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Prediction of surface roughness and optimization of cutting parameters of stainless steel turning based on RSM

Prediction of surface roughness and optimization of cutting parameters of stainless steel turning based on RSM
Prediction of surface roughness and optimization of cutting parameters of stainless steel turning based on RSM

The turning test of stainless steel was carried out by using the central composite surface design of response surface method (RSM) and Taguchi design method of central combination design. The influence of cutting parameters (cutting speed, feed rate, and cutting depth) on the surface roughness was analyzed. The surface roughness prediction model was established based on the second-order RSM. According to the test results, the regression coefficient was estimated by the least square method, and the regression equation was curve fitted. Meanwhile, the significance analysis was conducted to test the fitting degree and response surface design and analysis, in addition to establishing a response surface map and three-dimensional surface map. The life of the machining tool was analyzed based on the optimized parameters. The results show that the influence of feed rate on the surface roughness is very significant. Cutting depth is the second, and the influence of cutting speed is the least. Therefore, the cutting parameters are optimized and tool life is analyzed to realize the efficient and economical cutting of difficult-to-process materials under the premise of ensuring the processing quality.

1024-123X
Xiao, Maohua
84c4e27b-1f81-4880-a640-6fc43ac10602
Shen, Xiaojie
6a4457ee-8bbf-4a18-a394-e67daf42a371
Ma, You
54214921-9fc7-4369-ba46-a1a19dba045e
Yang, Fei
b472f050-c248-49ba-bde1-f3c92ca4a6de
Gao, Nong
9c1370f7-f4a9-4109-8a3a-4089b3baec21
Wei, Weihua
e3711fd6-9093-44b7-9534-70d8ae321ce3
Wu, Dan
febbf54b-9a0b-4f70-9c97-8f13f1e40972
Xiao, Maohua
84c4e27b-1f81-4880-a640-6fc43ac10602
Shen, Xiaojie
6a4457ee-8bbf-4a18-a394-e67daf42a371
Ma, You
54214921-9fc7-4369-ba46-a1a19dba045e
Yang, Fei
b472f050-c248-49ba-bde1-f3c92ca4a6de
Gao, Nong
9c1370f7-f4a9-4109-8a3a-4089b3baec21
Wei, Weihua
e3711fd6-9093-44b7-9534-70d8ae321ce3
Wu, Dan
febbf54b-9a0b-4f70-9c97-8f13f1e40972

Xiao, Maohua, Shen, Xiaojie, Ma, You, Yang, Fei, Gao, Nong, Wei, Weihua and Wu, Dan (2018) Prediction of surface roughness and optimization of cutting parameters of stainless steel turning based on RSM. Mathematical Problems in Engineering, 2018, [9051084]. (doi:10.1155/2018/9051084).

Record type: Article

Abstract

The turning test of stainless steel was carried out by using the central composite surface design of response surface method (RSM) and Taguchi design method of central combination design. The influence of cutting parameters (cutting speed, feed rate, and cutting depth) on the surface roughness was analyzed. The surface roughness prediction model was established based on the second-order RSM. According to the test results, the regression coefficient was estimated by the least square method, and the regression equation was curve fitted. Meanwhile, the significance analysis was conducted to test the fitting degree and response surface design and analysis, in addition to establishing a response surface map and three-dimensional surface map. The life of the machining tool was analyzed based on the optimized parameters. The results show that the influence of feed rate on the surface roughness is very significant. Cutting depth is the second, and the influence of cutting speed is the least. Therefore, the cutting parameters are optimized and tool life is analyzed to realize the efficient and economical cutting of difficult-to-process materials under the premise of ensuring the processing quality.

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9051084 - Version of Record
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Accepted/In Press date: 19 July 2018
e-pub ahead of print date: 2 August 2018

Identifiers

Local EPrints ID: 425782
URI: http://eprints.soton.ac.uk/id/eprint/425782
ISSN: 1024-123X
PURE UUID: ae7d7e6b-71ba-4966-a6d1-6d57253e9746

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Date deposited: 02 Nov 2018 17:30
Last modified: 16 Dec 2019 18:01

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Contributors

Author: Maohua Xiao
Author: Xiaojie Shen
Author: You Ma
Author: Fei Yang
Author: Nong Gao
Author: Weihua Wei
Author: Dan Wu

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