Dr. Jia (Peter) Liu
Industrial and Systems Engineering
Phone: (334) 844-1428
Address: 3301G Shelby Center, Auburn, AL 36849, USA
CV: Link below
I am from China. I have studied and worked on different engineering disciplines for more than 20 years, including electrical engineering, chemical engineering, energy, manufacturing, which lead to the research I am working on using AI/machine learning to solve all various engineering problems from a system point of view.
- Ph.D. in Industrial and Systems Engineering, Virginia Tech, USA
- M.S. in Statistics, Virginia Tech, USA
- M.S. in Electrical Engineering, Zhejiang University, China
- B.S. in Electrical Engineering, Zhejiang University, China
- Research Interests:
- Artificial intelligence and machine learning
- Applied statistics and Design of experiments
- Smart manufacturing and industry 4.0
- Data-driven process monitoring and quality control for advanced manufacturing
- Process optimization and quality modeling for additive manufacturing
- Major Publications:
- A Poudel, MS Yasin, J Ye, J Liu, A Vinel, S Shao, N Shamsaei. “Feature-based volumetric defect classification in metal additive manufacturing.” Nature Communications 13.1 (2022): 1-12.
- Liu, J., Ye, J., Momin, F., Zhang, X., & Li, A. (2022). Nonparametric bayesian framework for material and process optimization with nanocomposite fused filament fabrication. Additive Manufacturing, 54, 102765.
- Li, A., Baig, S., Liu, J., Shao, S., & Shamsaei, N. (2022). Defect Criticality Analysis on Fatigue Life of L-PBF 17-4 PH Stainless Steel via Machine Learning. International Journal of Fatigue, 107018.
- Liu, J., Ye, J., Silva, D., Vinel, A., Shamsaei, N., Shao, S. (2022), “A Review of Machine Learning for Process and Performance Optimization in Laser Beam Powder Bed Fusion Additive Manufacturing”, Journal of Intelligent Manufacturing
- Liu, J., Zheng, J., Rao. P., Kong, Z. (2020), “Machine Learning driven In-situ Process Monitoring with Vibration Frequency Spectra for Chemical Mechanical Planarization”, International Journal of Advanced Manufacturing Technology, 111(7-8), 1873-1888.
- Liu, J., Liu, C., Bai, Y., Rao. P., Williams, C., Kong, Z. (2019), “Layer-wise Spatial Modeling of Porosity in Additive Manufacturing”, IISE Transactions, Vol. 51, No. 2, pp. 109-123. Featured in ISE Magazine, January 2019 issue.
- Liu, J., Jin, R., Kong, Z. (2018), “Wafer Quality Monitoring using Spatial Dirichlet Process based Mixed-effect Profile Modeling Scheme”, Journal of Manufacturing Systems, Vol. 48, pp. 21-32.
- Liu, J., Beyca, O., Rao, P., Kong, Z., Bukkapatnam, S. (2017), “Dirichlet Process Gaussian Mixture Models for Real-Time Monitoring and Their Application to Chemical Mechanical Planarization”, IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 1, pp. 208-221.
- Rao, R., Liu, J., Roberson, D., Kong, Z. (2015), “Online Real-time Quality Monitoring in Additive Manufacturing Processes using Heterogeneous Sensors”, ASME Trans Journal of Manufacturing Science and Engineering, Vol. 137, No. 6, pp. 1007-1 – 1007-12.
- Rao, P., Liu, J., Roberson, D., Kong, Z., Williams, C. (2015), “Sensor-based Online Process Fault Detection in Additive Manufacturing”, Proceedings of the ASME 2015 10th International Manufacturing Science and Engineering Conference, Charlotte, NC, Jun 8-12, 2015.
- Synergistic Activities:
- Society Service: Co–PI Liu serves in the Industrial Relation Committee of Quality, Statistics, and Reliability (QSR) at INFORMS and session chairs in the INFORMS annual meeting. He is also an active reviewer for several high-impact journals in IEEE, IISE, and ASTM.
- Mentoring Students: Co-PI Liu continuously establishes prestigious research programs to mentor graduate and undergraduate students in performing research. He also leverages the Auburn Undergraduate Research program to involve passionate undergraduate students in his research projects.
- Teaching Activities: Co-PI Liu has developed and taught an undergraduate/graduate course entitled “Data analytics for Operations”, aiming to educate students to use machine learning and data analytics in manufacturing operations and product quality control.