[en] In this paper, the effects of two key process parameters of the selective laser melting process, namely laser power and scanning speed, on the single-track morphologies and the bead characteristics, especially the depth-to-width D/W and height-to-width H/W ratios, were investigated using both experimental and Machine Learning (ML) approaches. A total of 840 single tracks were fabricated with several combinations of laser power and scanning speed levels. Surface morphologies of the single tracks and bead profiles were thoroughly investigated, providing a track-type map and the evolutions of the bead characteristics as a function of laser power and scanning speed. The results indicate neither severe balling nor keyholing effect for all combinations of laser power and scanning speed. Besides, simple relationships cannot accurately describe the evolutions of the D/W and H/W ratios as a function of laser power and scanning speed. Two Machine Learning-based regression models, Random Forest and Artificial Neural Network, were chosen to estimate the D/W and H/W ratios using laser power and scanning speed. The Bayesian optimization algorithm was employed to optimize the model hyperparameter selection. Both Machine Learning-based models appear to be able to predict reasonably well the two aspect ratios, D/W and H/W, with an overall R2 value reaching about 90%, evaluated on the cross-validation dataset after a few seconds of training time, respectively.
Disciplines :
Mechanical engineering
Author, co-author :
Le-Hong, Thai; Institute of Development Strategies, Thu Dau Mot University, Thu Dau Mot, Viet Nam ; IMSIA, CNRS, EDF, CEA, ENSTA Paris, Institut Polytechnique de Paris, Palaiseau, France
Lin, Pai Chen; Advanced Institute of Manufacturing with High-Tech Innovations, National Chung-Cheng University, Chia-Yi, Taiwan ; Department of Mechanical Engineering, National Chung-Cheng University, Chia-Yi, Taiwan
Chen, Jian-Zhong; Advanced Institute of Manufacturing with High-Tech Innovations, National Chung-Cheng University, Chia-Yi, Taiwan ; Department of Mechanical Engineering, National Chung-Cheng University, Chia-Yi, Taiwan
Pham Quy Duc, Thinh ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Van Tran, Xuan ; Institute of Development Strategies, Thu Dau Mot University, Thu Dau Mot, Viet Nam
Language :
English
Title :
Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting
Ahsan, F., & Ladani, L. (2020). Temperature profile, bead geometry, and elemental evaporation in laser powder bed fusion additive manufacturing process. JOM Journal of the Minerals Metals and Materials Society, 72(1), 429–439. 10.1007/s11837-019-03872-3 DOI: 10.1007/s11837-019-03872-3
Aldous, D. (1993). The continuum random tree III. The Annals of Probability, 21(1), 248–289. 10.1214/aop/1176989404 DOI: 10.1214/aop/1176989404
Andreotta, R., Ladani, L., & Brindley, W. (2017). Finite element simulation of laser additive melting and solidification of Inconel 718 with experimentally tested thermal properties. Finite Elements in Analysis and Design, 135, 36–43. 10.1016/j.finel.2017.07.002 DOI: 10.1016/j.finel.2017.07.002
Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013). Improving deep neural networks for LVCSR using rectified linear units and dropout (pp. 8609–8613). Presented at the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/ICASSP.2013.6639346
Di, W., Yongqiang, Y., Xubin, S., & Yonghua, C. (2012). Study on energy input and its influences on single-track, multi-track, and multi-layer in SLM. The International Journal of Advanced Manufacturing Technology, 58(9), 1189–1199. 10.1007/s00170-011-3443-y DOI: 10.1007/s00170-011-3443-y
Dilip, J. J. S., Zhang, S., Teng, C., Zeng, K., Robinson, C., Pal, D., & Stucker, B. (2017). Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti–6Al–4V alloy parts fabricated by selective laser melting. Progress in Additive Manufacturing, 2(3), 157–167. 10.1007/s40964-017-0030-2 DOI: 10.1007/s40964-017-0030-2
Dutta, B., & Froes, F. H. (2017). The additive manufacturing (AM) of titanium alloys. Metal Powder Report, 72(2), 96–106. 10.1016/j.mprp.2016.12.062 DOI: 10.1016/j.mprp.2016.12.062
Frazier, P. I. (2018). A tutorial on Bayesian optimization. arXiv:1807.02811 [cs, math, stat]. Accessed 11 May 2021.
