Faculty Directory

Lee, Jay

Lee, Jay

Clark Distinguished Chair
Director of Industrial Artificial Intelligence Center
A. James Clark School of Engineering
Mechanical Engineering
Maryland Robotics Center
Center for Risk and Reliability
Maryland Energy Innovation Institute
3226 Jeong H. Kim Engineering Building

 

Dr. Jay Lee is the Clark Distinguished Chair Professor and Director of the Industrial AI Center in the Department of Mechanical Engineering at the University of Maryland, College Park. An internationally recognized authority in Industrial AI and smart manufacturing, his research focuses on next-generation machine learning methodologies—including transfer learning, domain adaptation, similarity-based learning, Stream-of-X AI, and Industrial Large Knowledge Models (ILKM)—to enable scalable, real-time intelligence for complex industrial systems. He also leads the Data Foundry, a unique platform comprising more than 100 diverse industrial datasets spanning semiconductor manufacturing, jet engines, wind turbines, electric vehicles, high-speed rail, robotics, machine tools, and medical applications. This infrastructure supports both rapid validation of industrial AI technologies and the development of a new generation of data-centric engineering talent. Complementing this effort, he is spearheading the AI Factory initiative, an open laboratory environment designed to cultivate industry-ready Industrial AI engineers.

Previously, Dr. Lee served as Ohio Eminent Scholar, L.W. Scott Alter Chair, and University Distinguished Professor at the University of Cincinnati. He was also Founding Director of the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS) from 2001 to 2019, which grew into a global consortium of more than 130 corporate partners and was recognized in the NSF Economic Impact Study as the most economically impactful I/UCRC. His mentorship has led to multiple technology startups, including Predictronics, launched through the NSF I-Corps program.

From 2019 to 2021, he took academic leave to serve as Vice Chairman and Board Member of Foxconn Technology Group, where he led development of the Foxconn Wisconsin Science Park and advised business units that collectively earned six World Economic Forum Lighthouse Factory Awards. Earlier in his career, he held leadership roles in both government and industry, including Director of Product Development and Manufacturing at United Technologies Research Center and Program Director at the U.S. National Science Foundation, overseeing major national research initiatives such as ERCs and I/UCRCs. He also contributed to early machine learning history as part of the team at the U.S. Postal Service that developed the first handwritten ZIP code dataset used in Yann LeCun’s landmark 1989 backpropagation research.

Dr. Lee currently serves on World Economic Forum’s Global Future Council in Advanced Manufacturing and Value Chains, the Board of Governors of the Manufacturing Executive Leadership Council of the National Association of Manufacturers, the Board of Trustees of MTConnect, and as Senior Advisor to McKinsey. He also serve as Editor-in-Chief of the IOP Journal on Machine Learning: Engineering. He is a Fellow of ASME, SME, ISEAM, and the PHM Society, and has received numerous honors, including SME’s 30 Visionaries in Smart Manufacturing (2016), 20 Most Influential Professors in Smart Manufacturing (2020), the SME Eli Whitney Productivity Award, and the SME/NAMRC S.M. Wu Research Implementation Award (2022). His book on Industrial AI was published by Springer in 2020, and he contributed to the NSF ERVA AI Engineering report in 2024.

Dr. Lee received his Doctor of Science in Mechanical Engineering from George Washington University, following doctoral studies at Columbia University. He holds master’s degrees in Industrial Management from Stony Brook University and in Mechanical Engineering from the University of Wisconsin–Madison, and a bachelor’s degree in Mechanical Engineering from Tamkang University, Taiwan.

For publication citations, highlights, and recent news:

 

Industrial AI, Industrial Big Data, Prognostics and Health Management (PHM), Smart Manufacturing, Digital Twin, etc.


The IAI Center is developing next-generation non-traditional machine learning techniques and methods including transfer learning, domain adaptation, topological graph analytics, stream-of-X, etc for highly connected and complex industrial systems.

The IAI Center has a collection of tools and methods for solving industrial problems. The tools support rapid development and deployment, and enable the systematic evaluation of solutions, enhancing efficiency in tackling diverse industrial challenges.

AI Factory at UMD: The New Engine for Future Manufacturing

The AI Factory concept is pushing the boundaries of AI-powered manufacturing. The core principles behind the AI Factory revolve around using real-time and real-world data to enable automated machine learning for predictive manufacturing systems.

Core Components of an AI Factory

  • Industrial AI & Non-Traditional Machine Learning: Real-time prediction, optimization, and autonomous control of manufacturing uncertainties through advanced machine learning algorithms.
  • Digital Twin & Simulation: Real-time digital replicas of physical assets, used for design, testing, operations, and maintenance.
  • Cyber-Physical Systems (CPS): Integration of physical machines with digital control systems, utilizing virtual metrology to manage the "invisible" aspects of production.
  • Industrial Agents & Agentic AI: Data-centric, sensor-driven automation, including intelligent maintenance and risk avoidance, powered by real-time AI.
  • Cloud-Edge-Hybrid Computing: Scalable intelligence that spans from edge devices to enterprise-level cloud systems, enabling AI decision-making from the ground up.
  • Human-Centric AI & Industrial Large Knowledge Models (ILKM): While AI automates processes, it also augments the role of operators and engineers, helping them make better decisions rather than replacing them.

Below Are A List of Projects:

  1. Digital Twin Methodology for Semiconductor Process Metrology.
  2. Industrial AI for Mechanical Components: Ball Screw Health Monitoring with Inertial Sensors
  3. Industrial AI for Semiconductor Manufacturing: Virtual Metrology for Semiconductor CMP Process using Gaussian Process Regression 
  4. Transfer Learning and Domain Adaptation Method for Classification Problems in Complex and Highly Connected Industrial System
  5.  Industrial AI-based Data Visualization Methodology: Topological Analysis and Analytics for Complex Industrial Systems
  6. Industrial AI using Synthetic Data: Intelligent Synthetic Data Creation for Machine Learning-based Manufacturing Quality Control
  7. Industrial AI for Continuous Flow Manufacturing: A Novel Methodology for Health Assessment in Printed Circuit Boards
  8. PHM for Spacecraft Propulsion Systems: Similarity-Based Model and Physics-Inspired Features (PHM Asia Pacific 2023 Data Challenge, 1st Place)
  9.  Non-Traditional Machine Learning: Similarity‑based Remaining Useful Life Prediction Method
  10. Data Quality Evaluation Methodology for Industrial AI
  11. Industrial AI for Data Challenges (2018-2023): Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review
  12.  A Unified Industrial Large Knowledge Model Framework in Industry 4.0 and Smart Manufacturing
  13. Non-Traditional Machine Learning: Similarity‑based Fault Diagnosis Framework
  14. Data Issues in Industrial AI Systems: A Meta-Review and Research Strategy
  15. An Advanced Diagnostic Model for Gearbox Degradation Prediction Under Various Operating Conditions and Degradation Levels
  16. Industrial AI for Machine Fleets: Collaborative Prognostics Using a Novel Federated Baseline Learner
  17. Data Preparation Techniques for Improved Machine Learning
  18. Vision-Augmented TimesNet for Anomaly Detection in Semiconductor Plasma Dry Etching
  19. Self-supervised Siamese Transformer for Surface Defect Segmentation in Silicon Wafers
  20. Rethinking Industrial Artificial Intelligence: A Unified Foundation Framework
  21. Agentic AI for Smart Manufacturing
  22. PHM Data Challenge 2025: Multi-Event Remaining Useful Life Prediction for Commercial Jet Engines
  23. Large Language Model-Enhanced Framework for Robust Time-Series Fault Diagnosis
  24. Novel Segmentation Methodology for Robust Feature Engineering of Time Series Data in Prognostics and Health Management
  25. Improving Machine Calibration Performance through Systematic Feature Design in Semiconductor Manufacturing
   

