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Augmented Intelligence for Smart Manufacturing (AISM) Lab

Scientific, technical, and societal advances are increasingly dependent on new insights, theories, and tools to exploit data effectively for the timely delivery of relevant and accurate information and for knowledge discovery. For the purpose of effective and efficient learning from the data to improve operational safety, manufacturing efficiency, energy efficiency, and sustainability in manufacturing, our team aims to explore machine learning (ML) and artificial intelligence (AI) for improved information extraction, pattern recognition, and decision making towards smart, data science-enhanced manufacturing.

Our team targets developing applicable and generalizable ML and AI techniques suitable for manufacturing data analytics that is featured by the large volume, high dimensionality, heterogeneity, non-linearity, and uncertainty. Current research thrusts include:

  • Integrating ML models (e.g., structures, training loss) with domain knowledge to improve model credibility and generalizability;
  • Self-supervised learning from big, unlabeled plant data and unsupervised continual ML model updates from continuous data streaming for machine and process monitoring on the shop floor;
  • In-situ process monitoring (i.e., defect detection and quality prediction) and real-time control of additive manufacturing processes;
  • Robotic automation of welding processes, by endowing robots with advanced perception, incremental learning, and critical thinking;
  • Development of cost-effective edge devices, communication protocols, semantic indexing for advanced data management, processing, and learning for building digitalized manufacturing factories;
  • Digital thread in life cycle analysis for improved product design and supply chain management.
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Projects

National Science Foundation, “CAREER: Transforming Machine Learning Models Developed in Labs to Manufacturing Plants” (CMMI-2237242), PI, $567,930, 05/01/2023-04/30/2028.

National Science Foundation, “Understanding Manufacturing Process Dynamics and Machine Tool Anomaly Detection Through Process Sensing and Machine Learning” (CMMI-2015889), PI (with Co-PI Dr. I.S. Jawahir), $444,405, 08/01/2020-07/31/2024.

National Science Foundation, “NRI: FND: Intelligent Co-robots for Complex Welding Manufacturing through Learning and Generalization of Welders Capabilities” (CMMI-2024614), Co-PI (with PI Dr. Yuming Zhang), $665,540 (Wang’s share: $317,355), 08/01/2020-07/31/2024.

National Institutes of Health, “Non-destructive optical spectroscopic assay for high-throughput metabolic characterization of in vitro cell models and patient-derived organoids”, Co-PI, (with PI Dr. Caigang Zhu), $600,437, 07/07/2022-03/31/2025.

UK Sustainable Grant, “UK-DiPP: Development of UK Digital iPad Product Passport for iPad Initiative Sustainability Improvement”, Co-PI (with PI Dr. Fazleena Badurdeen), $45,000, 07/01/2023-06/31/2024.

UK Department of Mechanical Engineering, Allan and Ginger Brown Foundation, “Module-Level Aircraft Engine Performance Degradation Tracking and Prediction”, PI, $10,368, 01/05/2021-12/31/2021.

UK Igniting Research Collaboration (IRC), “AI-Powered Augmented Reality Intubation Training for First Responders”, Co-PI (with PI Dr. Daniel Lau), $36,322, 12/01/2020-06/30/2021.

UK Energy Research Prioritization Partnership (ERPP) Grant Program, "Stochastic Modeling of Lithium-ion Battery Aging for Predictive Maintenance and Advanced Health Management", PI, $39,181, 05/01/2020-05/31/2021.

General Motors, “Modeling of Signals from Laser Welding ff Prismatic Battery Cell”, PI, $30,000, 12/01/2023-3/31/2024.

