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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/7388

Title: Development of Man-Machine Interface using Matlab: an adaptive network-based fuzzy inference system modeling for laser machining
Authors: Sivarao
Rizal, M.S.
Tajul, A.
Taufik
???metadata.dc.contributor.url???: sivarao@live.utem.edu.my
Keywords: Laser Machining;Man-Machine Interface;Human-machine systems;Adaptive Network-based Fuzzy Inference System (ANFIS);Human-machine systems -- Design and construction
Issue Date: 11-Oct-2009
Publisher: Universiti Malaysia Perlis
Citation: p.4B5 1 - 4B5 8
Series/Report no.: Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009)
Abstract: Development of GUI on MATLAB environment is rarely carried out by researchers especially for controlling complex and non-linear machining processes. Hence, it becomes more complicated and time consuming for one to explore artificial intelligent tools to model a process using MATLAB due to unfamiliarity and phobia of programming. 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 is 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 to achieve precision machining conditions. Knowing both laser processing and ANFIS programming are difficult and being fear of 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 error is calculated through Root Mean Square Error (RMSE) and Average Percentage Error. The RMSE values are compared with various type of trained variables and settings on ANFIS platform, so that the best ANFIS model can be finalized before prediction. The developed GUI can be used in industry of laser machining for an operator to optimize the best machine setting before the machine 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.
Description: Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
URI: http://hdl.handle.net/123456789/7388
Appears in Collections:Conference Papers

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