A New Approach of Adaptive Network-based Fuzzy Inference System Modeling in Laser Processing-A Graphical User Interface Based

• Main Author: Sivarao, Subramonian
• Format: Article
• Language: English
• Published: Science Publications 2009
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utem.edu.my/id/eprint/9163/1/Accepted_Paper.pdf; http://eprints.utem.edu.my/id/eprint/9163/
• Institution: Universiti Teknikal Malaysia Melaka

Problem statement: The power of Artificial Intelligent (AI) becomes more authoritative when the system is programmed to cater the need of complex applications. MATLAB 2007B, integrating artificial intelligent system and Graphical User Interface (GUI) has reduced researchers’ fear-to-model factor due to unfamiliarity and phobia to produce program codes. Approach: In this study, how GUI was developed on Matlab to model laser machining process using Adaptive Network based Fuzzy Inference System (ANFIS) was presented. Laser cutting machine is widely known for having the most number of controllable parameters among the advanced machine tools, hence become more difficult to engineer the process into desired responses; surface roughness and kerf width. Mastering both laser processing and ANFIS programming are difficult task for most researchers, especially for the difficult to model processes. Therefore, a new approach was ventured, where GUI was developed using MATLAB integrating ANFIS variables to model the laser processing phenomenon, in which the numeric and graphical output can be easily printed to interpret the results. Results: To investigate ANFIS variables’ characteristic and effect, error was analyzed via Root Mean Square Error (RMSE) and Average Percentage Error (APE). The RMSE values were then compared among various trained variables and settings to finalize best ANFIS predictive model. The results found was very promising and proved that, even a person with shallow knowledge in both artificial intelligence and laser processing can actually train the experimental data sets loaded into GUI, test and optimize ANFIS variables to make comparative analysis. Conclusion: The details of modeled work with prediction accuracy according to variable combinations were premeditated on another paper. The findings were expected to benefit precision machining industries in reducing their down time and cost as compared to the traditional way of trial and error method.