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Influence of Cutting Parameters on Surface Roughness When Surface Grinding C250 with Segmented

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Advances in Engineering Research and Application (ICERA 2021)

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Abstract

C250 steel is a Nickel-based alloy steel with really high hardness, strength and ductility. An excellent feature of this steel is its ability to limit surface cracks during machining. In this article, a study on the influence of cutting parameters on surface roughness when grinding this steel is presented. Segmented grinding wheels with 18 grooves were used and the experimental process was carried out with a total of 15 experiments in the form of a Box-Behnken design matrix. At each experiment the parameters were selected as the input parameters including the velocity of workpiece, the feed rate and the cutting depth. The surface roughness has been selected as the output parameter of the experimental process. The analysis of experimental results has determined the influence of the input parameters on the surface roughness. A regression model showing the relationship between the input parameters and the surface roughness was also developed. Genetic Algorithm (GA) was chosen as the instrument to solve the optimization problem. The results of the optimization problem have determined the values of the cutting parameters to ensure the minimum surface roughness. The experiments to verify the optimal results of the cutting parameters were also conducted. The results show that the optimal value has been achieved with a very high reliability degree, in which the deviation between the experimental and predicted values is only 5.79%. Some further directions when studying the grinding technology with segmented grinding wheel have also been proposed in this article.

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Acknowledgment

This work was supported by Thai Nguyen University of Technology.

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Correspondence to Le Thi Phuong Thao .

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Trung, D.D., Tuan, N.A., Lam, P.D., Ha, L.D., Thao, L.T.P. (2022). Influence of Cutting Parameters on Surface Roughness When Surface Grinding C250 with Segmented. In: Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H., Sattler, KU. (eds) Advances in Engineering Research and Application. ICERA 2021. Lecture Notes in Networks and Systems, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-030-92574-1_52

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  • DOI: https://doi.org/10.1007/978-3-030-92574-1_52

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