Prescriptive Data-Analytical Modeling of Laser Powder Bed Fusion Processes for Accuracy Improvement

Abstract

Laser powder bed fusion (LPBF) has the ability to produce three-dimensional lightweight metal parts with complex shapes. Extensive investigations have been conducted to tackle build accuracy problems caused by shape complexity. For metal parts with stringent requirements, surface roughness, laser beam positioning error, and part location effects can all affect the shape accuracy of LPBF built products. This study develops a data-driven predictive approach as a promising solution for geometric accuracy improvement in LPBF processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.