A Neural Network Approach for Image Classification of Welded Joints Using Evolutionary Computing Algorithm

In general, a variety of vision inspection system is used to detect the surface defects such as problem of inaccuracy in images, non-uniform illumination, noise and deficient contrast in welding joints. In this work, a new machine vision inspection system is introduced to inspect the quality level for imperfection of Metal Inert Gas (MIG) welded joints. In this proposed system, images of welded surfaces are captured through CCD camera. From these images, the regions of interest are segmented and features using principal component of the images (Eigen vector) are extracted. Principal component analysis provides good dimensionality reduction than other features. This procedure is repeated for four different types of welding joints. Finally, welded joints are classified using Differential Evolution Algorithm based Artificial Neural Networks (ANN). Eigen vectors of images are considered as input of ANN and different types of welded joints are considered as output of network. In this work, welding standard EN25817 is considered for surface quality level for imperfections. Differential Evolution Algorithm (DEA) based Artificial Neural Network is population based search algorithm, which is an improved version of genetic algorithm. It is found to be faster and robust in optimization. The result of this proposed system is 98.15 in overall accuracy level. This proposed system assures that convergence rate of DEA based ANN holds goods.