Högskolan i Skövde

This paper explores the use of digital technology stages and knowledge demand types for achieving energy efficiency. Digital technology stages are the steps toward developing an intelligent and networked factory: computerization, connectivity, visibility, transparency, predictive capacity, and adaptability. Knowledge demand types refer to the knowledge and skills needed to implement energy management through technical, process, and leadership knowledge. Empirical data were collected from a critical single case study at an industrial manufacturing company. The study made two significant contributions. Firstly, it identifies fourteen challenges and improvement potentials when working with energy monitoring, evaluation, and optimization, demonstrating the critical role of digital technology stages and knowledge demand types. Secondly, the study presents a conceptual framework indicating how companies could overcome pitfalls and enhance energy efficiency by combining digital technologies and knowledge demands. Future work will include technical implementations and its connection to knowledge management. 

Usability is a key factor for successfully integrating new technology to aid an operator in production. It is measured using three metrics: efficiency (productivity), effectiveness (quality), and user satisfaction. One prominent technology for operator support is augmented reality (AR), which is mostly handheld or head-mounted. A human-centered approach is required to align the AR integration with the operator’s capabilities. The underlying use case in this study is an energy dashboard visualized using AR and non-AR media, namely, a monitor, tablet, and HoloLens. The resulting media applications were evaluated for usability in terms of efficiency, effectiveness, and satisfaction in the within-study experiments by 16 participants. Overall, the results showed increased efficiency and satisfaction for traditional-monitor users and increased effectiveness for tablet users. Despite the participants’ lack of experience with AR, the AR applications performed comparably to the monitor and even slightly better in some aspects. With the ongoing development of AR software and hardware, AR can become increasingly useful for machine monitoring in production. However, to use AR for more comprehensive tasks, its strengths and weaknesses must be considered. 

In the manufacturing industry, automatic quality inspections can lead to improved product quality and productivity. Deep learning-based computer vision technologies, with their superior performance in many applications, can be a possible solution for automatic quality inspections. However, collecting a large amount of annotated training data for deep learning is expensive and time-consuming, especially for processes involving various products and human activities such as assembly. To address this challenge, we propose a method for automated assembly quality inspection using synthetic data generated from computer-aided design (CAD) models. The method involves two steps: automatic data generation and model implementation. In the first step, we generate synthetic data in two formats: two-dimensional (2D) images and three-dimensional (3D) point clouds. In the second step, we apply different state-of-the-art deep learning approaches to the data for quality inspection, including unsupervised domain adaptation, i.e., a method of adapting models across different data distributions, and transfer learning, which transfers knowledge between related tasks. We evaluate the methods in a case study of pedal car front-wheel assembly quality inspection to identify the possible optimal approach for assembly quality inspection. Our results show that the method using Transfer Learning on 2D synthetic images achieves superior performance compared with others. Specifically, it attained 95% accuracy through fine-tuning with only five annotated real images per class. With promising results, our method may be suggested for other similar quality inspection use cases. By utilizing synthetic CAD data, our method reduces the need for manual data collection and annotation. Furthermore, our method performs well on test data with different backgrounds, making it suitable for different manufacturing environments. 

Industry 5.0 places worker’s wellbeing at the center of the production process, prioritizing healthy and safety job conditions. Requirements to achieve occupational wellbeing are reducing risks for Work-related Musculoskeletal Disorders (WMSDs) and improving industry workstations. The traditional ergonomics risk assessments are based on human observational evaluation and the results are influenced by observers’ competence. Nowadays, advanced technologies such as motion capture systems are implemented to objectively monitor an operator’s movements over time. By providing real-time, data-driven insights into human movement and posture, systems offer the potential to reduce workplace injuries, enhance productivity, and promote long-term worker health. The purpose of the present study is to evaluate and compare three different approaches for assessing the quantitative biomechanical risk of an industrial task using the RULA method: the observational method, a wearable inertial measurement system, and a 4D stereophotogrammetry. The experiment involves one participant (female, 30 years old) performing a “pick-and-place” worker’s task in a controlled laboratory environment. RULA scores vary across the three approaches, with discrepancies primarily due to differences in how each system captures and measures joint angles. While this preliminary study provides valuable initial insights, the limitation of involving a single participant must be critically acknowledged. Future research will aim to include a larger sample size and conduct statistical analyses. The identification of benefits and limitations of each approach enables researchers, ergonomists, and industry stakeholders to critically select and integrate technology to support the worker’s safety, optimizing human wellbeing and overall system performance.

This paper addresses key aspects of domain randomization in generating synthetic data for manufacturing object detection applications. To this end, we present a comprehensive data generation pipeline that reflects different factors: object characteristics, background, illumination, camera settings, and post-processing. We also introduce the Synthetic Industrial Parts Object Detection dataset (SIP15-OD) consisting of 15 objects from three industrial use cases under varying environments as a test bed for the study, while also employing an industrial dataset publicly available for robotic applications. In our experiments, we present more abundant results and insights into the feasibility as well as challenges of sim-toreal object detection. In particular, we identified material properties, rendering methods, post-processing, and distractors as important factors. Our method, leveraging these, achieves top performance on the public dataset with Yolov8 models trained exclusively on synthetic data; mAP@50 scores of 96.4% for the robotics dataset, and 94.1%, 99.5%, and 95.3% across three of the SIP15-OD use cases, respectively. The results showcase the effectiveness of the proposed domain randomization, potentially covering the distribution close to real data for the applications. 

Rising energy costs, energy supply uncertainties, and the sustainability crisis have intensified the need for energy efficiency in industrial manufacturing. This adds complexity to balancing traditional production goals such as productivity, quality, and cost. While prior studies address energy-intensive processes or throughput bottlenecks, they often lack integrated decision-support for evaluating optimal trade-offs. To address this gap, this study proposes a novel simulation-based multi-objective optimization framework combined with a knowledge discovery module, demonstrated in an industrial case study. The framework systematically identifies energy and productivity losses, evaluates improvement strategies to determine optimal trade-off solutions, and extracts actionable rules to guide decision making. Case study results show a 23.9% reduction in specific energy consumption and a 27.9% increase in throughput, while emphasizing the need to balance inventory levels. The approach offers a robust, data-driven method for supporting energy-efficient manufacturing. Future research will explore integration with real-time monitoring and extension to additional objectives such as costs and emissions.

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. 

Recently the concept of Industry 5.0 has been introduced, reinforcing the human-centric perspective for future industry. The human-centric scientific discipline and profession ergonomics is applied in industry to find solutions that are optimized in regard to both human well-being and overall system performance. It is found, however, that most production development and preparation work carried out in industry tends to address one of these two domains at a time, in a sequential process, typically making optimization slow and complicated. The aim of this paper is to suggest, demonstrate, and evaluate a concept that makes it possible to optimize aspects of human well-being and overall system performance in an efficient and easy parallel process. The concept enables production planning and balancing of human work in terms of two parameters: assembly time as a parameter of productivity (system performance), and risk of musculoskeletal disorders as a parameter of human well-being. A software demonstrator was developed, and results from thirteen test subjects were compared with the traditional sequential way of working. The findings show that the suggested relatively unique parallel approach has a positive impact on the expected musculoskeletal risk and does not necessarily negatively affect productivity, in terms of cycle time and time balance between assembly stations. The time to perform the more complex two-parameter optimization in parallel was shorter than the time in the sequential process. The majority of the subjects stated that they preferred the parallel way of working compared to the traditional serial way of working.

This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms.

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.