Augmented Intelligence for Smart Manufacturing (AISM) Lab

Publication

Book Chapter

  1. R.X. Gao and P. Wang, “Sensors to Control Processing and Improve Lifetime and Performance for Sustainable Manufacturing”, in Encyclopedia of Sustainable Technologies (ed. Martin Abraham), Elsevier, 2016.
  2. R. Gao, P. Wang, and R. Yan, “Machine Tool Prognosis for Precision Manufacturing”, in Precision Manufacturing: Metrology (ed. Wei Gao), ch. 8, Springer Nature, 2019.
  3. R. Gao, R. Yan, and P. Wang, “Advanced Data Analytics for Health Monitoring and Prognostics in Manufacturing”, in Smart Manufacturing, World Scientific, 2020. In press.
  4. R. Gao, P. Wang, and J. Zhang, “Human Motion Tracking, Recognition, and Prediction for Robot Control”, in Advanced Human-Robot Collaboration in Manufacturing, pp. 261-282, Springer, Cham, 2021.

Journal articles

  1. 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. Link
  2. 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. Link
  3. 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. Link
  4. 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. Link
  5. 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. Link
  6. 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, 2021. In press. Link
  7. P. Wang, R. Gao, and W. Woyczynski, “Lévy Process-Based Stochastic Modeling for Machine Performance Degradation Prognosis”, IEEE Transactions on Industrial Electronics, 2021. In press. Link
  8. 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. 1334-343, 2021. Link
  9. 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. Link
  10. 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.  Link
  11. 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, 2020. Link
  12. 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. Link
  13. 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. Link
  14. 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. Link
  15. 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. Link
  16. 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. Link
  17. 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. Link
  18. 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, January, 2019. Link
  19. 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. Link
  20. J. Zhang, P. Wang, R. Yan, and R. Gao, “Long Short Time Memory for Machine Remaining Life Prediction”, SME Journal of Manufacturing Systems, Vol. 48, pp. 78-86, 2018. Link
  21. J. Zhang, P. Wang, R. Gao, C. Sun, and R. Yan, “Induction Motor Condition Monitoring for Sustainable Manufacturing”, Procedia Manufacturing, Vol. 33, pp. 802-809, 2018. Link
  22. D. Zhao, J. Li, W. Cheng, P. Wang, R. Gao and R. Yan, “Vold-Kalman generalized demodulation for multi-fault detection of gear and bearing under variable speed”, Vol. 26, pp. 1213-1220, 2018. Link
  23. J. Zhang, P. Wang, and R. Gao, “Modeling of Layer-Wise Additive Manufacturing for Part Quality Prediction”, Procedia Manufacturing, Vol. 16, pp. 155-162, 2018. Link
  24. P. Wang, H. Liu, L. Wang, and R. Gao, “Deep Learning-based Human Motion Recognition for Context-Aware Human Robot Collaboration”, CIRP Annals-Manufacturing Technology, Vol. 67, No. 1, pp. 17-20, July, 2018. Link
  25. J. Zhang, P. Wang, R. Gao, and R. Yan, “An Image Processing Approach to Machine Fault Diagnosis Based on Visual Words Representation”, Procedia Manufacturing, Vol. 19, pp. 42-49, 2018. Link
  26. S. Shao, W. Sun, R. Yan, P. Wang, and R. Gao, “A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing”, Chinese Journal of Mechanical Engineering, Vol. 30, No. 6, pp. 1347-1356, November, 2017. Link
  27. P. Wang, Z. Fan, D. Kazmer, and R. Gao, “Orthogonal Analysis of Multi-Sensor Data Fusion for Improved Quality Control”, ASME Journal of Manufacturing Science and Engineering, Vol. 139, No. 10, pp. 101008, August, 2017. Link
  28. P. Wang, R. Gao, and R. Yan, “A Deep Learning-Based Approach to Material Removal Rate Prediction in Polishing”, CIRP Annals-Manufacturing Technology, Vol. 66, No. 1, pp. 429-432, April, 2017. Link
  29. J. Wang, Y. Zheng, P. Wang, and R. Gao, “A Virtual Sensing based Augmented Particle Filtering for Tool Condition Prognosis”, SME Journal of Manufacturing Processes, Vol. 28, No. 3, pp. 472-478, April, 2017. Link
  30. P. Wang, Ananya, R. Yan, and R. Gao, “Virtualization and Deep Recognition for System Fault Classification”, SME Journal of Manufacturing Systems, Vol. 44, No. 2, pp. 310-316, April, 2017. Link
  31. P. Wang and R. Gao, “Automated Performance Tracking for Heat Exchangers in HVAC”, IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 2, pp. 634-645, March, 2017. Link
  32. P. Wang and R. Gao, “Markov Nonlinear System Estimation for Engine Performance Tracking”, ASME Journal  Engineering for Gas Turbine and Power, Vol. 138, No. 9, pp. 091201, March, 2016. Link
  33. P. Wang, R. Gao, and Z. Fan, “Cloud Computing for Manufacturing: Benefits and Limitations”, ASME Journal of Manufacturing Science and Engineering, Vol. 137, No. 4, pp. 040901, July, 2015. Link
  34. P. Wang and R. Gao, “Adaptive Resampling-Based Particle Filtering For Tool Life Prediction”, SME Journal of Manufacturing Systems, Vol. 37, No. 2, pp. 528-534, April, 2015. Link
  35. J. Wang, P. Wang, and R. Gao, “Enhanced Particle Filter for Tool Wear Prediction”, SME Journal of Manufacturing Systems, Vol. 36, pp. 35-45, April, 2015. Link
  36. P. Wang, D. Karg, R. Gao, Z. Fan, K. Kwolek, and A. Consiglio, “Non-Contact Identification of Rotating Blade Vibration”, Mechanical Engineering Journal, Japan Society of Mechanical Engineering, Vol. 2, No. 3, pp. 1-12, March, 2015.  Link

