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Optimization of process parameters for surface roughness and microhardness in dry milling of magnesium alloy using Taguchi with grey relational analysis

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Abstract

In this study, Taguchi with grey relational analysis of experimental data was carried out to determine an optimum combination of process parameters for promoting the surface roughness and the microhardness of dry milling magnesium alloy. According to Taguchi’s signal-to-noise ratio, the optimal process parameters were obtained for desired surface roughness and microhardness, respectively. Moreover, in order to achieve an improved surface quality, the optimum combination of process parameters was determined by grey relational grade. The analysis of variance for grey relational grade showed that feed rate was the most dominant factor influencing surface integrity in milling of magnesium alloy. Finally, the optimum combination of process parameters were validated by confirmation experiments that yield substantially improved multiple quality characteristics. The outcome of the confirmation experiment indicated that the Taguchi with grey relational analysis is an effective method to determine available cutting parameters for a desired surface quality for milling magnesium alloy under dry condition.

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Authors and Affiliations

  1. The Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University (NWPU), Xi’an, Shaanxi, People’s Republic of China

    Kaining Shi, Dinghua Zhang & Junxue Ren

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  1. Kaining Shi
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  2. Dinghua Zhang
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  3. Junxue Ren
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Correspondence to Kaining Shi.

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Shi, K., Zhang, D. & Ren, J. Optimization of process parameters for surface roughness and microhardness in dry milling of magnesium alloy using Taguchi with grey relational analysis. Int J Adv Manuf Technol 81, 645–651 (2015). https://doi.org/10.1007/s00170-015-7218-8

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  • DOI: https://doi.org/10.1007/s00170-015-7218-8

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