Machining Quality Predictions: Comparative Analysis of Neural Network and Fuzzy Logic

Sivarao, Subramonian and Taufik, , (2009) Machining Quality Predictions: Comparative Analysis of Neural Network and Fuzzy Logic. International Journal of Electrical & Computer Sciences, 9 (9). pp. 451-456. ISSN 2077-1231

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Abstract

Surface finish is an important objective function in manufacturing engineering. It holds the characteristic that could influence the performance of mechanical parts which is also proportional to production cost. It is also an aspect for designing mechanical elements and frequently presented as a quality and precision indicator of manufacturing processes. Various failures, sometimes catastrophic leading to high cost have been attributed to the surface finish of the components which left unanswered. Therefore, the quality of surface roughness is essential feature of drilling operation since most of hole applications are assembly works, especially focused on the relative movement and tight tolerance work. Hence, high standard quality control needs to be introduced. The aim of this experimental and analytical research is to identify the parameters which enable the prediction of surface roughness in drilling. Two expert systems were used to analyze the best fit model in predicting the output of surface roughness for this specific drill job. The prediction accuracy is then compared to analyze which model could give better results so that it can be recommended for machine learning and future work. From the findings, it is found that Sugeno Fuzzy model gives better the closest values as compared to the ANN model. Thus, the work conditions and Fuzzy environment is selected for predictions of surface roughness in drilling.

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 15:36
Last Modified:	28 May 2015 04:01
URI:	http://eprints.utem.edu.my/id/eprint/9166
Statistic Details:	View Download Statistic

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