A detailed introduction to the research on additive manufacturing process parameter optimization in complex parts forming

Abstract

This study comprehensively analyzes the unique process parameter requirements for complex geometries, explains the interactions between key parameters and forming quality, and summarizes the quantitative relationship between energy density and defect distribution. From this perspective, a multi-objective optimization scheme was developed that integrates forming accuracy, mechanical properties, efficiency, and cost. Parameters were optimized using data-driven agent modeling and Bayesian optimization/evolutionary algorithms. A case study of a Ti-6Al-4V laser powder bed fusion dataset revealed that parameter optimization significantly reduced porosity (from 2.5% to 0.9%) and increased yield strength from 900 MPa to 1000 MPa, while maintaining acceptable elongation levels. Compared to established processes, this technology achieves dual optimizations in both strength and density while optimizing forming efficiency and reliability.