Gao, J., Wu, C., Hao, Y., Xu, X., & Guo, L. (2020). Numerical simulation and experimental investigation on three-dimensional modelling of single-track geometry and temperature evolution by laser cladding. Optics & Laser Technology, 129, 106287. 10.1016/j.optlastec.2020.106287 DOI: 10.1016/j.optlastec.2020.106287
Garg, A., Lam, J. S. L., & Savalani, M. M. (2018). Laser power based surface characteristics models for 3-D printing process. Journal of Intelligent Manufacturing, 29(6), 1191–1202. 10.1007/s10845-015-1167-9 DOI: 10.1007/s10845-015-1167-9
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.
Greco, S., Gutzeit, K., Hotz, H., Kirsch, B., & Aurich, J. C. (2020). Selective laser melting (SLM) of AISI 316L—Impact of laser power, layer thickness, and hatch spacing on roughness, density, and microhardness at constant input energy density. The International Journal of Advanced Manufacturing Technology, 108(5), 1551–1562. 10.1007/s00170-020-05510-8 DOI: 10.1007/s00170-020-05510-8
Gu, H., Wei, C., Li, L., Han, Q., Setchi, R., Ryan, M., & Li, Q. (2020). Multi-physics modelling of molten pool development and track formation in multi-track, multi-layer and multi-material selective laser melting. International Journal of Heat and Mass Transfer, 151, 119458. 10.1016/j.ijheatmasstransfer.2020.119458 DOI: 10.1016/j.ijheatmasstransfer.2020.119458
Gunenthiram, V., Peyre, P., Schneider, M., Dal, M., Coste, F., Koutiri, I., & Fabbro, R. (2018). Experimental analysis of spatter generation and melt-pool behavior during the powder bed laser beam melting process. Journal of Materials Processing Technology, 251, 376–386. 10.1016/j.jmatprotec.2017.08.012 DOI: 10.1016/j.jmatprotec.2017.08.012
Guo, M., Gu, D., Xi, L., Du, L., Zhang, H., & Zhang, J. (2019). Formation of scanning tracks during Selective Laser Melting (SLM) of pure tungsten powder: Morphology, geometric features and forming mechanisms. International Journal of Refractory Metals and Hard Materials, 79, 37–46. 10.1016/j.ijrmhm.2018.11.003 DOI: 10.1016/j.ijrmhm.2018.11.003
He, Y., Montgomery, C., Beuth, J., & Webler, B. (2019). Melt pool geometry and microstructure of Ti6Al4V with B additions processed by selective laser melting additive manufacturing. Materials & Design, 183, 108126. 10.1016/j.matdes.2019.108126 DOI: 10.1016/j.matdes.2019.108126
Heumann, C., & Schomaker, M. (2016). Introduction to statistics and data analysis: With exercises, solutions and applications in R. Springer. 10.1007/978-3-319-46162-5 DOI: 10.1007/978-3-319-46162-5
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31–44. 10.1109/2.485891 DOI: 10.1109/2.485891
Kamath, C. (2016). Data mining and statistical inference in selective laser melting. The International Journal of Advanced Manufacturing Technology, 86(5), 1659–1677. 10.1007/s00170-015-8289-2 DOI: 10.1007/s00170-015-8289-2
Khairallah, S. A., Anderson, A. T., Rubenchik, A., & King, W. E. (2016). Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Materialia, 108, 36–45. 10.1016/j.actamat.2016.02.014 DOI: 10.1016/j.actamat.2016.02.014
Khanzadeh, M., Chowdhury, S., Marufuzzaman, M., Tschopp, M. A., & Bian, L. (2018). Porosity prediction: Supervised-learning of thermal history for direct laser deposition. Journal of Manufacturing Systems, 47, 69–82. 10.1016/j.jmsy.2018.04.001 DOI: 10.1016/j.jmsy.2018.04.001
Khanzadeh, M., Chowdhury, S., Tschopp, M. A., Doude, H. R., Marufuzzaman, M., & Bian, L. (2019). In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes. IISE Transactions, 51(5), 437–455. 10.1080/24725854.2017.1417656 DOI: 10.1080/24725854.2017.1417656
King, W. E., Barth, H. D., Castillo, V. M., Gallegos, G. F., Gibbs, J. W., Hahn, D. E., et al. (2014). Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. Journal of Materials Processing Technology, 214(12), 2915–2925. 10.1016/j.jmatprotec.2014.06.005 DOI: 10.1016/j.jmatprotec.2014.06.005
Kingma, D. P., Ba, J. (2017). Adam: A method for stochastic optimization. arXiv:1412.6980 [cs]. Accessed 11 May 2021.
