A new computational intelligence approach in formulation of functional relationship of open porosity of the additive manufacturing process

An additive manufacturing process of selective laser sintering (SLS) builds components of complex 3D shapes directly from metal powder. Past studies reveal that the properties of an SLS-fabricated prototype such as porosity, surface roughness, waviness, compressive strength, tensile strength, wear strength, and dimensional accuracy depend on the parameter settings of the SLS setup and can be improved by appropriate adjustment. In this context, the computational intelligence (CI) approach of multi-gene genetic programming (MGGP) can be used to formulate the model for understanding the process behavior. MGGP develops the model structure and its coefficients automatically. Despite being widely applied, MGGP generates models that may not give satisfactory performance on test data. The underlying reason is the inappropriate formulation procedure of the multi-gene model and the difficulty in model selection. Therefore, the present work proposes a new CI approach (ensemble-based MGGP (EN-MGGP)) that makes use of statistical and classification strategies for improving its generalization. The EN-MGGP approach is applied to the open porosity data obtained from the experiments conducted on an SLS machine, and its performance is compared to that of the standardized MGGP. The proposed EN-MGGP model outperforms the standardized model and is proven to capture the dynamics of the SLS process by unveiling dominant input process parameters and the hidden non-linear relationships.