Ti6Al4V single-track formation optimization for selective laser melting using a modified crow search algorithm

Abstract Selective Laser Melting (SLM) is a prevalent technology in additive manufacturing, primarily due to its high design freedom and quality. However, ascertaining the optimal process window remains challenging due to the complex nonlinear relationships among process parameters and the significant resource cost associated with traditional experimental methods. This paper is devoted to addressing aforementioned challenges by integrating numerical simulation with swarm intelligence algorithms. Specifically, a novel swarm intelligence optimizer based on the original Crow Search Algorithm (CSA), named Modified Crow Search Algorithm (MCSA), is proposed in this research. In an effort to overcome the tendency of CSA to easily converge towards local optima, MCSA incorporates four main improvements. The nonlinear perturbation factor and the stochastically adjusted sine-cosine mechanism are employed to strengthen the capability in both local and global search. A random opposition-based learning strategy is utilized to escape local optima. Then, two adaptive parameters are adopted to balance the weights between search methods. The effectiveness of MCSA in search accuracy and convergence speed is rigorously evaluated through testing on twenty-three benchmark functions. Finally, numerical simulation is employed to capture dynamic melt pool behavior, enabling the establishment of keyhole and no-continuity models. The performance of MCSA is further investigated for the Ti6Al4V single-track formation optimization problem in SLM. Experimental results demonstrate that MCSA effectively identifies the optimal process window.