ANFIS Modeling of Laser Machining Responses by Specially Developed Graphical User Interface

• Main Author: Sivarao, Subramonian
• Format: Article
• Language: English
• Published: IJENS Publishers 2009
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utem.edu.my/id/eprint/9164/1/Published_Paper.pdf; http://eprints.utem.edu.my/id/eprint/9164/
• Institution: Universiti Teknikal Malaysia Melaka

Utilizing MATLAB to develop graphical user interface in modeling of machining responses is a very rare case among researchers, especially for the complex and nonlinear machining processes. Since it is more complicated and time consuming for one to explore artificial intelligent tools to model a process or response using MATLAB due to unfamiliarity and phobia of programming, a new approach is ventured to model using graphical interface. In this paper, how GUI is developed and integrated to model laser machining process using Adaptive Network-based Fuzzy Inference System (ANFIS) together with GUI’s ability in generating the model output for laser responses are presented. Laser cutting machine is widely known for having the most number of controllable parameters among the advanced machine tools and it becomes more difficult for the process to be engineered into desired responses such as surface roughness and kerf width especially for precision machining settings. Knowing laser processing and ANFIS programming are both difficult and fears modelers, a novel GUI is developed and used as an interface to model laser processing using ANFIS with various setting capabilities where, numeric and graphical output can be printed. On the other hand, the GUI can also be used to predict the responses to conduct comparative analysis. To validate the accuracy of the ANFIS modeling, the deviations are calculated through Root Mean Square Error (RMSE) and Average Percentage Error. The RMSE values are compared with various types of trained variables and settings on ANFIS platform, so that the best ANFIS model can be finalized before prediction and validation. The developed GUI is currently being tested by a pressure vessel manufacturing industry for an operator to optimize the best machine setting before it is operated. Thus, the industry could reduce the production cost and down time by off-hand setting as compared to the traditional way of trial and error method.