Högskolan i Skövde

Production simulation holds great promise for industrial applications. Virtual commissioning and discrete event simulation are production simulation techniques that are both economically and operationally justified in theory. However, their practical use is limited by high initial costs and expertise, as well as time and effort requirements. This study proposes a model-based engineering framework with a focus on developing and utilizing these two types of techniques in parallel, with the aim of reducing overall costs while simultaneously harnessing the benefits of both methods’ complementary strengths. The proposed framework serves as a basis for future development of methodologies, processes, and tools aimed at streamlining the joint and parallel creation and utilization of said models through simulation-driven systems development. The study presents the framework and an illustrative example that demonstrates its feasibility and practical utility. 

Efficient energy management is particularly significant for the automotive industry due to its high consumption in foundry operations. To satisfy ambitious environmental goals, it is imperative to develop tools and methods for optimizing energy consumption in such contexts. The foundry is currently equipped with sensors and data collection equipment, which presents equipment-specific graphs of energy consumption over time. Although these graphs can reveal patterns and trends in energy consumption by themselves, more systematic methods are needed to utilize this data to investigate and optimize improvements that reduce energy waste. This article investigates the use of Discrete Event Simulation as a tool for leveraging collected historical energy consumption data. The aim is to explore how such data can be collected and translated into simulation variables to generate insights that support improvement initiatives. A case study was conducted to explore this approach, demonstrating that tracking energy consumption data provides a valuable input for Discrete Event Simulation modeling. The findings suggest some methods for data collection of different equipment, its modelling, and that further investigation in this direction could reveal opportunities for optimized energy management in energy-intensive industries.

Knowledge graphs are generating significant interest in industry and research. These graphs can be enriched with data to represent aspects of production systems such as their structure, component interrelationships, and conditions. This provides opportunities to gain insights into system behavior, performance, and states. Such insights could potentially be leveraged by a wide range of technologies for a multitude of purposes and applications such as system control, process optimization, and informed decision making. However, the existing literature addressing industrial applications of knowledge graphs related to production systems remains limited in scope and depth. This underscores the importance of developing methods for exploring the potential use and implementation of knowledge graphs in such systems. The primary focus of this study centers on facilitating such exploration by developing a virtual commissioning simulation framework. A modular production system is modelled that leverages physics, moving product dynamics, and incorporates authentic PLC and robot programs. A knowledge graph is integrated and enriched with data representing various aspects of the system. An application is developed to facilitate product routing and prioritization. A service-oriented approach is used that leverages graph data processing and exchange for service registration and matching. System simulations are conducted and subsequently the framework is evaluated for outcomes and findings. This study demonstrates the successful design and implementation of a production system simulation framework that uses knowledge graphs for system functionality. It demonstrates the exploration of knowledge graph applications through the development of a modular and service-oriented system that includes system functionality supported by the graph. The results highlight the potential of simulation suggesting its capacity for valuable exploration regarding potential applications of knowledge graphs within production systems. 

Production systems of the future may be in constant flux and reconfiguration, continuously adapting to changing production conditions. Digital models and simulation are powerful tools that can be used for their design and operation. These models must co-evolve with the physical system to sustain their usefulness and relevance. This poses a significant barrier, given the complexities involved in their efficient creation and maintenance. To understand whether certain system design concepts make the simulation process easier, this study aims to investigate a combination of concepts that promote reconfigurability and flexibility to explore whether they can positively influence the simulation process. By integrating modularization, interface standardization, and a service-oriented architecture it is believed to support faster and easier creation and updates of digital models. Modularization enhances flexibility by decomposing complex systems into independent, interchangeable modules. Standardizing interfaces ensures uniformity and compatibility among modules. Using a service-oriented architecture entails the encapsulation of various functionalities within modules as services, which can be dynamically requested. Shedding light on the advantages arising from modeling and simulating systems adhering to the mentioned concepts the research also aims to lay the groundwork for further investigation into the potential synergies of these promising production concepts. The study’s methodology includes modeling and programming of industrial robotic production modules adhering to predefined physical and logical interfaces. Interoperability and service orchestration are achieved through a service-oriented architecture. A simulated Manufacturing Execution System is integrated to facilitate handling of module services, product data and service requirements. Finally, a specialized software plugin was developed to support rapid module instantiation into a production system for evaluation. Results suggest that using a modular approach may ease modelling and simulation efforts and could be supported further by developing tailored tools for rapid system development. 

Flexibility is often cited as a desirable key characteristic of modern production systems. In ultra-flexible production, machinery and layouts are in a constant state of adaptation to accommodate changing orders, varying products, or evolving conditions. Cyber-physical integration has been proposed as a potential approach to increasing system flexibility with Cyber-Physical Production Systems (CPPS) and Digital Twins (DT) as central concepts. While numerous architectures, frameworks and approaches have been proposed for CPPS and DT development, further research is motivated regarding the development of a requirement-based framework that links together the high-level system property of flexibility and lower-level system components, enabling the analysis, prescription and comparison of systems. Such a framework could enable manufacturers to continuously evaluate and improve manufacturing systems' flexibility as well as make informed design decisions. Ultimately enhancing system flexibility and responsiveness to changing production conditions. This study aims to initiate the development and formulation of such a requirements-based framework linking flexibility and lower-level system components. Additionally, it seeks to introduce the concept of a”cyber-physical continuum, ” which the study aims to define as a potential quantifiable indicator reflecting flexibility within production systems. This is achieved by leveraging prior CPPS research based on high-level system requirements. These requirements were expanded by branching each requirement into lower-level components creating a more granular scope and providing a finer lens for analysis and assessment. The framework was then applied to assess a high-mix, low-volume manufacturing scenario. Application of the preliminary framework in the case study indicates its potential utility in providing a useful view of the cyber-physical content of a system. Moreover, it serves as a valuable guide for pinpointing areas for improvement and development. By developing a framework that seamlessly links high-level flexibility requirements with detailed implementation requirements, systems can be comprehensively evaluated, methodically prescribed, and effectively compared. As future work, further refinement and validation of this framework will be crucial to ensuring its validity and applicability across diverse manufacturing contexts. 

Industrial automated systems are mostly designed and pre-adjusted to always work at their maximum production rate. This leaves room for important energy consumption reductions considering the production rate variations of factories in reality. This article presents a multi-objective optimization application targeting cycle time and energy consumption of a robotic cell. A novel approach is presented where an existing emulation model of a fictitious robotic cell was extended with low-level electrical components modeled and encapsulated as FMUs. The model, commanded by PLC and Robot Control software, was subjected to a multi-objective optimization algorithm in order to find the Pareto front between energy consumption and production rate. The result of the optimization process allows selecting the most efficient energy consumption for the robotic cell in order to achieve the required cycle.