Wenjin Tao Ph.D.
Smart Manufacturing | AI-Driven Innovation | Industry 4.0 | Cyber-Physical Systems | Global Product Leadership

LinkedIn / Google Scholar / GitHub

Biography

Dr. Wenjin Tao is the Technical Product Lead at Foxconn iAI, spearheading initiatives in advanced Industry 4.0 and AI technologies. He leads the development and deployment of proof-of-concept solutions, drives global implementation strategies, and consistently delivers measurable improvements in operational efficiency.

He received his PhD degree in the Innovative Smart & Additive Manufacturing (ISAM) Laboratory at Missouri University of Science and Technology, advised by Prof. Ming C. Leu. During his Ph.D. studies, his research focused on smart manufacturing systems enhanced by deep learning and artificial intelligence. His areas of interest include smart manufacturing, Cyber-Physical Systems (CPS), Additive Manufacturing (AM), and design optimization.

Before he joined Missouri S&T, he received his Master and Bachelor degrees in Mechanical Engineering at Beijing Inistitute of Technology.

He is a recipient of 2018 IISE DAIS Track Best Paper Award. Beyond his professional interests, he is a lifelong learner and a hands-on DIY enthusiast, with a passion for emerging technologies—from flying cars to humanoid robots and beyond.

Interests

  • Robotics
  • Machine Learning and Deep Learning for Human-Centered Intelligent Manufacturing
  • Time-Series Signal/Image Recognition
  • Time-Frequency/Spatial-Temporal Data Analysis
  • Artificial Intelligence

Education

  • Ph.D. in Mechanical Engineering
    Missouri University of Science and Technology
  • M.S. in Mechanical Engineering
    Beijing Institute of Technology
  • B.S. in Mechanical Engineering
    Beijing Institute of Technology

R&D Projects

Field Robotics

This innovative robotic platform is designed to make outdoor work easier, cleaner, and more cost-effective. Powered by self-driving technology and zero-emission energy, it helps reduce labor and fuel expenses while shrinking the carbon footprint. With support for a wide range of tool attachments—like lawn mowers, weed controllers, snow plows, and salt spreaders—it’s built to handle field tasks all year round.

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Low-Cost Ventilator Prototype

This project was launched in response to the COVID-19 pandemic to design an affordable and easy-to-build ventilator. The goal was to support public health efforts, showcase our team’s capabilities, and lay the groundwork for future AI-powered features. The ventilator is built with cost-effective, widely available standard parts and includes custom components that can be 3D printed. It’s designed to be simple to assemble, making it accessible for rapid deployment when needed.

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Mask Defect Detection and Sorting System

This automated system uses computer vision and machine learning to check face masks for defects in real time. It quickly spots issues and sorts the masks into pass or fail categories, helping manufacturers speed up quality checks while keeping accurate records for production tracking.

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Pin Defect Detection System

This system uses deep learning to quickly spot bent or damaged pins in electronic components—sometimes among thousands at a time. It automatically highlights problem areas and sends alerts to the quality control team, helping catch defects early. The system also keeps detailed records, making it easy to track quality throughout the manufacturing process.

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FOLO: Human Operation Instruction, Digitization and Optimization

FOLO provides on-site smart work instructions for operators to follow. Engineers can create SOPs in a process builder by drag and drop. It records critical production data, and is able to connect external hardware.

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OPTIMO: AI Powered Software that Digitizes Human Operations for Performance Measurements and Optimization

Optimo implements an end-to-end industrial vision system that streams and stores IP camera feeds, enabling real-time monitoring, time-motion analysis, and AI model development. The modular platform includes a dashboard for visualization and analytics to optimize industrial operations.

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Worker Activity Recognition Using IMU and sEMG Signals with Convolutional Neural Networks

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In a smart manufacturing system involving workers, recognition of the worker’s activity can be used for quantification and evaluation of the worker’s performance, as well as to provide onsite instructions with augmented reality. In this paper, we propose a method for activity recognition using Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals obtained from a Myo armband.

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This research work is supported by the National Science Foundation grant CMMI-1646162 and also by the Intelligent Systems Center at Missouri University of Science and Technology.

