Data Science LabTexas A&M University

AI for Engineering Processes

Data-driven modeling, quality control and optimization for advanced engineering processes.

Research focus

Our research advances data-driven modeling, monitoring, design, control, and optimization methods for complex engineering processes. A central theme is to combine statistical and machine-learning models with process knowledge so that predictions are not only accurate, but also interpretable, uncertainty-aware, and useful for quality improvement and process design. Our work spans advanced manufacturing processes, nanoscale inkjet printing, graphene-based nanosensor manufacturing, robust parameter design, process monitoring, statistical calibration, and Bayesian optimization. Through these studies, we aim to transform high-dimensional process data into actionable knowledge for improving quality, robustness, and manufacturability.

Selected publications

Publications

Complete publication record

Projects & applications

Monitoring: Probability Modeling & Quality Control of Additive Nanosensor Manufacturing Process

Goal: Establishing a stochastic geometry model and a statistical quality control method using the image data

Methodologies: Stochastic geometry modeling, Stochastic process, physical property analysis

Publications

Research diagram: Monitoring: Probability Modeling & Quality Control of Additive Nanosensor Manufacturing Process
Monitoring: Probability Modeling & Quality Control of Additive Nanosensor Manufacturing Process View full-size figure

Data-driven Robust Design Optimization of Advanced Engineering System or Nanosensors

Goal: Data-driven design method minimizing the variance while satisfying mean outputs leveraging prediction & uncertainty

Methodologies: Bayesian optimization, Gaussian process modeling

Publications

Other Bayesian optimization (Process/System/Design optimization) papers:

Research diagram: Data-driven Robust Design Optimization of Advanced Engineering System or Nanosensors
Data-driven Robust Design Optimization of Advanced Engineering System or Nanosensors View full-size figure

Nonparametric Statistical Functional Data Modeling for Nanosensors & Statistical Calibration with Uncertainty Quantification

Goal: Uncertainty quantification, inference of underlying physical variable based on functional data

Methodologies: Functional data modeling, Gaussian Process modeling, Bayesian inference

Publications

Research diagram: Nonparametric Statistical Functional Data Modeling for Nanosensors & Statistical Calibration with Uncertainty Quantification
Nonparametric Statistical Functional Data Modeling for Nanosensors & Statistical Calibration with Uncertainty Quantification View full-size figure

Collaborated Research: Fully Inkjet-Printed, 2D Materials-Based Field-Effect Transistor for Water Sensing

Detail: Printing uniform channels by the (nano) inkjet printer is extremely challenging due to the well-known coffee-ring effects. The quality of the inkjet printed pattern hinges on many factors, including various material properties and temperature, which are often difficult to obtain. In this work, we proposed a method to achieve a uniform channel covered by an average of a single layer of graphene. By leveraging the fact that multiple printing passes can smooth out the coffee ring effects on the boundary, we formulate an optimization problem and achieve a uniform single layer coverage on the target region.

Publications

  • [J7] Xiaoyu Sui, Sonal V. Rangnekar, Jaesung Lee, Stephanie E. Liu, Julia R. Downing, Lindsay E. Chaney, Xiaodong Yan, Hyun-June Jang, Haihui Pu, Xiaoao Shi, Shiyu Zhou, Mark C. Hersam, and Junhong Chen, “Fully Inkjet-Printed, 2D Materials-Based Field-Effect Transistor for Water Sensing,” Advanced Materials Technologies, vol. 8, no. 22, article 2301288, 2023, doi:10.1002/admt.202301288.

    Publication record: [J7]

    • This paper was selected as the top viewed article in 2023.
Research diagram: Collaborated Research: Fully Inkjet-Printed, 2D Materials-Based Field-Effect Transistor for Water Sensing
Collaborated Research: Fully Inkjet-Printed, 2D Materials-Based Field-Effect Transistor for Water Sensing View full-size figure

Other research directions

01

AI for Engineering Systems

Monitoring, diagnostics, prognostics and decision-making for complex engineering systems.

02

AI for Engineering Processes

Data-driven modeling, quality control and optimization for advanced engineering processes.

03

Physics-Informed AI

Physics-informed inference of heterogeneous physical properties from noisy observations.