Experimental Investigation of Material Removal and Surface Roughness during Optical Glass Polishing

2016 ◽  
Vol 31 (12) ◽  
pp. 1613-1620 ◽  
Author(s):  
Raj Kumar Pal ◽  
Harry Garg ◽  
RamaGopal V. Sarepaka ◽  
Vinod Karar
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Material Removal ◽  
Optical Glass ◽  
Glass Polishing

scholarly journals Effect to the Surface Composition in Ultrasonic Vibration-Assisted Grinding of BK7 Optical Glass

2020 ◽  
Vol 10 (2) ◽  
pp. 516 ◽  
Author(s):  
Pei Yi Zhao ◽  
Ming Zhou ◽  
Xian Li Liu ◽  
Bin Jiang
Keyword(s):  
Surface Roughness ◽  
Ultrasonic Vibration ◽  
Material Removal ◽  
Surface Composition ◽  
Rotation Speed ◽  
Optical Glass ◽  
Machined Surface ◽  
Spindle Rotation ◽  
Ultrasonic Vibration Assisted Grinding ◽  
Ductile Removal

Because of the changes in cutting conditions and ultrasonic vibration status, the proportion of multiple material removal modes are of uncertainty and complexity in ultrasonic vibration-assisted grinding of optical glass. Knowledge of the effect of machined surface composition is the basis for better understanding the influence mechanisms of surface roughness, and also is the key to control the surface composition and surface quality. In the present work, 32 sets of experiments of ultrasonic vibration-assisted grinding of BK7 optical glass were carried out, the machined surface morphologies were observed, and the influence law of machining parameters on the proportion of different material removal was investigated. Based on the above research, the effect of surface composition was briefly summarized. The results indicated that the increasing of spindle rotation speed, the decreasing of feed rate and grinding depth can improve the proportion of ductile removal. The introduction of ultrasonic vibration can highly restrain the powdering removal, and increase the proportion of ductile removal. Grinding depth has a dominant positive effect on the surface roughness, whereas the spindle rotation speed and ultrasonic amplitude both have negative effect, which was caused by the reduction of brittle fracture removal.

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

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

Ultra Smooth Planarization Polishing Technique Based on the Cluster Magnetorheological Effect

2010 ◽  
Vol 135 ◽  
pp. 18-23 ◽  
Author(s):  
Qiu Sheng Yan ◽  
Jie Wen Yan ◽  
Jia Bin Lu ◽  
Wei Qiang Gao
Keyword(s):  
Surface Roughness ◽  
Carbonyl Iron ◽  
Material Removal ◽  
Optical Glass ◽  
Removal Rate ◽  
Polished Surface ◽  
Mr Fluid ◽  
Magnetorheological Effect ◽  
The Difference ◽  
Mr Effect

A new planarization polishing method based on the cluster magnetorheological (MR) effect is presented to polish optical glass in this paper. Some process experiments were conducted to reveal the influence of the content of carbonyl iron and the abrasive materials in the MR fluid on the machining effect, and the machining characteristic of polished surface was studied. The results indicate that the surface roughness of the polished workpiece can be reduced rapidly when the strong magnetic field is applied, and ultra smooth surface with Ra 1.4 nm can be achieved while the CeO2 abrasives are used in the MR fluid. The content of carbonyl iron obviously influences the machining effect of this planarization polishing method based on cluster MR-effect. With the increase of the content of carbonyl iron in the MR fluid, the material removal rate improves and the surface roughness reduces rapidly. However, the difference of abrasive material results in various machining effects. As for the K9 optical glass, the CeO2 abrasive is better polishing abrasive than the SiC abrasive in the planarization polishing technique based on the cluster MR-effect.