Industrial AI (Course Number ENME 485/691) 

Term: Spring/2026



Professor: Jay Lee



Pronouns: He/His/Him



Office Phone: 301-405-5205



Email: leejay@umd.edu 

Office Hours: Monday 3:30 pm- 4:30 pm, Jeong H. Kim Engineering Building 3226

Teaching Assistant: Hanqi Su, Meredith Wang, 



Pronouns: He/His/Him, She/Her/Her



Email: hanqisu@umd.edu & rwang135@umd.edu 



Office Hours: Monday 3:30 pm- 4:30 pm, Jeong H. Kim Engineering Building, 1210



Credits: 3



Course Dates: January 26, 2026 – May 22, 2026  



Course Times: Monday 12:30 pm - 3:15 pm



Classroom: CHM 1228

Course Description



With the proliferation of connected devices, industrial systems generate massive volumes of data at every level—from individual machines to 4F (factory, facility, field, and fleet) enterprises. Yet much of this data remains underutilized. This course introduces students to the principles and practices of Industrial Artificial Intelligence (Industrial AI), with a focus on data-centric engineering and applied machine learning for prognostics and health management (PHM) of complex engineering systems. 



Students will learn to harness industrial big data to enhance system reliability, monitor machine conditions, assess performance, and predict and prevent potential failures. Through case studies, hands-on coding, and real-world datasets, students will gain practical experience in developing machine learning from a system engineering perspective. 



Learning Outcomes

By the end of the course, students will be able to:



•    Understand core concepts of Industrial AI and its role in digital transformation and Industry 4.0.



•    Evaluate and manage data quality for industrial machine learning applications.



•    Apply machine learning techniques for machine condition monitoring, diagnostics, and prognostics.



•    Develop, validate, and deploy machine learning and AI models using real-world industrial datasets.



•    Collaborate on team projects using integrated knowledge of domain, data, and algorithms.



•    (Graduate Students only) Perform advanced data-model analysis using PHM Data Challenge Competition datasets (https://data.phmsociety.org/). 



Required Resources 

●    Course Website: elms.umd.edu There will be no formal textbook for this class, but all lecture notes will be posted online. The reference below is strongly recommended to use on a regular basis throughout the semester.



●    Text Book: 



1. Jay Lee, Industrial AI: Applications with Sustainable Performance, Springer, 2020.



●    Reference Books:



1. Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.



2. Gelman, Andrew, et al. Bayesian data analysis. Vol. 2. London: Chapman & Hall/CRC, 2014.



3. Randall, Robert Bond. Vibration-based condition monitoring: industrial, aerospace and automotive applications. John Wiley & Sons, 2011.

●    Software: Python or MATLAB



Prerequisites



•    Proficiency in Python or MATLAB



•    Or permission of the instructor



Course Structure

This is a 15-week in-person course. Attendance and participation are expected. Students are responsible for any content missed during absences.



Major Assignments

Homework Assignments

  • HW1    Literature review on Industrial AI in Industry 4.0 and digital transformation. Students write a short report and give a 5-minute class presentation.
  • HW2    Vibration-based fault detection using FFT and logistic regression. Group work with provided sample code. 10-minute presentation required.
  • HW3    SVM-based fault classification on bearing data. Group analysis with in-class presentati
  • HW4    Use of Self-Organizing Maps (SOM and SOM-MQE) for bearing fault detection. Sample code provided. Group presentation required.(Advanced Data-Model Analysis (ADDA) (Graduate students only)

Graduate students are required to complete 2 of 7 advanced analyses based on PHM Data Challenge competition datasets. Each analysis includes:



•    Paper Review (1 page): Summary of challenge task, dataset, and winning methods.



•    Exploratory Data Analysis (1–2 pages): Visualization, trends, data quality, and preprocessing.



•    Model Development (2–3 pages): At least two models, performance comparison, and critical reflection.



Possible datasets include:



•    2018 PHM NA – Ion Mill Etching System



•    2019 PHM NA – Fatigue Crack



•    2020 PHM EU – Filtration System



•    2021 PHM EU – Production Line



•    2021 PHM NA – Turbofan Engine



•    2022 PHM EU – PCBs



•    2022 PHM NA – Spacecraft Propulsion



A suggested reference paper: “Machine learning approaches for diagnostics and prognostics of industrial systems using open-source data from PHM data challenges: a review.”



Final Group Projects



Each group will select one of the following real-world datasets:



1.    Semiconductor Etching Fault Detection



2.    Spacecraft Propulsion System Monitoring



3.    Aero-engine Remaining Useful Life Prediction



4.    Gearbox Fault Diagnosis and Prognosis



5.    Bearing Fault Diagnosis and Prognostics



•    Each group will work independently with TA support.



•    A 15-minute presentation and a detailed project report are due during finals week.



•    Promising projects may be encouraged to submit for conference or journal publication.

Grading Structure

Assignment    ENME485 Percentage %    ENME691 Percentage %



Homework & Presentation    20%    15%



Midterm Test    40%    35%



Final Term-Project Report    40%    30%



Advanced Data-Model Analysis (ADDA)     0%    20%



Total    100%    100%

Grades



Final Letter Grades will be determined based on the table below. See the grading structure section for the breakdown of all assessments. 

All assessment scores will be posted on the course ELMS page.  If you would like to review any of your grades (including the exams), or have questions about how something was scored, please email me to schedule a time for us to meet and discuss.



Late work will not be accepted for course credit so please plan to have it submitted well before the scheduled deadline.  I am happy to discuss any of your grades with you, and if I have made a mistake, I will immediately correct it.  Any formal grade disputes must be submitted in writing and within one week of receiving the grade.



Final letter grades are assigned based on the percentage of total assessment points earned.  To be fair to everyone, I have to establish clear standards and apply them consistently, so please understand that being close to a cutoff is not the same as making the cut (89.99 ≠ 90.00).  It would be unethical to make exceptions for some and not others.

