Material removal predictions in the robot glass polishing process using machine learning

Robot polishing is increasingly being used in the production of high-end glass workpieces such as astronomy mirrors, lithography lenses, laser gyroscopes or high-precision coordinate measuring machines. The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. Whilst the trend towards sub nanometre level surfaces finishes and features progresses, matching both form and finish coherently in complex parts remains a major challenge. 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. Due to the modification of the polishing head by the installation of sensors and the resulting process influences, the first machine learning model could only make removal predictions with insufficient accuracy. The aim of this work is to show a polishing head optimised for the sensors, which is coupled with a machine learning model in order to predict the material removal and failure of the polishing head during robot polishing. The artificial neural network is developed in the Python programming language using the Keras deep learning library. It starts with a simple network architecture and common training parameters. The model will then be optimised step-by-step using different methods and optimised in different steps. The data collected by a design of experiments with the sensor-integrated glass polishing head are used to train the machine learning model and to validate the results. The neural network achieves a prediction accuracy of the material removal of 99.22%. Article highlights First machine learning model application for robot polishing of optical glass ceramics The polishing process is influenced by a large number of different process parameters. Machine learning can be used to adjust any process parameter and predict the change in material removal with a certain probability. For a trained model,empirical experiments are no longer necessary Equipping a polishing head with sensors, which provides the possibility for 100% control

A note on Guillaume Amontons and the laws of friction

Amontons’ widely cited paper of December 1699 on the subject of friction in machines is not the first to contain his statement of the laws of friction. An earlier paper on a novel heat engine, presented to the Académie Royale des Sciences 6 months earlier, describes the measurements of forces in glass polishing and contains a clear statement that the friction force is independent of contact area and proportional to normal load. The comments in Amontons’ paper on the physical origins of the friction force at surface irregularities (asperities) do not appear in the contemporary record of his lecture in December 1699, but were included in the published version after similar ideas had been presented by Philippe de la Hire; credit for these ideas should be given to La Hire rather than to Amontons.

Material Removal Predictions in the Robot Glass Polishing Process Using Machine Learning

Robot polishing is increasingly being used in the production of high-end glass workpieces such as astronomy mirrors, lithography lenses, laser gyroscopes or high-precision coordinate measuring machines. The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. Whilst the trend towards sub nanometre level surfaces finishes and features progresses, matching both form and finish coherently in complex parts remains a major challenge. 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. Due to the modification of the polishing head by the installation of sensors and the resulting process influences, the first Machine Learning model could only make removal predictions with insufficient accuracy. The aim of this work is to show a polishing head optimised for the sensors, which is coupled with a Machine Learning model in order to predict the material removal and failure of the polishing head during robot polishing. The artificial neural network (ANN) is developed in the Python programming language using the Keras deep learning library. It starts with a simple network architecture and common training parameters. The model will then be optimized step-by-step using different methods and optimized in different steps. The data collected by a design of experiments with the sensor-integrated glass polishing head are used to train the machine learning model and to validate the results. The neural network achieves a prediction accuracy of the material removal of 99.22 %.