Survey Paper on Visual Inspection of a Mechanical Part using Machine Learning

Abstract

Product manufacturing industries produce machine parts in vast quantities. Quality assurance and control of these parts is necessary to maintain the standard defined by industry. Traditional methods of inspection using quality control inspector leads to inconsistency and imprecise decisions. Thus, Current manual inspection procedure is erroneous. The purpose of this research is to overcome the flaws in the manual inspection using automated visual inspection system. Collecting data for processing is done with the help of Image acquisition system, which captures image of the part on production line. This captured image is pre-processed and feature extraction is performed resulting in generation of feature vectors. Using machine learning model for defect classification, we train ML algorithm with these feature vectors to classify the defects found during the inspection. defect detection and other quality control actions are performed based on the reference part. The detection of defects at an early stage helps in increasing quality control factor and smoothing the process of production. automated visual inspection system saves time and is efficient. This survey definitively answers the question regarding the defect detection of machine parts.