Jin Awarded NSF CAREER Award for High Precision Micromanufacturing
MIE Assistant Professor Xiaoning “Sarah” Jin was awarded a $500K NSF CAREER award for “Unifying Sensing, Machine Perception and Control for High-precision Micromanufacturing.”
Abstract Source: NSF
This Faculty Early Career Development (CAREER) grant investigates machine learning-based sensing and control methods to improve the efficiency and quality of scalable and high-precision subtractive and additive micromanufacturing processes. Machine learning-based methods take advantage of massive amounts of data collected from sensors and actuators to discover patterns and draw inference using mathematical algorithms and statistical models. This represents a new avenue for innovation in process monitoring and control for emerging micromanufacturing technologies (e.g., roll-to-roll printing of flexible electronics) where prior knowledge is limited but sensor data is rich. By leveraging advanced machine learning methods that are guided by physical manufacturing knowledge, this research increases understanding of sensing-based control, thus enabling significant enhancement in the process performance, stability and adaptiveness required for high-precision and high-rate manufacturing. Ultimately this work benefits society by enabling next-generation manufacturing that is more precise, more reliable and that produces more complex products with less material waste, lower defect rates, and higher efficiency. The knowledge obtained from this research is used to support the education and training of future manufacturing scientists and engineers recruited from a diverse and dynamic group that includes underrepresented minorities and women in this field.
The goal of this project is to advance the fundamental understanding of data-driven machine learning-based precision control for micromanufacturing processes. The novel unified framework and methods developed in this research transform state-of-the-art process control from a model-based standard to a data-driven model-free paradigm, ultimately pushing new levels of accuracy and precision of complex micromanufacturing systems. The major innovation involves a novel sensing-perception-learning-control framework that leads to meeting the following research objectives: 1) create a low-dimensional and low-noise latent state representation from abundant multimodal sensor data for process state estimation through probabilistic deep learning methods, and provide fundamental knowledge required to realize high-quality monitoring; 2) establish a novel Perception-based Iterative Learning Control (PILC) method with controllers to achieve unprecedented accuracy in precision process control, and 3) experimentally demonstrate and validate the unified framework and a set of sensor-based deep inference and learning-based control algorithms on two specific advanced micromanufacturing processes, ion-mill etching (subtractive) and roll-to-roll gravure printing (additive). The fundamental understandings directly advance the real-time control capability of high-precision manufacturing processes with tighter tolerance, and guide potential routes for achieving manufacturing capabilities augmented by sensing technologies and advanced data analytics.