Search

The manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making., CC BY 4.0CONTACT Andreas Lind andreas.lind@his.se Global Industrial Development, Scania CV AB, Södertälje, SwedenThe authors appreciatively thank the support of Scania CV AB, the research school Smart Industry Sweden (20200044) and the research project Virtual Factories with Knowledge-Driven Optimisation (2018-0011) funded by the Knowledge Foundation via the University of Skövde. With this support the research was made possible.The work was supported by the Stiftelsen för Kunskaps- och Kompetensutveckling [20200044]; Stiftelsen för Kunskaps- och Kompetensutveckling [2018-0011].

As we find ourselves in the midst of the fourth industrial revolution, also known as Industry 4.0, the digital transformation of products, processes, and systems, along with their interconnectedness, is of utmost interest. To ensure future competitiveness in the manufacturing sector, the integration of advanced manufacturing technologies and advanced information technology is essential. Information technologies and knowledge are deeply intertwined with industrial equipment, processes, products, and systems, posing a challenge in transitioning today’s manufacturing industry into the digital era. The manufacturing sector will require adequate methods, a conducive working environment, new tools, and lifelong training to support its employees. This article describes a joint effort of five Swedish universities with the ambition to strengthen the competitiveness and innovativeness of the national manufacturing industry through highly competent researchers and future leaders. The collaboration is in the form of an industrial graduate school, combining the efforts of five universities, 16 graduate students, and 12 companies or organisations. This article will outline how the graduate school has been organized, the joint efforts that have been made to assure the development of all parties, organisations and individuals, and will also outline some of the key success factors that have been identified thus far in the project., SMART PM

Augmented reality smart glasses (ARSG) have been available as a commercial product since 2015. Many potential usage areas have been identified, including industrial use. The needs from industry have evolved, with more emphasis being put on sustainability. While ARSG can help improve efficiency and sustainability, there are also similarly associated costs to their implementation and use. This paper aims to present a process for how to choose ARSG for specific use cases as assembly operator support while considering the sustainability of their implementation. A narrative review of the literature was made to identify the current understanding of the environmental impact of ARSG, as well as what has been considered in regards to ARSG being integrated into a manufacturing environment. The analysis of the literature resulted in a proposed decision process. The decision process serves as a baseline for how to guide the decision of whether ARSG could be a suitable solution and, if so, what aspects to consider in the choosing of the ARSG model. Future work includes collaboration with industry to further improve the decision process based on empirical input., CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: O. Danielsson; University of Skövde, Sweden; email: oscar.danielsson@his.se

OCCUPATIONAL APPLICATIONS In the context of Industry 5.0, our study advances manufacturing factory layout planning by integrating multi-objective optimization with nature-inspired algorithms and a digital human modeling tool. This approach aims to overcome the limitations of traditional planning methods, which often rely on engineers’ expertise and inputs from various functions in a company, leading to slow processes and risk of human errors. By focusing the multi-objective optimization on three primary targets, our methodology promotes objective and efficient layout planning, simultaneously considering worker well-being and system performance efficiency. Illustrated through a pedal car assembly station layout case, we demonstrate how layout planning can transition into a transparent, cross-disciplinary, and automated activity. This methodology provides multi-objective decision support, showcasing a significant step forward in manufacturing factory layout design practices. TECHNICAL ABSTRACT Rationale: Integrating multi-objective optimization in manufacturing layout planning addresses simultaneous considerations of productivity, worker well-being, and space efficiency, moving beyond traditional, expert-reliant methods that often overlook critical design aspects. Leveraging nature-inspired algorithms and a digital human modeling tool, this study advances a holistic, automated design process in line with Industry 5.0. Purpose: This research demonstrates an innovative approach to manufacturing layout optimization that simultaneously considers worker well-being and system performance. Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) alongside a Digital Human Modeling (DHM) tool, the study proposes layouts that equally prioritize ergonomic factors, productivity, and area utilization. Methods: Through a pedal car assembly station case, the study illustrates the transition of layout planning into a transparent, cross, CC BY 4.0Taylor & Francis Group an informa businessCONTACT Andreas Lind andreas.lind@scania.com, alt. andreas.lind@his.se Scania CV AB, Södertälje, SwedenThe authors appreciatively thank the support of Scania CV AB, the research school Smart Industry Sweden (20200044) and the research project Virtual Factories with Knowledge-Driven Optimization (2018-0011) funded by the Knowledge Foundation via the University of Skövde. With this support the research was made possible.

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, CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: M. Birtic; School of Engineering Science, University of Skövde, Skövde, Högskolevägen, Box 408, 541 28, Sweden; email: martin.birtic@his.se

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 w, CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: M. Birtic; School of Engineering Science, University of Skövde, Skövde, Högskolevägen, Box 408, 541 28, Sweden; email: martin.birtic@his.se