples 0.4, 5.4, 10.4 generated at the highest $E_d$ of 170 J/mm³.

<figure><img src="image_219.png" /><figcaption>Fig. 2. Top layer overviews of each sample, varying the amount of SiC and $E_d$. Melt pools morphologies are highlighted.</figcaption></figure>
Fig. 3 shows the melt pool depth of each sample. In general, the depth clearly increases as E<sub>d</sub> increases. Furthermore, the addition of SiC leads to a decrease in melt pool depth for all E<sub>d</sub>. For E<sub>d</sub> of 40 J/mm<sup>3</sup>, the depth is similar for S2 and S2 + 5% SiC, being around 100 µm. Similarly, for E<sub>d</sub> of 80 J/mm<sup>3</sup> the depth is between 90 – 120 µm with 5 and 10% SiC, increasing up to 190 µm for S2. For E<sub>d</sub> of 115 J/mm<sup>3</sup>, the melt pool depth is within the range 150 – 250 µm. Finally, for E<sub>d</sub> of 170 J/mm<sup>3</sup>, all the melt pool depths are higher than 250 µm, with S2 reaching a value close to 350 µm.

<figure><img src="image_216.png" /><figcaption>Fig. 3. Melt pool depths changes varying the amount of SiC and E<sub>d</sub>.</figcaption></figure>

Discussion

The nature of the melt pools generated during the LPBF process is responsible for the achievement
of a sound part, fully dense and defect-free or, on the contrary, for the appearance of defects such
as lack of fusion or key-hole porosities. According to equation 1, E<sub>d</sub> depends on four different
parameters: laser power, laser scan velocity, layer thickness and hatch distance. The type of melt
pool generated depends on the final value of E<sub>d</sub> as well as on the individual value of each single
parameter. To consider this aspect, in this work, different values of E<sub>d</sub> are investigated as a result
of the change of the laser scan velocity as the only parameter that is varied. Laser power, layer
thickness and hatch distance are fixed. Furthermore, samples obtained by processing the mixture
of S2 + 10% (in vol.) SiC exhibited cracks at all E<sub>d</sub>. Nevertheless, these samples are still considered
in this work for a more comprehensive investigation on the effect of SiC addition on the
processability of S2 tool steel powder.

Fig. 2 shows the melt pool formed depending on the initial amount of SiC and on the applied E<sub>d</sub>. A semi-circular shape with a depth close to 100 µm (Fig. 3) is observed for low E<sub>d</sub> of 40 J/mm<sup>3</sup>. Such morphology is associated with a conductive melt pool type, which leads to the appearance of lack of fusion defects. Indeed, the melt pool generated is not sufficient to melt a significant amount of powders. Only a limited re-melting of the underneath material occurs, thus creating poor overlapping between each track and layer. Despite the similar depth of melt pools of samples 0.1 and 5.1 (Fig. 3), the addition of 5% SiC leads to the generation of more irregular melt pools in sample 5.1 compared to sample 0.1 (Fig. 2), worsening the overlapping and the quality of each track and layer with the advancement of the process. Thus, there is a significant increase in the amount of porosity ε, from 1.4 up to 19% when passing from 0 to 5% SiC respectively (Fig. 1). With 10% SiC, the printing of sample 10.1 was interrupted due to the appearance of an irregular surface. It can be assumed that, in this case, the melt pool formed is shallow and conductive,
Fig. 3 shows the melt pool depth of each sample. In general, the depth clearly increases as E<sub>d</sub> increases. Furthermore, the addition of SiC leads to a decrease in melt pool depth for all E<sub>d</sub>. For E<sub>d</sub> of 40 J/mm<sup>3</sup>, the depth is similar for S2 and S2 + 5% SiC, being around 100 µm. Similarly, for E<sub>d</sub> of 80 J/mm<sup>3</sup> the depth is between 90 – 120 µm with 5 and 10% SiC, increasing up to 190 µm for S2. For E<sub>d</sub> of 115 J/mm<sup>3</sup>, the melt pool depth is within the range 150 – 250 µm. Finally, for E<sub>d</sub> of 170 J/mm<sup>3</sup>, all the melt pool depths are higher than 250 µm, with S2 reaching a value close to 350 µm.

<figure><img src="image_220.png" /><figcaption>Fig. 3. Melt pool depths changes varying the amount of SiC and E<sub>d</sub>.</figcaption></figure>

Discussion

The nature of the melt pools generated during the LPBF process is responsible for the achievement
of a sound part, fully dense and defect-free or, on the contrary, for the appearance of defects such
as lack of fusion or key-hole porosities. According to equation 1, E<sub>d</sub> depends on four different
parameters: laser power, laser scan velocity, layer thickness and hatch distance. The type of melt
pool generated depends on the final value of E<sub>d</sub> as well as on the individual value of each single
parameter. To consider this aspect, in this work, different values of E<sub>d</sub> are investigated as a result
of the change of the laser scan velocity as the only parameter that is varied. Laser power, layer
thickness and hatch distance are fixed. Furthermore, samples obtained by processing the mixture
of S2 + 10% (in vol.) SiC exhibited cracks at all E<sub>d</sub>. Nevertheless, these samples are still considered
in this work for a more comprehensive investigation on the effect of SiC addition on the
processability of S2 tool steel powder.

Fig. 2 shows the melt pool formed depending on the initial amount of SiC and on the applied E<sub>d</sub>. A semi-circular shape with a depth close to 100 µm (Fig. 3) is observed for low E<sub>d</sub> of 40 J/mm<sup>3</sup>. Such morphology is associated with a conductive melt pool type, which leads to the appearance of lack of fusion defects. Indeed, the melt pool generated is not sufficient to melt a significant amount of powders. Only a limited re-melting of the underneath material occurs, thus creating poor overlapping between each track and layer. Despite the similar depth of melt pools of samples 0.1 and 5.1 (Fig. 3), the addition of 5% SiC leads to the generation of more irregular melt pools in sample 5.1 compared to sample 0.1 (Fig. 2), worsening the overlapping and the quality of each track and layer with the advancement of the process. Thus, there is a significant increase in the amount of porosity ε, from 1.4 up to 19% when passing from 0 to 5% SiC respectively (Fig. 1). With 10% SiC, the printing of sample 10.1 was interrupted due to the appearance of an irregular surface. It can be assumed that, in this case, the melt pool formed is shallow and conductive,
leading to bad overlapping between each track causing irregular layers that finally affect the surface quality.

Increasing $E_d$ up to 80 J/mm³ leads to the appearance of a stable key-hole melt pool for samples 0.2 and 5.2. However, sample 0.2 alone does not exhibit any defect (Fig. 1). With the addition of 5% SiC, few lack of fusion defects are still present in sample 5.2 (Fig. 1), due to the generation of a shallow melt pool that leads to poor overlapping (Fig. 2). Indeed, despite a stable key-hole morphology, the depth of the melt pool is around 120 µm (Fig. 3), which remains close to the melt pool depth at 40 J/mm³ (100 µm). In contrast, sample 0.2 exhibits a deeper melt pool around 200 µm, which allows for good overlapping between each track and layer. With 10% SiC (sample 10.2), the melt pool has still a conductive morphology (Fig. 2) with a depth of 90 µm (Fig. 3). Thus, as in the case of an $E_d$ of 40 J/mm³, lack of fusion defects are present.

At 115 J/mm³, for all compositions a stable key-hole melt pool is generated, avoiding the presence of defects. Only few little gas porosities are spotted. The depth of the melt pool is between 150 and 250 µm.

Finally, for the highest $E_d$ of 170 J/mm³, an unstable key-hole melt pool is visible for all compositions, leading to presence of key-hole defects at the bottom of the melt pool (Fig. 1 and Fig. 2). Such defects are present in similar amounts for all compositions, i.e. less than 1%.

For all the processed powder mixtures, a sound part free of process-induced defects such as lack of fusion or key-hole porosities is obtained when the melt pool exhibits a stable key-hole like morphology with a depth between 150 and 250 µm. The threshold for the change from conductive to stable key-hole melt pool is observed to be of $E_d$ of 80 J/mm³ for the powder mixtures S2 and S2 + 5% SiC. However, with the addition of 5% SiC, a proper stable key-hole melt pool with sufficient depth is only generated at 115 J/mm³, which remains appropriate for S2 powder alone. With 10% SiC, the threshold for the change from conductive to stable key-hole melt pool is shifted to $E_d$ of 115 J/mm³. When further increasing $E_d$ up to 170 J/mm³, an unstable key-hole melt pool is generated for all compositions. Therefore, the addition of SiC restrains the optimal process window to obtain a sound part. Such restriction of the optimal process window may be ascribed to powder properties such as particle size distribution, laser energy absorptivity and reflection as well as packing density and conductivity of both the powders and solidified material. Further investigations are being conducted to gain a deeper understanding of these phenomena. Fig. 4 shows a schematic representation of the melt pool types and corresponding defects with varying $E_d$ and SiC additions.
leading to bad overlapping between each track causing irregular layers that finally affect the surface quality.

