ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Optimization of Lathe Cutting Parameters Using Taguchi Method and Grey Relational Analysis

Bo-Lin Jian, Cheng-Chi Wang, Her-Terng Yau, Li-Wei Wu, and An-Hong Tian

(Received February 8, 2019; Accepted December 27, 2019)

Keywords: Taguchi method, lathe, optimization, grey relational analysis

In the precision industry of today, the rapid production of high-quality parts in bulk quantities has led to high competitiveness. In this study the Taguchi method and grey relational analysis (GRA) approach were used in a practical investigation of precision lathe processing. The purpose was to find optimal parameters for single-target and multitarget cutting. The production of targets of the highest quality was the research focus, with the aim of strengthening the links between this study and application to the processing industry. Precision, surface roughness, and material removal rate were selected as targets for improvement. Parameters commonly used for lathe processing were set as the control factors, and cutting depth, spindle speed, feed rate, and material elongation were experimental factors. The results showed that in the cutting of materials, cutting precision was mainly affected by the depth of cut and spindle speed, surface roughness by spindle speed, and the material removal rate by the cutting depth. In a comparison of the quality loss for the same materials using previous parameters, the cutting precision has about 64 to 99% optimization, the surface roughness has 69 to 96% optimization, and the material removal rate has over 90% optimization. Grey relational analysis was also employed to analyze the sequences of parameters from the Taguchi experiments to obtain the target relationships and to find the various combinations of factors for improvement.

Corresponding author: An-Hong Tian




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