Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L …

Abstract:

This article analyzes the surface roughness and mechanical properties of 316L samples produced by Selective Laser Melting (SLM) through the application of statistical regression and Machine Learning techniques. Response Surface Methodology, Multi-layer Perceptron, Support Vector Regression, Random Forests, Gaussian Process, and Adaptive Neuro-Fuzzy Inference System were fitted and compared to pbkp_redict essential features in laser-printed metallic parts. Using the Box-Behnken design of experiment (DOE), a range of process parameters was selected to assess the impact of laser power, scanning speed, hatch spacing, and layer thickness on the surface roughness and mechanical properties. It is worth noticing that the simultaneous inclusion of hatch spacing and layer thickness as input variables hasn't been widely investigated in the literature. The generalization capabilities of the fitted models were …

Año de publicación:

2022

Keywords:

Fuente:

google