Anomaly detection is an important process that needs to be automated. Metal is an essential material that is heavily used in manufacturing of many different products. Automated detection of anomalies on metal surfaces is a topic that has been widely studied both by the researchers in the academia and by the practitioners. In the recent years, many studies have been conducted that apply deep learning for anomaly detection on metal surfaces. In this study, using NEU-DET data set, we have applied YOLO family models (YOLOv4, YOLOv5, YOLOv6, YOLOv7 and YOLOv8) in order to classify defects of images of steel. The results presented that with respect to mAP50 YOLOv8 has outperformed the other models. In the short term, we aim to utilize real images taken from home appliances products and perform a replication study. As for the long-term research we aim to develop and test our model based on real data set belonging to a Horizon Europe project. YOLOv8 has presented the best performance.

In this article, the decision mechanism of a control system has been created by using big data and by applying an ontology model. This type of control is important in order to minimize, or even eliminate human influence in systematic industrial processes. The development presented in the article in order to make the ontology model compatible with a relational database in the decision mechanism, is a step taken to eliminate the human effect. The method used in the article aims to carry out a decision mechanism under the guidance of big data by using relational databases integrated with the ontology model. In line with this goal, the ontology model associated with relational databases with high prevalence will be able to access the continuous data required for the decision process and enrich the decision mechanism. In this study, when the necessary parameters for the decision process are obtained, dynamic threshold determination is provided by a machine learning model with these parameters. This dynamic threshold varies over various time periods, with the combination of inputs provided to the machine learning model and differences in value. Our test results state that the Decision Tree model predictions’ accuracy is 100%.

In the contemporary rotating machinery, bearings are critical and indispensable parts. Early detection of rolling bearing defects carries crucial importance, because undetected defects on the rolling bearings may end in loss of time, resources, money and even lives. In parallel to the accelerated utilization of deep learning applications in the manufacturing industry, different studies have been conducted to determine and evaluate defects on the surfaces of rolling bearings. In this study, a new system, that contains a hardware platform and software components in order to detect surface defects of the metal rolling bearings has been developed. To detect defects, optic image data of the bearings were used, and then computer vision and artificial intelligence techniques were applied to them. In the system, TC-VISION, the source of big data is the platform designed and developed using the optical camera. The results of the applied CNN algorithms performed better than the targeted values with respect to several metrics. The F1 score obtained is close to 100%. The developed system is aimed to be enhanced further in order to develop a fully automated inspection and quality control system for metal rolling bearing systems appropriate for serial production in real industrial environments.