Increasing $E_d$ up to 80 J/mm³ leads to the appearance of a stable key-hole melt pool for samples 0.2 and 5.2. However, sample 0.2 alone does not exhibit any defect (Fig. 1). With the addition of 5% SiC, few lack of fusion defects are still present in sample 5.2 (Fig. 1), due to the generation of a shallow melt pool that leads to poor overlapping (Fig. 2). Indeed, despite a stable key-hole morphology, the depth of the melt pool is around 120 µm (Fig. 3), which remains close to the melt pool depth at 40 J/mm³ (100 µm). In contrast, sample 0.2 exhibits a deeper melt pool around 200 µm, which allows for good overlapping between each track and layer. With 10% SiC (sample 10.2), the melt pool has still a conductive morphology (Fig. 2) with a depth of 90 µm (Fig. 3). Thus, as in the case of an $E_d$ of 40 J/mm³, lack of fusion defects are present.

At 115 J/mm³, for all compositions a stable key-hole melt pool is generated, avoiding the presence of defects. Only few little gas porosities are spotted. The depth of the melt pool is between 150 and 250 µm.

Finally, for the highest $E_d$ of 170 J/mm³, an unstable key-hole melt pool is visible for all compositions, leading to presence of key-hole defects at the bottom of the melt pool (Fig. 1 and Fig. 2). Such defects are present in similar amounts for all compositions, i.e. less than 1%.

For all the processed powder mixtures, a sound part free of process-induced defects such as lack of fusion or key-hole porosities is obtained when the melt pool exhibits a stable key-hole like morphology with a depth between 150 and 250 µm. The threshold for the change from conductive to stable key-hole melt pool is observed to be of $E_d$ of 80 J/mm³ for the powder mixtures S2 and S2 + 5% SiC. However, with the addition of 5% SiC, a proper stable key-hole melt pool with sufficient depth is only generated at 115 J/mm³, which remains appropriate for S2 powder alone. With 10% SiC, the threshold for the change from conductive to stable key-hole melt pool is shifted to $E_d$ of 115 J/mm³. When further increasing $E_d$ up to 170 J/mm³, an unstable key-hole melt pool is generated for all compositions. Therefore, the addition of SiC restrains the optimal process window to obtain a sound part. Such restriction of the optimal process window may be ascribed to powder properties such as particle size distribution, laser energy absorptivity and reflection as well as packing density and conductivity of both the powders and solidified material. Further investigations are being conducted to gain a deeper understanding of these phenomena. Fig. 4 shows a schematic representation of the melt pool types and corresponding defects with varying $E_d$ and SiC additions.
<figure><img src="image_217.png" /><figcaption>Fig. 4. Schematic representation of the types of melt pool generated during the LPBF process with varying E<sub>d</sub> and SiC additions. A stable key-hole like melt pool with a depth between 150 and 250 µm is desired to obtain a sound part. For S2 parts, such melt pool is obtained for E<sub>d</sub> between 80 and 115 J/mm<sup>3</sup>, being limited to 115 J/mm<sup>3</sup> when 5 and 10% SiC is added.</figcaption></figure>

## Summary and conclusions

* SiC powder was added in proportion of 5 and 10 % (in volume) to a tool steel AISI S2 powder and processed by LPBF. Processing parameters were varied using different scan velocity, keeping a fixed laser power, layer thickness and hatch distance. Four values of E<sub>d</sub> were considered (40, 80, 115, 170 J/mm<sup>3</sup>) for investigations.

* For AISI S2 samples, lack of fusion defects are present only for the lowest E<sub>d</sub> (40 J/mm<sup>3</sup>). When adding SiC in proportion of either 5 and 10% in volume, such defects are present even for E<sub>d</sub> of 80 J/mm<sup>3</sup>.

* The restriction of the optimal process window can be linked to a change in melt pool morphology and dimensions. Conductive melt pools are present for the lowest E<sub>d</sub> of 40 J/mm<sup>3</sup> causing lack of fusion defects for S2 and S2 + 5% SiC. With S2 + 10% SiC, a conductive melt pool is still present for E<sub>d</sub> of 80 J/mm<sup>3</sup>. Unstable key-hole like melt pools are present at the highest E<sub>d</sub> of 170 J/mm<sup>3</sup>, causing key-hole porosities for all the investigated compositions.

* A stable key-hole melt pool with a depth between 150 and 250 µm is found for E<sub>d</sub> ranging from 80 J/mm<sup>3</sup> to 115 J/mm<sup>3</sup> for S2, limited to 115 J/mm<sup>3</sup> when SiC is added in proportion of 5 or 10% in volume.

* The threshold for the change from conductive to key-hole like melt pool is shifted from E<sub>d</sub> of 80 J/mm<sup>3</sup> for S2 and S2 + 5% SiC to E<sub>d</sub> of 115 J/mm<sup>3</sup> for S2 + 10% SiC.

## Acknowledgments

This work was supported by the FNRS (Fonds National de la Recherche Scientifique of Belgium, PDR number 35298295). The authors acknowledge the help of Sylvie Salieri for samples preparation.
<figure><img src="image_221.png" /><figcaption>Fig. 4. Schematic representation of the types of melt pool generated during the LPBF process with varying E<sub>d</sub> and SiC additions. A stable key-hole like melt pool with a depth between 150 and 250 µm is desired to obtain a sound part. For S2 parts, such melt pool is obtained for E<sub>d</sub> between 80 and 115 J/mm<sup>3</sup>, being limited to 115 J/mm<sup>3</sup> when 5 and 10% SiC is added.</figcaption></figure>

## Summary and conclusions

* SiC powder was added in proportion of 5 and 10 % (in volume) to a tool steel AISI S2 powder and processed by LPBF. Processing parameters were varied using different scan velocity, keeping a fixed laser power, layer thickness and hatch distance. Four values of E<sub>d</sub> were considered (40, 80, 115, 170 J/mm<sup>3</sup>) for investigations.

* For AISI S2 samples, lack of fusion defects are present only for the lowest E<sub>d</sub> (40 J/mm<sup>3</sup>). When adding SiC in proportion of either 5 and 10% in volume, such defects are present even for E<sub>d</sub> of 80 J/mm<sup>3</sup>.

* The restriction of the optimal process window can be linked to a change in melt pool morphology and dimensions. Conductive melt pools are present for the lowest E<sub>d</sub> of 40 J/mm<sup>3</sup> causing lack of fusion defects for S2 and S2 + 5% SiC. With S2 + 10% SiC, a conductive melt pool is still present for E<sub>d</sub> of 80 J/mm<sup>3</sup>. Unstable key-hole like melt pools are present at the highest E<sub>d</sub> of 170 J/mm<sup>3</sup>, causing key-hole porosities for all the investigated compositions.

* A stable key-hole melt pool with a depth between 150 and 250 µm is found for E<sub>d</sub> ranging from 80 J/mm<sup>3</sup> to 115 J/mm<sup>3</sup> for S2, limited to 115 J/mm<sup>3</sup> when SiC is added in proportion of 5 or 10% in volume.

* The threshold for the change from conductive to key-hole like melt pool is shifted from E<sub>d</sub> of 80 J/mm<sup>3</sup> for S2 and S2 + 5% SiC to E<sub>d</sub> of 115 J/mm<sup>3</sup> for S2 + 10% SiC.

