In this study, crack identification and detection process in sheet metals, which is one of the most important issues especially for sheet metal forming companies, was investigated. Cracks in sheet material, which are frequently seen in mass production, cause vehicle scraps, so the degree of impact and cost are high. Crack tests were performed on 490 different sheets on sheet material in the 0.6-3 mm thickness range, and a crack image dataset was created for sheet material. In addition, 121 non-cracked "accepted-approved" parts were included in the dataset. Due to its superior feature extraction capability, convolutional neural network (CNN) has been widely researched, applied and outperformed other traditional machine learning methods in the field of intelligent fault diagnosis. After that, the cracked and non-cracked part data in the sheet material was divided into training and test data, and the accuracy values. Here, accuracy and verification accuracy rates as 98.5% and 90% were achieved respectively. Accuracy values with VGGNet architecture are 98.75% and 90%. This gives information about the reliability of both the picture data of the faults and the model presented. The results of the research are important as they will form the basis for the detection of crack defects in sheet material, which is very important in mass production applications.