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Mechanism and Parameter Optimization in Grinding and Polishing of M300 Steel by an Elastic Abrasive

School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
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Materials 2019, 12(3), 340; https://doi.org/10.3390/ma12030340
Received: 11 December 2018 / Revised: 16 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
(This article belongs to the Special Issue Additive Manufacturing: Alloy Design and Process Innovations)
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Abstract

In order to achieve high quality polishing of a M300 mold steel curved surface, an elastic abrasive is introduced in this paper and its polishing parameters are optimized so that the mirror roughness can be achieved. Based on the Preston equation and Hertz Contact Theory, the theoretical material removal rate (MRR) equation for surface polishing of elastic abrasives is obtained. The effects of process parameters on MRR are analyzed and the polishing parameters to be optimized are as follows: particle size (S), rotational speed (Wt), cutting depth (Ap) and feed speed (Vf). The Taguchi method is applied to design the orthogonal experiment with four factors and three levels. The influence degree of various factors on the roughness of the polished surface and the combination of parameters to be optimized were obtained by the signal-to-noise ratio method. The particle swarm optimization algorithm optimized with the back propagation (BP) neural network algorithm (PSO-BP) is used to optimize the polishing parameters. The results show that the rotational speed has the greatest influence on the roughness, the influence degree of abrasive particle size is greater than that of feed speed, and cutting depth has the least influence. The optimum parameters are as follows: particle size (S) = #1200, rotational speed (Wt) = 4500 rpm, cutting depth (Ap) = 0.25 mm and feed speed (Vf) = 0.8 mm/min. The roughness of the surface polishing with optimum parameters is reduced to 0.021 μm. View Full-Text
Keywords: M300 mold steel; elastic abrasive; PSO-BP neural network algorithm; parameter optimization M300 mold steel; elastic abrasive; PSO-BP neural network algorithm; parameter optimization
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Tong, X.; Wu, X.; Zhang, F.; Ma, G.; Zhang, Y.; Wen, B.; Tian, Y. Mechanism and Parameter Optimization in Grinding and Polishing of M300 Steel by an Elastic Abrasive. Materials 2019, 12, 340.

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