Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian Optimization Algorithm

• Published in: Materials Vol. 16; no. 3; p. 1050
• Main Authors: Chepiga, Timur, Zhilyaev, Petr, Ryabov, Alexander, Simonov, Alexey P., Dubinin, Oleg N., Firsov, Denis G., Kuzminova, Yulia O., Evlashin, Stanislav A.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.01.2023; MDPI
• Subjects: 3D printing; Algorithms; Bayesian analysis; Data points; Gaussian process; Hardness; Lasers; Mathematical models; Mathematical optimization; Mechanical properties; Methods; Multiple objective analysis; Optimization; Porosity; Powder beds; Powders; Process parameters; Production data; Stainless steel; Stainless steels; Steel, Stainless; powder bed fusion; multi-objective optimization; machine learning; metal additive manufacturing; printing parameters; SS316L
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma16031050

Additive manufacturing is a modern technique to produce parts with a complex geometry. However, the choice of the printing parameters is a time-consuming and costly process. In this study, the parameter optimization for the laser powder bed fusion process was investigated. Using state-of-the art multi-objective Bayesian optimization, the set of the most-promising process parameters (laser power, scanning speed, hatch distance, etc.), which would yield parts with the desired hardness and porosity, was established. The Gaussian process surrogate model was built on 57 empirical data points, and through efficient sampling in the design space, we were able to obtain three points in the Pareto front in just over six iterations. The produced parts had a hardness ranging from 224–235 HV and a porosity in the range of 0.2–0.37%. The trained model recommended using the following parameters for high-quality parts: 58 W, 257 mm/s, 45 µm, with a scan rotation angle of 131 degrees. The proposed methodology greatly reduces the number of experiments, thus saving time and resources. The candidate process parameters prescribed by the model were experimentally validated and tested.