Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review

Abstract

The integration of LPBF printing technologies in various innovative applications relies on the resilience and reliability of parts and their quality. Reducing or eliminating the factors leading to defects in final parts is crucial to producing satisfactory high-quality parts. Extensive efforts have been made to understand the material properties and printing process parameters of LPBF-printed geometries that trigger defects. Studies of interest include the use of various sensing technologies, numerical modeling, and artificial intelligence (AI) to enable a better understanding of the phenomena under investigation. The primary objectives of this article are to introduce the reader to the most widely read published data on (1) the roles of numerical and analytical models in LPBF defect detection; (2) AI algorithms and models applicable to predict LPBF metal defects and causes; and (3) the integration of modeling, AI, and sensing technology, which is commonly used in material characterization and has been proven efficient and applicable to LPBF metal part defect detection over extended periods.

Keywords

• LPBF
• metal part defect detection
• melt pool modeling, sensing technologies
• artificial intelligence