CAREER: Transforming Machine Learning Models Developed in Labs to Manufacturing Plants for In-Process Quality Prediction

职业：将实验室开发的机器学习模型转变为制造工厂，以进行过程中的质量预测

• 批准号: 2237242
• 负责人: Peng Wang
• 金额: $ 56.79万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237242&HistoricalAwards=false
• 关键词: CAREER; Transforming; Machine; Learning; Models

Recent advances in areas such as automation, data science, and artificial intelligence, are creating new opportunities for advanced manufacturing. However, most machine learning-based solutions are developed in lab environments, which require extensive model tuning and expensive data labeling to be implemented in manufacturing plants. The technical barrier arises from the major discrepancies in the amount, distributions, veracity, and modality between lab and plant data. This Faculty Early Career Development (CAREER) award will investigate new machine learning methodology to make machine learning generalizable and deployable. If successful, the project will accelerate the deployment of artificial intelligence in manufacturing plants and lower the entrance barrier to Industry 4.0 for small and medium manufacturers. This project is also expected to contribute to the development of new manufacturing workforce by engaging middle/high school students and local industries. This project aims to develop a machine learning architecture with expandable modules to learn from massive unlabeled data streaming and adapt to dynamically changing manufacturing conditions in plants. The lab-to-plant transformation will be realized upon testing two scientific hypotheses: (1) a generic model for characterizing massive unlabeled data can effectively learn the similarities of plant data; (2) an established model can be fully adapted to unseen but related scenarios with limited tuning. A transformer architecture-based novel machine learning framework will be configured to simultaneously realize: i) task-agnostic self-supervised contrastive learning from massive plant data for multi-level data characterization; ii) normalizing flow for building one-to-one mapping between sensing data toward virtual sensing data generation in plants for improved quality prediction; and iii) prompt model turning for effectively and efficiently adapting models between different manufacturing conditions. If successful, this project will enable generalizable, deployment-ready machine learning solutions that will be readily scalable for a broad scope of manufacturing applications and help U.S. manufacturers adopt smart manufacturing technologies at an accelerated pace.This project is jointly funded by the Advanced Manufacturing Program 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.

自动化，数据科学和人工智能等领域的最新进展正在为高级制造创造新的机会。 但是，大多数基于机器学习的解决方案都是在实验室环境中开发的，这些解决方案需要在制造工厂中实施广泛的模型调整和昂贵的数据标签。技术障碍源于实验室数据和植物数据之间的数量，分布，真实性和方式的主要差异。该教师早期职业发展（职业）奖将研究新的机器学习方法，以使机器学习可推广和可部署。如果成功，该项目将加速制造工厂中人工智能的部署，并降低中小型制造商的行业4.0的入口屏障。预计该项目将通过吸引中学/高中学生和当地行业来为新制造劳动力的发展做出贡献。该项目旨在开发具有可扩展模块的机器学习体系结构，以从大量未标记的数据流中学习并适应植物中动态不断变化的制造条件。在测试两个科学假设时，将实现实验室对植物转化：（1）用于表征大量未标记数据的通用模型可以有效地学习植物数据的相似性； （2）建立的模型可以完全适应看不见的，但与调整有限的相关场景。基于变压器架构的小说机器学习框架将被配置为同时实现：i）从大型植物数据中从大型植物数据中进行任务无关的对比度学习，以进行多层数据表征； ii）将传感数据构建到植物中虚拟传感数据生成之间建立一对一映射的流量正常化，以改善质量预测； iii）迅速转向有效，有效地适应不同制造条件之间的模型。如果成功的话，该项目将启用可通用的，准备就绪的机器学习解决方案，这些解决方案将很容易扩展到广泛的制造应用程序范围内，并帮助美国制造商在加速的速度上采用智能制造技术。该项目由先进的制造计划和既定的计划通过启发竞争性研究（EPSCOR）进行了支持（EPSCOR）。基金会的智力优点和更广泛的影响审查标准。

项目成果

Virtual Sensing by Dense Encoder For Process Signals In Resistance Spot Welding

通过密集编码器对电阻点焊过程信号进行虚拟传感

• 发表时间: 2024
• 期刊: 2024 International Symposium on Flexible Automation (ISFA
• 作者: Kershaw, J.;Ghassemi-Armaki, H.;Carlson, B.;Wang, P.
• 通讯作者: Wang, P.

Advanced process characterization and machine learning-based correlations between interdiffusion layer and expulsion in spot welding

• DOI: 10.1016/j.jmapro.2023.12.013
• 发表时间: 2024-01
• 期刊: Journal of Manufacturing Processes
• 影响因子: 6.2
• 作者: J. Kershaw;Hassan Ghassemi-Armaki;Blair E. Carlson;Peng Wang

Ontology-Integrated Tuning of Large Language Model for Intelligent Maintenance

智能维护大语言模型本体集成调优

• 发表时间: 2024
• 期刊: CIRP annals
• 作者: Wang, Peng;Karigiannis, John;Gao, Robert
• 通讯作者: Gao, Robert