Training centers and labs offer many applications suitable for beginners who want to know how to set and operate a computer numerical control (CNC) milling machine. However, few applications address a basic understanding of the machining process founded on mathematical principals in line with new high-speed and high-precision machining technologies. The purpose of this paper is to present a complex mechanism in a simplified way, explaining the subject at an elementary level.,The authors have developed an application of the CNC milling machine in a Matlab/Simulink package, obtaining the appropriate parameters mathematically. The project developed an analytical method using Matlab code to test the step response (the actual cutting force) under various parameters to ensure comparability of the designed model. The analytical results are in line with the developed model. The Matlab/Simulink user interface allows the application to better explain machining for educational purposes. Furthermore, by combining this mathematical model and the fuzzy controller, the high-speed constant-force milling control model has a user interface for data entry. The addition of two kinds of fuzzy controllers (look-up table and Mamdani) achieve a more educational environment compared with existing models.,The developed technique can be used in CNC milling machine centers and laboratories. For virtual training purposes, this paper provides a two-stage educational model, giving students the necessary feedback on what they have learned at each stage from the beginning use of the CNC milling machine, with and without the controller. The system also offers to track the step-response analysis method. This method overcomes the shortage of milling processes modeled by the traditional transfer function, which more accurately establishes the relationship between cutting force and cutting parameters.,This technique can be used in the CNC machine centers and laboratory for teaching beginner students and trainees. Real data from the workshop had been used.,The earlier versions of this manuscript were presented in: JVE International LTD. Vibroengineering Procedia. +2017. 14.; IEEE 4th International Conference on Information Science and Control Engineering (ICISCE) +2017.
[1]
J. C. Wu,et al.
A sliding-mode approach to fuzzy control design
,
1996,
IEEE Trans. Control. Syst. Technol..
[2]
Tetsuo Morimoto,et al.
Optimization of a fuzzy controller for fruit storage using neural networks and genetic algorithms
,
1997
.
[3]
Chuen-Chien Lee,et al.
Fuzzy logic in control systems: fuzzy logic controller. II
,
1990,
IEEE Trans. Syst. Man Cybern..
[4]
Bingsheng He,et al.
A Variation-Aware Adaptive Fuzzy Control System for Thermal Management of Microprocessors
,
2017,
IEEE Transactions on Very Large Scale Integration (VLSI) Systems.
[5]
Bimal K. Bose,et al.
Expert system, fuzzy logic, and neural network applications in power electronics and motion control
,
1994,
Proc. IEEE.
[6]
M. Sugeno,et al.
Derivation of Fuzzy Control Rules from Human Operator's Control Actions
,
1983
.
[7]
Yoram Koren,et al.
Adaptive Control System for Turning
,
1980
.
[8]
Habibollah Haron,et al.
Fuzzy logic for modeling machining process: a review
,
2013,
Artificial Intelligence Review.
[9]
Haitao Zhu,et al.
Modeling and Analysis of Milling Machine Control Process without/with Using Fuzzy Interface Mamdani Style Based On Matlab/Simulink
,
2017
.
[10]
Chien-Hsu Chen,et al.
Symbol detection in low-resolution images using a novel method
,
2014
.
[11]
Juntang Yuan,et al.
High Speed Constant Force Milling Based on Fuzzy Controller and BP Neural Network
,
2014
.
[12]
Haitao Zhu,et al.
Analytical method of designing a comparable milling machine model based on Matlab/Simulink
,
2017
.
[13]
Benjamin Paris,et al.
Hybrid PID-fuzzy control scheme for managing energy resources in buildings
,
2011,
Appl. Soft Comput..
[14]
K. F. Man,et al.
A Practical Method for Identification of Dynamic Processes
,
1983
.