AI for Engineering Processes

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

[J3] J. Lee, S. Zhou, and J. Chen, "Statistical modeling and analysis of k-layer coverage of two-dimensional materials in inkjet printing processes," Technometrics, vol. 63, no. 3, pp. 410–420, 2021.

[C3] J. Lee, C. Wang, S. Zhou, and J. Chen, "Spatial distribution quantification and control of ink flakes in reduced graphene oxide FET inkjet printing," Procedia Manufacturing, vol. 34, pp. 19–25, 2019. 47th SME North American Manufacturing Research Conference, NAMRC 47, Pennsylvania, USA.
[J6] J. Lee, S. Zhou, and J. Chen, "Robust Parameter Design on Dual Stochastic Response Models with Constrained Bayesian Optimization,", IEEE Transactions on Automation Science and Engineering, 2023, doi:10.1109/TASE.2023.3251973

[J5] C. Huang, J. Lee, Y. Zhang, S. Zhou, and J. Tang, "Mixed-input Bayesian Optimization Method for Structural Damage Diagnosis," IEEE Transactions on Reliability, vol 72, no. 2, pp. 678-691, 2023. doi:10.1109/TR.2022.3179602
[C4] J. Lee, S. Zhou, and J. Chen, "Sequential Robust Parameter Design With Sample Size Selection", ASME 2022 17th International Manufacturing Science and Engineering Conference, Indiana, USA, doi:10.1115/MSEC2022-85690
[J4] J. Lee, C. Wang, X. Sui, S. Zhou, and J. Chen, "Landmark-embedded Gaussian process with applications for functional data modeling," IISE Transactions, vol. 54, no. 11, pp. 1033-1046, 2022.
[J8] X. Sui, S. Rangnekar, J. Lee, S. Liu, J. Downing, L. Chaney, X. Yan, H. Jang, H.Pu, S. Zhou, M. Hersam, and J. Chen, “Fully Inkjet-Printed, 2D Materials-Based Field-Effect Transistor for Water Sensing,” Advanced Materials Technologies, 2023, doi:10.1002/admt.202301288

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

[J3] J. Lee, S. Zhou, and J. Chen, "Statistical modeling and analysis of k-layer coverage of two-dimensional materials in inkjet printing processes," Technometrics, vol. 63, no. 3, pp. 410–420, 2021.

[C3] J. Lee, C. Wang, S. Zhou, and J. Chen, "Spatial distribution quantification and control of ink flakes in reduced graphene oxide FET inkjet printing," Procedia Manufacturing, vol. 34, pp. 19–25, 2019. 47th SME North American Manufacturing Research Conference, NAMRC 47, Pennsylvania, USA.

Data-driven Robust Design Optimiation 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
[J6] J. Lee, S. Zhou, and J. Chen, "Robust Parameter Design on Dual Stochastic Response Models with Constrained Bayesian Optimization,", IEEE Transactions on Automation Science and Engineering, 2023, doi:10.1109/TASE.2023.3251973

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

[J5] C. Huang, J. Lee, Y. Zhang, S. Zhou, and J. Tang, "Mixed-input Bayesian Optimization Method for Structural Damage Diagnosis," IEEE Transactions on Reliability, vol 72, no. 2, pp. 678-691, 2023. doi:10.1109/TR.2022.3179602
[C4] J. Lee, S. Zhou, and J. Chen, "Sequential Robust Parameter Design With Sample Size Selection", ASME 2022 17th International Manufacturing Science and Engineering Conference, Indiana, USA, doi:10.1115/MSEC2022-85690

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
[J4] J. Lee, C. Wang, X. Sui, S. Zhou, and J. Chen, "Landmark-embedded Gaussian process with applications for functional data modeling," IISE Transactions, vol. 54, no. 11, pp. 1033-1046, 2022.
- This paper was featured in the ISE Magazine (October 2022 issue)
- This work was selected as the Best Paper in IISE Transactions in 2024.

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.

[J8] X. Sui, S. Rangnekar, J. Lee, S. Liu, J. Downing, L. Chaney, X. Yan, H. Jang, H.Pu, S. Zhou, M. Hersam, and J. Chen, “Fully Inkjet-Printed, 2D Materials-Based Field-Effect Transistor for Water Sensing,” Advanced Materials Technologies, 2023, doi:10.1002/admt.202301288
- This paper was selected as the top viewed article in 2023.

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