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Gear defects are a major reason for poor quality and of embarrassment for manufacturers. 0 Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying gear defects requires more automotive and accurate inspection process. Considering this lacking, this research implements a Gear Defect Recognizer which uses computer vision methodology with the combination of local thresholding to identify possible defects. The recognizer identifies the gear defects within economical cost and produces less error prone inspection system in real time. In order to generate data set, primarily the recognizer captures digital gear images by image acquisition device and converts the RGB images into binary images by restoration process and local threshold techniques. Later, the outputs of the processed image are the area of the faulty portion and compute the possible defective and non -defective gear as an output. Detection of bad quality plastic gears is critical for any manufacturing unit trying to make a mark in the market in terms of quality standard and cost. Here we explore the possibility of using image segmentation and algorithms like non-smooth surface detection algorithms to automate the process of defect detection. In these plastics we have picked industrial strength plastic gears used typically in applications like robotic arms where quality in paramount for the functioning of the device. In this paper review of various gear defects and the possible automated solutions using image processing techniques for defect detection is given.

Analyze Gear Failures and Identify Defects in Gear System for Vehicles Using Digital Image Processing