Kistler, N. A., Corbin, D. J., Nassar, A. R., Reutzel, E. W., & Beese, A. M. (2019). Effect of processing conditions on the microstructure, porosity, and mechanical properties of Ti–6Al–4V repair fabricated by directed energy deposition. Journal of Materials Processing Technology, 264, 172–181. 10.1016/j.jmatprotec.2018.08.041 DOI: 10.1016/j.jmatprotec.2018.08.041
Kusuma, C., Ahmed, S. H., Mian, A., & Srinivasan, R. (2017). Effect of laser power and scan speed on melt pool characteristics of commercially pure titanium (CP-Ti). Journal of Materials Engineering and Performance, 26(7), 3560–3568. 10.1007/s11665-017-2768-6 DOI: 10.1007/s11665-017-2768-6
Le, T.-N., & Lo, Y.-L. (2019). Effects of sulfur concentration and Marangoni convection on melt-pool formation in transition mode of selective laser melting process. Materials & Design, 179, 107866. 10.1016/j.matdes.2019.107866 DOI: 10.1016/j.matdes.2019.107866
Le, T.-N., Lo, Y.-L., & Lin, Z.-H. (2020). Numerical simulation and experimental validation of melting and solidification process in selective laser melting of IN718 alloy. Additive Manufacturing, 36, 101519. 10.1016/j.addma.2020.101519 DOI: 10.1016/j.addma.2020.101519
Leal, R., Barreiros, F. M., Alves, L., Romeiro, F., Vasco, J. C., Santos, M., & Marto, C. (2017). Additive manufacturing tooling for the automotive industry. The International Journal of Advanced Manufacturing Technology, 92(5), 1671–1676. 10.1007/s00170-017-0239-8 DOI: 10.1007/s00170-017-0239-8
Li, C., Guo, Y. B., & Zhao, J. B. (2017). Interfacial phenomena and characteristics between the deposited material and substrate in selective laser melting Inconel 625. Journal of Materials Processing Technology, 243, 269–281. 10.1016/j.jmatprotec.2016.12.033 DOI: 10.1016/j.jmatprotec.2016.12.033
Mahamood, R. M., & Akinlabi, E. T. (2018). Heat affected zone relationship with processing parameter in additive manufacturing process. Materials Today: Proceedings, 5(9, Part 3), 18362–18367. 10.1016/j.matpr.2018.06.175 DOI: 10.1016/j.matpr.2018.06.175
Matthews, M. J., Guss, G., Khairallah, S. A., Rubenchik, A. M., Depond, P. J., & King, W. E. (2016). Denudation of metal powder layers in laser powder bed fusion processes. Acta Materialia, 114, 33–42. 10.1016/j.actamat.2016.05.017 DOI: 10.1016/j.actamat.2016.05.017
Meng, L., McWilliams, B., Jarosinski, W., Park, H.-Y., Jung, Y.-G., Lee, J., & Zhang, J. (2020). Machine learning in additive manufacturing: A review. JOM Journal of the Minerals Metals and Materials Society, 72(6), 2363–2377. 10.1007/s11837-020-04155-y DOI: 10.1007/s11837-020-04155-y
Mohajernia, B., Urbanic, R. J., & Nazemi, N. (2019). Predictive modelling of residual stresses for single bead P420 laser cladding onto an AISI 1018 substrate. IFAC-PapersOnLine, 52(10), 236–241. 10.1016/j.ifacol.2019.10.070 DOI: 10.1016/j.ifacol.2019.10.070
Mohd Yusuf, S., Cutler, S., & Gao, N. (2019). Review: The impact of metal additive manufacturing on the aerospace industry. Metals, 9(12), 1286. 10.3390/met9121286 DOI: 10.3390/met9121286
Mozaffar, M., Paul, A., Al-Bahrani, R., Wolff, S., Choudhary, A., Agrawal, A., et al. (2018). Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manufacturing Letters, 18, 35–39. 10.1016/j.mfglet.2018.10.002 DOI: 10.1016/j.mfglet.2018.10.002
