Analysis of Surface Roughness during Surface Polishing of ITO Thin Film Using Acoustic Emission Sensor Monitoring

Abstract

This study investigates the intricate process of surface polishing for ITO-coated Pyrex glass utilizing magnetic abrasive polishing (MAP) while employing acoustic emission (AE) sensors for real-time defect monitoring. MAP, known for its versatility in achieving nanoscale thickness processing and uniform surfaces, has been widely used in various materials. However, the complexity of the process, influenced by multiple variables like cutting conditions, material properties, and environmental factors, poses challenges to maintaining high surface quality. To address this, a sensor monitoring system, specifically one that uses AE sensors, was integrated into the MAP process to detect and confirm defects, providing real-time insights into machining conditions and outcomes. AE sensors excel in identifying material deterioration, microcrack formation, and wear, even in cases of minor damage. Leveraging AE sensor data, this study aims to minimize surface defects in ITO thin films during MAP while optimizing surface roughness. The investigation involves theoretical validation, magnetic density simulations, and force sensor pressure measurements to identify factors influencing surface roughness. ANOVA analysis is employed to determine optimal processing conditions. Additionally, this study compares the identified optimal roughness conditions with those predicted by AE sensor parameters, aiming to establish a correlation between predicted and achieved surface quality. The integration of AE sensor monitoring within the MAP process offers a promising avenue for enhancing surface quality by effectively identifying and addressing defects in real time. This comprehensive analysis contributes to advancing the understanding of surface polishing methodologies for ITO-coated Pyrex glass, paving the way for improved precision and quality in thin-film surface processes.

In Situ Measurement of the Machining State in Small-Diameter Drilling by Acoustic Emission Sensing

“…Numerous studies have been conducted in which AE sensing was used to monitor conditions during various machining operations, such as turning [8][9][10][11], milling [12][13][14][15], reaming [16], honing [17,18], grinding [19][20][21][22][23], or polishing [24][25][26]. Several AE sensing studies have also been reported for drilling [27][28][29]; for example, Gómez and Ferrari described correlations between various AE parameters and thrust force and tool wear [30,31], whereas Patra reported the usefulness of an artificial-neural-network model based on wavelet-packet features for evaluating flank wear [32].…”