Study on Analysis of Defect Identification Methods in Manufacturing Industry

Ensuring the quality of a product is crucial in the business, and it involves conducting checks, implementing control measures, and monitoring the process. Timely identification of product flaws is vital in the realm of manufacturing quality control. The utilization of automatic defect-detection technology offers more benefits compared to the manual identification of flaws. The initial section of the paper introduces a comprehensive classification system for various types of defects, which may be categorized into six distinct groups: Stain, pitted surface, Crack, black spots, Line, and Mono weld flaw. These faults would lead to a rise in the cost of the product and a decrease in the service life of the manufactured goods. The subsequent section of this article outlines the current state of traditional techniques and learning-based approaches in defect identification within the manufacturing business. We proceed with an examination of several defect detection methods, including statistical, spectral, model-based, and learning-based approaches. The primary objective of this study is to categorize the imperfections found in various items, including fabric material, steel, metal components, leather products, beverage products, and ceramic tiles. A comprehensive analysis has been conducted to evaluate and compare various automated defect detection methods and algorithms based on their distinctive features, accuracy in detecting defects, as well as their strengths and weaknesses.