Work

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

Year: 2024

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app14188534

Authors: Scott Wahlquist, Amir Ali

Institutions: Idaho State University, Center for Advanced Energy Studies

Language: English

Topic: Additive Manufacturing Materials and Processes

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: Idaho State University