The Slurry for Glass Polishing by Micro Abrasive Suspension Jets

2009 ◽  
Vol 69-70 ◽  
pp. 322-327 ◽  
Author(s):  
Yan Pei Liao ◽  
Cheng Yong Wang ◽  
Ying Ning Hu ◽  
Yue Xian Song
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Ph Dependence ◽  
Suspension Stability ◽  
Suspending Agents ◽  
Anionic Polyacrylamide ◽  
Abrasive Suspension ◽  
Glass Polishing

The influence of different dispersants and suspending agents of CeO2 polishing powder was investigated, including sodium hexametaphosphate, polyethyleneglycoll, polyacrylic acid, anionic polyacrylamide, and their mixtures. The suspension stability of ceria particles in slurries and the pH dependence were examined by the measurement of zeta potential, sedimentation and viscosity, etc. The material removal rate and surface roughness of different slurries for K9 glass polishing were studied. The slurry mixed by three kinds of dispersants can achieved the maximum material removal rate and the minimum surface roughness.

Investigation into Chemo-Mechanical Fixed Abrasive Polishing of Fused Silica with the Assistance of Ultrasonic Vibration

2012 ◽  
Vol 523-524 ◽  
pp. 155-160 ◽  
Author(s):  
Ya Guo Li ◽  
Yong Bo Wu ◽  
Li Bo Zhou ◽  
Hui Ru Guo ◽  
Jian Guo Cao ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Ultrasonic Vibration ◽  
Material Removal Rate ◽  
Material Removal ◽  
Optical Glass ◽  
Removal Rate ◽  
Fused Silica ◽  
Processing Efficiency ◽  
Machined Surface ◽  
Fixed Abrasive

Ultrasonic vibration assisted processing is well known for the improvement in machined surface quality and processing efficiency due to the reduced forces and tribology-generated heating when grinding hard-brittle materials. We transplanted this philosophy to chemo-mechanical fixed abrasive polishing of optical glass, namely fused silica, in an attempt to improve surface roughness and/or material removal rate. Experiments were conducted to elucidate the fundamental characteristics of chemo-mechanical fixed abrasive polishing of fused silica in the presence and absence of ultrasonic vibration on a setup with an in-house built gadget. The experimental results show that ultrasonic vibration assisted chemo-mechanical fixed abrasive polishing can yield increased material removal rate while maintaining the surface roughness of manufactured optics compared to conventional fixed abrasive polishing without ultrasonic vibration. The mechanism of material removal in fixed abrasive polishing was also delved. We found that the glass material is removed through the synergic effects of chemical and mechanical actions between abrasives and glass and the resultant grinding swarf contains ample Si element as well as Ce element, standing in stark contrast to the polisher that contains abundant Ce element and minor Si element.

Influence of Abrasive on Planarization Polishing with the Tiny-Grinding Wheel Cluster Based on the Magnetorheological Effect

2009 ◽  
Vol 76-78 ◽  
pp. 229-234 ◽  
Author(s):  
Qiu Sheng Yan ◽  
Yong Yang ◽  
Jia Bin Lu ◽  
Wei Qiang Gao
Keyword(s):  
Surface Roughness ◽  
Particle Size ◽  
Material Removal Rate ◽  
Material Removal ◽  
Magnetic Particles ◽  
Optical Glass ◽  
Removal Rate ◽  
Grinding Wheel ◽  
Material Particle ◽  
Material Removal Model

Experiments were conducted to polish optical glass with the magnetorheological (MR) effect-based tiny-grinding wheel cluster, and the influences of abrasive material, particle size and content on the material removal rate and surface roughness are investigated. The experimental results indicate that: the higher the hardness of abrasives, the higher the material removal rate, but the abrasives with lower hardness can obtain lower surface roughness. The better polishing quality of the workpiece can be obtained when the particle size of abrasives is similar to the particle size of magnetic particles. Moreover, the content of abrasives has an optimum value, and the material removal rate and the surface quality can not be improved further when the content of abrasives exceeds the optimum value. On the basis of above, the material removal model of the new planarization polishing technique is presented.

Sign in / Sign up

Export Citation Format

Share Document