Optimization of Laser Process Parameters Using Machine Learning Algorithms and Performance Comparison
Laser powder bed fusion (L-PBF) can be used to produce near net-shaped functional metal components. Despite offering high flexibility in producing components with intricate geometries, L-PBF has many constraints in terms of controllability and repeatability because of large number of processing parameters. There is a need for a robust computational model which can predict the properties of L-PBF parts using a wide range of processing parameters. In this work, several Machine learning-based algorithms like Random Forest, k Nearest Neighbors, XGBOOST, Support Vector Machine (SVM), and Deep Neural Networks are used to model the property- processing parameters relation for SS 316L samples prepared by LPBF. Laser power, scan speed, hatch spacing, scan strategy, volumetric energy density, and density are used as the input to these models. The developed model is then used to predict and analyze the surface roughness of as- fabricated SS 316L specimens. The prediction and experimental results are compared for the above-mentioned models to evaluate the capabilities and accuracy of each model.