A Bayesian Approach to Automated Optical Inspection for Solder Jet Ball Joint Defects in the Head Gimbal Assembly Process

摘要

Automation or selective automation is adopted as a solution to most productivity problems in the hard disk drive (HDD) industry as the industry continues to grow at a 40% compounded annual growth rate. An automated production line for manufacturing the head gimbal assembly (HGA) has been developed as part of the automation solution. In the automated HGA production line, a solder jet ball (SJB) soldering station connects the suspension circuit to the slider body. We propose a Bayesian approach to automated optical inspection (AOI) of the SJB joint in the HGA process, implementing Tree Augmented Naïve Bayes Network (TAN-BN) plus check classifier in-situ using GeNIe/SMILE within the inspection software. The system is further enhanced with a result checker, achieving an overall accuracy of 91.52% with 660 production parts in a blind test.