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Parameter Optimization of Polishing M300 Mold Steel with an Elastic Abrasive

机译:弹性磨料抛光M300模具钢的参数优化

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In order to achieve high-quality polishing of 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 equation for surface polishing of elastic abrasives is obtained, 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 range analysis method. The particle swarm optimization algorithm optimizes the BP neural network algorithm (PSO-BP), which 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 the influence of cutting depth is the least. The optimum parameters are as follows: particle size S 1200#, rotational speed Wt 4500rpm, cutting depth Ap 0.25mm, and feed speed Vf 0.8mm/min. The roughness of the surface polishing with optimum parameters is reduced to 0.021 mu m.
机译:为了实现M300模具钢曲面的高质量抛光,本文引入了弹性磨料,并对其抛光参数进行了优化,以达到镜面粗糙度。基于Preston方程和Hertz接触理论,获得了弹性磨料表面抛光的理论材料去除方程,待优化的抛光参数为:粒径S,转速Wt,切削深度Ap和进给速度Vf。采用田口法设计了四个因子,三个层次的正交实验。通过范围分析法求出各种因素对抛光表面粗糙度的影响程度以及需要优化的参数组合。粒子群优化算法优化了BP神经网络算法(PSO-BP),该算法用于优化抛光参数。结果表明,转速对粗糙度的影响最大,磨料粒度的影响程度大于进给速度的影响,切削深度的影响最小。最佳参数如下:粒径S 1200#,转速Wt 4500rpm,切削深度Ap 0.25mm和进给速度Vf 0.8mm / min。具有最佳参数的表面抛光粗糙度降低到0.021微米。

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