FMRG: Manufacturing USA: Cyber: Privacy-Preserving Tiny Machine Learning Edge Analytics to Enable AI-Commons for Secure Manufacturing

FMRG：美国制造业：网络：保护隐私的小型机器学习边缘分析，以实现 AI 共享以实现安全制造

• 批准号: 2134667
• 负责人: Ali Shakouri
• 金额: $ 300万
• 依托单位: Purdue 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: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134667&HistoricalAwards=false
• 关键词: FMRG; Manufacturing; USA; Cyber; Privacy

Current manufacturing systems based on a global supply chain work well, producing high-volume products at low cost. However, they are fragile to perturbations (e.g., COVID-19) and even processes that are essential to success, such as new product development and manufacturing ramp-up and optimization, can be prohibitively lengthy and expensive. Artificial Intelligence (AI) has revolutionized many industries and can be an important tool to improve the productivity and agility of manufacturing, but progress has been slow in manufacturing. This Future Manufacturing Research Grant (FMRG) Manufacturing USA: CyberManufacturing project is a fundamental reimagination of distributed AI/ML techniques to transform the future of manufacturing by establishing an AI-Commons that bridges multiple sites and companies using secure, distributed machine learning (ML), incentivized information sharing, and continuous quality improvement and training. This addresses a critical shortcoming of the current approach to AI in manufacturing; the limitation of training data to in-company data. Since AI algorithms increase in power with more data, secure data sharing and aggregation has the potential to provide vastly better AI solutions to all manufacturers. The project will also research the edge computation hardware needed to execute the resulting algorithms on the factory floor. The team will work closely with Ivy Tech Community College and the Vertically Integrated Projects program, which helps students from Purdue, Harvard, and Tuskegee University work on industry-defined manufacturing AI projects. This project’s goal will be achieved by fulfilling the following four objectives: (1) Co-optimization of Tiny Machine Learning (TinyML) hardware and software for manufacturing; (2) Design of privacy and confidentiality policies for an AI-Commons that encourages and incentivizes knowledge sharing; (3) Demonstration of data aggregation and predictive technical cost modeling for some foundational manufacturing processes; and (4) Introduction of AI in manufacturing curricula and integration with workforce development. A key focus is on AI system deployment to obtain necessary data for optimization of ML algorithms for common manufacturing processes in pharmaceutical, food processing, job shop machining, and hybrid composite materials. The project team has developed a strong partnership with small and large manufacturers in the Wabash Heartland Innovation Network (WHIN) region in Indiana. TinyML devices will be deployed throughout this region, where the sharing of data and AI could improve operations significantly.This project is jointly funded by the Division of Civil. Mechanical and Manufacturing Innovation, the Division of Computer and Network Systems, the Division of Engineering Education and Centers and the Office of Multidisciplinary Activities of the Directorate for Social, Behavioral and Economic Sciences.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.

目前基于全球供应链的制造系统运转良好，以低成本生产大量产品。然而，它们很容易受到干扰(例如，新冠肺炎)的影响，即使是对成功至关重要的过程，如新产品开发、制造升级和优化，也可能耗时长得令人望而却步，而且成本高昂。人工智能(AI)给许多行业带来了革命性的变化，可以成为提高制造业生产率和敏捷性的重要工具，但制造业的进展一直很缓慢。未来制造业研究资助(FMRG)美国制造业：网络制造项目是对分布式AI/ML技术的根本重新想象，旨在通过建立AI-Commons来改变制造业的未来，该AI-Commons使用安全的分布式机器学习(ML)连接多个站点和公司，激励信息共享，以及持续的质量改进和培训。这解决了目前制造业人工智能方法的一个严重缺陷：培训数据仅限于公司内部数据。由于AI算法的能力随着数据的增加而增加，因此安全的数据共享和聚合有可能为所有制造商提供更好的AI解决方案。该项目还将研究在工厂车间执行所产生的算法所需的边缘计算硬件。该团队将与常春藤理工社区学院和垂直整合项目项目密切合作，该项目帮助普渡大学、哈佛大学和塔斯基吉大学的学生在行业定义的制造业人工智能项目上工作。该项目的目标将通过实现以下四个目标来实现：(1)用于制造的微型机器学习(TinyML)硬件和软件的共同优化；(2)为鼓励和激励知识共享的AI-Commons设计隐私和保密政策；(3)为一些基本的制造过程演示数据聚合和预测性技术成本建模；以及(4)在制造课程中引入人工智能并与劳动力发展相结合。一个关键的重点是人工智能系统的部署，以获得必要的数据，以优化制药、食品加工、作业车间加工和混杂复合材料中常见制造过程的ML算法。该项目团队与印第安纳州沃巴什中心地带创新网络(WHIN)地区的大大小小的制造商建立了牢固的合作伙伴关系。TinyML设备将部署在整个地区，那里的数据共享和人工智能可以显著改善运营。该项目由土木工程司联合资助。机械和制造创新、计算机和网络系统分部、工程教育和中心分部以及社会、行为和经济科学局的多学科活动办公室。该奖项反映了NSF的法定使命，并通过使用基金会的智力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

Active learning approaches to analysis of thin-film printed sensors for determining nitrate levels in soil

用于分析薄膜印刷传感器以确定土壤中硝酸盐含量的主动学习方法

• DOI: 10.2352/ei.2023.35.15.color-194
• 发表时间: 2023
• 期刊: Electronic Imaging
• 作者: Wang, Xihui;Shakouri, Ali;Ribeiro, Bruno;Chiu, George T.C.;Allebach, Jan P.
• 通讯作者: Allebach, Jan P.

Improvements to color image and machine learning based thin-film nitrate sensor performance prediction: New texture features, repeated cross-validation, and auto-tuning of hyperparameters

基于彩色图像和机器学习的薄膜硝酸盐传感器性能预测的改进：新的纹理特征、重复交叉验证和超参数自动调整

• DOI: 10.2352/ei.2022.34.15.color-159
• 发表时间: 2022
• 期刊: Electronic Imaging
• 作者: Wang, Xihui;Mi, Ye;Shakouri, Ali;Chiu, George T.C.;Allebach, Jan P.
• 通讯作者: Allebach, Jan P.

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

• DOI: 10.1109/icit58465.2023.10143099
• 发表时间: 2023-04
• 期刊: 2023 IEEE International Conference on Industrial Technology (ICIT)
• 作者: Adrian Li;Elisa Bertino;Rih-Teng Wu;Ting Wu

Thin-Film Nitrate Sensor Performance Prediction Based on Image Analysis and Credibility Data to Enable a Certify As Built Framework

基于图像分析和可信度数据的薄膜硝酸盐传感器性能预测，以实现内置认证框架

• DOI: 10.1115/msec2022-85638
• 发表时间: 2022
• 期刊: ASME 2022 17th International Manufacturing Science and Engineering Conference
• 作者: Wang, Xihui;Saha, Ajanta;Mi, Ye;Shakouri, Ali;Ashraful Alam, Muhammad;Chiu, George T.;Allebach, Jan P.
• 通讯作者: Allebach, Jan P.

Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding

数据科学知识整合：计算认知学徒期对学生概念理解的启示

• DOI: 10.1002/cae.22580
• 发表时间: 2023
• 期刊: Computer Applications in Engineering Education
• 影响因子: 2.9
• 作者: Sánchez‐Peña, Matilde;Vieira, Camilo;Magana, Alejandra J.
• 通讯作者: Magana, Alejandra J.