The Welding Research Laboratory at the University of Kentucky is focused on system identification, adaptive and predictive control, neurofuzzy modeling and control, machine vision systems, and various sensors with applications to welding/manufacturing automation, especially to the development of innovative electrical arc welding processes with improved productivity/controllability. The Welding Research Laboratory projects include advanced sensing, control, learning/modeling, and intelligent robotic systems with application to real-time monitoring and control of innovative welding processes, human welder modeling, human welder centered control, human-robot collaborative systems, and intelligent welding robots as gained from efforts supported by the NSF, Navy, National Labs and industry with a total funding of $7.5 M.
Current research thrusts of the Welding Research Lab are focused primarily on:
- Learning and Modeling of Human Welder Response Behaviors for Intelligent Welding Robots
- Deep Learning of Reflection Pattern due to Weld Pool Oscillation for Intelligent Monitoring and Control of Weld Joint Penetration
- Learning of Complex Human Welder Intelligence from Big Data Generated from Augmented and Virtual Reality Welding Systems
- Analytic Weld Pool Model Calibrated by Measurements with Application to Real-Time Monitoring and Control of Invisible Weld Pool underneath the Work-piece
The Research interest :
Automation of industrial processes; Welding processes; Numerical modeling of welding processes; Adaptive control systems, robust control theory and systems, dynamic process identification; Machine vision, signal processing.
Director: Dr. Yuming Zhang