Peng Wang is an assistant professor in the Department of Electrical and Computer Engineering, with a joint appointment in the Department of Mechanical Engineering, and holds the Robley D. Evans Faculty Fellowship in Electrical Engineering. Wang has received an NSF CAREER award for his project titled, “Transforming Machine Learning Models Developed in Labs to Manufacturing Plants for In-Process Quality Prediction." This project will explore new machine learning methodology, and if successful, will accelerate the deployment of artificial intelligence in manufacturing plants.
Abstract: 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.