Final Grade Cutoffs



+    97.00%    +    87.00%    +    77.00%    +    67.00%    +    



A    94.00%    B    84.00%    C    74.00%    D    64.00%    F    <60.0%



-    90.00%    -    80.00%    -    70.00%    -    60.00%    -    

  • Grading Structure

Assignment

Percentage %

Homework & Presentation

20%

Midterm Test

40%

Final Term-Project Report

40%

Total

100%
 

Course Outline

Week #

Topic

Assignment

1

Industrial AI Introduction (Part 1):Data Issues on Industrial Big Data System:Data Source, Data Quality, and Data Context

HW1

 2

Fundamentals of Signal Processing and Feature Extraction andCommonly used tools and Case studies in Industrial AI and Industrial AI (Part 2)

 

 3

Industrial AI Introduction (Part 3):Machine Learning tools (Part 1): Logistic Regression & Student HW 1 Presentations and Discussions

HW2 (Bearing data sets)

4

Machine Learning tools (Part 2): Support Vector Machine, Self-Organizing Map, K-Means and Others.and Student HW 2 Presentations and Discussions

HW3 (Bearing – SVM)

 5

Review ML Tools and Case StudiesMachine Health Monitoring using Industrial Big Data:Case Study I: Machine Level Heath Monitoring

Case Study II: System Level Health Monitoringand Student HW 3 Presentations and Discussions

HW4 (Bearing – SOM)

 6

Industrial AI tool for Prognostics and Health Management and Introduction to PHM Data Challenge and Case Studies and Student HW 4 Presentations and Discussions,

Preview Midterm Preparation

 

 7

Midterm

 

 8

Midterm Review

and Industrial AI for Networked and Complex Engineering Systems:

Case Study III: Wind Turbine and Wind Farm

Case Study IV: Smart EV Battery & Mobility

 

9

Introduction of Group Projects and Assignments

Project 1: Fault detection in semiconductor Etching

Project 2: Gearbox fault diagnosis and prognosis

Project 3: Bearing fault diagnosis and prognosis.

Project 4: Virtual metrology for critical semiconductor manufacturing processes

Project 5: Machine tool degradation prognosis and remaining useful life prediction.

Project 6: Aero-engine remaining useful life prediction

Project 7: EV Batteries

Project 8: Spacecraft propulsion system

 

 10

Group Project Presentation 1

 

11

Invited Guest Lecture

Group Project Presentation 2

 

12

Group Project Presentation 3

 

13

Invited Lecture

Group Project Presentation 4

 

 14

Final Group Project Presentation

 

 15

Final Report Due

 

 

 

 

Selected Books and Book Chapters (for the past 10 years):

1.    Book Chapter, Lee, J., Ardakani, H. D., Kao, H. A., Siegel, D., Rezvani, M., Chen, Y., Deployment of Prognostics Technologies and Tools for Asset Management: Platforms and Applications, Engineering Asset Review Series, Springer London, 2015.

2.    Book Chapter, Lee, J., Handbook of Industry 4.0, edited by Prof. Birgit Vogel Heuser (TUM, Germany), Integrated Industrial Big Data Analytics and Cyber Physical Systems for Industry 4.0 Design and Applications, Springer, 2016.

3.    Book Chapter, Lee, J., Jin, C., & Liu, Z. (2017). Predictive Big Data Analytics and Cyber Physical Systems for TES Systems. In Advances in Through-life Engineering Services (pp. 97-112). Springer International Publishing.

4.    Book Chapter, Lee, J., Jin, C., Liu, Z., & Ardakani, H. D., “Introduction to Data-Driven Methodologies for Prognostics and Health Management. In Probabilistic Prognostics and Health Management of Energy Systems (pp. 9-32). Springer International Publishing, 2017.

5.    Co-editor, special issue on CPS for Industry 4.0 to IEEE Trans on SMC: Systems, 2017.

6.    Guest Editorial Special Section on Smart Agents and Cyber-Physical Systems for Future Industrial Systems, Volume 13, Issue: 2, April 2017. 

7.    Guest Editor, Special Issue on "Artificial Intelligence for Cyber-Enabled Industrial Systems" Journal of Machines, 2018.

8.    Guest Editor, Data-Driven Cognitive Manufacturing - Applications in Predictive Maintenance and Zero Defect Manufacturing, Organized by: Dimitris Kiritsis, Melinda Hodkiewicz, Oscar Lazaro, Jay Lee, Journal of Frontiers in Computer Science - Mobile and Ubiquitous Computing, 2020. 

9.    Book Chapter, Industrial AI and Predictive Analytics for Smart Manufacturing Systems, Elsevier, 2020. 

10.    Book Chapter, Analyzing Data Obtained via Wind Farm Supervisory Control and Aata Acquisition,” Book on Utility-Scale Wind Turbines and Wind Farms, Institution of Engineering and Technology (IET), ISBN: 9781839530999, e-ISBN: 9781839531002, 2020.

11.    Lee, J., Book on “Industrial AI: Applications with Sustainable Performance”, Springer, ISBN 978-981-15-2143-0, 2020. 

12.    Book Chapter, Lee, J. etc. 'Cyber-Physical System Framework of AI in Manufacturing and Maintenance,” Book on “Artificial Intelligence in Manufacturing,” Academic Press, ISBN 978-0-323-99135-3, 2024. https://www.sciencedirect.com/book/9780323991353/artificial-intelligence...

13.    Editors, Adolfo Crespo Márquez, Turuna Seecharan, Georges Abdul-Nour, Joe Amadi-Echendu, Jay Lee, Volume 4, Case Studies in Digital Transformation, Integration of Digital Technologies to Enhance Asset Management Processes. Digital Transformation for Engineering Asset Management. EAMR book series for Springer https://www.springer.com/series/8663 https://doi.org/10.1007/978-3-032-05592-7 , 2026

14.    Book Chapter, Design of Industrial Artificial Intelligence Augmented Digital-Twin Systems, Digital Twins in Manufacturing: Concepts and Methods,” to be published by Elsevier in early 2026. 

15.    Book Chapter, Generative and Agentic AI Reliability: Architectures, Challenges, and Trust for Autonomous Systems, April 2026, ISBN 978-3-032-18584-6

Selected Journal Papers (for the past 10 years)

2015

1.    Birgit Vogel-Heuser, Leitao, P, Lee, J., Enabling Cyber-Physical Production Systems,  Automatisierungstechnik, 2015.

2.    Lee, J., Cyber-Controlled Energy and Transport Systems, Special Issue of Scientific American, Jan. 2015. 

3.    Kao, H.A., Jin, W.J., Siegel, D., Lee, J., A Cyber Physical Interface for Automation Systems - Methodology and Examples, Journal of Machines, 2015. 

4.    Lee, J., Smart Factory Systems, Journal of Informatik-Spektrum, Germany, Springer, May 2015.

5.    Lee, J., Bagheri, B., and Kao, H. A., A Cyber-Physical Systems Architecture for Industry 4.0-based Manufacturing Systems, Journal of Manufacturing Letters, 2015.

