Fault Detection and Classification for Slider Attachment Process using Convolution Neural Network

Abstract

Hard Disk Drive (HDD) utilizes automation machines for the assembly processes used in the industry to achieve higher production rates and lower costs. The Head Gimbal Assembly (HGA) production process has two main parts: glue dispensing and slider attaching by an Auto Core Adhesion mounting Machine (ACAM). The slider attaching process produces a mounted head to the suspension utilizing vacuum pressure to hold and position a slider. The errors from a vacuum leak from any step trigger system alarms resulting in machine downtime and slider loss defective (SLD). This paper proposes a classification algorithm derived from 250x250 micron images of mounted heads are 4 different categories: Good, Fault I, Fault II and Fault III using Convolution Neural Networks (CNN). CNN is a performance model for predictive maintenance before failure. The method has achieved a 95 % accuracy for detection and classification