Högskolan i Skövde

In industry, simulation software is used to simulate production, making it possible to predict events in production, calculate times and plan production, even in the early phases of the production development process. Software known as digital human modelling (DHM) tools can also be used to simulate humans working in production. When simulating digital human models, ergonomics evaluations can be carried out to assess whether workstation designs offer appropriate ergonomic conditions for the workers. However, simulations performed to predict and plan production are usually done separately from the human simulations performed to evaluate ergonomics. This can lead to suboptimal solutions in which a factory is optimized to improve either productivity or ergonomics. This thesis outlines the hypothesis that more optimal solutions for workstation design, layout and line balancing can be obtained in simulations by optimizing productivity and ergonomic factors simultaneously instead of considering them separately. Hence, the aim is to carry out research on the development of a simulation-based multi-objective optimization method for productivity and ergonomic factors and to realize the method as a software tool in order to test and communicate it. From an application and societal-impact perspective, the overall objective is to offer a new approach for designing production systems that focuses on both over-all system performance and the well-being of workers, reduces the effort of engineers and helps industry create more productive and sustainable workspaces.

Simuleringsverktyg används i industrin för att simulera produktion. Det gör det möjligt att förutsäga händelser, uppskatta tider och planera produktionen, även i tidiga stadier av produktionsutvecklingsprocessen. På motsvarande vis används ergonomisimuleringsverktyg för att simulera människor som arbetar i produktionen. Ergonomisimulering kan till exempel utföras för att bedöma om utformningen av arbetsstationen erbjuder lämpliga ergonomiska förhållanden för arbetarna. Emellertid görs produktionssimuleringar vanligtvis separat från ergonomisimuleringar. Det kan leda till bristfälliga lösningar där en fabrik är optimerad för att förbättra antingen produktivitet eller ergonomi. Denna avhandling utgår från hypotesen att mer optimala lösningar för arbetsstationsutformning, layout och linje-balansering kan uppnås i simuleringar genom att samtidigt optimera produktivitetsfaktorer och ergonomiska faktorer istället för att beakta dem separat. Målet är därför att utforska utvecklingen av en simulerings- och flermålsbaserad optimeringsmetod för produktivitetsfaktorer och ergonomiska faktorer och att realisera metoden som ett mjukvaruverktyg för att testa och beskriva den. Ur ett tillämpnings- och samhällspåverkansperspektiv är det övergripande målet att erbjuda ett nytt tillvägagångssätt för att utforma produktionssystem som fokuserar på både systemprestanda och människans välbefinnande, som stödjer ingenjörer i deras dagliga arbete för att hitta goda lösningar, och hjälper industrin att skapa mer produktiva och hållbara arbetsplatser.

The paper reports an investigation conducted during the DHM2020 Symposium regarding current trends in research and application of DHM in academia, software development, and industry. The results show that virtual reality (VR), augmented reality (AR), and digital twin are major current trends. Furthermore, results show that human diversity is considered in DHM using established methods. Results also show a shift from the assessment of static postures to assessment of sequences of actions, combined with a focus mainly on human well-being and only partly on system performance. Motion capture and motion algorithms are alternative technologies introduced to facilitate and improve DHM simulations. Results from the DHM simulations are mainly presented through pictures or animations.

Recently, exoskeletons have been gaining popularity in many industries, primarily for supporting manual assembly tasks. Due to the relative novelty of exoskeleton technologies, knowledge about the consequences of using these devices at workstations is still developing. Digital human modelling (DHM) and ergonomic evaluation tools may be of particular use in this context. However, there are no standard integrations of DHM and ergonomic assessment tools for assessing exoskeletons. This paper proposes a general method for evaluating the ergonomic effects of introducing an exoskeleton in a production context using DHM simulation tools combined with a modified existing ergonomic assessment framework. More specifically, we propose adapting the Assembly Specific Force Atlas tool to evaluate exoskeletons by increasing the risk level threshold proportionally to the amount of torque that the exoskeleton reduces in the glenohumeral joint. We illustrate this adaptation in a DHM tool. We believe the proposed methodology and the corresponding workflow can be helpful for decision-makers and stakeholders when considering implementing exoskeletons in a production environment.

The type of ergonomics assessment methods typically used in digital human modelling (DHM) tools and automated assessment processes were rather developed to be used by ergonomists to assess ergonomics by observing the characteristics of the work. Direct measurement methods complement observation methods. Direct measurement methods have a design that suits being implemented into DHM tools. A drawback of direct measurement methods is that they traditionally do not include action levels. However, action levels in direct measurement methods have recently been suggested. The aim of this paper is to illustrate how these recent physical load exposure calculations and recommendations can be integrated in a DHM tool and in an automated assessment process. A demonstrator solution was developed that inputs exposure data from simulations in the DHM tool IPS IMMA as well as exposure data that originate from tracking real workers’ motions, using the motion capture system Xsens MVN. The demonstrator was applied in two use cases: one based on predicted human motions and one based on captured human motions. In the demonstrator, head posture, upper left and right arm posture and velocity, as well as left and right wrist velocity were calculated. Exposure data were compared with action levels, and extreme action levels were indicated by colouring the information. The results are promising, and the demonstrator illustrates that it is possible to follow the trends in Industry 4.0 and Industry 5.0 to automate and digitalize ergonomics assessment processes in industry.

