Development of Polishing Process for Optical Glass Dome Used in Airborne Electro Optical Payloads

2021 ◽  
pp. 771-774
Author(s):  
Kamal K. Pant ◽  
Neraj Pandey ◽  
Sandeep Nayak ◽  
Amitava Ghosh
Keyword(s):  
Optical Glass ◽  
Polishing Process

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

Experimental study on surface generation in optical glass with fluid jet polishing process

2018 ◽  
Vol 8 (3) ◽  
pp. 245
Author(s):  
Narayanaperumal Arunachalam ◽  
Kumaresan Gladys Anbarasu ◽  
Lakshmanan Vijayaraghavan
Keyword(s):  
Experimental Study ◽  
Optical Glass ◽  
Surface Generation ◽  
Polishing Process

scholarly journals EXPERIMENT PLAN FOR RESEARCHES OF POLISHING PROCESS OF OPTICAL DETAILS / OPTINIŲ DETALIŲ POLIRAVIMO PROCESO EKSPERIMENTINIO TYRIMO PLANAVIMAS

2017 ◽  
Vol 8 (6) ◽  
pp. 563-566
Author(s):  
Ieva Švagždytė ◽  
Mindaugas Jurevičius
Keyword(s):  
Experimental Data ◽  
Optical Glass ◽  
Polishing Process ◽  
High Quality ◽  
Quality Requirements ◽  
Multicriterion Optimization

Descriptions of polishing process, using technologies, materials of optical details are given in this article. High quality requirements are raising for optical details – smoothness of surface, non-existence of effects, form deflections, which are obtainable in polishing process. Results of modelling and experimental data are given in published scientist researches; various cutting rates were used in polishing process. Experiment plan for polishing plane details from optical glass BK7 with different cutting rates is given in this article. Multicriterion optimization in polishing process using for high quality optical details. Straipsnyje aprašomas optinių detalių poliravimo procesas, naudojamosios technologijos ir medžiagos. Optinėms detalėms keliami aukšti tikslumo reikalavimai – paviršiaus glotnumo, defektų nebuvimo, formos nuokrypių ir kt., jie gau­nami poliravimo proceso metu. Mokslininkų paskelbtuose tyrimuose pateikiami modeliavimo rezultatai, gauti eksperimentų metu, tirtos įvairios apdirbamosios medžiagos, jų paviršiaus kokybė, poliruota įvairiais pjovimo režimais. Šiame straipsnyje aprašomas eksperimento planas, skirtas plokščioms detalėms iš optinio stiklo BK7 poliruoti, kai varijuojama keliais apdirbimo režimais. Siekiama gauti aukštą gaminių kokybę daugiakriterio optimizavimo būdu.

Investigation of the optimal parameters for the surface finish of K9 optical glass using a soft abrasive rotary flow polishing process

2020 ◽  
Vol 49 ◽  
pp. 26-34 ◽  
Author(s):  
Jun Zhao ◽  
Jinfeng Huang ◽  
Rui Wang ◽  
HaoRan Peng ◽  
Wei Hang ◽  
...  
Keyword(s):  
Surface Finish ◽  
Optical Glass ◽  
Optimal Parameters ◽  
Polishing Process

A Global Correction Process for Flat Optics With Patterned Polishing Pad

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Weisi Li ◽  
Ping Zhou ◽  
Zhichao Geng ◽  
Ying Yan ◽  
Dongming Guo
Keyword(s):  
Optical Glass ◽  
Removal Rate ◽  
Surface Profile ◽  
Prediction Method ◽  
Correction Method ◽  
Polishing Process ◽  
Polishing Pad ◽  
Edge Exclusion ◽  
Optics Fabrication ◽  
Surface Figure

To improve the efficiency of flat optics fabrication, a global correction method with the patterned polishing pad is developed in this paper. Through creating grooves on a polishing pad, the contact pressure distribution on the optics surface can be adjusted to change the material removal rate (MRR) distribution during polishing. Using the patterned pad, the selective removal ability of the polishing process is greatly enhanced. The predictability and stability of the MRR distribution are the preconditions to efficiently implement the proposed global correction method. Relying on the MRR distribution prediction method proposed and validated in this paper, the pad pattern can be designed based on the original surface figure of the workpieces. The designed groove pattern is created on the polishing pad using the custom-developed equipment. Then, the optical glass is polished on the designed pad with the optimized polishing time. A flat optical glass sample (Φ 100 mm) is polished with the global correction method to show its feasibility and advantage. The correction instance shows that the peak-to-valley (PV) value of the surface profile (with 3 mm edge exclusion) dropped from 1.17 µm to 0.2 µm in 14 min using a polyurethane pad with two ring grooves. Comparing with the conventional polishing process, which usually takes hours or days, the global correction method proposed in this paper can improve the efficiency of the optics manufacturing significantly.

Experimental study on surface generation in optical glass with fluid jet polishing process

2018 ◽  
Vol 8 (3) ◽  
pp. 245 ◽  
Author(s):  
Kumaresan Gladys Anbarasu ◽  
Lakshmanan Vijayaraghavan ◽  
Narayanaperumal Arunachalam
Keyword(s):  
Experimental Study ◽  
Optical Glass ◽  
Surface Generation ◽  
Polishing Process

Polishing process effect on the image of dispersed precipitates

1984 ◽  
Vol 42 ◽  
pp. 534-535
Author(s):  
C.T. Hu ◽  
C.W. Allen
Keyword(s):  
Matrix Material ◽  
Specimen Preparation ◽  
Second Phase ◽  
Precipitate Particle ◽  
Polishing Process ◽  
Second Phase Particles ◽  
The Matrix ◽  
Surface Profiles ◽  
Thinning Process

One important problem in determination of precipitate particle size is the effect of preferential thinning during TEM specimen preparation. Figure 1a schematically represents the original polydispersed Ni3Al precipitates in the Ni rich matrix. The three possible type surface profiles of TEM specimens, which result after electrolytic thinning process are illustrated in Figure 1b. c. & d. These various surface profiles could be produced by using different polishing electrolytes and conditions (i.e. temperature and electric current). The matrix-preferential-etching process causes the matrix material to be attacked much more rapidly than the second phase particles. Figure 1b indicated the result. The nonpreferential and precipitate-preferential-etching results are shown in Figures 1c and 1d respectively.

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