Finite Element-Based Optimization of Additive Manufacturing Process Using Statistical Modelling and League of Champion Algorithm

Abstract

The study develops a 2D (two-dimensional) finite element model with a Gaussian heat source to simulate powder bed-based laser additive manufacturing process of Ti6Al4V alloy. The modelling approach provides insight into the process by correlating laser power and scan speed with melt pool temperature distribution and size. To tackle the FEA result in optimization environment, statistical approach of data normalization and regression modelling is adopted. Statistical treatment is not only able to deduce the interdependence of various objectives consider but also make the representation of objectives and constraint computationally simple. Adoption of a new stochastic algorithm namely league of a champion algorithm (LCA) together with penalty function approach for non-linear constraint handling reduces the effort required and computational complexity involved in determining the optimum parameter setting.