Multi-View Recognition of Complex Hand Gesture

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  • Considering the challenges of the ASL alphabet recognition task, we choose CNN as the basic model to build the classifier because of its powerful learning ability that has been shown
  • To fully exploit the 3D information provided by depth images, we develop a novel multi-view augmentation strategy. It generates more views from different perspectives, in order to augment the perspective variations that cannot be achieved using traditional image augmentation methods
  • To solve the interclass similarity issues caused by perspective variations and partial occlusions, we first make predictions for all individual views and then fuse information from them for the final prediction
  • Design and develop a real-time demo of American Sign Language (ASL) alphabet recognition [demo]
  • Establish and Publish a dataset of ASL alphabet [dataset]

American Sign Language Alphabet Recognition Using Leap Motion Controller

Design of Lattice Structure for Additive Manufacturing

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Additive Manufacturing (AM) technology provides new opportunities to automatically and flexibly fabricate parts with complicated shapes and architectures that could not be produced by conventional manufacturing processes, thus enabling unprecedented design flexibilities and application opportunities.

The lattice structure possesses many superior properties to solid material and conventional structures. It is able to integrate more than one function into a physical part, which makes it attractive to a wide range of applications. With AM technology the lattice structure can be fabricated by adding material layer-by-layer directly from a Computer-Aided Design (CAD) model, rather than the conventional processes with complicated procedures.

AM lattice structures have been intensively studied for more than ten years with significant progress having been made. This paper reviews and discusses AM processes, design methods and considerations, mechanical behavior, and applications for lattice structures enabled by this emerging technology.

Design of Lattice Structure for Additive Manufacturing

Design of Lattice Structure for Additive Manufacturing

Other Projects

I think getting hands dirty is the best way to learn. Here lists some interesting projects I’ve done.

Robotics

Self-Driving Car

Semantic Segmentation

Semantic Segmentation

Semantic Segmentation

Semantic Segmentation

Semantic Segmentation

Semantic Segmentation

Deep Learning

Flying Car

Artificial Intelligence

Publications

Journal Papers

  1. Tao, W., Lai, Z. H., Leu, M. C., Yin, Z., & Qin, R. (2019). A Self-Aware and Active-Guiding Training System for Worker-Centered Intelligent Manufacturing. Manufacturing Letters. (Accepted)
  2. Tao, W., Leu, M. C., & Yin, Z. (2019). Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing. (Submitted to the Journal of Manufacturing Systems)
  3. Tao, W., Moniruzzaman, M., Leu, M. C., Yin, Z., & Qin, R. (2019). Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals. (Submitted to the journal of Information Fusion)
  4. Lai, Z. H., Tao, W.*, Leu, M. C., & Yin, Z. (2019). Smart Augmented Reality Instructional System for Mechanical Assembly Towards Worker-Centered Intelligent Manufacturing. (Submitted to the Journal of Manufacturing Systems)
  5. Tao, W., Leu, M.C. and Yin, Z., 2018. American Sign Language alphabet recognition using Convolutional Neural Networks with multiview augmentation and inference fusion. Engineering Applications of Artificial Intelligence, 76, pp.202-213. Preprint Cite
  6. Shen, J., Li, G., Yan, W., Tao, W., Xu, G., Diao, D., & Green, P. (2018). Nighttime driving safety improvement via image enhancement for driver face detection. IEEE Access, 6, 45625-45634.
  7. Wei, C., Chen, G., Luan, Z., Tao, W.* (2016). Optimization on the Hydrodynamic Groove Geometry of Rotary Seals for Automotive Transmissions. Transactions of Beijing Institute of Technology 36 (1), 25-30.
  8. Qiao, J., Tao, W., & Sun, B. (2012). Design for the Braking System of a FSAE Racing Car [J]. Chinese Journal of Automotive Engineering, 2.
  9. Wei, W., Tao, W., & Yan, Q. (2010). Exploratory discussion on virtual laboratory teaching information platform system based on visual simulation technique. Experimental Technology and Management, 27(3), 78-81.