## Acknowledgments

This work was supported by the FNRS (Fonds National de la Recherche Scientifique of Belgium, PDR number 35298295). The authors acknowledge the help of Sylvie Salieri for samples preparation.
## References

[1] J. Platl, H. Leitner, C. Turk, A. G. Demir, B. Previtali, R. Schnitzer, Defects in a Laser Powder Bed Fused Tool Steel, Adv. Eng. Mater. 23 (2021) 1-11. https://doi.org/10.1002/adem.202000833

[2] A. Lüddecke, O. Pannitz, H. Zetzener, J. T. Sehrt, A. Kwade, Powder properties and flowability measurements of tailored nanocomposites for powder bed fusion applications, Mater. Des. 202 (2021). https://doi.org/10.1016/j.matdes.2021.109536

[3] J. Wang, R. Zhu, Y. Liu, L. Zhang, Understanding melt pool characteristics in laser powder bed fusion: An overview of single- and multi-track melt pools for process optimization, Adv. Powder Mater. 2 (2023). https://doi.org/10.1016/j.apmate.2023.100137

[4] W. E. King et al., Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing, J. Mater. Process. Technol. 214 (2014) 2915-2925. https://doi.org/10.1016/j.jmatprotec.2014.06.005

[5] E. Gärtner et al., Melt pool signatures of TiN nanoparticle dry-coated Co25Cr25Fe25Ni25 metal powder in laser-powder-bed-fusion, Mater. Des. 226 (2023). https://doi.org/10.1016/j.matdes.2023.111626

[6] S. Zhang, B. Lane, J. Whiting, K. Chou, On thermal properties of metallic powder in laser powder bed fusion additive manufacturing, J. Manuf. Process. 47 (2019) 382-392. https://doi.org/10.1016/j.jmapro.2019.09.012

[7] M. Khorasani et al., The effect of absorption ratio on meltpool features in laser-based powder bed fusion of IN718, Opt. Laser Technol. 153 (2022) 18263. https://doi.org/10.1016/j.optlastec.2022.108263

[8] Z. Zhang et al., 3-Dimensional heat transfer modeling for laser powder-bed fusion additive manufacturing with volumetric heat sources based on varied thermal conductivity and absorptivity, Opt. Laser Technol. 109 (2019) 297-312. https://doi.org/10.1016/j.optlastec.2018.08.012

[9] B. Brandau, A. Da Silva, C. Wilsnack, F. Brueckner, A. F. H. Kaplan, Absorbance study of powder conditions for laser additive manufacturing, Mater. Des. 216 (2022) 110591. https://doi.org/10.1016/j.matdes.2022.110591

[10] O. Pannitz, A. Lüddecke, A. Kwade, J. T. Sehrt, Investigation of the in situ thermal conductivity and absorption behavior of nanocomposite powder materials in laser powder bed fusion processes, Mater. Des. 201 (2021). https://doi.org/10.1016/j.matdes.2021.109530

[11] O. Pannitz, F. Großwendt, A. Lüddecke, A. Kwade, A. Röttger, J. T. Sehrt, Improved process efficiency in laser-based powder bed fusion of nanoparticle coated maraging tool steel powder, Materials (Basel) 14 (2021) 1-22. https://doi.org/10.3390/ma14133465

[12] C. Tan, J. Zou, D. Wang, W. Ma, K. Zhou, Duplex strengthening via SiC addition and in-situ precipitation in additively manufactured composite materials, Compos. Part B Eng. 236 (2022) 109820. https://doi.org/10.1016/j.compositesb.2022.109820

[13] M. H. Mosallanejad, B. Niroumand, A. Aversa, A. Saboori, In-situ alloying in laser-based additive manufacturing processes: A critical review, J. Alloys Compd. 872 (2021) 159567. https://doi.org/10.1016/j.jallcom.2021.159567

[14] E. Saggionetto, E. Filippi, O. Dedry, J. T. Tchuindjang, and A. Mertens, Development and processability of AISI S2 tool steel by laser powder bed fusion, Mater. Res. Proc. 28 (2023) 41-48. https://doi.org/10.21741/9781644902479-5
## References

[1] J. Platl, H. Leitner, C. Turk, A. G. Demir, B. Previtali, R. Schnitzer, Defects in a Laser Powder Bed Fused Tool Steel, Adv. Eng. Mater. 23 (2021) 1-11. https://doi.org/10.1002/adem.202000833

[2] A. Lüddecke, O. Pannitz, H. Zetzener, J. T. Sehrt, A. Kwade, Powder properties and flowability measurements of tailored nanocomposites for powder bed fusion applications, Mater. Des. 202 (2021). https://doi.org/10.1016/j.matdes.2021.109536

[3] J. Wang, R. Zhu, Y. Liu, L. Zhang, Understanding melt pool characteristics in laser powder bed fusion: An overview of single- and multi-track melt pools for process optimization, Adv. Powder Mater. 2 (2023). https://doi.org/10.1016/j.apmate.2023.100137

[4] W. E. King et al., Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing, J. Mater. Process. Technol. 214 (2014) 2915-2925. https://doi.org/10.1016/j.jmatprotec.2014.06.005

[5] E. Gärtner et al., Melt pool signatures of TiN nanoparticle dry-coated Co25Cr25Fe25Ni25 metal powder in laser-powder-bed-fusion, Mater. Des. 226 (2023). https://doi.org/10.1016/j.matdes.2023.111626

[6] S. Zhang, B. Lane, J. Whiting, K. Chou, On thermal properties of metallic powder in laser powder bed fusion additive manufacturing, J. Manuf. Process. 47 (2019) 382-392. https://doi.org/10.1016/j.jmapro.2019.09.012

[7] M. Khorasani et al., The effect of absorption ratio on meltpool features in laser-based powder bed fusion of IN718, Opt. Laser Technol. 153 (2022) 18263. https://doi.org/10.1016/j.optlastec.2022.108263

[8] Z. Zhang et al., 3-Dimensional heat transfer modeling for laser powder-bed fusion additive manufacturing with volumetric heat sources based on varied thermal conductivity and absorptivity, Opt. Laser Technol. 109 (2019) 297-312. https://doi.org/10.1016/j.optlastec.2018.08.012

[9] B. Brandau, A. Da Silva, C. Wilsnack, F. Brueckner, A. F. H. Kaplan, Absorbance study of powder conditions for laser additive manufacturing, Mater. Des. 216 (2022) 110591. https://doi.org/10.1016/j.matdes.2022.110591

[10] O. Pannitz, A. Lüddecke, A. Kwade, J. T. Sehrt, Investigation of the in situ thermal conductivity and absorption behavior of nanocomposite powder materials in laser powder bed fusion processes, Mater. Des. 201 (2021). https://doi.org/10.1016/j.matdes.2021.109530

[11] O. Pannitz, F. Großwendt, A. Lüddecke, A. Kwade, A. Röttger, J. T. Sehrt, Improved process efficiency in laser-based powder bed fusion of nanoparticle coated maraging tool steel powder, Materials (Basel) 14 (2021) 1-22. https://doi.org/10.3390/ma14133465

[12] C. Tan, J. Zou, D. Wang, W. Ma, K. Zhou, Duplex strengthening via SiC addition and in-situ precipitation in additively manufactured composite materials, Compos. Part B Eng. 236 (2022) 109820. https://doi.org/10.1016/j.compositesb.2022.109820

[13] M. H. Mosallanejad, B. Niroumand, A. Aversa, A. Saboori, In-situ alloying in laser-based additive manufacturing processes: A critical review, J. Alloys Compd. 872 (2021) 159567. https://doi.org/10.1016/j.jallcom.2021.159567

[14] E. Saggionetto, E. Filippi, O. Dedry, J. T. Tchuindjang, and A. Mertens, Development and processability of AISI S2 tool steel by laser powder bed fusion, Mater. Res. Proc. 28 (2023) 41-48. https://doi.org/10.21741/9781644902479-5
# Numerical modeling of the compressive behavior of 316L body-centered cubic lattice structures

RÍOS Ignacio¹,²,a, MARTÍNEZ Alex¹,ᵇ, SAGGIONETTO Enrico³,ᶜ,
MERTENS Anne³,ᵈ, DUCHÊNE Laurent⁴,ᵉ, HABRAKEN Anne Marie⁴,>f,
TUNINETTI Víctor¹,*,g

¹Department of Mechanical Engineering, Universidad de La Frontera, Temuco, Chile

²Master in Engineering Sciences, Universidad de La Frontera, Temuco, Chile

³Department A&M-MMS, University of Liège, Liège, Belgium

⁴Department ArGEnCo-MSM, University of Liège, Liège, Belgium

aignacio.rios@ufrontera.cl, b.a.martinez16@ufromail.cl, cenrico.saggionetto@uliege.be,

danne.mertens@uliege.be, el.duchene@uliege.be, fanne.habraken@uliege.be,

gvictor.tuninetti@ufrontera.cl

**Keywords:** Lattice Structure, Additive Manufacturing, Relative Density

**Abstract.** With additive manufacturing, innovative porous structures emerge for generating lightweight components with high mechanical responses. Body-centered cubic lattice structures are the focus of this study, with customizable lattice density depending on the strut diameter. To predict the properties of lattice structures and thus reduce the number of tests in experimental campaigns, several numerical and analytical models have been developed. In this work, the elastoplastic response was determined. Buckling phenomena of vertical struts depend on the different boundary conditions applied in Finite Element simulations. As shown the number of cells within the model affects the results. This size effect was quantified for different lattice density cases. The numerical results obtained for lattice structures with different relative density were also compared with the well-known Gibson-Ashby model.

