Optimization of processing parameters in metal additive manufacturing

Metal additive manufacturing (AM) has emerged as a transformative technology for producing complex geometries with tailored mechanical properties. However, achieving optimal part quality and performance requires precise control over processing parameters such as laser power, scan speed, layer thickness, and hatch spacing. This study presents an analytical framework for predicting optimized processing parameters in metal AM, focusing on the interplay between thermal dynamics, microstructure evolution, and mechanical properties. The proposed approach integrates thermal modeling, solidification kinetics, and machine learning algorithms to identify optimized parameter sets that maximize mechanical strength. The framework is validated through experimental data from laser powder bed fusion (LPBF) of titanium alloys, demonstrating its capability to predict parameter combinations that yield superior part quality. This work provides a systematic pathway for accelerating the development of optimized metal AM processes, reducing trial-and-error experimentation, and enhancing the reliability of AM-produced components.