Embedded fuzzy-control system for machining processes: Results of a case study

In this paper a fuzzy-control system has been designed, implemented and embedded in an open CNC. The integration process, design steps and results of applying an embedded fuzzy-control system are shown through the example of real machining operations. The controller uses internal CNC signals (i.e. spindle-motor current) that are gathered and mathematically processed by means of an integrated application. The results show that, at least in rough milling operations, internal CNC signals can double as an intelligent, sensorless control system. Actual industrial tests show a higher machining efficiency (i.e. in-process time is reduced by 10% and total estimated savings the system would provide are about 78%).

[1]  Osita D. I. Nwokah,et al.  A Digital Robust Controller for Cutting Force Control in the End Milling Process , 1997 .

[2]  Rodolfo E. Haber,et al.  Dynamic Model of the Machining Process on the Basis of Neural Networks: From Simulation to Real Time Application , 2002, International Conference on Computational Science.

[3]  R. Haber,et al.  Application of Knowledge Based Systems for Supervision and Control of Machining Processes , 2022 .

[4]  Hao Ying,et al.  Analytical Structure of the Typical Fuzzy Controllers Employing Trapezoidal Input Fuzzy Sets and Nonlinear Control Rules , 1999, Inf. Sci..

[5]  Chaojun Wang,et al.  Intelligent adaptive control in milling processes , 1999, Int. J. Comput. Integr. Manuf..

[6]  Yoram Koren,et al.  Control of Machine Tools , 1997 .

[7]  Tae-Yong Kim,et al.  Adaptive cutting force control for a machining center by using indirect cutting force measurements , 1996 .

[8]  Ronald R. Yager,et al.  Essentials of fuzzy modeling and control , 1994 .

[9]  A. Alique,et al.  Multivariable circle criterion: stable fuzzy control of a milling process , 2002, Proceedings of the International Conference on Control Applications.

[10]  Lih-Chang Lin,et al.  Hierarchical Fuzzy Control for C-Axis of CNC Turning Centers Using Genetic Algorithms , 1999, J. Intell. Robotic Syst..

[11]  C. Wang,et al.  Neural Network based Adaptive Control and Optimisation in the Milling Process , 1999 .

[12]  A. Galip Ulsoy,et al.  Model Reference Adaptive Force Control in Milling , 1989 .

[13]  Yusuf Altintas,et al.  Prediction of Cutting Forces and Tool Breakage in Milling from Feed Drive Current Measurements , 1992 .