Högskolan i Skövde

The shift towards electric vehicle production has introduced new manufacturing challenges, particularly in tasks that require operators to handle flexible components such as electrical wire harnesses and high-voltage cables. Assembly tasks such as picking, carrying, deforming, and mounting flexible components are usually performed by operators and can result in high force demands, affecting both operator well-being and production efficiency. Ensuring that these work demands do not exceed an operator’s physical capacity is essential for maintaining a sustainable work environment, improving worker well-being, and reducing risks of work-related musculoskeletal disorders. This paper addresses this challenge by simulating and evaluating a real-world use case at Volvo Cars AB, where operators manually install electrical wire harnesses in an automotive assembly station. The study integrates the Arm Force Field method within a DHM tool to compare forces demanded by the assembly task to force capacity of the operators. Additionally, RULA and REBA are used to evaluate postural risks during the assembly. The simulation estimates force demands for picking, carrying, deforming, and mounting the harness. By analysing the ratio between work demand and human capacity, this study provides insights into how DHM tools can assist engineers and ergonomists to proactively assess assembly work of flexible objects, in turn assisting workstation design and supporting sustainable manual assembly conditions.

This study examines how specific collaborative features for independent viewpoint control, collaborative pointing, sketching, and manikin representations support design reviews in a virtual environment. Participants conducted design reviews using the collaborative design tool Gravity Sketch while engaging with these features. Results show that friction situations were minimal, with viewpoint control eliminating the need for perspective adjustments. Collaborative pointing and sketching effectively clarified design modifications, while manikin representations aided discussions on ergonomics. The findings highlight how the evaluated features facilitate communication in remote design reviews.

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.

To ensure a sustainable work life, work demands should not exceed the capacity of members of the workforce. Digital human modelling (DHM) tools can be used to consider ergonomics issues in computer simulated settings, supporting engineers and ergonomists to proactively find design solutions that fulfil well-being related criteria. For this, DHM tools need to be able to assess risks for musculoskeletal disorders (MSDs). One key risk factor for MSDs is force exertions. This load dose on the human body is influenced by aspects such as the magnitude of the force, the direction of the force, the frequency of force exertion, the duration of the force exertion, and the posture held during force exertion. This paper compares force capacities for three different work postures and six force directions given by two methods: the Assembly Specific Force Atlas and the Arm Force Field. The study takes a DHM tool user’s point of view, envisioning the methods being used to assess a design proposal of a work scenario being simulated in a DHM tool. The results show that the two methods, for some conditions, predict quite similar force capacities, while for other conditions there are larger differences. Reasons for these findings are discussed.

Aims: Recent world events have resulted in companies using remote meeting tools more often in design processes. The shift to remote meeting tools has had a notable impact on collaborative design activities, such as design reviews (DRs). When DRs depend on computer-aided design (CAD) software, the lack of direct support for CAD functionalities in videoconferencing applications introduces novel communication challenges, i.e. friction. This study investigates friction encountered in real world remote DRs when using a combination of standard CAD and videoconferencing applications. The objective was to understand the main sources of friction when carrying out DRs using a combination of CAD and videoconferencing applications.

Methods: At a single Swedish automobile manufacturer, 15 DRs of a fixture component were passively observed. These observations were subjected to a qualitative thematic analysis to identify categories and sources of friction during these DRs. The DRs were carried out using a combination of CATIA CAD software and Microsoft Teams for videoconferencing.

Results: The analysis of the 15 remote DRs identified four recurring friction categories: requesting specific viewpoints, indicating specific elements, expressing design change ideas, and evaluating ergonomics. Each category highlights specific challenges that were observed during the DRs and emerged due to constraints imposed by existing methods and technologies for remote meetings.

Conclusion: This study provides a framework for understanding the current sources of friction in remote DRs using videoconferencing tools. These insights can support the future development of DR software tools, guiding the integration of features that address these friction points. Additionally, the results serve as a guideline for organizations to implement methods that reduce friction in remote DRs and improve DR quality and efficacy.

Occupant packaging design is usually done using computer-aided design (CAD) and digital human modelling (DHM) tools. These tools help engineers and designers explore and identify vehicle cabin configurations that meet accommodation targets. However, studies indicate that current working methods are complicated and iterative, leading to time-consuming design procedures and reduced investigations of the solution space, in turn meaning that successful design solutions may not be discovered. This paper investigates potential advantages and challenges in using an automated simulation-based multi-objective optimization (SBMOO) method combined with a DHM tool to improve the occupant packaging design process. Specifically, the paper studies how SBMOO using a genetic algorithm can address challenges introduced by human anthropometric and postural variability in occupant packaging design. The investigation focuses on a fabricated design scenario involving the spatial location of the seat and steering wheel, as well as seat angle, taking into account ergonomics objectives and constraints for various end-users. The study indicates that the SBMOO-based method can improve effectiveness and aid designers in considering human variability in the occupant packaging design process.

Optimization-based digital human modelling (DHM) can compute manikin motions for unique work tasks, requiring no motion capturing or motion data manipulation to simulate new work tasks. Also, optimization-based DHM can consider prevailing force and torque exertions, e.g. pushing or twisting, in the motion computations. This makes optimization-based DHM well suited for assessing workstation designs early in virtual development phases. When using optimization-based DHM to simulate work tasks and determine task times in settings such as manual assembly, it is crucial that the manikin motion durations can be set to comply with predetermined motion time systems (PMTS) data. As part of realizing this objective, this study compares assembly times generated by an optimization-based DHM tool, where durations of discrete manikin motions are determined based on PMTS data, against an industrial use case with known assembly times, determined according to the company standard. The comparison aims to identify the differences between the times generated by the DHM tool and the times determined in accordance with the company standard, understand why they occur and how they potentially can be addressed. The findings support establishing a road map for future research and development for improving task time estimations in optimization-based DHM.

Ergonomics evaluation methods can be used to assess risks for work-related musculoskeletal disorders and promote physical well-being of people However, research has shown that different assessors often comes to different conclusions in regard to risks. Hence, the subjective nature of observation-based ergonomics evaluations can cause reliability issues, while also being time-consuming to perform. Recent developments in technologies such as camera-based and inertial measurement unit (IMU) sensor-based motion capture systems facilitate the measurement and digitalization of human postures over time. Hence, it is assumed that such technology can become integrated into the process of performing ergonomics evaluations, to evaluate workers’ well-being more objectively and efficiently. This study investigates the use of a motion capture system to record hand and finger motions, and the application of the random forest machine learning algorithm to classify hand postures into categories of grip types. The results show that random forests can, based on the motion capture data, automatically and successfully classify hand postures into three grip types defined by the HandPak ergonomics evaluation method. The random forest models did not exhibit the overfitting issues typically associated with decision trees in similar classification problems. However, the training and test data were obtained from only two subjects. Including more subjects in the training and test data to account for posture variation could improve the accuracy of the random forest models.

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.

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.