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Research on Precision Compensation of Cross-Cutting Flying Shear based on BP Neural Network

机译:基于BP神经网络的跨切削剪切精度补偿研究

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In allusion to the numerous nonlinearity factors affecting precision in the cut process of cross-cutting line modeling shear and the difficulties to build precision mathematical model controlled by shear, the essay adopts BP neural network to build model of shear precision compensation system. By Matlab simulation experiment, the data demonstrates nonlinearity complex system can be identified fast and effectively by neural network. The predicted time of the model is 97 ms with the control precision within 0-2 mm hitting 89%, proving that the model can fully fulfil production demands regarding precision and pace and prove that probability that BP neural network is used in precision compensation of cross-cutting line modeling shear.
机译:阐明了影响跨切割线材建模剪切切割过程精度的众多非线性因素的差异剪切难以构建剪切控制的精度数学模型,采用BP神经网络来构建剪切精密补偿系统模型。 通过Matlab仿真实验,数据演示了非线性复杂系统,可以通过神经网络快速有效地识别。 模型的预测时间为97毫秒,控制精度在0-2 mm的击中89%内,证明该模型可以充分满足精度和步伐的生产要求,并证明BP神经网络用于交叉的精确补偿概率 - 切割线材建模剪切。

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