Fabrication of Optical Components by Ultraprecision Finishing Processes

Ghosh, Gourhari; Sidpara, Ajay; Bandyopadhyay, P.P.

doi:10.1007/978-3-319-68801-5_5

Cited by 14 publications

(8 citation statements)

References 96 publications

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“…Surface finish is an important constraint to assess the materials functional performance. Ghosh (2018) concluded that the optical materials to be utilised in optical industries are expected to acquire nanometric finish without any surface or sub-surface damage.…”

Section: Surface Roughness Of Al 7075mentioning

confidence: 99%

Experimental and theoretical investigation into surface roughness and residual stress using ball end magnetorheological finishing

Sharma

Niranjan

2022

JER is an international, peer-reviewed journal that publishes f

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The generation of residual stresses by grinding process on the surface for a soft material results in poor performance and diminished life. In this paper, grinding-induced residual stresses is addressed and an attempt is made to relieve these residual stresses while achieving nano level surface finish using ball end magnetorheological finishing (BEMRF) process. BEMRF is a recently developed process that is effectively used to for fine figuring and polishing of a variety of magnetic and non-magnetic materials. In our present work we will demonstrate the relieving of residual stresses and reduction in surface roughness generated after surface grinding of Aluminium 7075 workpiece using BEMRF process. The impact of various BEMRF variables on reduction in surface roughness and residual stresses are discussed statistically and graphically. Residual stresses of workpiece surfaces have been measured by using portable X-ray residual stress analyzer. Significant process parameters affecting the residual stresses during polishing of workpiece are obtained using Analysis of variance (ANOVA) and F-test. It has been observed that reduction in residual stress is achieved along with nano level surface finish on aluminium workpiece surface using BEMRF technique.

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Section: Surface Roughness Of Al 7075mentioning

confidence: 99%

Experimental and theoretical investigation into surface roughness and residual stress using ball end magnetorheological finishing

Sharma

Niranjan

2022

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

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“…Further the early research was undertaken by Hertzfeld, who showed a high pressure phase transformation of Germanium in ductile milling [ 6 , 30 , 31 , 32 , 33 ]. Recent research has focused heavily on ductile regime grinding of brittle materials [ 30 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Ductile Cutting Regime in Face Milling of Sintered Silicon Carbide

et al. 2022

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In this study, sintered silicon carbide is machined on a high-precision milling machine with a high-speed spindle, closed-loop linear drives and friction-free micro gap hydrostatics. A series of experiments was undertaken varying the relevant process parameters such as feedrate, cutting speed and chip thickness. For this, the milled surfaces are characterized in a process via an acoustic emission sensor. The milled surfaces were analyzed via confocal laser scanning microscopy and the ISO 25178 areal surface quality parameters such as Sa, Sq and Smr are determined. Moreover, scanning electron microscopy was used to qualitatively characterize the surfaces, but also to identify sub-surface damages such as grooves, breakouts and pitting. Raman laser spectroscopy is used to identify possible amorphization and changes to crystal structure. We used grazing incidence XRD to analyze the crystallographic structure and scanning acoustic microscopy to analyze sub-surface damages. A polycrystalline diamond tool was able to produce superior surfaces compared to diamond grinding with an areal surface roughness Sa of below 100 nm in a very competitive time frame. The finished surface exhibits a high gloss and reflectance. It can be seen that chip thickness and cutting speed have a major influence on the resulting surface quality. The undamaged surface in combination with a small median chip thickness is indicative of a ductile cutting regime.

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“…The American counterpart "Harnessing Light" describes CCP and the production of highprecision optics, for example for EUV technology (extreme ultraviolet radiation), as one of the key technologies of the twenty-first century. Here, roughnesses of 0.1 nm rms and 1 nm peak-to-valley (PV) are achieved [18]. For sustained repeatability, the performance of the manufacturing processes must be increased [19].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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Fabrication of Optical Components by Ultraprecision Finishing Processes

Cited by 14 publications

References 96 publications

Experimental and theoretical investigation into surface roughness and residual stress using ball end magnetorheological finishing

Experimental and theoretical investigation into surface roughness and residual stress using ball end magnetorheological finishing

Experimental Analysis of Ductile Cutting Regime in Face Milling of Sintered Silicon Carbide

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

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