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

Title
Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning
Authors
Erfan Zal Nezhad
Keywords
Fuzzy logic; Aluminum; Surface roughness; Antimony; Bismuth
Issue Date
2015-02
Publisher
ELSEVIER SCI LTD
Citation
MEASUREMENT, Page. 150-161
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%.
URI
http://www.sciencedirect.com/science/article/pii/S0263224114004679http://hdl.handle.net/20.500.11754/22015
ISSN
0263-2241
DOI
doi:10.1016/j.measurement.2014.10.003
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > MECHANICAL ENGINEERING(기계공학부) > Articles
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