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

工艺-缺陷关系是附加制造(AM)部件认证的关键要素之一，这一直是加速AM技术在行业中部署的主要挑战。已针对AM认证研究了利用大量数据来表征过程-缺陷关系的高级机器学习方法。然而，一些AM的制造和认证课程，特别是对于高价值的金属部件，是漫长和昂贵的；因此，如果认证可以在不同的AM系统之间转移，它可能会极大地扩大AM技术的工业应用。尽管理论上可行，但出于保护知识产权和敏感数据的需要，将来自共享平台上的多个AM系统的数据组合用于认证目的是不现实的。因此，缺乏一种整体策略来共享从不同AM系统学到的知识，而不会损害私人信息。这项教师早期职业发展(Career)奖支持在不同系统中保护隐私的AM过程缺陷建模和认证方法的基础研究。该项目旨在建立一个迁移学习基础，同时保护过程和部分的机密性，以了解和建立不同系统之间的金属AM中的过程-缺陷关系。此外，与研究密切结合的教育活动将为来自不同群体的下一代制造工程师，包括少数族裔和妇女，提供保护隐私的制造系统建模方面的基本培训。当前的数据驱动的AM认证方案主要集中于描述单个系统的过程-缺陷关系(即，每个单个系统一个模型，并且不能推广到其他系统，甚至类似的系统)。虽然最先进的迁移学习方法可以利用从多台机器收集的数据进行跨系统研究，但研究需要保持一定的机密性-无论是对于部件还是过程-以实现这种协作。因此，本项目的目标是通过建立一个数据共享平台，在不泄漏关键部件和处理数据的情况下，在多个AM系统之间进行工艺缺陷建模，从而推动金属AM技术的规模化。如果成功，该研究项目的主要贡献将是来自以下研究活动的隐私保护转移学习框架，该框架以定向能沉积AM为例：1)通过使用物理信息张量分解方法解耦可分配给产品设计和过程质量的可变性组件来构建掩蔽的过程特征；2)通过多任务转移学习建立跨系统的过程-缺陷关系以表征系统内和系统间的可变性；3)通过基于基本物理原理的部件级密度和过程级热数据的集成来增强AM认证能力。该项目由土木、机械和制造业创新部门(CMMI)和既定的激励竞争研究计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.