Högskolan i Skövde

The increasing complexity of modern automotive lighting systems requires advanced validation strategies that ensure both functional performance and regulatory compliance. This study presents a structured integration of Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) testing within a digital twin (DT) framework for validating headlamp systems. A gated validation process (G10–G120) is proposed, aligning each development phase with corresponding simulation stages from early requirements and concept validation to real-world scenario testing and continuous integration. A key principle of this approach is the adoption of a framework built upon the V-Cycle, adapted to integrate DT technology with SiL and HiL workflows. This architectural configuration ensures a continuous data flow between the physical system, the digital twin, and embedded software components, enabling real-time feedback, iterative model refinement, and traceable system verification throughout the development lifecycle. The paper also explores strategies for effective DT integration, such as digital twin-as-a-service, which combines virtual testing with physical validation to support earlier fault detection, streamlined simulation workflows, and reduced dependency on physical prototypes during lighting system development. Unlike the existing literature, which often treats SiL, HiL, and DTs in isolation, this work proposes a unified, domain-specific validation framework. The methodology addresses a critical gap by aligning simulation-based testing with development milestones and regulatory standards, offering a foundation for industrial adoption.

Digital technologies are critical in advancing a human-centered approach to warehouses that account for productivity and staff well-being. These technologies generate data addressing the negative conditions affecting the well-being of staff during order picking (OP), a labour intensive activity. This study analyzes artificial intelligence (AI)-enabled vision systems to enhance human-centricity and improve the generation and analysis of information about tasks executed by staff in OP. The study presents results from a pilot study in automotive manufacturing applying a design science research approach. The results show that AI-enabled vision systems enhance task identification, analysis, and efficiency in OP. The study suggests five actions including staff information, data acquisition, access restriction, data storage, and protection addressing the privacy concerns of these systems. The study discusses how these systems can integrate staff well-being by identifying human factors and outcomes. It offers three contributions: (1) an overview of activities for collecting task information through AI-enabled vision systems in human-centered OP; (2) evidence that existing architectures for human-centered manufacturing are essential for managing privacy implications; and (3) a discussion of the systems' impact on human factors and performance, and guidelines for developing and implementing these systems in future studies and operational environments.

With the emergence of Industry 5.0 and an increasing focus on human-centric approaches in manufacturing, the analysis of workers in production systems has gathered significant interest among researchers and practitioners. Previous studies have explored the impact of various aspects, such as skills, fatigue, and circadian rhythms, on human performance. However, the cumulative effect of these aspects as disturbances on work performance has yet to be fully elucidated. This study introduces an approach using the Functional Resonance Analysis Method (FRAM) to investigate the impact of multiple disturbances on workers’ performance. Furthermore, this approach explored how the resilience-related skill aspects of workers affect their performance under multiple disturbances. A case study on engine test and repair processes was conducted, employing qualitative data collection and semi-quantitative simulation studies examining the impact of combined disturbances across 4,094 scenarios. The results show that a larger number of compounded variabilities expressed in Common Performance Conditions (CPCs) made it significantly challenging to recover work performance, and CPCs with particularly critical effects were identified. In addition, the FRAM model of skilled workers was shown to sustain higher performance across more scenarios. The approach of this study has demonstrated its ability to provide insights for effectively and safely managing production systems while considering complex disturbances.

Dynamic order picking has usually demonstrated significant impacts on production efficiency in warehouse management. In the context of an automotive-part warehouse, this paper addresses a dynamic multi-tour order-picking problem based on a novel attention-aware deep reinforcement learning-based (ADRL) method. The multi-tour represents that one order-picking task must be split into multiple tours due to the cart capacity and the operator’s workload constraints. First, the multi-tour order-picking problem is formulated as a mathematical model, and then reformulated as a Markov decision process. Second, a novel DRL-based method is proposed to solve it effectively. Compared to the existing DRL-based methods, this approach employs multi-head attention to perceive warehouse situations. Additionally, three improvements are proposed to further strengthen the solution quality and generalization, including (1) the extra location representation to align the batch length during training, (2) the dynamic decoding to integrate real-time information of the warehouse environment during inference, and (3) the proximal policy optimization with entropy bonus to facilitate action exploration during training. Finally, comparison experiments based on thousands of order-picking instances from the Swedish warehouse validated that the proposed ADRL could outperform the other twelve DRL-based methods at most by 40.6%, considering the optimization objective. Furthermore, the performance gap between ADRL and seven evolutionary algorithms is controlled within 3%, while ADRL can be hundreds or thousands of times faster than these EAs regarding the solving speed.

