Achieving effective and adaptable production in smart manufacturing requires integrating simulation models at many system levels, including machines, cells, and plants. Because many data formats, modeling techniques, and technologies exist, connecting these models is still challenging. Existing frameworks and standards, including the Asset Administration Shell (AAS), aim to support such integration but often fall short of enabling tool independent and vendor neutral interoperability. This paper tackles these challenges by offering a practical framework for multi-level simulation integration using currently available tools and resources. Using examples from the literature, the framework shows practical ways to connect simulations across manufacturing hierarchies and highlights the importance of careful tool selection and alignment for effective implementation.

Bringing together simulation models and real manufacturing systems is central to creating effective digital twins (DTs) and cyber-physical production systems. This study emphasis on the communication standards that make such integration possible, showing how information can move smoothly from a single machine to an entire production cell and, ultimately, the whole plant. The analysis is based on academic sources and global standards. We look at the structure, semantic modeling, time performance, and how well standardized communication protocols work for system integration. The research demonstrates that these standards collectively facilitate a seamless exchange of information between digital and physical realms. Drawing from existing literature, it formulates an integrated framework for communication spanning from the machine level to the cell and plant levels, grounded in these standards. It concludes that standardized communication is what makes the ISO 23247 digital twin framework operate and is what makes it possible to have scalable, secure, and interoperable manufacturing environments.

ABSTRACT Digital twin (DT) technology has demonstrated advantages in various industries, including manufacturing, service, and healthcare. In particular, the significance of this technology has grown substantially in the manufacturing sector, particularly in light of Industry 4.0 developments. In the past ten years, much research has been conducted on the framework and methods for DT implementation in a manufacturing system. However, the current status of DT implementation remains unclear, particularly about the level of integration, which refers to the extent and direction of data exchange between the physical system and its digital counterpart, and the extent of coverage across manufacturing system components such as machines, tools, workers, and control systems. This study presents a systematic literature review to examine the current status of DT implementation about key manufacturing system components and provides directions for future research. It first explores the purpose and historical background of DTs, as well as the key challenges associated with their implementation. Based on these insights, the study then proposes a four-step planning framework for implementing an intelligent DT in manufacturing systems.

Virtual commissioning has gained significance in the contemporary manufacturing scene, as it can employ powerful emulations with the control logic of a machine, work cell, or even an entire production system that can be tested and optimized before being deployed in real-time. The literature contains plenty of studies that address virtual commissioning independently at the machine, cell, and plant levels. However, it lacks examples of connected virtual commissioning on several levels. Using a simple example, this study tried to perform virtual commissioning on several levels one by one with an emphasis on the potential for integrating several levels. As a result, this paper discusses possibilities and challenges for integrating virtual commissioning at the machine, cell, and plant level, enabling more cohesive and efficient commissioning processes across the entire production system hierarchy.

Energy efficiency plays a crucial role in the field of industrial automation, particularly in relation to conveyor belt systems that operate continuously in various sectors, including mining, logistics, and manufacturing. Smart Industrial Components (SIC) are required to process, manage, and produce a substantial amount of different types of IIOT (Industrial Internet of Things) data. This historical data is the main source for analysing the current and future conditions of the components. In this paper, a predictive analytics algorithm is proposed to analyse the trend of real-time and future power consumption. The proposed algorithm seamlessly combines generative AI with IIoT data to enhance predictive modelling capabilities. This study highlights key challenges, methodologies, and future directions in leveraging AI-driven analytics for industrial energy management, as well as investigates the application of predictive data analytics for conveyor belt systems focusing on two key aspects: (1) historical data analysis to monitor the current condition of the conveyor belt system and (2) the use of generative AI for future prediction essential for analysing future trends of power consumption running at the specific condition.

This study explores how CEE (Cyclic Event Evaluator) function of a simulation tool can be used for the virtual commissioning of a robotic work cell. By using this approach, control logic can be continuously developed and tested throughout the work cells design process. The entire work cell operates within an event-based simulation, where each eventsuch as a sensor signal or a robot actiontriggers another event. To keep things organized, the logic for each event is built as a separate module. These modules are tested individually and then combined to ensure they work together seamlessly. Once the work cell is fully developed, these modules can be directly used to create a virtual PLC program in a step-by-step manner. This method simplifies virtual commissioning because the PLC logic is essentially pre-built and validated within the simulation. As a result, transitioning from simulation to real-world implementation becomes much more efficient and reliable.