6.    Yang, Shanhu, Bagheri, B., Kao, H. A., Lee, J., A Unified Framework and Platform for Designing of Cloud-based Machine Health Monitoring and Manufacturing Systems, Journal of Manufacturing Science and Engineering, 2015.

7.    Lee, J., Ardakani H.A, Bagheri, B., Liu, Z.C., Jin, W.J., Recent Advances in Smart Industrial Internet and Big Data Analytics with Industrial Applications, Journal of Japan Industrial Management Association (JIMA), pp.77-83, vol. 25, No. 2, July 2015.

2016

8.    Inaki Bravo-imaz, Hossein Davari Ardakani, Zongchang Liu, Alfred García-Arribas, Aitor Arnaiz, and Jay Lee, Motor current signature analysis for gearbox condition monitoring under transient speeds using wavelet analysis and dual-level time synchronous averaging, Journal of Expert Systems with Applications, July 2016.

9.    Lee, Jay, Behrad Bagheri, and Chao Jin. "Introduction to Cyber Manufacturing." Journal of Manufacturing Letters 8, 11-15, 2016.

10.    Bravo-Imaz, I., Ardakani, H. D., Liu, Z., García-Arribas, A., Arnaiz, A., & Lee, J., Motor Current Signature Analysis for Gearbox Condition Monitoring under Transient Speeds using Wavelet Analysis and Dual-level Time Synchronous Averaging, Mechanical Systems and Signal Processing, 94, 73-84, July 2016.

2017

11.    Tong, X.; Davari Ardakani, Hossein; Siegel, D., Gamel, E., Lee, J., A Novel Methodology for Fault Detection of Multi-stage Manufacturing Processes using Product Quality Measurement, Int. Journal of Prognostics and Health Management, June 2017. 

12.    Jia, X., Zhao, M., Di, Y., Jin, C., & Lee, J., Investigation on the Kurtosis Filter and the Derivation of Convolutional Sparse Filter for Impulsive Signature Enhancement, Journal of Sound and Vibration, 386, 433-448, 2017.

13.    Jia, X., Zhao, M., Buzza, M., Di, Y., & Lee, J., A Geometrical Investigation on the Generalized lp/lq Norm for Blind Deconvolution, Signal Processing, 134, 63-69, 2017. 

14.    Lee, J., Jin, C., & Bagheri, B., Cyber Physical Systems for Predictive Production Systems, Production Engineering, 11(2), 155-165, 2017.

15.    Di, Y., Jia, X., & Lee, J., Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach, International Journal of Prognostics and Health Management, 031, 1-8, 2017.

2018

16.    Shi, Z., Liu, Z., & Lee, J., An Auto-associative Residual-based Approach for Railway Point System Fault Detection and Diagnosis, Measurement, 119, 246-258, 2018.

17.    Hossein Davari Ardakani, and Lee. J., "A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements," Machines 6.1, 2018. 

18.    Di, Y., Jin, C., Bagheri, B., Shi, Z., Ardakani, H. D., Tang, Z., & Lee, J., Fault Prediction of Power Electronics Modules and Systems Under Complex Working Conditions, Computers in Industry, 97, 1-9, 2018.

19.    Zhao, M., Jia, X., Lin, J., Lei, Y., & Lee, J., Instantaneous Speed Jitter Detection via Encoder Signal and its Application for the Diagnosis of Planetary Gearbox, Journal of Mechanical Systems and Signal Processing, 98, 16-31, 2018.

20.    Li, Pin, Jia, X, Feng, J.S., Davari, H., Qiao, G., Hwang Y.C., Lee, J., Prognosability Study of Ball Screw Degradation Using Systematic Methodology, Journal of Mechanical Systems and Signal Processing, Feb 2018.

21.    Jia, X., Zhao, M., Di, Y., Yang, Q., & Lee, J., Assessment of Data Suitability for Machine Prognosis using Maximum Mean Discrepancy, IEEE Transactions on Industrial Electronics, 65(7), 5872-5881, 2018. 

22.    Jia, X., Di, Y., Feng, J., Yang, Q., Dai, H., and Lee, J., "Adaptive Virtual Metrology for Semiconductor Chemical Mechanical Planarization Process using GMDH-type Polynomial Neural Networks," Journal of Process Control, vol. 62, pp. 44-54, 2018.

23.    Jia, X., Zhao, M., Di, Y., Li, P., and Lee, J., "Sparse Filtering with the Generalized lp/lq Norm and its Applications to the Condition monitoring of Rotating Machinery," Mechanical Systems and Signal Processing, vol. 102, pp. 198-213, 2018.

24.    Jia, X., Jin, C., Buzza, M., Di, Y., Siegel, D., and Lee, J., "A Deviation Based Assessment Methodology for Multiple Machine Health Patterns Classification and Fault Detection," Mechanical Systems and Signal Processing, vol. 99, pp. 244-261, 2018. 

25.    Jia, X., Zhao, M., Di, Y., Yang, Q., and Lee, J., "Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy," IEEE Transactions on Industrial Electronics, 2018. 

26.    Lee, J., Davari, H., Singh, J. and Pandhare, V., Industrial Artificial Intelligence for industry 4.0-based Manufacturing Systems, Manufacturing letters, 18, pp.20-23, 2018.

2019

27.    Li, P., Jia, X., Sumiya, M., Kamaji, Y., Ishiguro, M., Pahren, L. and Lee, J., A Novel Method for Deposit Accumulation Assessment in Dry Etching Chamber, IEEE Transactions on Semiconductor Manufacturing, 32(2), pp.183-189, 2019.

28.    Cai, H., Jia, X., Feng, J., Yang, Q., Hsu, Y.M., Chen, Y. and Lee, J., A Combined Filtering Strategy for Short Term and Long-Term Wind speed Prediction with Improved Accuracy, Journal of Renewable energy, 136, pp.1082-1090, 2019.

29.    Li, F., Ren, G. and Lee, J., Multi-step Wind Speed Prediction based on Turbulence Intensity and Hybrid Deep Neural Networks, Journal of Energy Conversion and Management, 186, pp.306-322, 2019.

30.    Lee, J., Singh, J. and Azamfar, M., “Industrial AI: is it manufacturing’s guiding light?” Journal of Manufacturing Leadership 2019:26–36, 2019.

31.    Lee, J., Azamfar, M. and Singh, J., A Blockchain enabled Cyber-Physical System Architecture for Industry 4.0 Manufacturing Systems, Manufacturing Letters, 20, pp.34-39, 2019.

32.    Pan, Y., Hong, R., Chen, J., Singh, J. and Jia, X., Performance Degradation Assessment of a Wind Turbine Gearbox based on Multi-sensor Data Fusion, Mechanism and Machine Theory, 137, pp.509-526, 2019.

