A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0.

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10-30 min, depending on the car model. If 200,000-300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes.

Control of a Production Manipulator with the Use of BCI in Conjunction with an Industrial PLC.

Research in the field of gathering and analyzing biological signals is growing. The sensors are becoming more available and more non-invasive for examining such signals, which in the past required the inconvenient acquisition of data. This was achieved mainly by the fact that biological sensors were able to be built into wearable and portable devices. The representation and analysis of EEGs (electroencephalograms) is nowadays commonly used in various application areas. The application of the use of the EEG signals to the field of automation is still an unexplored area and therefore provides opportunities for interesting research. In our research, we focused on the area of processing automation; especially the use of the EEG signals to bridge the communication between control of individual processes and a human. In this study, the real-time communication between a PLC (programmable logic controller) and BCI (brain computer interface) was investigated and described. In the future, this approach can help people with physical disabilities to control certain machines or devices and therefore it could find applicability in overcoming physical disabilities. The main contribution of the article is, that we have demonstrated the possibility of interaction between a person and a manipulator controlled by a PLC with the help of a BCI. Potentially, with the expansion of functionality, such solutions will allow a person with physical disabilities to participate in the production process.

Data Integration from Heterogeneous Control Levels for the Purposes of Analysis within Industry 4.0 Concept.

Small- and medium-sized manufacturing companies must adapt their production processes more quickly. The speed with which enterprises can apply a change in the context of data integration and historicization affects their business. This article presents the possibilities of implementing the integration of control processes using modern technologies that will enable the adaptation of production lines. Integration using an object-oriented approach is suitable for complex tasks. Another approach is data integration using the entity referred to as tagging (TAG). Tagging is essential to apply for fast adaptation and modification of the production process. The advantage is identification, easier modification, and generation of data structures where basic entities include attributes, topics, personalization, locale, and APIs. This research proposes a model for integrating manufacturing enterprise data from heterogeneous levels of management. As a result, the model and the design procedure for data integrating production lines can efficiently adapt production changes.

Proposal for an IIoT Device Solution According to Industry 4.0 Concept.

Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features they provide; therefore, we decided to design an IIoT device taking advantage of the benefits arising from OPC UA. The design procedure was based on the creation of sequences of steps resulting in a workflow that was transformed into a finite state machine (FSM) model. The FSM model was transformed into an OPC UA object, which was implemented in the proposed IIoT. The OPC UA object makes it possible to monitor events and provide important information based on a client's criteria. The result was the design and implementation of an IIoT device that provides improved monitoring and data acquisition, enabling improved control of the manufacturing process.

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0.

One of the big problems of today's manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.