Simulation technologies are widely used in industry as they enable efficient creation, testing, and optimization of the design of products and production systems in virtual worlds, rather than creating,testing, and optimizing prototypes in the physical world. In an industrial production context, simulation of productivity and ergonomics helps companies to find and realize optimized solutions that uphold profitability, output, quality, and worker well-being in their production facilities. However, these two types of simulations are typically carried out using separate software, used by different users, with different objectives. This easily causes silo effects, leading to slow development processes and sub-optimal solutions. This paper reports on research related to the realization of an optimization framework that enables the concurrent optimization of aspects relating to both ergonomics and productivity. The framework is meant to facilitate the inclusion of Ergonomics 4.0 in the Industry 4.0 revolution.

Recently, the concept of Industry 5.0 has been introduced to complement, among other things, Industry 4.0’s focus on efficiency and productivity with a focus on humans in digital design and production processes. The inclusion of human interaction with digital realities, extended reality (XR) technologies, such as augmented reality (AR) and virtual reality (VR), can play an essential role in Industry 5.0. While rapid advances in XR technologies are solidifying and finding their place in the product and production development process, terminology and classification scheme remain under-determined. As a result, there have been numerous classifications of XR technologies from different perspectives, but little widespread agreement. They have been classified by their level of immersion or how well they meet a specific purpose (such as training). In addition to that, the classifications are usually made for one particular field (e.g. marketing, healthcare, engineering, architecture, among others). Therefore, to set the basis for future research, it is essential to identify and outline the dimensions that intervene in product and production design in regards to XR facilitated collaboration. With the ideas proposed in this paper, we want to identify basic concepts that classify a collaborative XR system by analyzing how users interact with the environment and other users. Our motivation is that collaborative design involves not only the physical dimension but also a social dimension. Defining when an XR system contributes to increasing social and/or physical presence could clarify and simplify its categorization.

Work-related musculoskeletal disorders (WMSDs) constitute a large part of work absences among industry workers, together with all the health and economic problems that it carries. Exoskeletons developed for overhead operations can potentially be a solution to reduce risks for WMSDs. However, some companies are still hesitant to implement exoskeletons in their workplace, since the effects of using exoskeletons are still not fully proved. Digital human modeling (DHM) could help with this dilemma by facilitating studies of the viability of the exoskeletons for specific work tasks. This paper proposes a DHM based framework to implement the study of upper body exoskeletons focused on overhead assembly operations. The framework emphasizes the kinematics and forces interaction between the human and the exoskeleton. 

The manufacturing industry is becoming increasingly more complex as the paradigm of mass-production moves, via mass-customization, towards personalized production, and Industry 4.0. This increased complexity in the production system also makes everyday work for shop-floor operators more complex. To take advantage of this complexity, shop-floor operators need to be properly supported in order to perform their important work. The shop-floor operators in this future complex manufacturing industry, the Operator 4.0, need to be supported with the implementation of new cognitive automation solutions. These automation solutions, together with the innovativeness of new processes and organizations will increase the competitiveness of the manufacturing industry. This paper discusses three different aspects of production innovation in the context of the needs and preferences of information for Operator 4.0. Conclusively, product innovations can be applied in the manufacturing processes, and thus becoming process innovations, but the implementation of such innovations require organizational innovations.

Some anthropometric measurements, such as body weight often show a positively skewed distribution. Different types of transformations can be applied when handling skewed data in order to make the data more normally distributed. This paper presents and visualises how square root, log normal and, multiplicative inverse transformations can affect the data when creating boundary confidence ellipses. The paper also shows the difference of created manikin families, i.e. groups of manikin cases, when using transformed distributions or not, for three populations with different skewness. The results from the study show that transforming skewed distributions when generating confidence ellipses and boundary cases is appropriate to more accurately consider this type of diversity and correctly describe the shape of the actual skewed distribution. Transforming the data to create accurate boundary confidence regions is thought to be advantageous, as this would create digital manikins with enhanced accuracy that would produce more realistic and accurate simulations and evaluations when using DHM tools for the design of products and workplaces.

Virtual reality (VR) technologies can support the planning and implementation of new workstations in various industry sectors, including in automotive assembly. Starting in the early planning stages, VR can help in identifying potential problems of new design ideas, e.g. through ergonomics analyses. Designers can then quickly change the virtual representations of new workstations to test solutions for the emerging difficulties. For this purpose, the actions and motions of prospective workers can be captured while they perform the work tasks in VR. The information can also be used as input for digital human modelling (DHM) tools, to instruct biomechanical human models. The DHM tools can then construct families of manikins that differ on anthropometric characteristics, like height, to simulate work processes. This paper addresses both existing technologies for gathering data on human actions and motions during VR usage and ways in which these data can be used to assist in designing new workstations. Here, a novel approach to translate a VR user’s actions into instructions for DHM tools through an event-based instruction sampling method is presented. Further, the challenges for utilizing VR are discussed through an industrial use case of the manual assembly of flexible cables in an automotive context.