33.    Feng, J., Jia, X., Zhu, F., Iskandar, J, Lee, J., An Online Virtual Metrology Model with Sample Selection for Tracking of Dynamic Manufacturing Processes with Slow Drift, IEEE Transactions on Semiconductor Manufacturing, Vol. 32, No. 4, Nov. 2019.

34.    Li, F., Ren, G., Lee, J., Multi-step Wind Speed Prediction based on Turbulence Intensity and Hybrid Deep Neural Networks, Journal of Energy Conversion and Management, Vol. 185, pp.306-322, 2019.

35.    Feng, J., Jia, X., Zhu, J., Yang, Q., Y., Pan, and Lee, J., An Intelligent System for Offshore Wind Farm Maintenance Scheduling Optimization Considering Turbine Production Loss, Journal of Intelligent & Fuzzy Systems, Vol. 37, pp.6911-6923, 2019.

2020

36.    Bagheri Behrad, Rezapoor Maryam, Lee, J., A Unified Data Security Framework for Federated Prognostics and Health Management in Smart Manufacturing, Manufacturing Letters, 2020.

37.    Yang, Q., Jia, X., Li, X., Feng, J., Li, W., Lee, J., Evaluating Feature Selection and Anomaly Detection Methods of Hard Drive Failure Prediction, IEEE Transaction on Reliability, March 2020.  

38.    Cai, Haoshu, Jia, X., Feng, J., Li, W., Hsu, YM., and Lee, J., Gaussian Process Regression for Numerical Wind Speed Prediction Enhancement, Journal of Renewable Energy, Vol. 146, pp.2112-213, 2020.

39.    Li, X., Jia, X., Yang, Q., Lee, J., Quality Analysis in Metal Additive Manufacturing with Deep Learning, Journal of Intelligent Manufacturing, 2020.

40.    Cai, H., Jia, X., Feng, J., Li, W., Pahren, L., Lee, J., A Similarity based Methodology for Machine Prognostics by Using Kernel Two Sample Test, ISA Transactions, 2020.

41.    Cai, H., Feng, J., Yang, Q., Li, W., Li, X., Lee, J., A Virtual Metrology Method with Prediction Uncertainty Based on Gaussian Process for Chemical Mechanical Planarization, Journal of Computers in Industry, 2020.

42.     Moslem, A., Li, Xiang, Lee, J., Intelligent Ball Screw Fault Diagnosis using a Deep Domain Adaptation Methodology, Journal of Mechanism and Machine Theory, Vol. 151, Sept. 2020.

43.    Dai, H., Jia, X., Pahren, L., Lee, J., & Foreman, B. Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: a narrative overview and conceptual data science framework, Frontiers in Neurology, 959, section Neurotrauma, 2020.

44.    Yang, QB., Li, X., Cai, HS., Hsu, YM., Lee, J., Chun Hung Yang, Zong Li Li, Ming Yi Lin, Fault Prognosis of Industrial Robots in Dynamic Working Regimes: Find Degradation in Variations, Journal of Measurement, 2020.

45.    Negri, E., Pandhare, V., Cattaneo, L., Singh, J., Macchi, M., Lee, J., Synchronized Digital Twin Framework for Production Scheduling with Uncertainty, Journal of Intelligent Manufacturing, DOI 10.1007/s10845-020-01685-9, 2020.

46.    Li, W., Jia, X., Li, X., Wang, YL., Lee, J., A Markov Model for Short Term Wind Speed Prediction by Integrating the Wind Acceleration Information, Journal of Renewable Energy, 2020.

47.    Cai, Haoshu, Jianshe Feng, Feng Zhu, Qibo Yang, Xiang Li, Jay Lee, Adaptive Virtual Metrology Method based on Just-in-time Reference and Particle Filter for Semiconductor Manufacturing, Journal of Measurement, 2020.

48.    Siahpour, S., Li, X., Lee, J., Deep Learning-based Cross-sensor Domain Adaptation for Fault Diagnosis of Electro-mechanical Actuators, International Journal of Dynamics and Control, 2020.

49.    Cai, Haoshu, Jianshe Feng, Wenzhe Li, Yuan-Ming Hsu, Jay Lee, Similarity-based Particle Filter for Remaining Useful Life Prediction with Enhanced Performance, Journal of Applied Soft Computing, 2020.

50.    Lee, J., Moslem Azamfar, Jaskaran Singh, Shahin Siahpour, Integration of Digital Twin and Deep Learning in Cyber-Physical Systems: Towards Smart Manufacturing, IET Collaborative Intelligent Manufacturing, 2020.

51.    Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J., Industrial Artificial Intelligence in Industry 4.0-Systematic Review, Challenges and Outlook, IEEE Access, 8, 220121-220139, 2020.

52.    Li X, Jia X, Wang Y, Yang S, Zhao H, Lee J. (2020). Industrial Remaining Useful Life Prediction by Partial Observation Using Deep Learning with Supervised Attention, IEEE Transactions on Mechatronics, 2020.

53.    Lee J, Li X, Xu Y, Yang S, Sun K., Recent Advances and Prospects in Industrial AI and Applications, ACTA Automatic Sinica, 2020.

54.    Pandhare, V., Li, X., Miller, M., Jia, X., & Lee, J., Intelligent Diagnostics for Ball Screw Fault Through Indirect Sensing Using Deep Domain Adaptation, IEEE Transactions on Instrumentation and Measurement, 70, 1-11, 2020.

55.     Lee, J., Ni, Jun, Jaskaran Singh, Baoyang Jiang, Moslem Azamfar, Jianshe Feng Intelligent Maintenance Systems and Predictive Manufacturing , J. Manuf. Sci. Eng.142(11): 110805, Nov. 2020.

2021

56.    Zhu, F., Jia, X., Miller, M., Li, X., Li, F., Wang, Y., Lee, J., Methodology for Important Sensor Screening for Fault Detection and Classification in Semiconductor Manufacturing," Transactions on Semiconductor Manufacturing, 2021. 

57.    Cai, H., Feng, J., Yang, Q., Li, F., Li, X., & Lee, J. Reference-based Virtual Metrology method with uncertainty evaluation for Material Removal Rate prediction based on Gaussian Process Regression, International Journal of Advanced Manufacturing Technology, 116(3), 1199-1211, 2021.

58.    Feng, J., Zhu, F., Li, P., Davari, H., & Lee, J., Development of An Integrated Framework for Cyber Physical System (CPS)-Enabled Rehabilitation System. International Journal of Prognostics and Health Management, 12(4), 2021.

59.    Cai, H., Jia, X., Feng, J., Yang, Q., Li, W., Li, F., & Lee, J., A Unified Bayesian Filtering Framework for Multi-horizon Wind Speed Prediction with Improved Accuracy, Renewable Energy, 2021.