**Introduction**

The development of additive manufacturing technologies and their fabrication capacities has enabled the production of parts with complex three dimensional geometries. These capabilities allow producing parts with innovative designs [1], optimized functionalities [2] and lightweight structures [3] while reducing production cost. Indeed, this process decreases material consumption, production time and post-processing. The lattice structures belong to the complex components built by additive manufacturing.

Lattice structures are based on a cellular representative unit, which is repeated in an orderly fashion in a three-dimensional space. The basic cells – generally called struts-based cells – are defined by constituent rods, with particular geometric dimensions and connectivity at certain points in space called nodes, thus defining their topology. From a cellular point of view, lattice structures can be considered indeed as local structures [4], but from a more macroscopic point of view, they can be considered as a homogenized meta-material distributed in a three-dimensional space with its own mechanical properties [5].

One of the main features of lattice structures is that their global mechanical response can be altered by modifying their topology or geometry parameters such as relative density, cell topology, cell size, among other design parameters. Depending on its design, the lattice structure leads to mechanical properties that significantly differ from those of the constituent material [6].

<figure><img src="image_218.png" /></figure>
# Numerical modeling of the compressive behavior of 316L body-centered cubic lattice structures

RÍOS Ignacio¹,²,a, MARTÍNEZ Alex¹,ᵇ, SAGGIONETTO Enrico³,ᶜ,
MERTENS Anne³,ᵈ, DUCHÊNE Laurent⁴,ᵉ, HABRAKEN Anne Marie⁴,>f,
TUNINETTI Víctor¹,*,g

¹Department of Mechanical Engineering, Universidad de La Frontera, Temuco, Chile

²Master in Engineering Sciences, Universidad de La Frontera, Temuco, Chile

³Department A&M-MMS, University of Liège, Liège, Belgium

⁴Department ArGEnCo-MSM, University of Liège, Liège, Belgium

aignacio.rios@ufrontera.cl, b.a.martinez16@ufromail.cl, cenrico.saggionetto@uliege.be,

danne.mertens@uliege.be, el.duchene@uliege.be, fanne.habraken@uliege.be,

gvictor.tuninetti@ufrontera.cl

**Keywords:** Lattice Structure, Additive Manufacturing, Relative Density

**Abstract.** With additive manufacturing, innovative porous structures emerge for generating lightweight components with high mechanical responses. Body-centered cubic lattice structures are the focus of this study, with customizable lattice density depending on the strut diameter. To predict the properties of lattice structures and thus reduce the number of tests in experimental campaigns, several numerical and analytical models have been developed. In this work, the elastoplastic response was determined. Buckling phenomena of vertical struts depend on the different boundary conditions applied in Finite Element simulations. As shown the number of cells within the model affects the results. This size effect was quantified for different lattice density cases. The numerical results obtained for lattice structures with different relative density were also compared with the well-known Gibson-Ashby model.

**Introduction**

The development of additive manufacturing technologies and their fabrication capacities has enabled the production of parts with complex three dimensional geometries. These capabilities allow producing parts with innovative designs [1], optimized functionalities [2] and lightweight structures [3] while reducing production cost. Indeed, this process decreases material consumption, production time and post-processing. The lattice structures belong to the complex components built by additive manufacturing.

Lattice structures are based on a cellular representative unit, which is repeated in an orderly fashion in a three-dimensional space. The basic cells – generally called struts-based cells – are defined by constituent rods, with particular geometric dimensions and connectivity at certain points in space called nodes, thus defining their topology. From a cellular point of view, lattice structures can be considered indeed as local structures [4], but from a more macroscopic point of view, they can be considered as a homogenized meta-material distributed in a three-dimensional space with its own mechanical properties [5].

One of the main features of lattice structures is that their global mechanical response can be altered by modifying their topology or geometry parameters such as relative density, cell topology, cell size, among other design parameters. Depending on its design, the lattice structure leads to mechanical properties that significantly differ from those of the constituent material [6].

<figure><img src="image_222.png" /></figure>
Cellular structures have been used in several applications, such as aerospace [7], automotive [8], marine [9] and medical [10] industries, owing to the lightweight nature, high strength-to-weight ratio, potential for customization and specific energy absorption capacity [11, 12].

On the basis of the mechanical response, lattice structures can be categorized into bending-dominated or stretch-dominated. Bending-dominated lattice structures experience mostly internal bending moments and are therefore compliant, while stretch-dominated lattice structures experience mainly internal axial loads and are therefore stiffer than bending-dominated structures. In general, the cell topology is the main factor that defines the deformation mechanism of the lattice structure [13].

The most studied strut-based lattice structure topologies are those based on body-centered cubic (BCC), face-centered cubic (FCC) geometries and their variants with reinforcing struts, with the most common one being z reinforcement in the direction of the load (BCCZ and FCCZ). The behavior of these strut-based cells can be characterized by the connectivity of the struts defined in Eq. 1 with the Maxwell number [5, 14], (M). This M-value depends on the number of struts (s) of the representative cell, and the number of nodes, (n).

$$M = s - 3n + 6 \qquad (1)$$

For $M < 0$, the system is bending-dominated, while for $M \ge 0$ the structure will display a stretch-dominated behavior. This number is just a general indicator, as the boundary between the two types of behavior is not so clear. It also depends on the orientation of struts versus the load direction.

To decrease the experimental campaigns, many computational models, based on the finite element method, have been developed with the goal of predicting the mechanical behavior of lattice structures, especially those related with the energy absorption capacity of such structures. For instance, Rodrigo et al. [15] presented the quasi-static and dynamic compression behavior of functionally graded lattices by means of experiments and simulations using finite element modeling. The successful validation of their model allowed parametric simulations of the structure subjected to higher compression rates. Wang et al. [13] also investigated the mechanical response and deformation mechanism of a hierarchical lattice structure by means of quasi-static compression tests and numerical simulations. They also found out a good correlation between both methods and proved a superior performance of strut-reinforced hierarchical lattice structures compared to conventional ones. Favre et al. [6] used a continuous crystallographic approach to generate cubic strut-based lattice cells with different topologies and evaluated them through finite element modeling. They found a relationship between the relative elastic modulus and relative density equivalent to the Gibson-Ashby model, with a constant $C = 1$ and an exponent $n$ dependent on the cell topology and geometry.

Computational models have proven to be reliable tools to predict the mechanical behavior of lattice structures, validating analytical models such as the Gibson-Ashby model [16]. However, there is a lack of models evaluating the effect of different boundary conditions in simulations compared with real physical experiments. The present article assesses the impact of the size effect, in terms of number of cells in the studied volume, on the global elastoplastic response of a body centered cubic lattice. The simulations are performed with various boundary conditions and relative densities. They demonstrate the great importance of taking into account the edge effect in lattice structures. Indeed different numbers of cells generate different mechanical responses. The insights and findings reported in this work must be considered when modeling lattice structures as a homogenized material in small domains.
Cellular structures have been used in several applications, such as aerospace [7], automotive [8], marine [9] and medical [10] industries, owing to the lightweight nature, high strength-to-weight ratio, potential for customization and specific energy absorption capacity [11, 12].

On the basis of the mechanical response, lattice structures can be categorized into bending-dominated or stretch-dominated. Bending-dominated lattice structures experience mostly internal bending moments and are therefore compliant, while stretch-dominated lattice structures experience mainly internal axial loads and are therefore stiffer than bending-dominated structures. In general, the cell topology is the main factor that defines the deformation mechanism of the lattice structure [13].

