Skills possessed by human welders typically require a long time to develop. Especially, maintaining the torch to travel in desired speed is challenging. In this paper, a feedback control system is designed to assist the welder to adjust the torch movement for the desired speed in manual gas tungsten arc welding process. To this end, an innovative helmet based manual welding platform is proposed and developed. In this system, vibrators are installed on the helmet to generate vibration sounds to instruct the welder to speed or slow down the torch movement. The torch movement is monitored by a leap motion sensor. The torch speed is used as the feedback for the control algorithm to determine how to change the vibrations. To design the control algorithm, dynamic experiments are conducted to correlate the arm movement (torch speed) to the vibration control signal. Linear model is firstly identified using standard least squares method, and the model is analyzed. A nonlinear adaptive neuro-fuzzy inference system (ANFIS) model is then proposed to improve the model accuracy. The resultant nonlinear ANFIS model can estimate the welder’s response on the welding speed. Based on the response model, a PID control algorithm has been designed and implemented to control the welder arm movement for desired torch speed. Experiments verified the effectiveness of the system for the desired speed with acceptable accuracy.
|Number of pages||10|
|Journal||Journal of Intelligent Manufacturing|
|State||Published - Aug 1 2017|
Bibliographical noteFunding Information:
Acknowledgements This research is funded in part by the National Science Foundation (CMMI-0927707), National Natural Science Foundation of China (51375023) and the China Scholarship Council (201306540014).
© 2015, Springer Science+Business Media New York.
- Human welder
- Machine–human system
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence