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GUI Based ANFIS Modeling: Back Propagation Optimization Method for CO2 Laser Machining

Sivarao, Subramonian (2009) GUI Based ANFIS Modeling: Back Propagation Optimization Method for CO2 Laser Machining. International Journal of Intelligent Information Technology Application, 2 (4). pp. 191-198. ISSN 1999-2459

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Abstract

Past few decades have seen a resurgent trend towards establishment of intelligent manufacturing systems which are capable of using advanced knowledge-bases and intelligence techniques in aiding critical operational procedures in manufacturing. Increasing demands on productivity and quality with the increase in global competitiveness have necessitated development of sound predictive models and optimization strategies. This paper presents the modeling technique and prediction of surface roughness for 2.5mm Manganese Molybdenum pressure vessel plate by Hybrid Intelligence, namely, adaptive neurofuzzy inference system (ANFIS). Back propagation optimization method has been employed to optimize the epoch number and training of data sets. To compare the accuracy of the ANFIS model, the errors were calculated through Root Mean Square Error (RMSE) which yielded 0.3 and below. On the other hand, the prediction accuracy by the finalized ANFIS model has yielded up to 90% and above proving the prediction stability. The uniqueness of this modeling technique is that, all modeling, variable selection, model validation, prediction, etc. was done using a graphical user interface (GUI) developed on our own using Matlab. The non-traditional laser machining, was used in the modeling investigation as this machining process requires controlling of more than seven critical parameters and to date, no researchers has used ANFIS to model this exact phenomenon. The modeling technique has been successfully developed to predict the cut edge quality with excellent degree of accuracy and strongly believe that ANFIS could be the best hybrid AI tool with the capability of data training and rule setting which has to be further explored with critical consideration in producing precise part of any material in the field of precision manufacturing. The RMSE values were compared with various training variables to develop the best predictive model yielding 0.3 and below. The model was then used to predict the surface roughness and the prediction accuracy obtained was above 90% proving the optimizing technique and methods were accurate in producing excellent ANFIS model.

Item Type: Article
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Manufacturing Engineering > Department of Manufacturing Process
Depositing User: Assoc. Pror. Ir. Dr. Sivarao Subramonian
Date Deposited: 13 Aug 2013 15:30
Last Modified: 28 May 2015 04:01
URI: http://eprints.utem.edu.my/id/eprint/9162

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