In most cases, the system control is made in a sampled manner, measuring the controlled value at a predefined frequency given by the sampling time. However, not all processes provide relevant information at regular intervals, especially in manufacturing. To reduce the costs and complexity of systems, event-based measuring is necessary. To control this kind of process, an event-based controller is needed. This poses some challenges, especially between the event-triggered measurement, as the process runs in an open loop. In the literature, most event-based controllers are based on the comparison of the error value with a predefined value and activate the controller if this value is crossed. However, in this type of controller, the measured value is measured at a predefine interval and is not suited for most event-based processes. In manufacturing systems, the most usual event-based process is represented by the conveyor transportation system. In this process, the product position is measured only in key locations on the conveyor. For the optimal operation of a flexible manufacturing system, the presence of a product in a key location at predetermined intervals is necessary. For this purpose, this article presents an event-based PID controller implemented on a conveyor transportation system.

In recent years, most of the research in the field of smart grids integrating renewable energy sources assumed energy efficiency as a scheduling objective. However, the aspects of energy consumption or energy demand have not been described clearly, even though they have been proven to be an effective way of reducing energy consumption. In this context, this study aimed to cover a key research challenge in the field, such as the development of an intelligent strategy for solving energy consumption scheduling problems. The added value of our proposal consists of classifying individual consumption profiles assigned to each operation cycle phase, instead of considering an average of non-varying consumption of household appliances. Within this hybrid approach, the proposed explainable system, based on self-organizing maps of neural networks, fuzzy clustering algorithm, and scheduling technics, correlates the complex interrelation between power generated from renewable energy sources in a smart grid, prosumers’ load behaviors, and the consumption profile of controllable or uncontrollable appliances. The tests were made using green energy consumption and production from real monitored data sets. The load-shifting algorithm that was used to reduce energy consumption from the national energy grid proved its effectiveness. In fact, consumers paid 25% less for the energy they used from the national energy grid during the times when the amount of electricity produced from renewable sources was reduced as a result of weather conditions.

This paper proposes a redesigned model for transforming an assembly/disassembly mechatronics line into a flexible manufacturing system. The flexible manufacturing system model considered is based on a multi-agent manufacturing paradigm in the design of the manufacturing process but borrows elements from the reconfigurable manufacturing paradigm, proposing a modular extension of the system. With a minimal hardware update of the manufacturing system, the number of customizable products that can be produced was extended from one model to five models. This redesign to produce more models in a flexible way was possible primarily due to the characteristics of the initial product. To better understand and test the proposed modification, a Petri net model is proposed and simulations for two of the models are made. Using the simulation data and the proposed manufacturing process models a workstation equipped with an ABB robot and a transportation system equipped with a SCARA robot are introduced. Both systems have dual porpoises, the workstation can operate on the fabrication line or as an independent flexible manufacturing cell, and the transportation system is used to transport products from both the manufacturing cell and from the fabrication line considering the modularization and the configuration task from the reconfigurable manufacturing paradigm.

This paper presents a revolutionary approach to image analysis, specific to quality testing (QT) processes performed in industrial manufacturing processes, which integrates specific concepts of Industry 4.0 and Industry 5.0. The results of the research were translated into an innovative software interface, finally customized for testing the manufacturing quality on the mechatronic assembly and disassembly lines. The QT control structure, subordinated to the new technologies of Industry 4.0 and Industry 5.0, proposes that the acquisition sequences, analysis, decision -making and historical recording of quality data, be implemented through a server application (SA) and remotely through client applications (CA). Additionally, the system’s hardware configuration and embedded software components function analogously to an Internet of Things (IoT) sensor, providing a framework for real-time process analysis and control. The QT system is structured as a Machine Vision (MV) framework, leveraging advanced image processing techniques to extract relevant information, thereby enhancing the reliability and detail of quality assessments. In this setup, the mechatronic line is seamlessly integrated with cutting-edge analytical and control functionalities, enabling robust production monitoring and remote-control capabilities through the SA. Concurrently, the CA software offers enhanced flexibility for customizing production orders in response to customer demands. Following the implementation of this MV-based control structure, which utilizes the developed software interface for image analysis, a series of performance tests were conducted to validate system efficacy and adaptability within an automated production environment.