Peer-Reviewed Conference Papers

  1. Al-Amin, M., Tao, W., Doell, D., Lingard, R., Yin, Z., Leu, M. C., & Qin, R. (2019, August). Action Recognition in Manufacturing Assembly using Multimodal Sensor Fusion. 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, August 9–14, 2019 Chicago, Illinois, USA.
  2. Tao, W., Lai, Z.H., Leu, M.C. and Yin, Z., 2018. Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks. Procedia Manufacturing, 26, pp.1159-1166. Preprint Project Cite
  3. Tao, W., Lai, Z.H., Leu, M.C. and Yin, Z., 2018. American Sign Language Alphabet Recognition Using Leap Motion Controller (IISE Annual 2018, Data Analytics & Information Systems Division Best Track Paper Award) Preprint Project
  4. Wu, S., Tao, W.*, Leu, M. C., & Long, S. (2018). Engine Sound Simulation and Generation in Driving Simulator. In Proceedings of the 2018 Institute of Industrial and Systems Engineers Annual Conference (IISE 2018).
  5. Al-Amin, M., Qin, R., Tao, W., & Leu, M. C. (2018, January). Sensor Data Based Models for Workforce Management in Smart Manufacturing. In Proceedings of the 2018 Institute of Industrial and Systems Engineers Annual Conference (IISE 2018).
  6. Hu, L., Nguyen, N.T., Tao, W.*, Leu, M.C., Liu, X.F., Shahriar, M.R. and Al Sunny, S.N., 2018. Modeling of cloud-based digital twins for smart manufacturing with MT connect. Procedia Manufacturing, 26, pp.1193-1203.
  7. Tao, W., Liu, Y., Sutton, A., Kolan K. and Leu, M.C., 2018. Design of Lattice Structures with Graded Density Fabricated by Additive Manufacturing. Preprint
  8. Tao, W., & Leu, M. C. (2016, August). Design of lattice structure for additive manufacturing. In 2016 International Symposium on Flexible Automation (ISFA) (pp. 325-332). IEEE.

Books & Book Chapters

  1. NX 12 for Engineering Design. Leu, M. C., Tao, W.*, Ghazanfari, A., & Kolan, K. (2017). Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology. eBook
  2. Manufacturing Assembly Simulations in Virtual and Augmented Reality. Tao, W., Lai, Z. H., & Leu, M. C. (2018). Augmented, Virtual, and Mixed Reality Applications in Advanced Manufacturing. Accepted Draft
  3. Virtual Bone Surgery. Leu, M. C., Tao, W., Niu, Q., & Chi, X. (2018). In Virtual Prototyping & Bio Manufacturing in Medical Applications (2nd Edition). Springer, Boston, MA under review

Patents

  1. A Novel Method for Analyzing the Wear of Rotary Seal with Micro-textured Contacting Surface. Wei, C., Zhao, Y., Hu, J., Yuan, S., & Tao, W. (2015). CN104679990A
  2. A Novel Optimization Method for Designing of Rotary Seal in Automotive Transmission. Wei, C., Hu, J., Tao, W., & Chen, G. (2014). CN103955581A
  3. A Novel Rotary Seal with Wavy Contacting Surface for Automotive Transmissions. Hu, J., Tao, W., Yuan, S., & Wei, C. (2012). CN102797854A
  4. A Self-adaptive Deformable Wing for Racing Cars. Xiang, C., Xu, B., Tao, W., & Lou, R. (2011). CN102248968B

Google Scholar for a full list.

Teaching & Mentoring Experience

I am a teaching instructor for the following courses at Missouri University of Science and Technology

  • Fall 2018: ME 5763: Computer Aided Design: Theory and Practice / Course Site / Book / YouTube Channel / Vimeo
  • Fall 2017: ME 5763: Principles And Practice Of Computer Aided Design
  • Fall 2016: ME 5763: Principles And Practice Of Computer Aided Design

Invited Talk

  • Human Behavior Understanding for Worker-Centered Intelligent Manufacturing. Xi’an University of Technology, Dec. 2018

Reviews

Active Reviewer for

Publons Profile

Honors & Awards

  • Data Analytics & Information Systems Division Best Track Paper Award, IISE Annual 2018
  • NSF Travel Grant, IISE Annual conference, 2018
  • NSF Travel Grant, ISFA conference, 2016
  • Award in the Innovation Cup of Science Popularization, 2012
  • Outstanding Student Leader in the School of Mechanical Engineering at BIT, 2012
  • The Final Champion in 2011 Formula Student Competition China, 2011
  • Qualification Certificate of Automotive Specialized Technique by SAE of China, 2011
  • The Final Champion in 2010 Formula Student Competition China, 2010
  • Beijing Outstanding Graduate, 2010
  • The First Prize of FAST Scholarship, 2010
  • The DEC Scholarship, 2010
  • National Scholarship, 2009
  • The University Award for 6 times at BIT, 2006-2010

Service Activities

Miscellaneous