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

FMSG：网络：电子束粉末床熔融增材制造过程产品质量改进的过程监控方法

• 批准号: 2229245
• 负责人: Xiaolei Fang
• 金额: $ 49.81万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229245&HistoricalAwards=false
• 关键词: FMSG; Cyber; Process; Monitoring; Methods

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.

加法制造是一种由计算机控制的过程，它通过逐层沉积材料来创造具有复杂结构的三维物体。在众多的添加剂制造工艺中，电子束粉末床熔化(EB-PBF)在金属零件的生产中显示出了巨大的前景。阻碍EB-PBF和许多其他添加剂制造工艺得到更广泛采用的最大障碍之一是缺乏有效的产品质量控制方法。因此，EB-PBF机器制造质量达不到规格的缺陷产品的可能性非常高。为了应对这一挑战，本项目的重点是促进建立EB-PBF添加剂制造工艺的现场质量控制框架的可能性的研究。该框架使用实时传感数据来监控产品质量，并在必要时自适应调整过程控制参数以提高质量。这项研究探索了EB-PBF显著提高制造零件和制造过程质量的新能力。该项目还包括各种教育和宣传部分，为吸引和培训下一代制造专业人员奠定了基础。该项目的目标是为EB-PBF添加剂制造工艺的现场质量控制提供基础。主要研究方向有三个。第一种是一种计算效率高的运行时缺陷检测方法，该方法使用一种新的统计学习模型，该模型建立在基于实时电子数据的基础上，用于检测制造过程中可能对产品质量产生不利影响的缺陷。第二个是隐私保护联合模型，用于让制造社区参与根本原因缺陷诊断；它构建在一种新的基于正则化的统计学习模型上，特别是对于EB-PBF，以确定导致产品缺陷的关键过程控制参数。第三个重点是设计和进行实验，以验证这些模型的有效性。自动缺陷检测和根本原因诊断方法不仅是EB-PBF现场质量控制的基本基础，而且极大地丰富了统计学习、数据分析和机器学习领域的知识库。这个未来的制造奖得到了计算机和信息科学与工程局(CEISE)计算机与网络系统(CNS)司和土木工程和土木工程司的支持，这一奖项反映了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。