Nair, V., Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on international conference on machine learning (pp. 807–814). Madison, WI, USA: Omnipress. Accessed 11 May 2021.
Panda, B., Shankhwar, K., Garg, A., & Savalani, M. M. (2019). Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing. Journal of Intelligent Manufacturing, 30(2), 809–820. 10.1007/s10845-016-1282-2 DOI: 10.1007/s10845-016-1282-2
Papazoglou, E. L., Karkalos, N. E., & Markopoulos, A. P. (2020). A comprehensive study on thermal modeling of SLM process under conduction mode using FEM. The International Journal of Advanced Manufacturing Technology, 111(9), 2939–2955. 10.1007/s00170-020-06294-7 DOI: 10.1007/s00170-020-06294-7
Park, H. S., Nguyen, D. S., Le-Hong, T., & Van Tran, X. (2021). Machine learning-based optimization of process parameters in selective laser melting for biomedical applications. Journal of Intelligent Manufacturing. 10.1007/s10845-021-01773-4 DOI: 10.1007/s10845-021-01773-4
Qi, T., Zhu, H., Zhang, H., Yin, J., Ke, L., & Zeng, X. (2017). Selective laser melting of Al7050 powder: Melting mode transition and comparison of the characteristics between the keyhole and conduction mode. Materials & Design, 135, 257–266. 10.1016/j.matdes.2017.09.014 DOI: 10.1016/j.matdes.2017.09.014
Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives. Engineering, 5(4), 721–729. 10.1016/j.eng.2019.04.012 DOI: 10.1016/j.eng.2019.04.012
Ren, K., Chew, Y., Zhang, Y. F., Fuh, J. Y. H., & Bi, G. J. (2020). Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Computer Methods in Applied Mechanics and Engineering, 362, 112734. 10.1016/j.cma.2019.112734 DOI: 10.1016/j.cma.2019.112734
Roy, M., & Wodo, O. (2020). Data-driven modeling of thermal history in additive manufacturing. Additive Manufacturing, 32, 101017. 10.1016/j.addma.2019.101017 DOI: 10.1016/j.addma.2019.101017
Santos, E. C., Shiomi, M., Osakada, K., & Laoui, T. (2006). Rapid manufacturing of metal components by laser forming. International Journal of Machine Tools and Manufacture, 46(12), 1459–1468. 10.1016/j.ijmachtools.2005.09.005 DOI: 10.1016/j.ijmachtools.2005.09.005
Scime, L., & Beuth, J. (2019). Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 25, 151–165. 10.1016/j.addma.2018.11.010 DOI: 10.1016/j.addma.2018.11.010
Scipioni Bertoli, U., Wolfer, A. J., Matthews, M. J., Delplanque, J.-P.R., & Schoenung, J. M. (2017). On the limitations of volumetric energy density as a design parameter for selective laser melting. Materials & Design, 113, 331–340. 10.1016/j.matdes.2016.10.037 DOI: 10.1016/j.matdes.2016.10.037
Shi, X., Ma, S., Liu, C., & Wu, Q. (2017). Parameter optimization for Ti–47Al–2Cr–2Nb in selective laser melting based on geometric characteristics of single scan tracks. Optics & Laser Technology, 90, 71–79. 10.1016/j.optlastec.2016.11.002 DOI: 10.1016/j.optlastec.2016.11.002
Snoek, J., Larochelle, H., Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. In Proceedings of the 25th international conference on neural information processing systems—Volume 2 (pp. 2951–2959). Red Hook, NY, USA: Curran Associates Inc. Accessed 11 May 2021.