The most studied strut-based lattice structure topologies are those based on body-centered cubic (BCC), face-centered cubic (FCC) geometries and their variants with reinforcing struts, with the most common one being z reinforcement in the direction of the load (BCCZ and FCCZ). The behavior of these strut-based cells can be characterized by the connectivity of the struts defined in Eq. 1 with the Maxwell number [5, 14], (M). This M-value depends on the number of struts (s) of the representative cell, and the number of nodes, (n).

$$M = s - 3n + 6 \quad (1)$$

For $M < 0$, the system is bending-dominated, while for $M \ge 0$ the structure will display a stretch-dominated behavior. This number is just a general indicator, as the boundary between the two types of behavior is not so clear. It also depends on the orientation of struts versus the load direction.

To decrease the experimental campaigns, many computational models, based on the finite element method, have been developed with the goal of predicting the mechanical behavior of lattice structures, especially those related with the energy absorption capacity of such structures. For instance, Rodrigo et al. [15] presented the quasi-static and dynamic compression behavior of functionally graded lattices by means of experiments and simulations using finite element modeling. The successful validation of their model allowed parametric simulations of the structure subjected to higher compression rates. Wang et al. [13] also investigated the mechanical response and deformation mechanism of a hierarchical lattice structure by means of quasi-static compression tests and numerical simulations. They also found out a good correlation between both methods and proved a superior performance of strut-reinforced hierarchical lattice structures compared to conventional ones. Favre et al. [6] used a continuous crystallographic approach to generate cubic strut-based lattice cells with different topologies and evaluated them through finite element modeling. They found a relationship between the relative elastic modulus and relative density equivalent to the Gibson-Ashby model, with a constant $C = 1$ and an exponent $n$ dependent on the cell topology and geometry.

Computational models have proven to be reliable tools to predict the mechanical behavior of lattice structures, validating analytical models such as the Gibson-Ashby model [16]. However, there is a lack of models evaluating the effect of different boundary conditions in simulations compared with real physical experiments. The present article assesses the impact of the size effect, in terms of number of cells in the studied volume, on the global elastoplastic response of a body centered cubic lattice. The simulations are performed with various boundary conditions and relative densities. They demonstrate the great importance of taking into account the edge effect in lattice structures. Indeed different numbers of cells generate different mechanical responses. The insights and findings reported in this work must be considered when modeling lattice structures as a homogenized material in small domains.
# Materials and Methods

## *Strut-based lattice structure*

The investigated body-centered cubic lattice cells with strut reinforcements on the x, y and z axes (BCCXYZ lattice) at different relative densities are shown in Fig. 1. Four BCCXYZ cells with different relative densities of 20%, 40%, 60% and 80% were modeled by adapting the diameter of the representative strut. The cell size *L* was set constant at a value of 3 mm. The parameters for defining the topology of the cells shown in Fig.1 are the relative density (*ρ*<sub>r</sub>), the strut diameter (*d*) and the cell size (*L*).

<figure><img src="image_219.png" /><figcaption>Fig. 1 Lattice cell designs at different relative densities ρ<sub>r</sub> and their geometric parameters: strut diameter d and cell size L.</figcaption></figure>

## Finite element modeling

Finite element models of the four lattice structures shown in Fig. 1 were designed with their different relative density using ANSYS mechanical. To reveal the different mechanical responses of these structures under different boundary conditions, four models (see Fig. 2) were implemented:

* Model 1 is made up of a single cell without any displacement constraints applied to the lateral faces. In the bottom face, the degree of freedom in the Y direction (direction perpendicular to the face) is constrained, allowing lateral displacements within this face. A reference line in the center of the cell was defined having only its degree of freedom of displacement in the Y direction free. It aims to prevent any side slip or rigid movement of the whole sample. A constant displacement of 0.3 mm was applied to all the nodes in the upper face of the cell to apply an overall compression strain up to 10%.

* Model 2 is made up of a single cell with three symmetry planes in faces which are respectively perpendicular to each of the three x, y and z coordinate axes. By this way, a structure of eight cells is represented. A constant displacement of 0.6 mm was applied to all the nodes in the upper face of the cell.

* Model 3 is made up of a single cell with three symmetry planes perpendicular to each of the three x, y and z coordinate axes. Additionally, on the two remaining lateral faces, the constraint that all the nodes of these faces should remain in the same plane is applied. In other words, these lateral faces are allowed to move as long as they stay flat, preventing buckling or any similar effect. A constant displacement of 0.6 mm was applied to all the nodes in the upper face of the cell.

* Model 4 is made up of 8 cells with three symmetry planes in faces perpendicular to each of the three x, y and z coordinate axes. In this way, a structure of 64 cells is represented. A constant displacement of 1.2 mm was applied to all the nodes in the upper face of the model.

For all the simulations, an overall compression strain up to 10% was applied.
# Materials and Methods

## Strut-based lattice structure

The investigated body-centered cubic lattice cells with strut reinforcements on the x, y and z axes (BCCXYZ lattice) at different relative densities are shown in Fig. 1. Four BCCXYZ cells with different relative densities of 20%, 40%, 60% and 80% were modeled by adapting the diameter of the representative strut. The cell size L was set constant at a value of 3 mm. The parameters for defining the topology of the cells shown in Fig. 1 are the relative density ($ρ_r$), the strut diameter (d) and the cell size (L).

<figure><img src="image_223.png" /><figcaption>Fig. 1 Lattice cell designs at different relative densities $ρ_r$ and their geometric parameters: strut diameter $d$ and cell size $L$.</figcaption></figure>

## Finite element modeling

Finite element models of the four lattice structures shown in Fig. 1 were designed with their different relative density using ANSYS mechanical. To reveal the different mechanical responses of these structures under different boundary conditions, four models (see Fig. 2) were implemented:

* Model 1 is made up of a single cell without any displacement constraints applied to the lateral faces. In the bottom face, the degree of freedom in the Y direction (direction perpendicular to the face) is constrained, allowing lateral displacements within this face. A reference line in the center of the cell was defined having only its degree of freedom of displacement in the Y direction free. It aims to prevent any side slip or rigid movement of the whole sample. A constant displacement of 0.3 mm was applied to all the nodes in the upper face of the cell to apply an overall compression strain up to 10%.

* Model 2 is made up of a single cell with three symmetry planes in faces which are respectively perpendicular to each of the three x, y and z coordinate axes. By this way, a structure of eight cells is represented. A constant displacement of 0.6 mm was applied to all the nodes in the upper face of the cell.

* Model 3 is made up of a single cell with three symmetry planes perpendicular to each of the three x, y and z coordinate axes. Additionally, on the two remaining lateral faces, the constraint that all the nodes of these faces should remain in the same plane is applied. In other words, these lateral faces are allowed to move as long as they stay flat, preventing buckling or any similar effect. A constant displacement of 0.6 mm was applied to all the nodes in the upper face of the cell.

* Model 4 is made up of 8 cells with three symmetry planes in faces perpendicular to each of the three x, y and z coordinate axes. In this way, a structure of 64 cells is represented. A constant displacement of 1.2 mm was applied to all the nodes in the upper face of the model.

For all the simulations, an overall compression strain up to 10% was applied.
The struts of the lattice structures were meshed using solid elements SOLID187 of size between 0.13 and 0.2 mm. The material properties were set based on the 316L stainless steel. A bilinear elastic-plastic model was chosen considering the 316L strain-hardening after the yield point. The elastic modulus was set to 150 GPa, the yield stress to 225 MPa and the hardening modulus to 0.95 GPa. Poisson's ratio was established as 0.3.

<figure><img src="image_220.png" /><figcaption>Fig. 2 Graphical representation of the four models with different boundary conditions applied on a 20% relative density lattice cell.</figcaption></figure>

## Methodology to assess lattice mechanical properties

The mechanical properties of lattice structures have a slightly different interpretation with respect to the standard definitions of solid materials. For example, elastic modulus, yield strength and tangent modulus, when determined for lattice structures, refer to the apparent macroscopic properties that converge to certain values when the number of unit cells is sufficiently large [17].

Usually the mechanical properties of lattice structures are expressed as a fraction of the corresponding mechanical property of the solid material. This ratio depends on the relative density of the cellular unit. Density fraction ($ρ/ρ_0$) is defined as the ratio between the density of the lattice structure ($ρ$) and the density of the bulk material from which it is built ($ρ_0$) [17]. In general, and regardless of their topology, the mechanical properties of lattice structures tend to increase with an increment in their relative density. Another factor that plays a relevant role regarding the mechanical response of anisotropic lattice structures is the orientation of the cells with respect to the loading direction.

