Fuzzy logic based model for predicting surface roughness of machined Al-Si-Cu-Fe die casting alloy using different additives-turning

Barzani, M.M. and Zalnezhad, E. and Sarhan, A.A.D. and Farahany, S. and Ramesh, S. (2015) Fuzzy logic based model for predicting surface roughness of machined Al-Si-Cu-Fe die casting alloy using different additives-turning. Measurement, 61. pp. 150-161. ISSN 0263-2241

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Abstract

This paper presents a fuzzy logic artificial intelligence technique for predicting the machining performance of Al-Si-Cu-Fe die casting alloy treated with different additives including strontium, bismuth and antimony to improve surface roughness. The Pareto-ANOVA optimization method was used to obtain the optimum parameter conditions for the machining process. Experiments were carried out using oblique dry CNC turning. The machining parameters of cutting speed, feed rate and depth of cut were optimized according to surface roughness values. The results indicated that a cutting speed of 250 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 0.15 mm were the optimum CNC dry turning conditions. The results also indicated that Sr and Sb had a negative effect on workpiece machinability. The workpiece containing Bi exhibited the lowest surface roughness value, likely due to the formation of pure Bi that acted as lubricant during turning. A confirmation experiment was performed to check the validity of the model developed in this paper, and the predicted surface roughness came had an error rate of only 5.4. (C) 2014 Elsevier Ltd. All rights reserved.

Item Type: Article
Additional Information: Ay3ub Times Cited:1 Cited References Count:30
Uncontrolled Keywords: Fuzzy logic, aluminum, turning, surface roughness, antimony, bismuth, inference system, al7075-t6 alloy, coated tools, parameters, microstructure, optimization, composites, integrity, life, pvd,
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 04 Aug 2015 01:58
Last Modified: 04 Aug 2015 01:58
URI: http://eprints.um.edu.my/id/eprint/13846

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