Abstract Scope |
Laser Powder Bed Fusion (LPBF) provides a rapid fusion and solidification of the metal powders to form 3D models, enlarging the design and manufacturability compared with the traditional subtractive machining process. However, there are still challenges in repeatability and reproducibility of the LPBF-ed AM parts that hinder the on-demand product needs. This is particularly difficult for an LPBF-ed AM part needs to be qualified through destructive measurements, such as tensile test, fatigue, and hardness. The as-built mechanical properties are highly correlated to the process parameters. In this study, LPBF-ed 316L tensile testing samples are analyzed for investigating the correlation between input process parameters and the tensile strength measures. This study establishes the quantitative comprehensive process-quality modeling with a proposed novel multi-dimensional machine learning framework, as-built tensile strength can be maintained as desired or enhanced through adjusting the process parameters. |