Optimizing additive manufacturing path pattern for Ti-6Al-4V thin rods using a combinatorial radial basis function surrogate-assisted genetic algorithm

Abstract

Path pattern is one of the most significant parameters in the additive manufacturing (AM) process because it influences the specimen's final shape and residual stress distribution. Generally, the optimal path pattern is a computationally expensive, high-dimensional, and black-box permutation optimization problem. In this paper, we propose a combinatorial radial basis function surrogate-assisted genetic algorithm (CRBF-GA) to effectively generate the optimal path pattern by integrating the combinatorial radial basis function surrogate model (CRBF) with the genetic algorithm (GA). To demonstrate the effectiveness of the proposed CRBF-GA, a Ti-6Al-4V thin rod, a component of lattice, is chosen as the research object. Through numerical simulation, experimental verification, and error comparison analysis, the RBF-GA pattern is demonstrated to be the best path pattern among the random forest-assisted evolutionary algorithm (RF-EA), GA, spiral, and zigzag patterns, and it excels in achieving a more precise rod shape compared to the other patterns examined.