Most mechanical characterizations of lattice structures are performed through compression tests, due to their simple performance in contrast with tensile tests. The problem in the tensile case is linked to the design of a specimen that avoids stress concentration [18]. The compressive deformation mechanism of lattice structures can be divided into three stages: a linear elastic deformation, followed by plastic deformation and ending in densification or compaction (Fig. 3) [19]. Within the compression test, the individual struts of the lattice structures are prone to failure by 3 mechanisms: yielding, buckling and fracture [5].

During the elastic deformation stage, the stress-strain response of the cellular material is linear elastic, with an elastic modulus proportional to the elastic modulus of the parent material. Once the elastic limit is reached, plastic deformation takes place and yielding or buckling begins in the cells of the lattice structure. Often plastic deformation continues with an almost constant stress, known as plateau stress [5]. Additionally, at this stage, it is possible to experience an oscillating stress behavior, due to the consecutive failure of the different cell layers of the lattice structure.
The struts of the lattice structures were meshed using solid elements SOLID187 of size between 0.13 and 0.2 mm. The material properties were set based on the 316L stainless steel. A bilinear elastic-plastic model was chosen considering the 316L strain-hardening after the yield point. The elastic modulus was set to 150 GPa, the yield stress to 225 MPa and the hardening modulus to 0.95 GPa. Poisson's ratio was established as 0.3.

<figure><img src="image_224.png" /><figcaption>Fig. 2 Graphical representation of the four models with different boundary conditions applied on a 20% relative density lattice cell.</figcaption></figure>

## Methodology to assess lattice mechanical properties

The mechanical properties of lattice structures have a slightly different interpretation with respect to the standard definitions of solid materials. For example, elastic modulus, yield strength and tangent modulus, when determined for lattice structures, refer to the apparent macroscopic properties that converge to certain values when the number of unit cells is sufficiently large [17].

Usually the mechanical properties of lattice structures are expressed as a fraction of the corresponding mechanical property of the solid material. This ratio depends on the relative density of the cellular unit. Density fraction ($ρ/ρ_0$) is defined as the ratio between the density of the lattice structure ($ρ$) and the density of the bulk material from which it is built ($ρ_0$) [17]. In general, and regardless of their topology, the mechanical properties of lattice structures tend to increase with an increment in their relative density. Another factor that plays a relevant role regarding the mechanical response of anisotropic lattice structures is the orientation of the cells with respect to the loading direction.

Most mechanical characterizations of lattice structures are performed through compression tests, due to their simple performance in contrast with tensile tests. The problem in the tensile case is linked to the design of a specimen that avoids stress concentration [18]. The compressive deformation mechanism of lattice structures can be divided into three stages: a linear elastic deformation, followed by plastic deformation and ending in densification or compaction (Fig. 3) [19]. Within the compression test, the individual struts of the lattice structures are prone to failure by 3 mechanisms: yielding, buckling and fracture [5].

During the elastic deformation stage, the stress-strain response of the cellular material is linear elastic, with an elastic modulus proportional to the elastic modulus of the parent material. Once the elastic limit is reached, plastic deformation takes place and yielding or buckling begins in the cells of the lattice structure. Often plastic deformation continues with an almost constant stress, known as plateau stress [5]. Additionally, at this stage, it is possible to experience an oscillating stress behavior, due to the consecutive failure of the different cell layers of the lattice structure.
This effect is further enhanced due to the inherent fabrication defects of additive manufacturing. Once the deformations of the cells are high enough to present contact between the different layers of the lattice material, the densification or compaction stage is reached. At this stage, the stress necessary to increase the deformation abruptly grows and the cellular material adopts a behavior closer to that of the solid material (see Fig. 3).

From the macroscopic point of view of cellular material, three main modes of compressive failure have been found: i) successive collapse of cells, in which each layer collapses due to buckling or crushing, and compresses on the layer below, making the structure stronger after the compaction of each layer; ii) brittle fracture in the cell walls, usually originated by the existence of some defects and characterized by the propagation of the crack or cracks through the lattice struts; and iii) diagonal shear, resulting in an initial loss of about 50% of strength followed by a stiffening during densification [20].

<figure><img src="image_221.png" /><figcaption>Fig. 3 Stress-strain compression curves characteristic of bending-dominated and stretch-dominated lattice structures. Modified from [19]</figcaption></figure>

The Gibson-Ashby model is one of the most widely accepted models for computing the mechanical properties of cellular structures including strut-based lattice structures. This model predicts mechanical properties, such as elastic modulus and yield strength, expressed as fractions of the base material properties. These properties depend on the type of mechanical response of the lattice structure, whether bending-dominant or stretch-dominant. They are expressed as a positive power relation with the relative density of the lattice structure. The formulation for the elastic modulus and yield strength for lattice structures is shown in Eq. 2 and Eq. 3, respectively [16]:

$$ \frac{E}{E_0} = C \left(\frac{\rho}{\rho_0}\right)^n \qquad (2) $$

$$ \frac{\sigma}{\sigma_0} = C \left(\frac{\rho}{\rho_0}\right)^n \qquad (3) $$

where $E$ and $\sigma$ are the apparent elastic modulus and yield strength of the lattice structure, $E_0$ and $\sigma_0$ are the elastic modulus and yield strength of the bulk parent material. Following this way, the fractions ($E/E_0$) and ($\sigma/\sigma_0$) can be defined as the relative modulus and the relative strength of the lattice structure, with $C$ being the Gibson-Ashby constant. Its value depends on the cell topology and geometry and should be derived from experimental results. The exponent $n$ depends on the mechanical response of the lattice structure, bending or stretch-dominated. It has different values depending on cell geometry size and materials. However, this exponent value is also usually derived from experimental results.

In this work, the presented model with different relative densities, numbers of cells and boundary conditions were simulated up to a global strain of 10%. The global stress-strain curves
This effect is further enhanced due to the inherent fabrication defects of additive manufacturing. Once the deformations of the cells are high enough to present contact between the different layers of the lattice material, the densification or compaction stage is reached. At this stage, the stress necessary to increase the deformation abruptly grows and the cellular material adopts a behavior closer to that of the solid material (see Fig. 3).

From the macroscopic point of view of cellular material, three main modes of compressive failure have been found: i) successive collapse of cells, in which each layer collapses due to buckling or crushing, and compresses on the layer below, making the structure stronger after the compaction of each layer; ii) brittle fracture in the cell walls, usually originated by the existence of some defects and characterized by the propagation of the crack or cracks through the lattice struts; and iii) diagonal shear, resulting in an initial loss of about 50% of strength followed by a stiffening during densification [20].

<figure><img src="image_225.png" /><figcaption>Fig. 3 Stress-strain compression curves characteristic of bending-dominated and stretch-dominated lattice structures. Modified from [19]</figcaption></figure>

The Gibson-Ashby model is one of the most widely accepted models for computing the mechanical properties of cellular structures including strut-based lattice structures. This model predicts mechanical properties, such as elastic modulus and yield strength, expressed as fractions of the base material properties. These properties depend on the type of mechanical response of the lattice structure, whether bending-dominant or stretch-dominant. They are expressed as a positive power relation with the relative density of the lattice structure. The formulation for the elastic modulus and yield strength for lattice structures is shown in Eq. 2 and Eq. 3, respectively [16]:

$$ \frac{E}{E_0} = C \left(\frac{\rho}{\rho_0}\right)^n \qquad (2) $$

$$ \frac{\sigma}{\sigma_0} = C \left(\frac{\rho}{\rho_0}\right)^n \qquad (3) $$

where $E$ and $\sigma$ are the apparent elastic modulus and yield strength of the lattice structure, $E_0$ and $\sigma_0$ are the elastic modulus and yield strength of the bulk parent material. Following this way, the fractions ($E/E_0$) and ($\sigma/\sigma_0$) can be defined as the relative modulus and the relative strength of the lattice structure, with $C$ being the Gibson-Ashby constant. Its value depends on the cell topology and geometry and should be derived from experimental results. The exponent $n$ depends on the mechanical response of the lattice structure, bending or stretch-dominated. It has different values depending on cell geometry size and materials. However, this exponent value is also usually derived from experimental results.

In this work, the presented model with different relative densities, numbers of cells and boundary conditions were simulated up to a global strain of 10%. The global stress-strain curves
were calculated and used to derive the Gibson-Ashby exponent *n*, assuming a constant *C* value of 1. The procedure was applied on the modulus of elasticity, the tangent modulus and the yield stress (defined for 0.2% of plastic strain).

