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学院和大学 - 卓越的研究（HBCU -EIR）赠款支持研究，这些研究贡献了与增材制造业质量保证有关的新知识，从而促进了科学的进步和国家繁荣的进步。喷墨打印是一种代表性的添加剂制造过程，基于热或声学形成以及通过喷嘴弹出液滴的弹射。它在电子，能源，医疗保健和生物医学行业中已证明了它的巨大希望。但是，喷墨打印对环境，材料，机械和电子因素敏感，并且该过程很容易偏离所需的工作状态，从而导致部分有缺陷。这往往会导致物质和能源浪费，并影响许多重要的工程系统的结构健康和功能完整性。该奖项支持基本研究，以提供所需的知识，以开发一个整体框架，涉及神经网络，以进行喷墨印刷中的质量保证。该项目具有显着提高基于喷墨的增材制造过程的生产率，质量和材料效率，从而使美国经济和社会受益。这项研究使用涉及制造，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的小组在研究和促进工程教育中的参与。该项目的目的是建立一个全面的框架，以无缝整合基于过程中的视频监控，从而通过封闭式控制和对墨水进行启动和随后的绘制范围，并随后纠正了较高的图像，并随后纠正了一部分，并随后又纠正了一部分。该框架由基于神经网络的三个协同数字双胞胎组成，该技术模仿了人工智能领域人类大脑的操作。第一个数字双胞胎旨在闭环控制微滴的运动学和形态状态。第二个数字双胞胎着重于印刷模式的几何和形态状态的闭环控制。第三个数字双胞胎确定并实施有缺陷模式的补偿策略。 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从神经网络的角度打印。预计该项目将提供对添加剂制造中网络物理系统的设计，开发和实施的基本理解。开发的框架可以适应其他宏观和微型尺度添加剂制造过程。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。

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.

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

In-situ Droplet Monitoring of Inkjet 3D Printing Process using Image Analysis and Machine Learning Models

• DOI: 10.1016/j.promfg.2021.06.045
• 发表时间: 2021-01-01
• 期刊: 49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021)
• 作者: Ogunsanya, Michael;Isichei, Joan;Cai, Yi
• 通讯作者: Cai, Yi

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