Predicting Faulty Production Lines Causing Head Gimbal Assembly Damage from Electrostatic Discharge Using Machine Learning

Abstract

This research was conducted to analyze abnormal production lines leading to Head Gimbal Assembly (HGA) damage, mainly caused by electrostatic discharge. HGAs are critical component in hard disk. The HGA was constantly developed to produce higher storage capacity products to delight customer demands. Newer product design HGAs are highly sensitive to electrostatic discharge. This results in more damage and higher scrap costs. The main goal of this research is to leverage artificial intelligence technology to predict abnormal production lines that cause electrostatic damage in the assembly process of HGA. The research aims to develop timely solutions to reduce damage during production. This proactive approach is essential in lowering production costs and ensuring the HGAs have a longer lifespan and increased reliability.