Mathematical Modeling of Multi-Performance Metrics and Process Parameter Optimization in Laser Powder Bed Fusion

This study aims to develop mathematical models improve multi-performance metrics, such as relative density and operating costs, in laser powder bed fusion (LPBF), also known selective melting, a metallic additive manufacturing technique, by optimizing the printing process parameters. The work develops data-driven model for based on measurements an analytical costs related Optimization are formulated maximize or minimize determining optimal set of parameters, while meeting target level other performance metrics (i.e., costs). Furthermore, new devised test sensitivity optimization solutions, which used novel robust acquire less sensitive analysis examines effect varying some parameters fabricated specimens. Samples with greater than 99% machine cost USD 1.00 per sample can be produced, utilizing combination low power (100 W), high scan speed (444 mm/s), moderate layer thickness (0.11 mm), large hatch distance (0.4 mm). is first investigate relationship between quality samples LPBF process. achieved parameter values that may more suitable real operations. equations verified via 10-fold cross-validation, predicted results further comparing them experimental data literature. framework presented this pave way techniques material grades successful industrial-level implementation.