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

职业：用于工艺缺陷建模的隐私保护迁移学习，以加速金属增材制造的跨系统认证

• 批准号: 2046515
• 负责人: Wenmeng Tian
• 金额: $ 51.57万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046515&HistoricalAwards=false
• 关键词: CAREER; Privacy; preserving; Transfer; Learning

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.

工艺缺陷关系是增材制造（AM）部件认证的关键因素之一，这是加速增材制造技术在行业中部署的主要挑战。先进的机器学习方法利用大量数据来表征过程缺陷关系，已经研究了增材制造认证。然而，一些增材制造和认证课程，特别是高价值的金属零件，是漫长而昂贵的；因此，如果认证可以在不同的增材制造系统之间转移，它可能会大大扩大增材制造技术的工业应用。虽然理论上可行，但由于希望保护知识产权和敏感数据，将来自多个AM系统的数据合并到共享平台上进行认证是不切实际的。因此，缺乏的是从不同AM系统中共享知识而不损害私人信息的整体策略。该学院早期职业发展（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，同时保护工艺和零件的机密性，了解和建立金属增材制造中不同系统之间的工艺缺陷关系。此外，与研究紧密结合的教育活动将为来自不同群体（包括少数民族和女性）的下一代制造工程师提供隐私保护制造系统建模的基本培训。当前数据驱动的增材制造认证方案主要侧重于描述单个系统的过程缺陷关系（即，每个单一系统的一个模型，而不能推广到其他系统，甚至是类似的系统）。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-对于部件和过程-以实现这种协作。因此，该项目的目标是通过建立一个数据共享平台来推进金属增材制造技术的扩大，该平台可以在多个增材制造系统之间进行工艺缺陷建模，而不会泄露关键部件和加工数据。如果成功，该研究项目的主要贡献将是一个保护隐私的迁移学习框架，该框架源自以下研究活动，以定向能沉积AM为例：1)使用物理信息张量分解方法，通过可分配给产品设计和过程质量的解耦变异性组件构建掩模过程特征；2)通过多任务迁移学习建立跨系统过程缺陷关系，表征系统内和系统间变异性；3)基于基本物理原理，通过集成部件级密度和过程级热数据，增强增材制造认证能力。该项目由民用、机械和制造创新部门（CMMI）和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

FFF-based metal and ceramic additive manufacturing: Production scale-up from a stream of variation analysis perspective

• DOI: 10.1016/j.mfglet.2023.08.126
• 发表时间: 2023-08
• 期刊: Manufacturing Letters
• 影响因子: 3.9
• 作者: Blake Ray;Boris Oskolkov;Chenang Liu;Zacary Leblanc;Wenmeng Tian

Morphological Dynamics-Based Anomaly Detection Towards In Situ Layer-Wise Certification for Directed Energy Deposition Processes

基于形态动力学的异常检测，实现定向能量沉积过程的原位逐层认证

• DOI: 10.1115/1.4054805
• 发表时间: 2022
• 期刊: Journal of Manufacturing Science and Engineering
• 作者: Bappy, Mahathir Mohammad;Liu, Chenang;Bian, Linkan;Tian, Wenmeng
• 通讯作者: Tian, Wenmeng

Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes

定向能量沉积过程协同过程缺陷建模的热历史设计去识别

• DOI: 10.1115/1.4056488
• 发表时间: 2023
• 期刊: Journal of Manufacturing Science and Engineering
• 作者: Fullington, Durant;Bian, Linkan;Tian, Wenmeng
• 通讯作者: Tian, Wenmeng

In-situ layer-wise certification for direct laser deposition processes based on thermal image series analysis

• DOI: 10.1016/j.jmapro.2021.12.041
• 发表时间: 2022-03
• 期刊: Journal of Manufacturing Processes
• 影响因子: 6.2
• 作者: M. N. Esfahani;M. Bappy;L. Bian;Wenmeng Tian

When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development

• DOI: 10.1016/j.jmsy.2022.04.010
• 发表时间: 2022-07-01
• 期刊: JOURNAL OF MANUFACTURING SYSTEMS
• 影响因子: 12.1
• 作者: Liu，Chenang;Tian，Wenmeng;Kan，Chen
• 通讯作者: Kan，Chen