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Although the automation level is high within the automotive industry, there are still a large number of manual tasks, especially is the final assembly of the vehicle. Overhead assembly operations is an example of a problematic manual task that can cause workers to develop musculoskeletal disorders in the shoulder complex. Exoskeletons may be a solution to reduce the risk for developing musculoskeletal disorders from the work tasks. This study evaluates and compares how the use of three different passive upper body exoskeletons affects the range of motion (ROM) of workers at overhead assembly tasks. An experiment consisting of three tasks was set up in order to analyze the differences between the models. Seventeen subjects were involved in the study. Interviews, observations, videos and motion capture recordings were the methods of collecting data. The results show agreement from all the subjects that the exoskeletons help the worker at this specific assembly operation. The results also show that different exoskeleton models cause different levels of ROM reductions. The subjects’ opinions about how the different exoskeletons influence the ROM corresponds with the analysis of the motion capture data. Positive and negative aspects of each exoskeleton from a ROM and an implementation point of view are discussed. In general, the results indicate that the exoskeleton models can be applicable for the type of work tasks studied. However, the exoskeletons would benefit from further development in order to decrease ROM limitations and therefore cover a larger number of different manual assembly tasks.

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This paper presents a solution that integrates a smart textiles system with virtual reality to assess the design of workstations from an ergonomics point of view. By using the system, ergonomists, designers, engineers, and operators, can test design proposals of workstations in an immersive virtual environment while they see their ergonomics evaluation results displayed in real-time. The system allows its users to evaluate the ergonomics of the workplace in a pre-production phase. The workstation design can be modified, enabling workstation designers to better understand, test and evaluate how to create successful workstation designs, eventually to be used by the operators in production. This approach uses motion capture together with virtual reality and is aimed to complement and integrate with the use of digital human modelling (DHM) software at virtual stages of the production development process.

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Work-related musculoskeletal disorders (WMSDs) are often associated with high costs for manufacturing companies and society, as well as negative effects on sustainable working life of workers. To both ensure workers’ well-being and reduce the costs of WMSDs, it is important to consider worker well-being in the design and operations of production processes. To facilitate the simulation of humans in production and improve worker well-being, there are numerous digital human modelling (DHM) tools available on the market. Besides simulation of humans in production, there are numerous production simulation software to simulate production flows of factories, robots and workstations that offer the possibility of improving the productivity of the stations, optimizing the layout and the configuration of the production lines. Despite of the capabilities of DHM and production flow simulation software, there is a lack of tools that can handle an overall optimization perspective, where it is possible to concurrently treat aspects related to both worker well-being and productivity within one tool. This study presents a prescribed tool that enables concurrent multi-objective optimization of worker well-being and productivity in DHM tools by analyzing the impact of different design alternatives. The tool was assessed in a workstation layout optimization use case. In the use case, risk scores of an ergonomics evaluation method was used as a measure of well-being, and total walking distance and workstation area were used as measures of productivity. The results show that the optimized solutions improve both total walking distance, workstation area and ergonomic risk scores compared to the initial solution. This study suggests that the concurrent multi-objective optimization of worker well-being and productivity could generate more optimal solutions for industry and increase the likelihood for a sustainable working life of workers. Therefore, further studies in this field are claimed to be beneficial to industry, society and workers.

Usually, optimizing productivity and optimizing worker well-being are separate tasks performed by engineers with different roles and goals using different tools. This results in a silo effect which can lead to a slow development process and suboptimal solutions, with one of the objectives, either productivity or worker well-being, being given precedence. Moreover, studies often focus on finding the best solutions for a particular use case, and once solutions have been identified and one has been implemented, the engineers move on to analyzing the next use case. However, the knowledge obtained from previous use cases could be used to find rules of thumb for similar use cases without needing to perform new optimizations. In this study, we employed the use of data mining methods to obtain knowledge from a real-world optimization dataset of multi-objective optimizations of worker well-being and productivity with the aim to identify actionable insights for the current and future optimization cases. Using different analysis and data mining methods on the database revealed rules, as well as the relative importance of the design variables of a workstation. The generated rules have been used to identify measures to improve the welding gun workstation design.