FMSG: Cyber: Process Monitoring Methods for the Product Quality Improvement of Electron Beam Powder Bed Fusion Additive Manufacturing Processes

North Carolina State University, Raleigh NC

Investigators

Abstract

Additive manufacturing is a computer-controlled process that creates three-dimensional objects with complex structures by depositing materials layer-by-layer. Among many additive manufacturing processes, Electron Beam Powder Bed Fusion (EB-PBF) has shown its great promise in the production of metallic parts. One of the most significant barriers that prevents the EB-PBF and many other additive manufacturing processes from becoming more widely adopted is the lack of effective product quality control methods. Consequently, EB-PBF machines have a very high probability of fabricating defective products whose quality fails to meet specifications. To address this challenge, this project focuses on research to facilitate the possibility of establishing an in-situ quality control framework for EB-PBF additive manufacturing processes. The framework uses real-time sensing data to monitor the quality of products and adaptively adjusts process control parameters for quality improvement whenever necessary. This research explores new capabilities for EB-PBF to significantly improve the quality of manufactured parts and manufacturing processes. The project also has a variety of educational and outreach components that form a basis for attracting and training the next generation of manufacturing professionals. The objective of this project is to provide a basis for in-situ quality control of EB-PBF additive manufacturing processes. There are three primary research directions. The first is a computationally-efficient run-time defect detection method using a novel statistical learning model that builds on real-time electron-based data for detecting defects during manufacturing that would adversely affect product quality. The second is a privacy-preserving federated model to engage the manufacturing community on root-cause defect diagnostics; it is constructed on a new regularization-based statistical learning model, particularly for EB-PBF, to identify crucial process control parameters responsible for product defects. The third focuses on designing and conducting experiments to validate the effectiveness of these models. The automatic defect detection and root-cause diagnostics methods not only serve as the fundamental basis of the in-situ quality control of EB-PBF, but also significantly enrich the knowledge base of the statistical learning, data analytics, and machine learning communities. This Future Manufacturing award is supported by the Division of Computer and Network Systems (CNS) of the Directorate for Computer and Information Science and Engineering (CISE), and by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.