Author:
Slimani Abdesselem,Kherraf Allaoua,Zidani Kamel
Abstract
The implementation of condition monitoring tools can improve plant availability and lower downtime costs in general. A reliable adaptive control system can prevent machine downtime or undesired situations such as chatter vibration and excessive tool wear, permitting the best utilization of a tool's life. This study used dynamic force analysis to create an adaptive dynamic control system for Computer Numerical Control (CNC) milling to adjust a controlled system for signals from offline measurements that will be processed and supplied back to the machine tool controller to correct cutting parameters. This paper describes a better adaptive control system for peripheral milling with helical end mills based on a dynamic cutting force model. This theoretical model is based on the oblique cutting principle and takes into account the effects of the size of undeformed chip thickness and the effective rake angle. Simulation results showed that the enhanced dynamic cutting-force model accurately predicted cutting forces in peripheral milling.
Publisher
Engineering, Technology & Applied Science Research
Reference17 articles.
1. J. W. Sutherland and R. E. DeVor, "An Improved Method for Cutting Force and Surface Error Prediction in Flexible End Milling Systems," Journal of Engineering for Industry, vol. 108, no. 4, pp. 269–279, Nov. 1986.
2. S. Smith and J. Tlusty, "An Overview of Modeling and Simulation of the Milling Process," Journal of Engineering for Industry, vol. 113, no. 2, pp. 169–175, May 1991.
3. B. S. Prasad, M. M. M. Sarcar, and B. S. Ben, "Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach," The International Journal of Advanced Manufacturing Technology, vol. 51, no. 1, pp. 57–67, Nov. 2010.
4. B. S. Prasad, M. M. M. Sarcar, and B. S. Ben, "Real-time tool condition monitoring of face milling using acousto-optic emission â an experimental approach," International Journal of Computer Applications in Technology, vol. 41, no. 3/4, 2011, Art. no. 317.
5. F. Cus, U. Zuperl, E. Kiker, and M. MIlfelner, "Adaptive controller design for feedrate maximization of machining process," Journal of Achievements in Materials and Manufacturing Engineering, vol. 17, no. 1, 2006.