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Statistical Optimization of Electromagnetic Stirring Parameters for Semisolid AM60 Slurry Using Taguchi-Based Grey Relational Analysis

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Abstract

In the present study, the effect of electromagnetic stirring parameters, including stirring time, pouring temperature and stirring frequency, on the morphology of the AM60 alloys was investigated using Taguchi-based grey relational analysis (TGRA). Each parameter of the process was analyzed at three levels. To accommodate all parameters, an orthogonal array L9 based on the Taguchi approach was used. The optimum parameters were chosen, using different microstructural responses such as shape factor, roundness, average grain size, average grain density and average aspect ratio. The optimal processing parameters were found to be 240 s, 30 Hz and 610 °C for stirring time, stirring frequency and pouring temperature, respectively. Results showed that the shape factor, roundness and average grain density were improved by 33%, 12.8% and 5.5%, respectively. Moreover, the average grain size and the aspect ratio decreased compared to those related to the preliminary set of experimental parameters, which represents the refining of the primary α-Mg phases. In addition, optimal experiment shows a dramatic improvement in tensile strength, uniform elongation and hardness compared to the initial conditions. Besides, the overall grey relational grade was compared to an initial set of experimental parameters, showing an improvement of 64%.

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Acknowledgement

Financial support provided by Shahid Chamran University of Ahvaz (Ahvaz, Iran) through Grant No. SCU.EM98.788 is gratefully appreciated.

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  1. Department of Materials Science and Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, 6135783151, Ahvaz, Iran

    A. Habibi Eftekhar, S. Mohsen Sadrossadat & M. Reihanian

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  1. A. Habibi Eftekhar
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Correspondence to S. Mohsen Sadrossadat.

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Eftekhar, A.H., Sadrossadat, S.M. & Reihanian, M. Statistical Optimization of Electromagnetic Stirring Parameters for Semisolid AM60 Slurry Using Taguchi-Based Grey Relational Analysis. Inter Metalcast 16, 212–222 (2022). https://doi.org/10.1007/s40962-021-00586-8

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