Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

Shen Jenn Hwang; Yi-Hung Tsai

doi:10.20944/preprints201704.0127.v1

Preprint Article Version 1 This version is not peer-reviewed

Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

Shen Jenn Hwang ^* and

Yi-Hung Tsai

Version 1 : Received: 19 April 2017 / Approved: 19 April 2017 / Online: 19 April 2017 (16:37:38 CEST)

How to cite: Hwang, S.J.; Tsai, Y. Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics. Preprints 2017, 2017040127 (doi: 10.20944/preprints201704.0127.v1). Hwang, S.J.; Tsai, Y. Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics. Preprints 2017, 2017040127 (doi: 10.20944/preprints201704.0127.v1).

Abstract

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for AA7050-T7451 by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of cutting variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is proved that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were performed to make a comparison between the experimental results. Experimental results have shown that machining performance in precision turn-boring process can be improved effectively through this approach

Subject Areas

turn-boring; AA 7050-T7451; surface roughness; roundness error; power consumption; Taguchi; grey relational analysis.

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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