Excellence in Research: A Cyber-Physical System Framework for In-process Quality Assurance of Inkjet-based Additive Manufacturing

卓越的研究：基于喷墨的增材制造过程质量保证的网络物理系统框架

• 批准号: 2100850
• 负责人: Salil Desai
• 金额: $ 39.99万
• 依托单位: North Carolina Agricultural & Technical 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: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100850&HistoricalAwards=false
• 关键词: Excellence; Research; Cyber; Physical; System

This Historically Black Colleges and Universities - Excellence in Research (HBCU-EiR) grant supports research that contributes new knowledge related to quality assurance for additive manufacturing, promoting both the progress of science and the advancement of national prosperity. Inkjet printing is one representative additive manufacturing process based on thermal or acoustic formation and ejection of liquid droplets through a nozzle. Its great promise has been demonstrated in electronics, energy, healthcare and biomedical industries. However, inkjet printing is sensitive to environmental, material, mechanical and electronical factors, and the process can easily deviate from the desirable working status, resulting in defective parts. This tends to lead to material and energy waste and affects the structural health and functional integrity of many important engineering systems. This award supports fundamental research to provide needed knowledge for the development of a holistic framework involving neural networks for quality assurance in inkjet printing. This project holds the potential to significantly improve productivity, quality and material efficiency for inkjet-based additive manufacturing processes, thus benefiting the U.S. economy and society. Using a multi-disciplinary approach involving manufacturing, computer vision, control theory, and machine learning, this research helps broaden participation of underrepresented groups in research and promotes engineering education.The goal of this project is to establish a comprehensive framework that seamlessly integrates in-process video-based monitoring with closed-loop control and compensation to effectively detect and subsequently correct the process drift and anomalies toward high-quality inkjet printing. The framework consists of three synergic digital twins based on neural networks, a technique that mimics the operations of a human brain in the artificial intelligence field. The first digital twin aims at closed-loop control of the kinematic and morphological status of the micro droplets. The second digital twin focuses on closed-loop control of the geometrical and morphological status of the printed patterns. The third digital twin determines and implements compensation strategies for defective patterns. Specific objectives are to 1) identify methodology for creation and integration of digital twins to maintain desirable droplet status, obtain required patterns and implement effective compensation, 2) derive practical guidelines of using neural network in quality assurance, including input selection and preparation, network design and optimization, output selection and usage, and transferability and adaptability, and 3) understand the relationship between material properties, control variables, in-process parameters and print outcome in inkjet printing from the perspectives of neural network. This project is expected to provide fundamental understanding of the design, development, and implementation of cyber-physical systems in additive manufacturing. The developed framework can be adapted to other macro- and micro-scale additive manufacturing processes.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.

这项历史悠久的黑人大学卓越研究（HBCU-EiR）资助支持为增材制造质量保证提供新知识的研究，促进科学进步和国家繁荣的发展。喷墨打印是一种典型的增材制造工艺，它基于热或声形成和通过喷嘴喷射液滴。它的巨大前景已经在电子、能源、医疗保健和生物医学行业得到了证明。然而，喷墨印刷对环境、材料、机械和电子等因素比较敏感，在打印过程中很容易偏离理想的工作状态，造成零件缺陷。这往往会导致材料和能源的浪费，并影响许多重要工程系统的结构健康和功能完整性。该奖项支持基础研究，为涉及喷墨打印质量保证的神经网络的整体框架的发展提供所需的知识。该项目具有显著提高喷墨增材制造工艺的生产率、质量和材料效率的潜力，从而使美国经济和社会受益。本研究采用涉及制造、计算机视觉、控制理论和机器学习的多学科方法，有助于扩大代表性不足群体在研究中的参与，并促进工程教育。该项目的目标是建立一个全面的框架，将过程中基于视频的监控与闭环控制和补偿无缝集成，以有效地检测并随后纠正过程漂移和异常，以实现高质量的喷墨打印。该框架由基于神经网络的三个协同数字双胞胎组成，神经网络是人工智能领域模仿人脑运作的技术。第一个数字孪生体旨在对微液滴的运动和形态状态进行闭环控制。第二个数字孪生集中在印刷图案的几何和形态状态的闭环控制。第三个数字孪生确定并实施缺陷模式的补偿策略。具体目标是：1)确定数字孪生体的创建和集成方法，以保持理想的液滴状态，获得所需的模式并实施有效的补偿；2)推导在质量保证中使用神经网络的实用指南，包括输入选择和准备、网络设计和优化、输出选择和使用、可转移性和适应性；3)理解材料特性、控制变量、从神经网络的角度研究喷墨打印的过程参数和打印效果。该项目有望为增材制造中网络物理系统的设计、开发和实施提供基本的理解。所开发的框架可以适用于其他宏观和微观尺度的增材制造工艺。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

Grid Search Hyperparameter Tuning in Additive Manufacturing Processes

增材制造过程中的网格搜索超参数调整

• 发表时间: 2023
• 期刊: Manufacturing letters
• 影响因子: 3.9
• 作者: Ogunsanya, M.;Isichei, J.;Desai, S.
• 通讯作者: Desai, S.

Predictive Modeling of Additive Manufacturing Process using Deep Learning Algorithm

使用深度学习算法对增材制造过程进行预测建模

• 发表时间: 2022
• 期刊: Proceedings of the IISE Annual Conference & Expo 2022
• 作者: Michael Ogunsanya;Salil Desai
• 通讯作者: Salil Desai

Smart Agent System for Cyber Nano-Manufacturing in Industry 4.0

• DOI: 10.3390/app12126143
• 发表时间: 2022-06-01
• 期刊: APPLIED SCIENCES-BASEL
• 影响因子: 2.7
• 作者: Almakayeel, Naif;Desai, Salil;Qureshi, Mohamed Rafik Noor Mohamed
• 通讯作者: Qureshi, Mohamed Rafik Noor Mohamed