CAREER: Privacy-preserving Transfer Learning for Process-defect Modeling toward Accelerated Cross-system Certification for Metal Additive Manufacturing

About This Grant

The process-defect relationship is one of the key elements to the certification of additive manufacturing (AM) parts, which has been a major challenge in accelerating AM technology deployments in the industry. Advanced machine learning methods that leverage massive data to characterize the process-defect relationship have been studied for AM certifications. However, some AM fabrications and certification courses, especially for high-valued metallic parts, are lengthy and costly; thus, if the certification could be transferrable between different AM systems, it may greatly broaden the industrial use of AM technologies. Though feasible in theory, combining data from multiple AM systems on a shared platform for the certification purpose is not practical because of the desire to protect intellectual properties and sensitive data. What is lacking, therefore, is a holistic strategy to share knowledge learned from different AM systems without compromising the private information. This Faculty Early Career Development (CAREER) award supports fundamental research on privacy-preserving AM process-defect modeling and certification means across different systems. The project aims to establish a transfer learning groundwork, while protecting the process and part confidentiality, to understand and establish the process-defect relationship in metal AM between different systems. In addition, educational activities closely integrated with the research will provide basic training in privacy-preserving manufacturing systems modeling to next-generation manufacturing engineers from diverse groups, including minorities and women. Current data-driven AM certification schemes largely focus on characterizing the process-defect relationship of individual systems (i.e., one model for each single system and not generalizable to other systems, even similar ones). Although the state-of-the-art transfer learning methods can leverage data collected from multiple machines for cross-system studies, the research need is to maintain certain confidentiality—for both the part and process—to realize such collaboration. The goal of this project, hence, is to advance the scale-up of metal AM technologies by establishing a data-sharing platform, which enables process-defect modeling among multiple AM systems without divulging critical part and processing data. If successful, the major contribution of the research project will be a privacy-preserving transfer learning framework derived from the following research activities using directed energy deposition AM as an example: 1) constructing masked process features through de-coupling variability components assignable to product designs and process quality using a physics-informed tensor decomposition method, 2) establishing cross-system process-defect relationship through multi-task transfer learning to characterize intra- and inter-system variability and 3) enhancing AM certification capability by integrating part-level density and process-level thermal data based on fundamental physics principles. This project is jointly funded by the division of Civil, Mechanical and Manufacturing Innovation (CMMI) and the Established Program to Stimulate Competitive Research (EPSCoR). 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.