基于神经网络的机器人抛光材料去除提升模型（英文）  

An improved material removal model for robot polishing based on neural networks

作　　者：余熠 孔令豹 张海涛[2] 徐敏[1] 王丽萍[2] Yu Yi;Kong Lingbao;Zhang Haitao;Xu Min;Wang Liping(Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing,Department of Optical Science andEngineering,Fudan University,Shanghai 200433,China;State Key Laboratory of Applied Optics,Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China)

机构地区：[1]复旦大学光科学与工程系上海超精密光学制造工程技术研究中心,上海200433 [2]中国科学院长春光学精密机械与物理研究所应用光学国家重点实验室,吉林长春130033

出　　处：《红外与激光工程》2019年第3期223-230,共8页Infrared and Laser Engineering

基　　金：中国科学院长春光学精密机械研究所应用光学国家重点实验室开放基金;复旦-长光所合作项目基金(FC2017-010)

摘　　要：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。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.

关 键 词：机器人抛光 材料去除 机器学习 深度神经网络 建模与仿真 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]