## Results

Figure 4 shows the global true stress-strain curves of simulated BCCXYZ lattice structure with 5 different relative densities and the boundary condition expressed in model 2, thus representing an 8 cell lattice structure.

<figure><img src="image_222.png" /><figcaption>Fig. 4 True stress-strain curves of simulated compression specimens of different relative densities for model 2 boundary conditions.</figcaption></figure>

Similar curves were obtained for the other boundary models (models 1, 3, 4). The elastic modulus, the yield stress and the tangent modulus of each case were obtained. These results are summarized in Table 1.

The main observations are that the principal differences between the models are found in the plastic zone. Indeed, the tangent modulus presents the largest deviations for different boundary conditions, especially for the low densities. Contrarily, the elastic modulus and the yield stress do not show important variations for the different models (5% or less). In general, the model with the lowest mechanical properties is model 1, which was expected since this model represents a single cell, so its bars are more prone to bending or buckling. This model, representative of a single cell has poor real applications, its interest is to provide a lower limit of the mechanical properties in lattice structures. Model 2 better simulates mechanical properties of lattice structures due to its 8-cell arrangement. However, it prevents by symmetry conditions some buckling that can happen in model 4. This latter model represents an array of 64 cells, and its lateral vertical bars can experience high deformations due to buckling. Model 3, the most constraint, is the stiffest, having the highest mechanical properties. This model represents an 8-cell lattice structure but in the ideal case in which its lateral faces will always remain flat, in spite of the values of the displacements. Model 3 aims to simulate a lattice structure portion that is inside a larger array, gaining resistance from contact with the surrounding cells or bulk material. Despite being an ideal case whose application in experiments is unlikely, it serves to establish an upper limit in terms of mechanical properties of lattice structures subjected to compression. It is expected that when simulating lattice structures with a larger number of cells, but with the boundary conditions of models 2 or 4, their behavior will tend to approach that of model 3 as the number of cells in the structure increases and the bending or buckling of the vertical external edge face becomes less relevant for the whole structure. This difference in mechanical properties due to changes in boundary conditions is exacerbated in structures with lower relative density, since they imply smaller diameter in the struts. This slenderness tends to increase the buckling in these struts, resulting in lower mechanical properties and even a softening effect once the plastic zone is reached. These effects are shown in Fig. 5, which shows the true stress-strain curves for the lattice structure at 20% and 40% of density fraction for the different boundary condition cases analyzed.
were calculated and used to derive the Gibson-Ashby exponent *n*, assuming a constant *C* value of 1. The procedure was applied on the modulus of elasticity, the tangent modulus and the yield stress (defined for 0.2% of plastic strain).

## Results

Figure 4 shows the global true stress-strain curves of simulated BCCXYZ lattice structure with 5 different relative densities and the boundary condition expressed in model 2, thus representing an 8 cell lattice structure.

<figure><img src="image_226.png" /><figcaption>Fig. 4 True stress-strain curves of simulated compression specimens of different relative densities for model 2 boundary conditions.</figcaption></figure>

Similar curves were obtained for the other boundary models (models 1, 3, 4). The elastic modulus, the yield stress and the tangent modulus of each case were obtained. These results are summarized in Table 1.

The main observations are that the principal differences between the models are found in the plastic zone. Indeed, the tangent modulus presents the largest deviations for different boundary conditions, especially for the low densities. Contrarily, the elastic modulus and the yield stress do not show important variations for the different models (5% or less). In general, the model with the lowest mechanical properties is model 1, which was expected since this model represents a single cell, so its bars are more prone to bending or buckling. This model, representative of a single cell has poor real applications, its interest is to provide a lower limit of the mechanical properties in lattice structures. Model 2 better simulates mechanical properties of lattice structures due to its 8-cell arrangement. However, it prevents by symmetry conditions some buckling that can happen in model 4. This latter model represents an array of 64 cells, and its lateral vertical bars can experience high deformations due to buckling. Model 3, the most constraint, is the stiffest, having the highest mechanical properties. This model represents an 8-cell lattice structure but in the ideal case in which its lateral faces will always remain flat, in spite of the values of the displacements. Model 3 aims to simulate a lattice structure portion that is inside a larger array, gaining resistance from contact with the surrounding cells or bulk material. Despite being an ideal case whose application in experiments is unlikely, it serves to establish an upper limit in terms of mechanical properties of lattice structures subjected to compression. It is expected that when simulating lattice structures with a larger number of cells, but with the boundary conditions of models 2 or 4, their behavior will tend to approach that of model 3 as the number of cells in the structure increases and the bending or buckling of the vertical external edge face becomes less relevant for the whole structure. This difference in mechanical properties due to changes in boundary conditions is exacerbated in structures with lower relative density, since they imply smaller diameter in the struts. This slenderness tends to increase the buckling in these struts, resulting in lower mechanical properties and even a softening effect once the plastic zone is reached. These effects are shown in Fig. 5, which shows the true stress-strain curves for the lattice structure at 20% and 40% of density fraction for the different boundary condition cases analyzed.
<table><caption>Table 1 Mechanical properties of simulated lattice structures at different relative densities and different boundary conditions. The maximum absolute difference (Δ) between the results for each relative density is computed, its percentage relative to the smallest result of the simulations of this case is also provided.</caption><thead><tr><th>Relative density</th><th>Boundary condition</th><th>Elastic modulus (GPa)</th><th>Yield stress (MPa)</th><th>Tangent modulus (GPa)</th></tr></thead><tbody><tr><td rowspan="4">20%</td><td>Model 1</td><td>6.433</td><td>Δ= 0.113</td><td>12.6</td><td>Δ= 0.4</td><td>-0.026</td></tr><tr><td>Model 2</td><td>6.487</td><td>= 1.76%</td><td>12.8</td><td>= 3.17%</td><td>-0.008</td></tr><tr><td>Model 3</td><td>6.546</td><td></td><td>13.0</td><td></td><td>0.031</td></tr><tr><td>Model 4</td><td>6.526</td><td></td><td>13.0</td><td></td><td>0.010</td></tr><tr><td rowspan="4">40%</td><td>Model 1</td><td>18.562</td><td>Δ= 0.243</td><td>33.2</td><td>Δ= 1.5</td><td>0.078</td></tr><tr><td>Model 2</td><td>18.675</td><td>= 1.31%</td><td>33.9</td><td>= 4.52%</td><td>0.103</td></tr><tr><td>Model 3</td><td>18.789</td><td></td><td>34.7</td><td></td><td>0.111</td></tr><tr><td>Model 4</td><td>18.805</td><td></td><td>34.5</td><td></td><td>0.109</td></tr><tr><td rowspan="4">60%</td><td>Model 1</td><td>41.706</td><td>Δ= 0.659</td><td>69.6</td><td>Δ= 4.2</td><td>0.227</td></tr><tr><td>Model 2</td><td>42.015</td><td>= 1.58%</td><td>71.7</td><td>= 6.03%</td><td>0.233</td></tr><tr><td>Model 3</td><td>42.309</td><td></td><td>73.8</td><td></td><td>0.239</td></tr><tr><td>Model 4</td><td>42.365</td><td></td><td>73.3</td><td></td><td>0.239</td></tr><tr><td rowspan="4">80%</td><td>Model 1</td><td>85.604</td><td>Δ= 1.063</td><td>132.6</td><td>Δ= 1.6</td><td>0.504</td></tr><tr><td>Model 2</td><td>86.065</td><td>= 1.24%</td><td>133.9</td><td>= 1.21%</td><td>0.509</td></tr><tr><td>Model 3</td><td>86.553</td><td></td><td>135.1</td><td></td><td>0.516</td></tr><tr><td>Model 4</td><td>86.667</td><td></td><td>134.2</td><td></td><td>0.516</td></tr><tr><td colspan="2">100% (Bulk material)</td><td>14.940</td><td></td><td>226.7</td><td></td><td>0.927</td></tr></tbody></table>

<figure><img src="image_223.png" /><figcaption>Fig. 5 True stress-strain curves of simulated compression specimens at (a) 20% and (b) 40% for different boundary condition models.</figcaption></figure>

As it can be observed Fig. 5a, in the extreme case of structures with low densities (20%), a large difference in the mechanical properties is observed, especially in the tangent modulus, presenting a softening effect in the plastic zone for models 1 and 2 due to the buckling in the vertical bars at the edge of the structure. This behavior implies that to have a better mechanical response, an arrangement with a larger amount of cells should be considered. In Fig. 5, the results of model 4 (64 cells) and particularly of model 3 present ideal cases without buckling. These findings show the importance of the size effect on lattice structure, especially in the ones with low densities or small strut diameters. For the 40% relative density structure, no softening is observed, but there is still a considerable difference in the tangent modulus between the different models (up to 30%). Figure 6 shows the predicted deformed cells of model 1 at different densities, in which the buckling effect of the exterior vertical struts can be observed.

