Optimization of CO2 Laser Cutting Parameters Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Authors

  • Renann G. Baldovino Manufacturing Engineering and Management (MEM) Department De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
  • Ira C. Valenzuela Electronics and Communications Engineering (ECE) Department De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
  • Argel A. Bandala Electronics and Communications Engineering (ECE) Department De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
  • Elmer P. Dadios Manufacturing Engineering and Management (MEM) Department De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines

Keywords:

ANFIS, Fuzzy Logic, Laser Cutting, Process Optimization,

Abstract

Laser cutting is a manufacturing technology that uses laser light to cut almost any materials. This type of cutting technology has been applied in many industrial applications. Problems seen with a laser is the cutting efficiency and the quality wherein these two parameters are both affected by the laser power and its process speed. This study presents the modelling and simulation of an intelligent system for predicting and optimising the process parameters of CO2 laser cutting. The developed model was trained and tested using actual data gathered from actual laser cut runs. For the system parameters, two inputs were used: the type of material used and the material thickness (mm). For the desired response, the output is the process speed or cutting rate (mm/min). Adaptive neuro-fuzzy inference system (ANFIS) was the tool used to model the optimisation cutting process. Moreover, grid partition (GP) and subtractive clustering were both used in designing the fuzzy inference system (FIS). Among the training models used, GP Gaussian bell membership function (Gbellmf) provided the highest performance with an accuracy of 99.66%.

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Published

2018-02-19

How to Cite

Baldovino, R. G., Valenzuela, I. C., Bandala, A. A., & Dadios, E. P. (2018). Optimization of CO2 Laser Cutting Parameters Using Adaptive Neuro-Fuzzy Inference System (ANFIS). Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-9), 103–107. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3885

Issue

Vol. 10 No. 1-9: Breakthrough To Excellence in Communication and Computer Engineering V

Section

Articles

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