60.    Lee, J., Azamfar, M., & Bagheri, B., A Unified Digital Twin Framework for Shop Floor Design in Industry 4.0 Manufacturing Systems, Manufacturing Letters, 27, 87-91, 2021.

61.     Li, W., Jia, X., Li, X., Wang, Y., & Lee, J., A Markov Model for Short Term Wind Speed Prediction by Integrating the Wind Acceleration Information, Renewable Energy, 164, 242-253, 2021.

62.    Negri, E., Pandhare, V., Cattaneo, L., Singh, J., Macchi, M., & Lee, J., Field-synchronized Digital Twin framework for Production Scheduling with Uncertainty, Journal of Intelligent Manufacturing, 32(4), 1207-1228, 2021.

2022

63.    Siahpour, S., Li, X., Lee, J., A Novel Transfer Learning Approach in Remaining Useful Life Prediction for Incomplete Dataset, J. of Mechanical Systems and Signal Processing, Ref:  MSSP21-1356, 2022.

64.    Ainapure, Abhijeet, A., Siahpour, S., Li, X., Majid, F., Lee, J., Intelligent Robust Cross-Domain Fault Diagnostic Method for Rotating Machines Using Noisy Condition Labels, Mathematics, 2022.

65.    Lee, J., Siahpour, S., Jia, X., Patrick. B., " Introduction to Resilient Manufacturing," Manufacturing Letters, 2022. 

66.    Hsu, Y.M., Jia, Xiaodong, Lee, J., et. al. "A Novel Methodology for Lens Matching in Compact Lens Module Assembly." IEEE Transactions on Automation Science and Engineering, 2022.

67.    Hsu, YM, Jia, X., Lee, J., et. al. " Sequential Optimization of the Injection Molding Gate Locations Using Parallel Efficient Global Optimization." International Journal of Advanced Manufacturing Technology, 2022. 

68.    Lu, C., Jia, X., Lee, J., and Shi, J., "Knowledge Transfer using Bayesian Learning for Predicting the Process-Property Relationship of Inconel Alloys Obtained by Laser Powder Bed Fusion," Virtual and Physical Prototyping, 1-19, 2022.

69.    Lee, J., Kundu, P., "Integrated Cyber-Physical System and Industrial Metaverse for Remote Manufacturing ,” Manufacturing Letters, accepted 2022.

70.    Lee, J., Gore, P., Jia, X., Siahpour S., Sun. Keyi " Novel Stream of Quality Methodology for Industrial Internet Based Production System,” Manufacturing Letters, accepted, 2022.

71.    Zhu, F., Jia, X., Lee, J.,et.al. “Cross-chamber Data Transferability Evaluation for Fault Detection and Classification in Semiconductor Manufacturing” IEEE Transactions on Semiconductor Manufacturing, under review, 2022.

72.    Kundu, P., Miller, M., Gore, P., Jia, X., Lee, J., Detection of Inception of Preload Loss and Remaining Life Prediction for Ball Screw Considering Change in Dynamics Due to Worktable Position, Mechanical Systems and Signal Processing, under review, 2022.

73.    Pandhare, V., Miller, M., Vogl, G., Lee, J., Ball Screw Health Monitoring with Inertial Sensors, Ball Screw Health Monitoring with Inertial Sensors, IEEE Transactions on Industrial Informatics, under review, 2022.

74.    Li, W., Jia, X., Kundu, P., Dong, H., Sumiya, M., Kamaji, Y., Lee, J., “A Fast Machine Calibration Methodology in High-Mix Semiconductor Manufacturing”, IEEE Transactions on Semiconductor Manufacturing, under review, 2022.

2023

75.    Kundu, Pradeep, Marcella Miller, Prayag Gore, Xiaodong Jia, and Jay Lee, "Detection of inception of preload loss and remaining life prediction for ball screw considering change in dynamics due to worktable position," Mechanical Systems and Signal Processing 189 (2023): 110075.

76.    Leitão, Paulo, Stamatis Karnouskos, Thomas I. Strasser, Xiaodong Jia, Jay Lee, and Armando Walter Colombo, "Alignment of the IEEE Industrial Agents Recommended Practice Standard with the Reference Architectures RAMI4. 0, IIRA, and SGAM." IEEE Open Journal of the Industrial Electronics Society 4 (2023): 98-111.

77.    Hsu, Yuan-Ming, Dai-Yan Ji, Marcella Miller, Xiaodong Jia, and Jay Lee, "Intelligent Maintenance of Electric Vehicle Battery Charging Systems and Networks: Challenges and Opportunities." International Journal of Prognostics and Health Management 14, no. 3 (2023).

78.    Lee, Jay, Huwei Dong, and D. J. Pradeep, "Cyber-physical systems framework for predictive metrology in semiconductor manufacturing process." International Journal of Precision Engineering and Manufacturing Smart Technology 1 (2023): 107-113.

79.    Zhu, Feng, Jianshe Feng, Min Xie, Lishuai Li, Jingzhe Lei, and Jay Lee, " An Optimization-based Subset Selection and Summarization Method for Profile Data Mining," IEEE Transactions on Industrial Informatics (2023).

80.    Pandhare, V., Miller, M., Vogel, W., Lee, J., Ball Screw Health Monitoring With Inertial Sensors, IEEE Transactions on Industrial Informatics, Volume: 19, Issue 6, (2023). 

2024

81.     An advanced diagnostic model for gearbox degradation prediction under various operating conditions and degradation levels H Su, J Lee Annual Conference of the PHM Society 16 (1)

82.    A novel technique for multiple failure modes classification based on deep forest algorithm, J Taco, P Kundu, J Lee, Journal of Intelligent Manufacturing 35 (7), 3115-3129

83.    A unified industrial large knowledge model framework in industry 4.0 and smart manufacturing, J Lee, H Su, International Journal of AI for Materials and Design 3681, 20

84.    Digital twin-enabled robust production scheduling for equipment in degraded state, V Pandhare, E Negri, L Ragazzini, L Cattaneo, M Macchi, J Lee, Journal of Manufacturing Systems 74, 841-857

85.    The Pharmacy 5.0 framework: A new paradigm to accelerate innovation for large-scale personalized pharmacy care, AC Lin, J Lee, MK Gabriel, RN Arbet, Y Ghawaa, AM Ferguson, American Journal of Health-System Pharmacy 81 (5), e141-e147

86.    Volumetric nondestructive metrology for 3D semiconductor packaging: A review, Y Su, J Shi, YM Hsu, DY Ji, AD Suer, J Lee, Measurement 225, 114065

87.    Cyber–physical systems framework for AI in smart manufacturing and maintenance, J Lee, W Li, YM Hsu, X Jia, Artificial Intelligence in Manufacturing, 233-272

88.    Data Analytics and Cyber-physical Systems for Smart Manufacturing and Maintenance, J Lee, W Li, YM Hu, XJ Jia, Artificial Intelligence in Manufacturing: Application and Case Studies.

