Vision based identification and classification of weld defects in welding environments: A review

Abstract

This paper is a review for the identification and classification of weld defects in welding environments based on vision. The revolution in research to develop an autonomous system to identify and classify types of weld defects has been attempted for a long time. According to the difficulty in identifying and classifying small flaws in an image of professional skills, they should be formed accordingly. However the identification process takes time depending on the job knowledge, experience, skills and prejudice. The techniques in the identification welding introduced in this paper are compared to each basic stage of development of welding defects classification system in the welding environment. The geometrical parameter and linguistic gray value is used to interpret the characteristics of the data extraction, including size, location, attributes and shape of weld defects. A perfect knowledge of geometry in the weld defect is an important step in assessing the quality of the weld. In welding classification there are several methods to inspect the weld defect term of type of welds, shapes, information of welding defects such as width, location and position used a statistical tools, neural networks, interference fit line profiles diffuse system and according to the average gray level. A good suggestion can be considered in this work are researchers should focus on some new development that work with grayscale profiles as input set for feature extraction which the welds defect area segmentation step it's not necessary. Further improvement can be taken is uses a CCD camera only to reduce the development cost.