Optimization and prediction of sintering process parameters for magnetic abrasives preparation using response surface methodology

Kumar, M.; Singh, S.; Farwaha, H.

Growing Science » International Journal of Data and Network Science » Optimization and prediction of sintering process parameters for magnetic abrasives preparation using response surface methodology

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 3 Issue 2 pp. 103-108 , 2019

Optimization and prediction of sintering process parameters for magnetic abrasives preparation using response surface methodology Pages 103-108 Download PDF

Authors: Mukesh Kumar, Sehijpal Singh, Harnam Singh Farwaha

DOI: 10.5267/j.ijdns.2018.12.005

Keywords: Magnetic Abrasive Finishing, Percentage Improvement in Surface Finish, Magnetic Strength

Abstract: Magnetic abrasives are important parts of Magnetic Assisted Abrasive Finishing (MAF). Magnetic abrasives are prepared by many processes, but sintering is the one of the best processes to prepare magnetic abrasives. The objective of this paper is to optimize the sintering process parameters. To do that, Response Surface Methodology (RSM) is used for the optimization of process parameters, Abrasive concentration in ferromagnetic particles (AC)%, Compacting Pressure (CP) N/mm2 and Sintering Time(ST)min. To check the performance of magnetic abrasives Percentage Improvement in Surface finish (PISF) is considered as a response variable. Optimization and prediction are executed through RSM and Central Composite Design (CCD) is used to conduct the experiments. The optimized values of process parameters obtained are AC (19.29%), ST (15min) and CP (6.9 N/mm2) and also predicted values for the response variable are obtained.

How to cite this paper

 Kumar, M., Singh, S & Farwaha, H. (2019). Optimization and prediction of sintering process parameters for magnetic abrasives preparation using response surface methodology.International Journal of Data and Network Science, 3(2), 103-108.

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