Publications

  • P. Wang, J. Karigiannis, R. Gao, “Ontology-Integrated Tuning of Lange Language Model for Intelligent Maintenance”, CIRP Annals, 2024. 
  • L. Nadeesha and P. Wang, “Efficient Stochastic Parametric Estimation for Lithium-Ion Battery Performance Degradation Tracking and Prognosis”, Journal of Manufacturing Processes, 2024.
  • J. Kershaw, H. Ghassemi-Armaki, B. Carlson, and P. Wang, “Advanced process characterization and machine learning-based correlations between interdiffusion layer and expulsion in spot welding”, Journal of Manufacturing Processes, Vol. 109, pp.222-234, 2024.
  • M. Russell and P. Wang, “Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning”, Journal of Manufacturing Systems, Vol. 71, pp. 274-285, 2023.
  • M. Russell, P. Wang, S. Liu, and I.S. Jawahir, “Mixed-Up Experience Replay for Adaptive Online Condition Monitoring”, IEEE Transactions on Industrial Electronics, Vol. 27, no. 2, pp. 1979-1986, 2023.
  • M. Russell, J. Kershaw, Y. Xia, T. Lv, Y. Li, H. Ghassemi-Armaki, B. Calson, P. Wang, “Comparison and Explanation of Data-Driven Modeling for Weld Quality Prediction in Resistance Spot Welding”, Journal of Intelligent Manufacturing, pp. 1-15, 2023.
  • R. Yu, J. Kershaw, P. Wang, and Y. Zhang, “How to Accurately Monitor the Weld Penetration from Dynamic Weld Pool Serial Images Using CNN-LSTM Deep Learning Model?”, IEEE Robotics and Automation Letters, Vol. 7, no. 3, pp. 6519-6525, 2022.
  • P. Wang, J. Kershaw, M. Russell, J. Zhang, Y. Zhang, and R. Gao, “Data-Driven Process Characterization and Adaptive Control in Robotic Arc Welding”, CIRP Annals, Vol. 71, no. 1, pp. 45-48, 2022.
  • M. Russell and P. Wang, “Physics-Informed Deep Learning for Signal Compression and Reconstruction of Big Data in Industrial Condition Monitoring”, Mechanical Systems and Signal Processing, Vol. 168, pp. 108709, 2022.
  • P. Wang, Y. Yang, and N. Moghaddam, “Process Modeling in Laser Powder Bed Fusion Towards Defect Detection and Quality Control via Machine Learning: The State-of-the-Art and Research Challenges”, Journal of Manufacturing Processes, Vol. 73, pp. 961-984, 2022.
  • R. Yu, J. Kershaw, P. Wang, and Y. Zhang, “Real-Time Recognition of Arc Weld Pool using Image Segmentation Network”, Journal of Manufacturing Processes, Vol. 72, pp. 159-167, 2021.
  • J. Kershaw, R. Yu, Y. Zhang, and P. Wang, “Hybrid Machine Learning-Enabled Adaptive Welding Speed Control”, Journal of Manufacturing Processes, Vol. 71, pp. 374-383, 2021.
  • J. Zhang, P. Wang, and R. Gao, “Hybrid Machine Learning for Human Action Recognition and Prediction in Assembly”, Robotics and Computer-Integrated Manufacturing, Vol. 72, pp. 102184, 2021.
  • M. Russell, E. King, C. Parrish, and P. Wang, “Stochastic Modeling for Tracking and Prediction of Gradual and Transient Battery Performance Degradation”, Journal of Manufacturing Systems, Vol. 59, pp. 663-674, 2021.
  • P. Wang, R. Gao, and W. Woyczynski, “Lévy Process-Based Stochastic Modeling for Machine Performance Degradation Prognosis”, IEEE Transactions on Industrial Electronics, Vol. 68, No. 12, pp. 12760 – 12770, 2021.
  • Q. Wang, W. Jiao, P. Wang, and Y. Zhang, “Digital Twin for Human-robot Interactive Welding and Welder Behavior Analysis”, IEEE/CAA Journal of Automatica Sinica, Vol. 8, No. 2, pp. 334-343, 2021.
  • Q. Wang, W. Jiao, P. Wang, and Y. Zhang, “A Tutorial on Deep Learning-Based Data Analytics in Manufacturing through A Welding Case Study”, Journal of Manufacturing Processes, Vol. 63, pp. 2-13, 2021.
