Machine learning model for robot polishing cell

M. Schneckenburger, L. Garcia-Barth, R. Börret
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引用次数: 2

Abstract

The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. With increasing optic sizes, the stability of the polishing process becomes more and more important. If not empirically known, the optical surface must be measured after each polishing step. One approach is to mount sensors on the polishing head in order to measure process relevant quantities. On the basis of these data, Machine Learning algorithms can be applied for surface value prediction. The aim of this work is the stepwise development of an artificial neural network (ANN) in order to improve the accuracy of the models' prediction. The ANN is developed in the Python programming language using the Keras deep learning library. Beginning with simple network architecture and common training parameters. The model will then be optimized step-by-step through the implementation of different methods and Hyperparameter optimization (HPO). Data, which is generated by the sensor-integrated glass polishing head, is used to train the ANN-model. A representative part of these data is held back before, in order to validate the models' prediction. The so-called dataset contains measured values from multiple polishing runs, preceded by a design of experiment. After the model is trained on the dataset, it is able to predict the result of not yet performed polishing runs, with given polishing parameters. Concrete, the ANN is used to predict the resulting glass-surface quality, which includes the surface roughness and the shape accuracy, calculated by the material removal over time. The prediction by artificial neural networks reduces the polishing iterations and thus the production time.
机器人抛光电池的机器学习模型
光学元件如透镜或反射镜的质量可以用形状误差和表面粗糙度来描述。随着光学尺寸的增大,抛光过程的稳定性变得越来越重要。如果没有经验,光学表面必须在每个抛光步骤后测量。一种方法是在抛光头上安装传感器,以测量工艺相关的量。在这些数据的基础上,机器学习算法可以应用于表面值预测。这项工作的目的是逐步发展人工神经网络(ANN),以提高模型预测的准确性。人工神经网络是使用Keras深度学习库用Python编程语言开发的。从简单的网络架构和常用的训练参数开始。然后通过不同方法和超参数优化(HPO)的实现逐步优化模型。由传感器集成的玻璃抛光头生成的数据用于训练神经网络模型。为了验证模型的预测,这些数据的代表性部分之前被保留下来。所谓的数据集包含多次抛光运行的测量值,之前是一个实验设计。在数据集上训练模型后,它能够预测尚未执行抛光运行的结果,具有给定的抛光参数。在混凝土中,人工神经网络用于预测最终的玻璃表面质量,包括表面粗糙度和形状精度,通过材料随时间的去除来计算。人工神经网络预测减少了抛光迭代,从而减少了生产时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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