Modern manufacturing systems are influenced by the growing complexity of mechatronics, control systems, IIoT, and communication technologies integrated into cyber-physical systems. These systems demand flexibility, modularity, and rapid project execution, making digital tools critical for their design. Virtual commissioning, based on digital twins, enables the testing and validation of control systems and designs in virtual environments, reducing risks and accelerating time-to-market. This research explores the development of digital twin models to bridge the gap between simulation and real-world validation. The models identify design flaws, validate the PLC control code, and ensure interoperability across software platforms. A case study involving a modular Festo manufacturing system modelled in Tecnomatix Process Simulate demonstrates the ability of digital twins to detect inefficiencies, such as collision risks, and to validate automation systems virtually. This study highlights the advantages of virtual commissioning for optimizing manufacturing systems. Communication testing showed compatibility across platforms but revealed limitations with certain data types due to software constraints. This research provides practical insights into creating robust digital twin models, improving the flexibility, efficiency, and quality of manufacturing system design. It also offers recommendations to address current challenges in interoperability and system performance.

The efficient and sustainable design of robotic manufacturing cells is a critical aspect of modern industrial processes, for which energy and time efficiency play significant roles in achieving sustainability goals. In industrial practice, robotic cell design often involves methods such as predefined layout templates, empirical rules for positioning, and simulation-based validation. While these approaches provide a practical starting point, they may not fully account for the complex interdependencies between robot configuration, energy consumption, and operational efficiency. Consequently, opportunities for optimizing resource usage are frequently overlooked. This paper presents a novel methodology for optimizing the deployment of industrial robots and their peripherals, focusing on minimizing energy and time costs to enhance the sustainability of industrial processes. The proposed approach, grounded in experimental measurements and simulations, was validated through an experimental model of a welding robot station. The methodology integrates the analysis of the relationship between the robot base position, trajectory, and energy consumption. The results indicate that adjusting the relative positions of robots and work points can achieve energy savings of approximately six percent. Specifically, optimization reduced energy consumption by 1.6731 Wh per work cycle, translating to an annual savings of 0.8794 MWh for a 60 s clock cycle. These findings highlight the practical applicability of the proposed methodology, demonstrating its potential to significantly improve the energy and time efficiency of robotic workplaces.

A digital twin is an effective tool for the design, testing, and validation of control strategies for electro-pneumatic actuators in industrial automation. This study presents and compares three different digital twin models of a pneumatic cylinder with varying levels of physical fidelity—from basic discrete control, through analogue control without pneumatic dynamics, to a complex model simulating pressure, friction, and airflow. The experiments were conducted using the Siemens NX Mechatronics Concept Designer, integrated with the SIMIT emulation platform and a PLC control system via the standardized OPC UA protocol. The main objective was to evaluate simulation accuracy, model flexibility for testing various control strategies, and the ability of the digital twin to reflect changes in PLC algorithms. The results showed that while simple models are suitable for verifying basic logic, only advanced models can realistically replicate the dynamic behaviour of pneumatic systems, including delay phases and pressure influence. A comparison with the experimental study by Jiménez confirmed a strong correlation between the simulated and actual actuator behaviour. In future work, the developed control algorithm will be connected to a physical cylinder to further validate the models and refine control strategies under real-world conditions.

Automated production lines are increasingly being expanded with Industrial Internet of Things (IIoT) devices, creating complex Cyber-Physical Systems (CPSs) that connect physical production with control and information infrastructure. However, the convergence of Information Technology (IT) and Operational Technology (OT) layers creates new entry points for attacks targeting communication availability. Most existing studies analyze Distributed Denial of Service (DDoS) attacks primarily in simulation or testbed environments, with limited experimental verification of their impact on real-world production systems. This article presents an experimental evaluation of the impact of DDoS and Distributed Reflection Denial of Service (DRDoS) attacks carried out directly on a physical automated production line with integrated IIoT infrastructure during real operation. Three attack scenarios (TCP SYN flood, TCP ACK flood, and ICMP reflected attack) were implemented, targeting Programmable Logic Controllers (PLCs), Radio-Frequency Identification (RFID) subsystems, and selected IIoT devices. The results showed rapid degradation of deterministic PROFINET communication, disruption of the link between the OT and IT layers, loss of digital product representation, and physical interruption of the production process. Based on the findings, a minimally invasive security solution based on perimeter protection was designed and experimentally verified. The results emphasize the need to design IIoT-based manufacturing systems with an emphasis on network segmentation and architectural separation of the IT and OT layers.