  • P. Hou, B. Zhao, O. Jolliet, J. Zhu, P. Wang, and M. Xu, “Rapid Prediction of Chemical Ecotoxicity Through Genetic Algorithm Optimized Neural Network Models”, ACS Sustainable Chemistry & Engineering, Vol. 8, No. 32, pp. 12168-12176, 2020. 
  • P. Wang and R. Gao, “Transfer Learning for Enhanced Machine Fault Diagnosis in Manufacturing”, CIRP Annals-Manufacturing Technology, Vol. 69, No. 1, pp. 413-416, 2020. 
  • Q. Xiong, J. Zhang, P. Wang, D. Liu, and R. Gao, “Transferable two-stream convolutional neural network for human action recognition”, Journal of Manufacturing Systems, Vol. 56, pp. 605-614, 2020.
  • J. Grezmak, J. Zhang, P. Wang, K. Loparo, and R. Gao, “Interpretable Convolutional Neural Network through Layerwise Relevance Propagation for Machine Fault Diagnosis”, IEEE Sensors, Vol. 20, No. 6, pp. 3172-3181, 2019.
  • S. Shao, R. Yan, Y. Lu, P. Wang, and R. Gao, “DCNN-based Multi-signal Induction Motor Fault Diagnosis”, IEEE Transactions on Instrument and Measurement, Vol. 69, No. 6, pp. 2658-2669, 2019. 
  • P. Wang, Z. Liu, R. Gao, and Y. Guo, “Heterogeneous Data-Driven Hybrid Machine Learning for Tool Condition Monitoring”, CIRP Annals-Manufacturing Technology, Vol. 68, No. 1, pp. 455-458, 2019.
  • D. Zhao, W. Cheng, R. Gao, R. Yan, and P. Wang, “Generalized Vold-Kalman Filtering for Compound Faults Detection of Bearing and Gearbox Under Nonstationary Condition”, IEEE Transactions on Instrument and Measurement, Vol. 26, pp. 1213-1220, 2019.
  • J. Zhang, P. Wang, and R. Gao, “Deep Learning-Based Tensile Strength Prediction in Fused Deposition Modeling”, Computers in Industry, Vol. 107, pp. 11-21, 2019.
  • R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. Gao, “Deep Learning and Its Applications to Machine Health Monitoring”, Mechanical Systems and Signal Processing, Vol. 115, pp.213-237, 2019.
  • C. Sun, P. Wang, R. Yan, R. Gao, and X. Chen, “Machine Health Monitoring based on Locally Linear Embedding with Sparse Representation for Neighborhood Optimization”, Mechanical Systems and Signal Processing, Vol. 114, pp. 25-34, 2019.
  • J. Kershaw, H. Ghasssemi-Armaki, B. Calson, and P. Wang, “Virtual Sensing by Dense Encoder for Process Signals in Resistance Spot Welding”, International Symposium on Flexible Automation, Seattle, WA, July, 2024.
  • X. Zhao and P. Wang, “A Deployable Edge Computing Solution For Machine Condition Monitoring”, IEEE International Instrument and Measurement Technology Conference, UK, May 2024.
  • J. Ko, P. Adoba, A. Deaton, P. Wang, and F. Badurdeen, “Developing an Effective Digital Product Passport for Circular Economy: A Framework and Case Study”, Proc. 19th Global Conference on Sustainable Manufacturing, Buenos Aires, Argentina, December, 2023.
  • M. Russell and P. Wang, “Normalizing Flow for Intelligent Manufacturing”, Manufacturing Science and Engineering Conference (MSEC 2023), New Brunswick, NJ, June, 2023.
  • S. Ibn Mohsin, B. Farhang, P. Wang, Y. Yang, N. Shayesteh, and F. Badurdeen, “Deep Learning based Automatic Defect Detection of Laser Powder Bed Fusion Additive Manufacturing”, 32nd International Conference on Flexible Automation, and Intelligent Manufacturing, Porto, Portugal, June, 2023.
  • S. Ippili, M. Russell, P. Wang, and D. Herrin, “Deep Learning Based Mechanical Fault Detection and Diagnosis of Electric Motors Using Directional Characteristics of Acoustic Signals”, Noise-Con 2023, Grand Rapids, MI, May 2023.