2025

89.    Data issues in industrial AI systems: A meta-review and research strategy, X Li, Y Cheng, C Møller, J Lee, Computers in Industry 173, 104361

90.    Introducing machine learning: engineering, J Lee, Machine Learning: Engineering, Volume 1, Number 1 1

91.    Engineering artificial intelligence: framework, challenges, and future direction, TM Jay Lee, Hanqi Su, Dai-Yan Ji, Machine Learning: Engineering 1 (1)

92.    Rethinking industrial artificial intelligence: a unified foundation framework, J Lee, H Su, arXiv preprint arXiv:2504.01797

93.    Novel topological machine learning methodology for stream-of-quality modeling in smart manufacturing, J Lee, DY Ji, YM Hsu, Manufacturing Letters 43, 60-63.

94.    V-TimesNet: Vision-Augmented TimesNet for Improved Anomaly Detection in Semiconductor Plasma Dry Etching, Ruoxin Wang, Dai-yan Ji, Changlin Liu, and Jay Lee, SSRN, DOI: 10.2139/ssrn.5955781

95.    UniFault: A Fault Diagnosis Foundation Model from Bearing Data, E Eldele, M Ragab, X Qing, Edward, Z Chen, M Wu, X Li, J Lee, arXiv:2504.01373

 Refereed Papers in Conference Proceedings ( for the past 10 years)

1.    Bagheri, B, Siegel, D., Zhao, W., and Lee, J., "A Stochastic Asset Life Prediction Method for Large Fleet Datasets in Big Data Environment," Proceeding of ASME 2015 International Mechanical Engineering Congress and Exposition, pp. V014T06A010-V014T06A010, 2015.

2.    Chen, Y., Lucas, C., Lee, J., Buehner, M. Neural Network-Based Gear Failure Prediction in a Brushless DC Actuation System, Proceeding of Annual Conference of PHM 2015.

3.    Lee, J. and Bagheri, B., Cyber-Physical Systems in Future Maintenance, Proceeding of 9th WCEAM Conference, Springer International Publishing: 299-305, 2015.

4.    Jin. W, Siegel. D, Lee. J, “Degradation analysis of ball screw lubrication starvation using sensor-less and sensor-rich approaches,” Proceeding of Machinery Failure Prevention Technology (MFPT) Conference, 2015.

5.    Chao, J., Djurdjanovic, D., Hossein D. Ardakani, Wang, K., Buzza, M., Begheri, B., Brown, P., and Lee, J., "A Comprehensive Framework of Factory-to-Factory Dynamic Fleet-level Prognostics and Operation Management for Geographically Distributed Assets," Proceeding of IEEE International Conference on Automation Science and Engineering (CASE), pp. 225-230, 2015.

6.    Jin, Wenjing, Shi, Z., Siegel, D., Dersin, P.,  Douziech, C., Pugnaloni, M., Piero La Cascia, and Lee, J., "Development and Evaluation of Health Monitoring Techniques for Railway Point Machines," Proceeding of 2015 IEEE Conference on, pp. 1-11. IEEE, 2015.

7.    Lee, J., Kao, H.A., Industrial Big Aata Analytics and Cyber-Physical Systems for Future Maintenance & Service Innovation, Procedia CIRP, 2015.

8.    Lee, Jay, and Behrad Bagheri. "Cyber-Physical Systems in Future Maintenance," Proceeding of  9th WCEAM Conference, pp. 299-305. Springer International Publishing, 2015.

9.    Ardakani, H. D., Phillips, B., Bagheri. B., A New Scheme for Monitoring and Diagnosis of Multistage Manufacturing Processes Using Product Quality Measurements, Proceeding of Annual Conference of the PHM, 2015. 

10.    M Buzza, M Buehner, J Lee, “Fault Diagnosis of Root Causes in Complex Systems using Bayesian Belief Networks,” Proceedings of the Machinery Failure Prevention Technology MFPT) Conference, 2016.

11.    Shi, Z., Lee, J., Peng C., “Prognostics and Health Management Solution Development in LabVIEW: Watchdog Agent® Toolkit and Case Studies, ” Proceeding of Annual Conference of the Prognostics and Health Management, 2016. 

12.    Lee, J., Liu, Z., & ARDAKANI, H. D., Recent Advances in Industrial Big Data Analytics and Cyber-Physical Systems for High-speed Railway Transportation Systems, Proceeding of 1st International Workshop on Structural Health Monitoring for Railway System, 2016.

13.    Pahren, L., Foreman, B., Hossein Davari Ardakani, Lee, J., “Self-Organizing Map for Modeling Intracranial Pressure with EEG for Traumatic Brain Injury,” Proceeding of Annual Conference of the Prognostics and Health Management, 2016. 

14.    Di, Y., Jia, X., Lee, J., Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach, Proceeding of PHM Conference, 2016.

15.    Huang, B., Di, Y., Jin, C., & Lee, J., Review of Data-Driven Prognostics and Health Management Techniques: Lessons Learned from PHM Data Challenge Competition, Proceeding of Machinery Failure Prevention Technology (MFPT) Conference,  2017. 

16.    Di, Y., Jia, X., Moyne, J., Iskandar, J., Hao, H., Schulz, B., Armacost, M., Lee, J., Advanced Segmentation and Pattern Recognition for Sensor Data in Semiconductor Manufacturing, Proceeding of Advanced Process Control Conference XXIX, 2017.

17.     Ravindranathan, S., Ardakani, H. D., Pimental, A., Lee, J., Care, N., & Clark, J. F., Performance Monitoring of Soccer Players using Physiological Signals and Predictive Analytics, Proceeding of Machinery Failure Prevention Technology (MFPT) Conference, Virginia Beach, VA, 2017.

18.    Feng, J., Jia, X., Moyne, J., Iskandar, J., Hao, K., Subrahmanyam, K., Armacost, M., Korobkov, A., Lee, J., Pattern-based Trace Segmentation and Feature Extraction for Semiconductor Manufacturing and Application to Fault Detection, Proceeding of Advanced Process Control 30th, Austin, TX, US, Oct 8-11,2018. 

19.    Jia, X., Huang, B., Feng, J., Cai, H. and Lee, J., A Review of PHM Data Competitions from 2008 to 2017, Proceedings of the Annual Conference of the PHM Society, Vol. 10, No. 1, Sept. 2018.

20.    Pin, L., Feng,J.,  Zhu, F., and Lee, J., A Deep Learning-Based Method for Cutting Parameter Optimization for Band Saw Machine, Proceeding of Annual Conference of the PHM Society 2019.

21.    Feng, J., Du, X., Salman, M., Wheel Bearing Fault Isolation and Prognosis using Acoustic based Approach, Proceeding of Annual Conference of the Prognostics and Health Management 2019, Sept 24-26, 2019, Tempe AZ.

