基于神经网络的机器人抛光材料去除提升模型

提出了一种基于深度神经网络的提高材料去除模型精度的策略。提出一种具有特征选择能力的深度学习算法。在机器人抛光的材料去除率模型的基础上, 生成由材料去除率和相应的抛光参数组成的一系列仿真样本。深度学习算法学习了仿真样本和实际样本, 建立了深度学习模型。通过使用所提出的深度学习模型, 根据抛光参数, 估测测试样本的材料去除深度, 并计算估测了测试样本的材料去除深度与实际的测试样本的材料去除深度之间的误差。结果表明: 改进后的模型可以获得比传统模型更高的精度。

Abstract

A strategy for improving the precision of material removal model based on deep neural networks was proposed. A deep learning algorithm with ability of feature selecting was proposed. A series of simulation samples composed of a material removal rate and corresponding polishing parameters were generated based on the model of material removal rate for robot polishing. The deep learning algorithm learned both the simulation samples and practical samples and then a deep learning model was established. The error between material removal depth of the test samples and material removal depth estimated by polishing parameters by using proposed deep learning model was calculated and compared. The results show that the improved model can achieve higher accuracy than the traditional models.

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余熠, 孔令豹, 张海涛, 徐敏, 王丽萍. 基于神经网络的机器人抛光材料去除提升模型[J]. 红外与激光工程, 2019, 48(3): 0317005. Yu Yi, Kong Lingbao, Zhang Haitao, Xu Min, Wang Liping. An improved material removal model for robot polishing based on neural networks[J]. Infrared and Laser Engineering, 2019, 48(3): 0317005.

基于神经网络的机器人抛光材料去除提升模型

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