Wear behavior of the abrasive grains used in optical glass polishing

The localized wear on grinding wheel edges is a common phenomenon on profile grinding since the abrasive grains are less attached to the bond. The grinding wheel wear depends heavily on the process parameters, workpiece and wheel composition, causing changes on the process and profile deviation behaviors. In order to cope with these uncertainties, many natural and synthetic materials have been used in different grinding processes. However, the influence of mixed compositions of different types of abrasive grains on external cylindrical grinding is not well known. In order to assess this relation, a methodology procedure was developed providing an overview of the cinematic edges behavior on a progressive wheel wear. The methodology procedure is based on the acoustic emission technology, using a transducer with a 50 μm radius diamond tip. The tip, when in contact with a rotating grinding wheel, enables the evaluation of the cinematic cutting edges. The abrasive grain density was evaluated for different grinding wheel compositions and specific wear removal values. Furthermore, these results were compared to the profile deviation observed on the same tool, allowing the assessment of the influence of different microcrystalline corundum grains on the overall grinding wheel wear behavior.

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C01 Fundamental performance of MCF (Magnetic Compound Fluid) wheel in optical glass polishing

The Proceedings of The Manufacturing & Machine Tool Conference ◽

10.1299/jsmemmt.2012.9.131 ◽

2012 ◽

Vol 2012.9 (0) ◽

pp. 131-132

Author(s):

Yongbo WU ◽

Masakazu FUJIMOTO

Keyword(s):

Optical Glass ◽

Glass Polishing

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Laser application for optical glass polishing

Optical Engineering ◽

10.1117/1.601614 ◽

1998 ◽

Vol 37 (1) ◽

pp. 272 ◽

Cited By ~ 28

Author(s):

Fidel Vega

Keyword(s):

Optical Glass ◽

Laser Application ◽

Glass Polishing

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

SN Applied Sciences ◽

10.1007/s42452-021-04916-7 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Max Schneckenburger ◽

Sven Höfler ◽

Luis Garcia ◽

Rui Almeida ◽

Rainer Börret

Keyword(s):

Neural Network ◽

Machine Learning ◽

Material Removal ◽

Optical Glass ◽

Learning Model ◽

Machine Learning Algorithms ◽

List Type ◽

Polishing Process ◽

Machine Learning Model ◽

Glass Polishing

Abstract 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

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