An Autoencoder with Convolutional Neural Network for Surface Defect Detection on Cast Components

There is unrealized potential in using automation to alleviate the visual inspection associated with non-destructive testing in manufacturing facilities. The identification of defects during the production can help avoid substantial manufacturing errors by indicating that preventative maintenance should be introduced. The use of an autoencoder for this application reduces the need to generate datasets for various defect types, instead only one training dataset would be needed. To address this, this paper proposes a convolution neural network (CNN) autoencoder approach to detect surface defects on cast components during the production. The proposed method categorizes the data into damaged and undamaged components by clustering based on the loss associated with the reconstructed image. The average F1-score and accuracy from retraining the model 10 times were 89.14 and 88.52%, respectively. Although previous studies have obtained higher metrics, they have focused their efforts on supervised training techniques whereas this research proposes an unsupervised training method with results comparable to the previous studies.