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The analysis of the grinding mechanism of ITO conductive glass is particularly important for revealing material damage and chip formation. This article proposes the removal mechanism of brittle thin film materials during grinding based on the indentation model. And based on the thin film composite structure of ITO conductive glass, a material model in finite element simulation was established. The surface morphology and chip state of ITO conductive glass were studied through microscale grinding experiments. By comparing the simulation results, it has been proven that the ITO film is removed in a brittle fracture mode during the grinding process, while the removal mode of the glass substrate is influenced by the process parameters. The interlayer fracture between the film and substrate will affect the processing quality. [ABSTRACT FROM AUTHOR]

The thermal error is a significant factor that influences the machining accuracy of machine tools, and error compensation is an economical and effective method for enhancing the accuracy of machine tools. However, establishing a precise thermal error prediction model is crucial for thermal error compensation. In this paper, the subtraction-average-based optimizer-based CNN-GRU neural network (SABO-CNN-GRU) is applied to integrated thermal error modeling. Through conducting a thermal characteristic experiment, temperature rise data and thermal error data were collected from the linear feed system of LXK300X helical groove CNC machine tool. The fuzzy c-means clustering and grey correlation analysis are employed to identify temperature-sensitive points in the linear feed system. By utilizing the temperature rise data from these sensitive points along with feed shaft thermal errors as data samples, and using the SABO algorithm to optimize the CNN-GRU prediction model, the thermal error prediction model of SABO-CNN-GRU is established. To validate its superiority and practicality, a comparative analysis is conducted with traditional thermal error prediction models based on CNN-GRU and SO-ELM. The results demonstrate that SABO-CNN-GRU model outperforms both models in terms of mean absolute error (MAE), root mean square error (RMSE), remaining prediction deviation (RPD), mean square error (MSE), and determination coefficient (R2) in accurately predicting results. Building upon this achievement, this paper develops a real-time thermal error compensation system which effectively reduces maximum thermal errors from 80.5 to 17.6 μm after implementing compensation measures. Effectively reducing the influence of thermal errors and improving the machining accuracy of machine tools. [ABSTRACT FROM AUTHOR]

To investigate the effect of microscale grinding on the quality of ITO conductive glass, this paper conducted microscale grinding experiments on ITO conductive glass. The influence of process parameters on machining quality was analyzed using microgrinding force, surface roughness, and sheet resistance as indicators. Meanwhile, soda-lime glass was set as the control group to analyze the effect of ITO film. The results indicate that brittle fracture is the main removal method for ITO films, and the glass substrate exhibits two different removal methods, brittle and ductile, depending on the process parameters. The ITO thin film layer suppresses the sheet deformation of the glass substrate, increasing the grinding force and reducing the processing quality. Increasing the spindle speed, reducing the feed speed, and reducing the cutting depth can improve the machining quality. The spindle speed has a significant impact on surface roughness, while the cutting depth has a significant impact on the sheet resistance of the thin film. In addition, soda-lime glass chips mainly appear in powder and granular form, while ITO conductive glass also has flaky thin film chips generated by interlayer fracture. [ABSTRACT FROM AUTHOR]