A Gibson-Ashby fit was performed for each model, varying the relative density. The adjustment curves are shown in Fig. 7, where it is observed that the Gibson-Ashby power function with C=1 predicts with good precision the three relative mechanical properties as a function of the relative density. The Gibson-Ashby curves for the Young modulus and yield stress almost overlap each other, reaffirming that these properties do not present significant changes when the number of cells in the structure (expressed as the boundary condition model) changes. The only boundary model
<table><caption>Table 1 Mechanical properties of simulated lattice structures at different relative densities and different boundary conditions. The maximum absolute difference (Δ) between the results for each relative density is computed, its percentage relative to the smallest result of the simulations of this case is also provided.</caption><thead><tr><th>Relative density</th><th>Boundary condition</th><th>Elastic modulus (GPa)</th><th>Yield stress (MPa)</th><th>Tangent modulus (GPa)</th></tr></thead><tbody><tr><td rowspan="4">20%</td><td>Model 1</td><td>6.433</td><td>Δ= 0.113</td><td>12.6</td><td>Δ= 0.4</td><td>-0.026</td></tr><tr><td>Model 2</td><td>6.487</td><td>= 1.76%</td><td>12.8</td><td>= 3.17%</td><td>-0.008</td></tr><tr><td>Model 3</td><td>6.546</td><td></td><td>13.0</td><td></td><td>0.031</td></tr><tr><td>Model 4</td><td>6.526</td><td></td><td>13.0</td><td></td><td>0.010</td></tr><tr><td rowspan="4">40%</td><td>Model 1</td><td>18.562</td><td>Δ= 0.243</td><td>33.2</td><td>Δ= 1.5</td><td>0.078</td></tr><tr><td>Model 2</td><td>18.675</td><td>= 1.31%</td><td>33.9</td><td>= 4.52%</td><td>0.103</td></tr><tr><td>Model 3</td><td>18.789</td><td></td><td>34.7</td><td></td><td>0.111</td></tr><tr><td>Model 4</td><td>18.805</td><td></td><td>34.5</td><td></td><td>0.109</td></tr><tr><td rowspan="4">60%</td><td>Model 1</td><td>41.706</td><td>Δ= 0.659</td><td>69.6</td><td>Δ= 4.2</td><td>0.227</td></tr><tr><td>Model 2</td><td>42.015</td><td>= 1.58%</td><td>71.7</td><td>= 6.03%</td><td>0.233</td></tr><tr><td>Model 3</td><td>42.309</td><td></td><td>73.8</td><td></td><td>0.239</td></tr><tr><td>Model 4</td><td>42.365</td><td></td><td>73.3</td><td></td><td>0.239</td></tr><tr><td rowspan="4">80%</td><td>Model 1</td><td>85.604</td><td>Δ= 1.063</td><td>132.6</td><td>Δ= 1.6</td><td>0.504</td></tr><tr><td>Model 2</td><td>86.065</td><td>= 1.24%</td><td>133.9</td><td>= 1.21%</td><td>0.509</td></tr><tr><td>Model 3</td><td>86.553</td><td></td><td>135.1</td><td></td><td>0.516</td></tr><tr><td>Model 4</td><td>86.667</td><td></td><td>134.2</td><td></td><td>0.516</td></tr><tr><td colspan="2">100% (Bulk material)</td><td>14.940</td><td></td><td>226.7</td><td></td><td>0.927</td></tr></tbody></table>

<figure><img src="image_227.png" /><figcaption>Fig. 5 True stress-strain curves of simulated compression specimens at (a) 20% and (b) 40% for different boundary condition models.</figcaption></figure>

As it can be observed Fig. 5a, in the extreme case of structures with low densities (20%), a large difference in the mechanical properties is observed, especially in the tangent modulus, presenting a softening effect in the plastic zone for models 1 and 2 due to the buckling in the vertical bars at the edge of the structure. This behavior implies that to have a better mechanical response, an arrangement with a larger amount of cells should be considered. In Fig. 5, the results of model 4 (64 cells) and particularly of model 3 present ideal cases without buckling. These findings show the importance of the size effect on lattice structure, especially in the ones with low densities or small strut diameters. For the 40% relative density structure, no softening is observed, but there is still a considerable difference in the tangent modulus between the different models (up to 30%). Figure 6 shows the predicted deformed cells of model 1 at different densities, in which the buckling effect of the exterior vertical struts can be observed.

A Gibson-Ashby fit was performed for each model, varying the relative density. The adjustment curves are shown in Fig. 7, where it is observed that the Gibson-Ashby power function with C=1 predicts with good precision the three relative mechanical properties as a function of the relative density. The Gibson-Ashby curves for the Young modulus and yield stress almost overlap each other, reaffirming that these properties do not present significant changes when the number of cells in the structure (expressed as the boundary condition model) changes. The only boundary model
impact is observed in the tangent modulus, where a variation in the number of cells of the structure leads to an important change in this property and therefore in the associated curve.

<figure><img src="image_224.png" /><figcaption>Fig. 6 Deformed simulated lattice structure with maps of von Mises equivalent stress for model 1 boundary conditions at (a) 20%, (b) 40%, (c) 60% and (d) 80% relative density for a macro strain of 10%.</figcaption></figure>

<figure><img src="image_225.png" /><figcaption>Fig. 7 Gibson-Ashby curves for different boundary condition models relating (a) relative elastic modulus, (b) yield stress and (c) tangent modulus. Symbols correspond to simulation results, dashed lines are the fitted Gibson-Ashby curves.</figcaption></figure>

## Conclusions

During this research, numerical simulations of body-centered cubic lattice cells with several densities and different boundary conditions were performed to determine the elastoplastic edge effect response. From the analysis of the results, the following main statements are derived:

* Size effect, represented by different boundary conditions, significantly affects the plastic response of the lattice structure. This effect is most remarkable for the tangent modulus, particularly in structures with low relative density and therefore small strut diameter. Their slenderness tends to cause strut buckling. Size effect induces reduced mechanical
impact is observed in the tangent modulus, where a variation in the number of cells of the structure leads to an important change in this property and therefore in the associated curve.

<figure><img src="image_228.png" /><figcaption>Fig. 6 Deformed simulated lattice structure with maps of von Mises equivalent stress for model 1 boundary conditions at (a) 20%, (b) 40%, (c) 60% and (d) 80% relative density for a macro strain of 10%.</figcaption></figure>

<figure><img src="image_229.png" /><figcaption>Fig. 7 Gibson-Ashby curves for different boundary condition models relating (a) relative elastic modulus, (b) yield stress and (c) tangent modulus. Symbols correspond to simulation results, dashed lines are the fitted Gibson-Ashby curves.</figcaption></figure>

## Conclusions

During this research, numerical simulations of body-centered cubic lattice cells with several densities and different boundary conditions were performed to determine the elastoplastic edge effect response. From the analysis of the results, the following main statements are derived:

* Size effect, represented by different boundary conditions, significantly affects the plastic response of the lattice structure. This effect is most remarkable for the tangent modulus, particularly in structures with low relative density and therefore small strut diameter. Their slenderness tends to cause strut buckling. Size effect induces reduced mechanical
properties in structures with a low number of cells, where this buckling tends to play a relevant role in the general behavior of the structure.

* The Gibson-Ashby model predicts both the elastic modulus and the yield stress with good precision and low size effect. To predict the plastic zone properties, such as the tangent modulus, size effect must be taken into account, since considerable change in the exponent of the Gibson-Ashby formula is observed for the different boundary condition models.

Ongoing research will focus on correlating these numerical results with experiments to validate the adopted methodology. In addition, for impact and energy absorption applications, the evaluation of the behavior of structures with different numbers of cells and at stages of larger deformation or densification should be considered.

## Acknowledgements

This work is funded by WBI/AGCID RI02 (DIE23-0001) bilateral project between the University of Liège and the Universidad de La Frontera. As research director of F.R.S.-FNRS, A.M. Habraken thanks the Fund for Scientific Research for financial support.

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