CerDef-Detector: automated detection of surface defects in buzzer ceramic discs based on deep learning and machine vision

Key Points

• Surface defects were automatically detected using deep learning algorithms, enhancing manufacturing processes.
• The model achieved an accuracy rate of 95% in identifying defects across various ceramic disc samples.
• Analysis utilized machine vision techniques to automate inspection, improving efficiency in quality control.
• These findings highlight the potential for deep learning to streamline defect detection, indicating a shift in manufacturing methods.