Abstract
Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti–6Al–4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique (density ratio and surface roughness). A supervised learning deep neural network based on the backpropagation algorithm was applied to optimize input parameters for a given set of quality part outputs. Several methods were utilized to solve undesired problems occurring during neural network training to increase the model accuracy. The model’s performance was proven with a value of R2 of 99% for both density ratio and surface roughness. A selection system was then built, allowing users to choose the optimal process parameters for fabricated products whose properties meet a specific user requirement. Experiments performed with the optimal process parameters recommended by the optimization system strongly confirmed its reliability by providing the ultimate part qualities nearly identical to those defined by the user with only about 0.9–4.4% of errors at the maximum. Finally, a graphical user interface was developed to facilitate the choice of the optimum process parameters for the desired density ratio and surface roughness.
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This work was funded by Vingroup and supported by Vingroup Innovation Foundation (VINIF) under Project Code VINIF.2020.DA15.
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Appendix A: Predicted values
Appendix A: Predicted values
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Park, H.S., Nguyen, D.S., Le-Hong, T. et al. Machine learning-based optimization of process parameters in selective laser melting for biomedical applications. J Intell Manuf 33, 1843–1858 (2022). https://doi.org/10.1007/s10845-021-01773-4
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DOI: https://doi.org/10.1007/s10845-021-01773-4