Extended reality (XR) technologies are gaining traction in technical education due to their potential for creating immersive and interactive training environments. This study presents the development and empirical evaluation of X-RAPT, a collaborative VR-based platform designed to train students in industrial robotics programming. The system enables multi-user interaction, cross-platform compatibility (VR and PC), and real-time data logging through a modular simulation framework. A pilot evaluation was conducted in a vocational training institute with 15 students performing progressively complex tasks in alternating roles using both VR and PC interfaces. Performance metrics were captured automatically from system logs, while post-task questionnaires assessed usability, comfort, and interaction quality. The findings indicate high user engagement and a distinct learning curve, evidenced by progressively shorter task completion times across levels of increasing complexity. Role-based differences were observed, with main users showing greater interaction frequency but both roles contributing meaningfully. Although hardware demands and institutional constraints limited the scale of the pilot, the findings support the platform’s potential for enhancing robotics education.

This chapter examines integrating the Functional Resonance Analysis Method (FRAM) with Discrete-Event Simulation (DES) to analyze and enhance socio-technical production systems in the context of Industry 5.0 and Society 5.0. It highlights the complexity of interactions among technologies, humans, and societal elements, emphasizing FRAM’s potential for quantitative analysis and its combination with DES for system improvement, risk prevention, and resilience. Practical case studies demonstrate these methods’ applications, illustrating their contributions to system performance and risk assessment, paving the way for more advanced industries and societies.

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.

This study applies an intersectional approach to address concerns about diversity of data acquisition when applying computer vision systems in human order picking. The study draws empirical data from a single case study conducted at an automotive manufacturer. It identifies critical factors of intersectionality for the use of vision systems to enrich data collection in human order picking at four levels including form and function, experience and services, systems and infrastructure, and paradigm and purpose. These findings are helpful for mitigating bias and ensuring accurate representation of the target population in training datasets. The results of our study are indispensable for enhancing human-centricity when applying computer vision systems, and facilitating the acquisition of unstructured data in human order picking. The study contributes to enhancing diversity in human order picking, a situation that is highly relevant because of the variations in age, gender, cultural background, and language of staff. The study discusses theoretical and managerial implications of findings, alongside suggestions for future research.

In the Industry 4.0 era, advanced analytical tools are essential for progressing with digital transformation, especially within complex socio-technical systems. However, the growing complexity of these systems in manufacturing impedes system improvement, and traditional analytical methods focusing solely on the technological aspect often fall short. To overcome this problem, this paper introduces an integrated methodology combining Discrete-Event Simulation, Functional Resonance Analysis Method, and Work Domain Analysis for analysing and enhancing manufacturing systems by considering factors like operator skill levels, demand changes, and production constraints. Implemented in two industrial case studies, this methodology effectively identifies system limitations and aids in structured data analysis, positioning it as a vital decision support system in the digital transformation of Industry 4.0. 

With robotics applications playing an increasingly significant role in our daily lives, it is crucial to develop effective methods for teaching and understanding their behavior. However, limited access to physical robots in educational institutions and companies poses a significant obstacle for many individuals. To overcome this barrier, a novel framework that combines realistic robot simulation and intuitive control mechanisms within a virtual reality environment is presented. By accurately emulating the physical characteristics and behaviors of various robots, this framework offers an immersive and authentic learning experience. Through an intuitive control interface, users can interact naturally with virtual robots, facilitating the acquisition of practical robotics skills. In this study, a qualitative assessment to evaluate the effectiveness and user satisfaction with the framework is conducted. The results highlighted its usability, realism, and educational value. Specifically, the framework bridges the gap between theoretical knowledge and practical application in robotics, enabling users to gain hands-on experience and develop a deeper understanding of robot behavior and control strategies. Compared to existing approaches, the framework provides a more accessible and effective alternative for interacting with robots, particularly for individuals with limited physical access to such devices. In conclusion, the study presents a comprehensive framework that leverages virtual reality technology to enhance the learning and training process in robotics. By combining realistic simulations and intuitive controls, this framework represents a significant advancement in providing an immersive and effective learning environment. The positive user feedback obtained from the study reinforces the value and potential of the framework in facilitating the acquisition of essential robotics skills. Ultimately, this work contributes to flattening the robotics learning curve and promoting broader access to robotics education.