Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach

The rapid prototyping (RP) processes, specifically selective laser melting (SLM), are popular for building complex 3D parts directly from the metal powder. The literature reveals that the properties such as surface roughness, waviness, bead width, compressive strength, tensile strength, wear, and dimensional accuracy of an SLM-fabricated prototype depend on the parameter settings of the SLM setup and can be improved by appropriate adjustment. For the selection of an optimal parameter setting, multi-gene genetic programming (MGGP), which develops the model structure and its coefficients automatically, can be applied. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. This paper proposes a modified MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated, and only the high performing genes are combined. The M-MGGP approach is applied on the bead width data obtained from the experiments conducted on the SLM machine, and its performance is found to be better than that of the standardized MGGP and artificial neural network (ANN) models. Between MGGP and ANN, ANN has shown better performance. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the SLM process by unveiling important process parameters and the hidden non-linear relationships.