Nonlinear prediction modeling of surface quality during laser powder bed fusion of mixed powder of diamond and Ni-Cr alloy based on residual analysis. (July 2022) - Li, Shichun; Mo, Bin; Wang, Kunming; Xiao, Gang; Zhang, Pengfei
Highlights: Surface quality is completely characterized by deficiency rate, crack and flatness. Nonlinear relationships and omitted terms in models are studied by residual analysis. Nonlinear regression models with superposition form are established. Defect inheritance and layer by layer stacking process are reflected in the models. The models are reasonable and have good interpretability and prediction accuracy. Abstract: In order to obtain better surface quality, this paper mainly studied the prediction model of surface quality during laser powder bed fusion of diamond grinding wheel, which will lay a foundation for optimizing process parameters of multi-layer grinding wheel. Firstly, mixed powder of diamond and Ni-Cr alloy was fused by laser powder bed fusion. The experimental data were obtained and the surface quality was characterized and quantitatively evaluated. Residual analysis method and regression modeling method were used to establish the prediction nonlinear model of surface quality of fusion layer. The results of test, analysis and experimental verification of the nonlinear model showed that the model had good fitting accuracy and prediction accuracy, which verified that the introduction of the nonlinear characteristics of parameters and the previously surface quality factor based on the residual analysis were reasonable and effective. With the introduction of the factor of previously formed surface quality, the connection and superposition of the models of the different layers were realized. The superimposed of models reflected the actual layer by layer stacking process and the process of defect inheritance. After analysis and discussion, it is concluded that the establishment process of the nonlinear prediction model in this paper is reasonable, and the form of model is highly interpretable for the layer by layer laser powder bed fusion process.