Zimo Wang
Assistant Professor
School of Systems Science and Industrial Engineering
Background
Zimo Wang obtained his PhD in 2020 in industrial engineering from Texas A&M University and he also holds an MS degree in mechatronics engineering and a BS in industrial engineering, both from Harbin Institute of Technology in China. Wang conducted research projects in broad aspects of smart manufacturing with strengths in sensors and AI for additive manufacturing and precision manufacturing processes. His research focuses on bridging sensor techniques, manufacturing processes and data science to create smart sensing approaches, develop machine learning approaches and integrate them in the cyber-physical platform to allow in-process characterizations of materials, diagnosis/prognosis of the processes to realize smart manufacturing processes and autonomous systems. Other current research interests include smart logistics on the shop floor using unmanned aerial vehicles (UAV), sustainable material characterizations and manufacturing as well as statistical modeling towards analyzing chaotic dynamic systems. Wang is a member of IISE, ASME and INFORMS, and he is also an amateur guitar player with interest in acoustic signal processing.Google ScholarEducation
- PhD in Industrial Engineering, Texas A&M University
- MS in Mechatronics Engineering, Harbin Institute of Technology, China
- BS in Industrial Engineering, Harbin Institute of Technology, China
Research Interests
- Statistical/machine learning
- Smart manufacturing
- Advanced manufacturing processes
- Material characterization
- Diagnosis and quality assurance
Awards
- National Science Foundation (NSF) Travel Awards (NAMRC 43, Charlotte, NC, May 2015; NAMRC 46, College Station, TX, May 2018)
- Best Student Paper Award of Quality Control and Reliability May 2014 - Engineering (QCRE) division, Industrial and Systems / Engineering Research Conference (ISERC), Montreal, Canada
- NFORMS Student Chapter Cum Laude Award (as the President of INFORMS student chapter at Texas A&M University). In recognition of outstanding participation and performance, acknowledged by INFORMS organization at INFORMS Annual Meeting Houston, TX, 2017.
More Info
- Z. Wang, P. Dixit, F. Chegdani, B. Takabi, B. Tai, M. El Mansori, S.T.S. Bukkapatnam. Bidirectional Gated Recurrent Neural Networks (GRNNs) based smart acoustic emission (AE) sensing of natural fiber reinforced plastic (NFRP) composite machining process (In press, Journal of ASTM, Smart and Sustainable Manufacturing Systems).
- Z. Wang, F. Chegdani, N. Yalamarti, B. Takabi, B. Tai, M. El Mansori, S.T.S. Bukkapatnam. Acoustic Emission (AE) characterization of natural fiber reinforced plastic (NFRP) composite machining using a random forest machine learning model, ASME Journal of Manufacturing Science and Engineering, 2020, 142(3): 031003.
- F. Chegdani, Z. Wang, M. El Mansori, S.T.S. Bukkapatnam. Multiscale tribo-mechanical analysis of natural fiber composites for manufacturing applications. Tribology International, 2018, 122: 143-150.
- Z. Wang and S.T.S. Bukkapatnam. A Dirichlet process Gaussian state machine model for change detection in transient processes. Technometrics, 2018, 60 (3): 373-385.
- Z. Wang , V. Kovvuri, A. Araujo, M. Bacci, W.N.P. Hung, S.T.S. Bukkapatnam. Built-up-edge effects on surface deterioration in micromilling processes. SME Journal of Manufacturing Processes, 2016, 24: 321-327.
- Z. Wang, S.T.S. Bukkapatnam, S.R.T. Kumara, Z. Kong, Z. Katz. Change detection in precision manufacturing processes under transient conditions. CIRP Annals- Manufacturing Technology, 2014, 63 (1): 449-452.
