Nguyen, V. B., Teo, A., Ba, T., Aramcharoen, A., Ahluwalia, K., Tran, S. B. Q., & Kang, C. W. (2021). Advanced model-based controller for cyber-physical shot peening process. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-021-07009-2
Abstract:
This study reports new development of a practical model-based controller (namely model predictive control, MPC) for shot peening, which is the first application to such a process for fully automated operation. In particular, the feedback MPC is developed based on a process model that links inlet air pressure of the machine to air pressure at a peening nozzle. In addition, a proxy model is developed to link measurement variable(s) to peening intensity as the intensity cannot be measured online for real-time feedback control. During process control, the process model is used to simulate future dynamics of the peening process to guide the controller for optimal control action, while the proxy model translates the setting intensity to air pressure reference set-point for real-time tracking. Both the controller and model development rely on the physical machine’s constraints and capabilities. The pressure sensors and sensor locations are carefully selected to ensure controllability and observability. Single input/single output feedback MPC with future process pre-view capability is developed and integrated into the actual shot peening machine. The MPC has been demonstrated and validated using both in-silico and onsite controls for different scenarios. The obtained results show that the developed MPC is stable, robust, and accurate as it can automatically adjust inlet air pressure to attain the desired intensity. Finally, MPC can also help to reduce up to 25% of production cost by eliminating the cost, time, materials waste, and labor in performing experimental trials to build the saturation curve for actual operational guidance.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the IAF-PP SOC - IAF-PP
Grant Reference no. : A1894a0032
Description:
This is a post-peer-review, pre-copyedit version of an article published in The International Journal of Advanced Manufacturing Technology. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00170-021-07009-2