Machine Learning-Based Wheel Monitoring for Sapphire Wafers

Abstract

The thinning of sapphire wafers is a key process that affects the quality of optoelectronic devices. In the grinding process for sapphire, a hard and brittle material, the grade and surface conditions (wearing and chip loading) of the grinding wheel are the key to continuous processing and reduction of defects. In industry, a common approach is to assume the possible causes of defects by observing the spindle current during the grinding process and the finished product after processing. Thus far, there has been no effective method to quantify the grade and no real-time monitoring technology for grinding wheels. This research proposes a wheel monitoring system that utilizes acoustic emission (AE) signals and radial/axial vibration signals to extract characteristic parameters via popular machine learning classification algorithms (k-NN, ANN, and SVM) in order to identify the signal and characteristic parameters of the wheel during the grinding process. The experimental results show that the AE signal identification accuracy of the grade is excellent, at 99%. The vibration signal of the wheel surface condition during the grinding process is significant as well, yielding an identification accuracy of 91%. The extracted signal characteristics in this research not only quantify the state of the grinding wheel, but also facilitate a wheel monitoring system based on the identification technology. The proposed method for grinding wheel processing status monitoring can be used to establish an automated intelligent grinding system.

Keywords

• Acoustic emission
• feature extraction
• grinding wheel grade
• machine learning
• radial and axial vibration
• wheel loading