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

Using fault detection and classification techniques for machine breakdown reduction of the HGA process caused by the slider loss defect

“…The Convolution Neural Network (CNN) was applied to detect the fault on the mount head surface in the slider attachment process. The result demonstrated that CNN could be used to classify the fault condition by using image data [1]. High-Speed automation machines are widely used in HGA production process, therefore Fault Tolerant Control (FTC) utilizing PI servo with observer was used to increase the reliability of the machine control system [2].…”