LAUSR

The detection of low-quality solder joints in hard disk drive manufacturing is a time-consuming, error-prone and costly process that is often performed manually. This paper thus proposes two automated optical solder jet ball joint defect inspection methods for head gimbal assembly (HGA) production. The first method uses a support vector machine (SVM) for fault detection, and the second method uses vertical edge detection to identify solder ball and pad burning defects. The methods were tested with 5530 HGA images, and their performance was compared to a Bayesian-based method. Experimental results show that the vertical edge detection method gave the best results, with an under reject rate of 0.75% and an over reject rate of 1.88%. The accuracy of the vertical edge detection method was 98.2%, which is higher than the accuracy of 89.9% for the Bayesian-based method, and 84.6% for the SVM-based method.