Conference Publications

  1. M. Russell and P. Wang, “Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction”, Proc. 2020 Prognostics and Health Management (PHM) Society Annual Conference, Nashville, October, 2020.
  2. Q. Wang, P. Wang, R. Yu, and Y. Zhang, "The Role Robot Plays in Virtual Reality Human-Robot Interactive", 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE 2020), Hong Kong, August 20-24, 2020.
  3. M. Russell and P. Wang, “Transferable Deep Learning For In-Situ Tool Wear Diagnosis”, Proc. ASME 2020 International Symposium on Flexible Automation, July, 2020.
  4. C. Wang, X. Zhang, X. Chen, R. Yan, and P. Wang, “Weak Chatter Detection in Milling based on Sparse Dictionary”, Proc. 2019 North American Manufacturing Research Conference, Cincinnati, June, 2020.
  5. C. Cooper, J. Zhang, R. Gao, P. Wang, and I. Ragai, "Novel anomaly detection in milling tool condition monitoring using acoustic signals and single-class generative adversarial networks", Proc. 2019 North American Manufacturing Research Conference, Cincinnati, June, 2020.
  6. M. Russell and P. Wang, “Transferable Deep Learning For In-Situ Tool Wear Diagnosis”, Proc. ASME 2020 International Symposium on Flexible Automation, July, 2020.
  7. Q. Xiong, J. Zhang, P. Wang, and R. Gao, "Transferable Two-stream Convolutional Neural Network in Human-robot Collaboration", Proc. 2020 North American Manufacturing Research Conference, Cincinnati, June, 2020.
  8. C. Cooper, J. Zhang, R. Gao, P. Wang, and I. Ragai, "Novel anomaly detection in milling tool condition monitoring using acoustic signals and single-class generative adversarial networks", Proc. 2019 North American Manufacturing Research Conference, Cincinnati, June, 2020.
  9. P. Wang and R. Gao, “Prognostic Modeling of Performance Degradation and Capacity Regeneration Phenomena in Lithium-ion Battery”, Proc. 2019 North American Manufacturing Research Conference, Eire, PA, USA, June, 2019
  10. 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.
  11. P. Wang, and R. Gao, “Lévy Process-Based Stochastic Modeling for Machine Performance Degradation Prognosis”, Proc. 44th Annual Conference of the IEEE Industrial Electronics Society, Washington DC, USA, October, 2018.
  12. P. Wang, R. Yan, and R. Gao, “Multi-Mode Particle Filtering for Rolling Bearing Remaining Life Prediction”, Proc. 2018 ASME Manufacturing Science and Engineering Conference, paper #: MSEC 2018-6638, College Station, TX, USA, June, 2018.
  13. J. Zhang, P. Wang, Y. Yan, and R. Gao, “Deep Learning for Improved System Remaining Life Prediction”, Procedia CIRP (Proc. CIRP Conference on Manufacturing Systems), Vol. 72, pp. 1033-1038, 2018.
  14. J. Zhang, P. Wang, Y. Yan, and R. Gao, “Induction Motor Fault Diagnosis and Classification Through Sparse Representation”, Proc. 2017 ASME Dynamic Systems and Control Conference, DSCC2017-5259, Tysons, Virginia, USA, October, 2017.
  15. P. Wang and R. Gao, “Through Life Analysis for Machine Tools: from Design to Remanufacture”, Procedia CIRP (Proc. CIRP Conference on Through-Life Engineering Services), Vol. 59, pp. 2-7, 2017.
