Högskolan i Skövde

Collaborative robot (cobot) applications enhance flexibility and efficiency in the manufacturing industry. Even though they are easier to program, their re-programming and transferability across applications remain challenging in fast-changing settings. Artificial Intelligence (AI) technologies reduce the entry barrier to utilising cobots by providing low-code or no-code solutions. This study identifies the requirements for AI-driven no-code solutions for cobot implementation, focusing on technical and managerial perspectives. Through a case study approach informed by the automotive innovation ecosystem, the authors have identified requirements to leverage AI technologies for generating low-code and no-code solutions. These solutions aim to reduce the entry barriers for cobots in manufacturing, enabling agile and adaptive production systems that respond swiftly to market demands. The study highlights the importance of addressing technical and managerial challenges to ensure the successful implementation and value co-creation of cobot applications. 

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. 

Industry 5.0 advocates a human-centric approach where humans play a central role in the design and implementation of industrial technologies. Collaborative robots, with their adjustable safety functions, are key enabling technologies in this paradigm, especially for manual assembly tasks. This study investigates initial trust in collaborative robots by examining whether familiarization through virtual reality (VR) influences the applicability of various trust-related factors. Two groups of university engineering students participated: an Online group that evaluated the factors based solely on images and provided general information, and an In-Person group that engaged in a VR interaction with the robot before evaluation. Participants were asked to assess whether they found factors such as safety, usability, competence, predictability, and adaptability applicable to the robots, selecting "Yes", "No", or "I don’t know". In a follow-up question, they were then asked to rate each factor on a 5-point Likert scale. Results indicate that the In-Person group more frequently affirmed the applicability of the factors. Limitations include the use of only positively framed statements and the participants’ prior experience with industrial and collaborative robots. 

This paper introduces MAXLabs, a distributed cyber-physical testbed designed for advanced manufacturing research. MAXLabs connects geographically dispersed academic production and digitalization labs across Sweden, overcoming localization barriers to create a unified research platform. By leveraging cyber-physical systems, MAXLabs enables remote collaboration, fostering the development, testing, and validation of cutting-edge manufacturing solutions. The initiative aims to enhance global accessibility to these research facilities, promoting innovation in the evolving manufacturing landscape. 

Factory layout planning involves allocating resources and arranging equipment in manufacturing facilities to enhance system performance and ensure a safe work environment. Integrating digital human modeling tools into factory layout planning facilitates early worker well-being analysis, mitigating musculoskeletal disorders. This paper presents methods for modeling factory layout planning as a multi-objective reinforcement learning problem, leveraging digital human modeling-based simulations. 

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. 

Engineers use modelling and simulation techniques to efficiently create, evaluate, and optimize design solutions.In an industrial production context, engineers often need to consider requirements related to both productivityand worker well-being in order to find successful design solutions. However, simulations related to productivityand worker well-being respectively, are typically carried out by different engineering roles, using different digitaltools. This lack of integrated work procedure could lead to inefficient development processes and suboptimaldesign solutions. Additionally, since performing multi-objective optimizations is likely to be seen as a complicated task by engineers in areas such as design engineering, production engineering, and ergonomics, requiringspecific knowledge and skills, such tasks are typically performed by engineers specialized on optimization. Thispaper presents the development and usability evaluation of a digital tool that supports engineers not specializedin optimization to define and perform simulation-based multi-objective optimizations of requirements related toboth productivity and worker well-being in an automated and simultaneous manner. The digital tool is the resultof research carried out over a period of four years, following an iterative development and assessment process bythe means of use cases, done in close collaboration with potential users of the digital tool, i.e. engineers at severalcompanies. The usability evaluation of the digital tool shows that potential users in the industry view the tool asa promising support for performing their engineering tasks in a more efficient and integrated manner.

The convergence of the focus of Industry 5.0 on human well-being and the prevalent problem of work-related musculoskeletal disorders necessitates advanced digital solutions due to limitations in manual risk assessment methods. This research aimed to compare usability of a newly developed video-based awkward posture identification software, the ergonomist assistant for evaluation (ERAIVA) with a conventional manual method. The risk assessment tool utilised in this study, integrated into the ERAIVA digital platform, is the risk management assessment tool for manual handling proactively (RAMP). Four assessors evaluated video-recorded tasks using both methods (manual and ERAIVA). The usability was assessed through the post-study system usability questionnaire, time consumption, number of video replays and video annotation deletions. The impact on identification of awkward posture durations was also studied. ERAIVA exhibited the highest usability score; it showed a higher number of video replays of specific sequences and annotations without significant differences in time consumption.

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.