Optimizing island sequencing in laser powder bed fusion using Genetic Algorithms

Abstract

Additive manufacturing, particularly laser powder bed fusion (L-PBF), is an emerging method for fabricating complex parts in various industries. However, it faces the persistent challenge of thermal deformation, a significant barrier to its wider application and reliability. Current strategies, while partially effective, do not fully address the intricate thermal dynamics of the process, indicating a clear research gap in optimizing manufacturing techniques for better thermal management. This study focuses on understanding and mitigating thermal deformation in L-PBF using Genetic Algorithms (GAs). The application of GAs as a ‘black-box’ approach is explored to gain insights into the complex physics of L-PBF. A comprehensive investigation into the optimization of island sequencing within L-PBF processes is presented, employing GAs to systematically reduce thermal deformation. Various island sequences in a bilayered block structure are analyzed to assess the effectiveness of GAs in minimizing deformation, including scenarios such as variations in block sizes and interlayer rotation angles. Statistical tools such as silhouette scores and probability density distribution plots are utilized to provide a thorough analysis of deformation patterns and their respective thermal behaviors. The results show GA's remarkable efficiency in enhancing thermal management, achieving a significant reduction in thermal deformation within a range of 12–15% across the examined scenarios. This achievement highlights GA's capability in rapid optimization of scan sequences for better thermal deformation control. The findings enhance the understanding of thermal dynamics in L-PBF and consequently open new avenues for improving the quality and reliability of other metal additive manufacturing processes as well.