  • Y. Wang and P. Wang, “Explainable machine learning for motor fault diagnosis”, IEEE International Instrument and Measurement Technology Conference, Malaysia, May, 2023.
  • H. Zhou, T. Wu, P. Wang, and J. Engel, “A Simple Four-Pole Solution with FEM/BEM Validation to Estimate the Effectiveness of Compact Resonators in Large Silencers”, NOISE-Conf, Lexington, KY, June, 2022.
  • H. Zannoun, J. Kene, T. Asensio, G. Guedes, F. Badurdeen, P. Wang, and I.S. Jawahir, “Leveraging Italian Design Principles for Creating Sustainable Products”, Proc. 18th Global Conference on Sustainable Manufacturing, GCSM, Berlin, Germany, October, 2022.
  • R. Yu, J. Kershaw, P. Wang, and Y. Zhang, “How to Accurately Monitor the Weld Penetration From Dynamic Weld Pool Serial Images using CNN-LSTM Deep Learning Model?”, IEEE 18th International Conference on Automation Science and Engineering (CASE 2022), Mexico City, Mexico, August, 2022.
  • R. Yu, J. Kershaw, P. Wang, and Y. Zhang, “Monitoring of Backside Weld Bead Width from High Dynamic Range Images Using CNN Network”, International Conference on Control, Decision and Information Technologies, Istanbul, Turkey, May 2022.
  • M. Russell and P. Wang, “Improved Representations for Continual Learning of Novel Motor Health Conditions through Few-Shot Prototypical Networks”, IEEE 18th International Conference on Automation Science and Engineering (CASE 2022), Mexico City, Mexico, August, 2022.
  • P. Wang, J. Kershaw, M. Russell, Y. Xia, T. Lv, Y. Li, H. Ghassemi-Armaki, B. Carlson, “Interpretable Data-Driven Prediction of Resistance Spot Weld Quality”, International Symposium on Flexible Automation, Japan, July, 2022.
  • M. Russell and P. Wang, “Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction”, Annual Conference of Prognostics and Health Management Society, paper# 1137, November, 2020.
  • M. Russell and P. Wang, “Transferable Deep Learning for In-Situ Tool Wear Diagnosis”, Proc. ASME 2020 International Symposium on Flexible Automation, July, 2020.
  • J. Zhang, P. Wang, and R. Gao, “Attention Mechanism-Incorporated Deep Learning for AM Part Quality Prediction”, Procedia Manufacturing (Proc. 53rd CIRP Conference on Manufacturing Systems), July 2020.
  • C. Wang, X. Zhang, X. Chen, R. Yan, and P. Wang, “Weak Chatter Detection in Milling based on Sparse Dictionary”, Procedia Manufacturing (Proc. 48th North American Manufacturing Research Conference, NAMRC), Vol. 48, pp. 839-843, 2020.
  • C. Cooper, J. Zhang, R. Gao, P. Wang, and I. Ragai, " Anomaly Detection in Milling Tools using Acoustic Signals and Generative Adversarial Networks", Procedia Manufacturing (Proc. 48th North American Manufacturing Research Conference, NAMRC), Vol. 48, pp. 372-378, 2020.
  • J. Grezmak, J. Zhang, P. Wang, K. Loparo, and R. Gao, “Multi-Stream Convolutional Neural Network-Based Fault Diagnosis for Variable Frequency Drives in Sustainable Manufacturing Systems”, Procedia Manufacturing (Proc. 17th Global Conference on Sustainable Manufacturing, GCSM), Vol. 43, pp. 511-518, 2020.
  • P. Wang and R. Gao, “Prognostic Modeling of Performance Degradation and Capacity Regeneration Phenomena in Lithium-ion Battery”, Procedia Manufacturing (Proc. 47th North American Manufacturing Research Conference, NAMRC), Vol. 34, pp. 911-920, 2019.
  • J. Grezmak, P. Wang, and R. Gao, “Explainable Deep Convolutional Neural Network for Rotary Machine Fault Diagnosis in Sustainable Manufacturing”, Proc. 26th CIRP Life Cycle Engineering (LCE) Conference, West Lafayette, IN, USA, May, 2019.