Taheri Andani, M., Dehghani, R., Karamooz-Ravari, M. R., Mirzaeifar, R., & Ni, J. (2018). A study on the effect of energy input on spatter particles creation during selective laser melting process. Additive Manufacturing, 20, 33–43. 10.1016/j.addma.2017.12.009 DOI: 10.1016/j.addma.2017.12.009
Tang, M., Pistorius, P. C., & Beuth, J. L. (2017). Prediction of lack-of-fusion porosity for powder bed fusion. Additive Manufacturing, 14, 39–48. 10.1016/j.addma.2016.12.001 DOI: 10.1016/j.addma.2016.12.001
Tapia, G., Khairallah, S., Matthews, M., King, W. E., & Elwany, A. (2018). Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel. The International Journal of Advanced Manufacturing Technology, 94(9), 3591–3603. 10.1007/s00170-017-1045-z DOI: 10.1007/s00170-017-1045-z
Trevisan, F., Calignano, F., Aversa, A., Marchese, G., Lombardi, M., Biamino, S., et al. (2018). Additive manufacturing of titanium alloys in the biomedical field: Processes, properties and applications. Journal of Applied Biomaterials & Functional Materials, 16(2), 57–67. 10.5301/jabfm.5000371 DOI: 10.5301/jabfm.5000371
Unpingco, J. (2019). Python for probability, statistics, and machine learning (2nd ed.). Springer. 10.1007/978-3-030-18545-9 DOI: 10.1007/978-3-030-18545-9
Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020a). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. 10.1016/j.addma.2020.101538 DOI: 10.1016/j.addma.2020.101538
Wang, L., Silva, L., Süß-Wolf, R., & Franke, J. (2020b). Prediction of surface roughness of laser selective metallization of ceramics by multiple linear regression and artificial neural networks approaches. Journal of Laser Applications, 32(4), 042013. 10.2351/7.0000198 DOI: 10.2351/7.0000198
Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., & Chen, S. (2021). Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. Journal of Intelligent Manufacturing. 10.1007/s10845-020-01725-4 DOI: 10.1007/s10845-020-01725-4
Xiong, J., Zhang, G., Hu, J., & Wu, L. (2014). Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. Journal of Intelligent Manufacturing, 25(1), 157–163. 10.1007/s10845-012-0682-1 DOI: 10.1007/s10845-012-0682-1
Yadroitsev, I., & Smurov, I. (2010). Selective laser melting technology: From the single laser melted track stability to 3D parts of complex shape. Physics Procedia, 5, 551–560. 10.1016/j.phpro.2010.08.083 DOI: 10.1016/j.phpro.2010.08.083
Yang, J., Han, J., Yu, H., Yin, J., Gao, M., Wang, Z., & Zeng, X. (2016). Role of molten pool mode on formability, microstructure and mechanical properties of selective laser melted Ti–6Al–4V alloy. Materials & Design, 110, 558–570. 10.1016/j.matdes.2016.08.036 DOI: 10.1016/j.matdes.2016.08.036
Yun, K., Huyen, A., & Lu, T. (2018). Deep neural networks for pattern recognition. In Advances in pattern recognition research (pp. 49–79).