Detection of contaminants using image processing on a hard drive reader

Abstract

This research has conducted experiments to detect small contaminants through image processing using the Python language. The objective is to develop an algorithm for detecting contaminants on the Head Gimbal Assembly (HGA) of hard disk drives. This is aimed at addressing the performance issues of the read/write head caused by dust particles. The small and delicate size of the read/write head makes it susceptible to dust, which can affect its efficiency. Traditional visual inspection by humans is time-consuming and prone to errors. The research has developed an algorithm to be used with machinery for detecting small contaminants, providing assistance and reducing the burden of visual inspections by humans alone. Experimental results show that this algorithm can successfully detect contaminants on the read/write heads of hard disk drives with an accuracy of 86.25% and a specificity of 91.67%. This is achieved through the use of image pixelation and logic gates in the image transformation process.