  16. S. Shao, W. Sun, P. Wang, R. Gao, and R. Yan, “Learning Features from Vibration Signals for Induction Motor Fault Diagnosis”, Proc. 2016 International Symposium on Flexible Automation, Cleveland, OH, USA, August, 2016.
  17. C. Sun, P. Wang, R. Yan, and R. Gao, “A Sparse Approach to Fault Severity Classification for Gearbox Monitoring”, Proc. 19th IEEE International Conference on Information Fusion, Heidelberg, Germany, July, 2016.
  18. P. Wang and R. Gao, “Online Fault Detection and Diagnosis for Chiller System”, Proc. 12th IEEE International Conference on Automation Science and Engineering, Fort Worth, Texas, USA, August, 2016.
  19. Z. Fan, R. Gao, P. Wang, and D. Kazmer, “Multi-sensor Data Fusion for Improved Measurement Accuracy in Injection Molding”, Proc. 2016 IEEE International Instrumentation and Measurement Technology Conference, Taipei, Taiwan, May, 2016.
  20. D. Wu, P. Wang, R. Yan, and R. Gao, “A Correlation-based Approach to Trustworthy Sensing for Cyber-Physical Systems”, Proc. 2016 IEEE International Instrumentation and Measurement Technology Conference, Taipei, Taiwan, May, 2016.
  21. P. Wang and R. Gao, “Stochastic Tool Wear Prediction for Sustainable Manufacturing”, Procedia CIRP (Proc. CIRP Conference on Life Cycle Engineering), Vol. 48, pp. 236-241, 2016.
  22. P. Wang, R. Gao, D. Wu, and J. Terpenny, “A Computational Framework for Cloud-Based Prognosis”, Procedia CIRP (Proc. CIRP Conference on Manufacturing Systems), Vol. 57, pp. 309-314, 2016.
  23. P. Wang, R. Gao, Z. Fan, and X. Tang, “Trustworthy Sensing for Product Quality”, Proc.48th CIRP Conference on Manufacturing Systems, paper #: PROCIR-D-15-00232, Naples, Italy, June, 2015.
  24. P. Wang, X. Tang, and R. Gao, “Automated Performance Tracking for Heat Exchanger in HVAC”, Proc. 11th IEEE International Conference on Automation Science and Engineering, pp. 949-954, Gothenburg, Sweden, August, 2015.
  25. P. Wang, R. Gao, and Z. Fan, “Switching Local Search Particle Filter for Heat Exchanger Degradation Prognosis”, Proc. 2015 IEEE International Instrumentation and Measurement Technology Conference, pp. 539-544, Pisa, Italy, May, 2015.
  26. P. Wang and R. Gao, “Particle Filtering-Based System Degradation Prediction Applied to Jet Engines”, Proc. 2014 Annual Conference of the Prognosis and Health Management Society, paper  #067, Fort Worth, TX, USA, September, 2014.
  27. P. Wang, R. Gao, Z. Fan, D. Karg, K. Kwolek, and A. Consiglio, “Noninterference identification of rotating blade vibration”, Proc. 12th International Conference on Motion and Vibration, paper #10205, Sapporo, Japan, August, 2014.
  28. J. Wang, P. Wang, and R. Gao, “Tool Life Prediction for Sustainable Manufacturing”, Proc. 11th Global Conference on Sustainable Manufacturing, pp. 230-234, Berlin, Germany, September, 2013.
  29. P. Wang, R. Gao, H. Wang, and H. Yuan, “Defect Growth Prediction in Rolling Bearing based on Approximate Entropy”, Proc. 2013 ASME Dynamic Systems and Control Conference, paper # V002T24A004, Stanford University, CA, USA, October, 2013.