Factor Effect on Material Removal Rate During Phosphate Laser Glass Polishing

Chen, Guokai; Yao, Kui; Yang, Minghong; Liu, Wenwen; Xu, Xueke

doi:10.1080/10426914.2014.893061

Cited by 13 publications

(3 citation statements)

References 18 publications

(16 reference statements)

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“…Thus, resulting in abrading the work material and increased material removal per unit area. This result is similar to the result reported by Zhang et al 39 and Chen et al 40 in their research studies.…”

Section: Effect Of Process Factors On Mrrsupporting

confidence: 93%

The influence of ultrasonic vibrations on material removal in the silicon wafer polishing using DDCAMRF: Experimental investigations and process optimization

Srivastava

Pandey

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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The present experimental investigation attempts to understand and address the effect of ultrasonic vibrations on material removal in the polishing of silicon wafers (1 0 0). The requisite finishing experimentations were performed on an indigenously developed experimental arrangement of double-disc chemical assisted magnetorheological finishing (DDCAMRF) process with longitudinal vibrations. The MR fluid used in the experiments consists of a water-based suspension prepared by mixing suitable amounts of carbonyl iron particles (CIPs), abrasive particles, and additives or stabilizers. The prepared MR fluid uses both mechanics and chemistry to finish the silicon surface. Mechanics is mainly responsible for micro-scratching of silicon surface, which gets “softened” by hydration utilizing DI water in the MR fluid. In this study, the ‘response surface methodology (RSM)’ was chosen for designing the experiments to evaluate the significance of different process factors, namely polishing speed, abrasive concentration, and ultrasonic power on the material removal rate (MRR) in DDCAMRF process. The material removed from the wafer surface was measured using the precision digital weighing balance. It was observed that the MRR was found to increase with the increase in various process factors used. Further, analysis of variance (i.e., ANOVA) technique with a 95% confidence interval was performed to analyze the significant contribution of different process factors on MRR. The validation of developed model was done by performing experiments on random and optimized set of process factors. From, the statistical investigation it was discovered that ultrasonic power has highest contribution of 57.9% on MRR, followed by the polishing speed (13.3%), and abrasive concentration (12.5%). Furthermore, a genetic algorithm optimization tool was utilized to obtain optimum set of process parameters to maximize MRR.

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Section: Effect Of Process Factors On Mrrsupporting

confidence: 93%

The influence of ultrasonic vibrations on material removal in the silicon wafer polishing using DDCAMRF: Experimental investigations and process optimization

Srivastava

Pandey

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Robotic polishing is used by the companies Safran (formerly Sagem) [12] and Zeiss Semiconductor Technologies [13]. Commercial production machines are available for smaller optics [14,15]. The company Coherent (formerly tin-Sley) uses a gantry portals approach as a polishing machine [16].…”

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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“…6,7 The form accuracy in deterministic polishing is generally related to kinematic accuracy of machine, reliability of surface data acquisition, generation of tool path, selection of machining parameters and so on. [8][9][10] Several studies have been conducted to improve the performances, such as preferable efficiency and less edge effect, in polishing. Cao and Cheung 11 carried out an in-depth research on contact mechanics, wear mechanisms and theory of kinematics in bonnet polishing to reach an insight of material removal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Deterministic polishing of freeform optical surface by iterative intersection tool path in an off-axial three-mirror anastigmat imaging system

Liang

Zhao

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Deterministic polishing as the final step of freeform surface machining can acquire preferable form accuracy. In this article, a deterministic polishing model based on iterative intersection tool path is presented to meet the requirement of high-form accuracy in freeform surface. In the polishing process, on-machine measurement and point set registration method are adopted for installation error extraction and form error calculation. The iterative polishing can be finished without discharging of workpiece by on-machine measurement strategy, which will reduce the processing time and improve the machining efficiency. In addition, a boundary extension method is employed to diminish the surface edge collapse generated by edge effect. Finally, the polishing experiment of freeform optical surface in an off-axial three-mirror anastigmat imaging system is conducted to verify the effectiveness of the proposed model.

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Factor Effect on Material Removal Rate During Phosphate Laser Glass Polishing

Cited by 13 publications

References 18 publications

The influence of ultrasonic vibrations on material removal in the silicon wafer polishing using DDCAMRF: Experimental investigations and process optimization

The influence of ultrasonic vibrations on material removal in the silicon wafer polishing using DDCAMRF: Experimental investigations and process optimization

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

Deterministic polishing of freeform optical surface by iterative intersection tool path in an off-axial three-mirror anastigmat imaging system

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