22.    Pandhare, V., Singh, J. and Lee, J., Convolutional Neural Network Based Rolling-Element Bearing Fault Diagnosis for Naturally Occurring and Progressing Defects Using Time-Frequency Domain Features, Proceeding of Prognostics and System Health Management Conference (PHM-Paris) (pp. 320-326), May 2019.

23.    Azamfar, M., Jia, X., Pandhare, V.,Davari, H.,Singh, J., Lee, J., Detection and Diagnosis of Bottle Capping Failures based on Motor Current, Proceeding of 47th SME North American Manufacturing Research Conference, NAMRC 47, Pennsylvania, 2019.

24.    Negri, E., Davari, H.,Cattaneo, L.,Singh, J., Macchi, M., Lee, J., A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms. Proceeding of 13th IFAC Workshop on Intelligent Manufacturing Systems Oshawa, Ontario, Canada, 12-14 August 2019.

25.    Yang, Q., Singh, J., & Lee, J., Isolation-Based Feature Selection for Unsupervised Outlier Detection, Proceedings of the Annual Conference of the PHM Society, 2019.

26.    Jia, X., Cai, H., Hsu, YM, Li, W., Feng, J., Lee, J., A Novel Similarity-based Method for Remaining Useful Life Prediction Using Kernel Two Sample Test, Proceedings of the Annual Conference of the PHM Society, 2019. 

27.    Jia, X., Duan, S., Lee, C., Radecki, P., Lee, J., A Methodology for the Early Diagnosis of Vehicle Torque Converter Clutch Degradation, Proceeding of 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 2019.

28.    Cai, H., Feng, J., Iskandar, J., Moyne, J., Armacost, M., and Lee, J., Pattern-based Trace Data Generator for Fault Detection of Fabrication Processes, Proceeding of Advanced Process Control 31st, 2019.

29.    Li, X., Siahpour, S., Lee, J., Wang, Y., Shi, J., Deep Learning-Based Intelligent Process Monitoring of Directed Energy Deposition in Additive Manufacturing with Thermal Images, Proceeding of 48th SME North American Manufacturing Research Conference, NAMRC 48, 2020.

30.    Yang, S., Li, X., Jia, X., Wang, Y., Zhao, H., Lee, J., Deep Learning-Based Intelligent Defect Detection of Cutting Wheels with Industrial Images in Manufacturing, Proceeding of 48th SME North American Manufacturing Research Conference, NAMRC 48, 2020.

31.    Yang, Q., Li, X., Wang, Y., Ainapure, A., Lee, J., Fault Diagnosis of Ball Screw in Industrial Robots Using Non-Stationary Motor Current Signals, Proceeding of 48th SME North American Manufacturing Research Conference, NAMRC 48, 2020.  

32.    Ainapure, A., Li, X., Singh, J.,Yang, Q.,  and Lee, J., Deep Learning-Based Cross-Machine Health Identification Method for Vacuum Pumps with Domain Adaptation, Proceeding of 48th SME North American Manufacturing Research Conference, 2020.

33.    Ainapure, A., Li, X., Singh, J., Yang, Q., Lee, J., Enhancing Intelligent Cross-Domain Fault Diagnosis Performance on Rotating Machines with Noisy Health Labels, Proceeding of 48th SME North American Manufacturing Research Conference, NAMRC 48, 2020. 

34.    Feng, J.S., Lee, J., A Framework for Adaptive Multivariate Limits Setting and Visualization in The Semi-automated Pattern-based Feature Extraction FD System, Proceeding of Advanced Process Control, 2020.

35.    Lee, Jay, Jun Ni, Singh, J., Intelligent Maintenance Systems and Predictive Manufacturing, Proceeding of 48th SME North American Manufacturing Research Conference, NAMRC 48, 2020.  

36.    Wang, Y, Jia X, Li X, Yang S, Zhao H, Lee J., A Machine Vision Based Monitoring System for the LCD Panel Cutting Wheel Degradation Assessment, Proceeding of Manufacturing Processes Research at NAMRC 48, 2020.

37.    Cai, H., Feng, J., Moyne, J., Lee, J., et al., “A Framework for Semi-Automated Fault Detection Configuration with Automated Feature Extraction and Limits Setting”, Proceeding of 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), 1-6, 2020.

38.    Siahpour, S., Ainapure, A., Li, X., and Lee, J., A Deep Learning Framework for Health Anomaly Detection of Multi-component Systems in Evolving Environments: A Case Study in PHM, Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, 2020.

39.    Jia, X., Li, W., Wang, W., W., Li, X., Lee, J., Development of Multivariate Failure Threshold with Quantifiable Operation Risks in Machine Prognostics, Annual Conference of the PHM Society 2020.

40.    Pahren, L., Lee, J., Data Augmentation for Quality Inspection in Product Manufacturing, submitted to IFAC AMEST 2022.

41.    Taco, J., Gore, P., Minami, T., Kundu, P., Suer, A., Lee, J., A Novel Methodology for Health Assessment in Printed Circuit Boards. PHM Society European Conference, 7(1), 566–562, 2022.

42.    Hsu, YM, Lee, J., Novel Wafer Map Image Analysis using Virtual Metrology, ASME MSEC, under review, 2022.

43.    John Taco, Brandon Foreman, Pradeep Kundu, and Jay Lee, “A Novel Technique for Data Quality Evaluation in Human Health PHM,” PHM Conference, under review, 2022. 

44.    Han, Xu, Xiaodong Jia, Dai-Yan Ji, and Jay Lee. "Designing Robust Topological Features for Wafer Map Pattern Classification." In 2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), pp. 1-6. IEEE, 2023.

45.    Takanobu Minami, Alexander Suer, Pradeep Kundu, Shahin Siahpour and Jay Lee, “Novel Ensemble Domain Adaptation Methodology for Enhanced Multi-class Fault Diagnosis of Highly-Connected Fleet of Assets, International Conference on Prognostics and Health Management Asian Pacific, Tokyo, Japan, Sept. 11-14, 2023.

46.    An advanced diagnostic model for gearbox degradation prediction under various operating conditions and degradation levels H Su, J Lee Annual Conference of the PHM Society 16 (1), 2024.

47.    PHM for Spacecraft Propulsion Systems: Developing Resilient Models for Real-World Challenges, T Minami, DY Ji, J Lee, PHM Society European Conference 8 (1), 7-7, 2024.

48.    Improving Machine Calibration Performance through Systematic Feature Design in Semiconductor Manufacturing, DY Ji, M Sumiya, Y Kamaji, S Matsukura, W Li, J Lee, 36th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), 2025.

 

 

UMD prominently showcased at ARPA-E summit

16 booths and 3 tech demos highlighted

Eminent Scholar in Metallurgy To Join Clark School as Distinguished Chair

The new faculty member will join Materials Science and Engineering in 2025.

  • American Society of Mechanical Engineers (ASME), Society of Manufacturing Engineers (SME), Prognostics and Health Management (PHM) Society, International Society of Engineering Asset Management (ISEAM)


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