This article presents a case study that was conducted at a renowned Danish manufacturing company that desired to employ AGVs (automated-guided vehicles) in one of its production facilities. The main goal was to create an AGV (automated-guided vehicle) system that is well synchronized with the manufacturing facility so that intralogistics problems are avoided during manufacturing activities. AGV routing and scheduling, loading, and waiting periods, battery management, and failure management were all considered when developing the AGV logic. As a result, it was confirmed that the AGV system in place can support a production system to meet pulse time requirements. A hierarchically structured discrete event simulation model was created to examine the logic of AGVs and the interplay between AGVs and manufacturing operations. The simulation study confirmed that AGV implementation will not affect the production system's ability to meet the set pulse time requirements. Furthermore, the simulation study offered meaningful insights regarding the layout, quantity, and control logic of AGVs.

Shifting from a dedicated or flexible manufacturingFlexible manufacturing system to a reconfigurable manufacturing systemReconfigurable Manufacturing Systems (RMS) (RMS) requires a significant amount of time, money, and effort. Therefore, it is vital to verify beforehand that the potential reconfigurable solution will be able to achieve the organizational objectives. Discrete event simulationDiscrete event simulation offers the opportunity of assessing several reconfigurable alternatives against the set objectives. This study signifies the importance of using discrete-event simulationDiscrete-Event Simulation as a tool to verify several reconfigurationReconfiguration options. An industrial case example has been presented in the study to elaborate on the role of discrete event simulationDiscrete event simulation in the implementationImplementation methodology of RMSs. The study concluded that discrete event simulationDiscrete event simulation is one of the important tools to consider in the RMS implementationImplementation methodology.

The development of injection molding tools is an expensive, time-consuming, and resource-intensive process offering little to no flexibility to adapt to variations in product design. Metal additive manufacturing can be used to produce these tools in a cost-effective way. Nevertheless, in an industrial context, effective methods are missing for the selection of the most suitable technology for the given tooling project. This paper presents a method to compare process chains based on additive and conventional subtractive technologies for the manufacturing of metal tooling for injection molding. The comparison is based on a technology focused-performance analysis (TFPA) through computer simulation performed using Tecnomatix Plant Simulation developed by Siemens Digital Industries Software combined with a customized cost–benefit economic analysis tool. The analysis of the technology comparison highlights potential bottlenecks for production, such as the printing phase and the heat treatment. It also gives a deeper understanding of the technology maturity level of conventional milling machines against laser powder bed fusion machines. The result is that the total costs for an insert made by AM and CM are indeed rather similar (the cost difference between the two tooling process chains is lower than 5%). The cost analysis reveals major costs drivers in the production of high-performance molding tools, such as the cutting tools employed for the milling steps and their changeover frequency. The industrial case of a 32-cavity mold insert for plastic injection molding is used to perform the study, develop the analysis, and validate the results.

This paper elaborates on the role of computer simulations in the small and medium enterprises (SMEs) collaborative robot (cobot) implementation process. The cobot implementation process of a Danish small-medium enterprise (SME) is studied and the challenges along the way are highlighted. The main challenges discovered are underutilization of the cobot and inefficient work cell design. This paper argues that the use of computer simulations along with PDCA methodology can help to overcome these challenges. This case study validates that the use of simulation can help SMEs in an efficient work cell design and layout planning.

This paper describes a framework for using 3D simulation as a safety assessment tool based on ISO/TS 15066:2016 guidelines for a cobot work cell. A human-robot collaboration-based work cell has been developed. The digital counterpart of this work cell is developed beforehand to perform several safety assessments based on ISO/TS 15066:2016. It is observed that the 3D simulation model can be used as a safety assessment tool to ensure the safety of a cobot work cell even before its creation. The study also signifies the simulation software for safety and human factors in the design of a cobot cell. .