Integration of meta-analysis, analytic hierarchy process, and Taguchi method to examine tool wear

Dyi-Cheng Chen, Ci Syong You, Jung Chu Ting

Research output: Contribution to journalArticle

Abstract

This study integrated the analytic hierarchy process (AHP), a metaanalysis, and the Taguchi method to assess tool wear. Designs that mitigate tool wear are crucial to achieving favorable production outcomes. Design factors for cutting tools, including cutting speed, cutting depth, cutting feed rate, cutting time, and cutting angle, were considered to determine their association with tool wear. These factors were subjected to the AHP to determine their relative importance. The analysis results for these factors were discussed and compared to determine the indicators for tool wear. Subsequently, specialists reviewed the results, and factors with low weightings were deleted. Thereafter, a meta-analysis was applied to an extensive litera- ture review, which conformed to the schema findings of this study. Studies published in English were obtained from EI Compendex and Science Cita- tion Index journals for the period of 1998-2015. First, the effects of various statistical values of different amounts of conversion into r were determined, and the success or failure of the estimates was calculated. Subsequently, the Q test for heterogeneity, ZC value significance test, real effect of the size of the parent, and 95% confidence interval were used to analyze results pertaining to the relationship between each factor and tool wear. Finally, Taguchi’s quality engineering strategy was used to determine how to mitigate wear by altering the cutting speed, cutting depth, and cutting feed rate. Taguchi’s three levels of design were employed to explore each tool’s cytokine responses and variance in the signal-to-noise ratio to obtain the optimal combination for curtailing tool wear.

Original languageEnglish
Pages (from-to)155-164
Number of pages10
JournalJournal of Technology
Volume33
Issue number3
Publication statusPublished - 2018 Sep 1

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Taguchi methods
Analytic hierarchy process
Wear of materials
Cutting tools
Signal to noise ratio

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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abstract = "This study integrated the analytic hierarchy process (AHP), a metaanalysis, and the Taguchi method to assess tool wear. Designs that mitigate tool wear are crucial to achieving favorable production outcomes. Design factors for cutting tools, including cutting speed, cutting depth, cutting feed rate, cutting time, and cutting angle, were considered to determine their association with tool wear. These factors were subjected to the AHP to determine their relative importance. The analysis results for these factors were discussed and compared to determine the indicators for tool wear. Subsequently, specialists reviewed the results, and factors with low weightings were deleted. Thereafter, a meta-analysis was applied to an extensive litera- ture review, which conformed to the schema findings of this study. Studies published in English were obtained from EI Compendex and Science Cita- tion Index journals for the period of 1998-2015. First, the effects of various statistical values of different amounts of conversion into r were determined, and the success or failure of the estimates was calculated. Subsequently, the Q test for heterogeneity, ZC value significance test, real effect of the size of the parent, and 95{\%} confidence interval were used to analyze results pertaining to the relationship between each factor and tool wear. Finally, Taguchi’s quality engineering strategy was used to determine how to mitigate wear by altering the cutting speed, cutting depth, and cutting feed rate. Taguchi’s three levels of design were employed to explore each tool’s cytokine responses and variance in the signal-to-noise ratio to obtain the optimal combination for curtailing tool wear.",
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Integration of meta-analysis, analytic hierarchy process, and Taguchi method to examine tool wear. / Chen, Dyi-Cheng; You, Ci Syong; Ting, Jung Chu.

In: Journal of Technology, Vol. 33, No. 3, 01.09.2018, p. 155-164.

Research output: Contribution to journalArticle

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