Högskolan i Skövde

With the increase in complexity of industrial systems it becomes more and more challenging to make well-grounded decisions for system design and operation. Following the concept of Virtual Factories with Knowledge-Driven Optimization (VF-KDO), this paper proposes a graph database approach to support knowledge-driven and simulation-based optimization. With the mapping of a VF-KDO ontology to a graph database, competency questions that facilitate traceability, transparency, and group decision making can be answered. This is exemplified with an industrial use case and a scenario form academic education.

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.

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.

Virtual manufacturing, simulation, and optimization provide a wealth of knowledge about the possibilities of future production systems so as to support decision makers. However, this knowledge usually remains with a handful of domain experts, is not captured and is hard to share even within the same team. At the same time, simulations can benefit from the incorporation of linked data from real factories once a process is running. Graph databases provide a possible approach to storing and managing this form of interrelated heterogeneous data, with powerful querying capabilities that can identify important or interesting patterns that might otherwise remain hidden. Current research focuses on one or two aspects of this problem but does not address all at once, despite the potential benefits of the combination. This paper provides a broad literature review of the current directions within research with a special focus on how graphs can support finding knowledge within Virtual Factories, used by larger teams for industrial planning and optimization.

Ergonomics evaluation methods are typically used for assessing risks of work-related musculoskeletal disorders (WMSDs) and the physical well-being of workers. Traditionally, these methods rely on assessors observing workers performing tasks and assessing potential risks based on observational ergonomics evaluation methods like the Rapid Entire Body Assessment (REBA) and the Rapid Upper Limb Assessment (RULA). While observational methods provide a structured risk assessment framework, they often depend on subjective evaluations, leading to inconsistent assessments between different ergonomists.

This study examines the application of motion capture technology to enhance the objectivity and efficiency of ergonomics evaluations and to enable the use of direct measurement ergonomics evaluation methods. The study was conducted at a medium-sized enterprise assembly station, where a worker’s tasks were recorded using motion capture technology. The captured motions were input into the Digital Human Modelling (DHM) tool IPS IMMA, and ergonomic assessments were performed using RULA, REBA, and the Arm Force Field (AFF) method.

The study followed a process comprising three main stages: data collection, data processing, and ergonomics evaluation. The recorded data were processed into XML format, imported into IPS IMMA, and exported to the Ergonomics in Production Platform (EPP) for RULA and REBA evaluations and to a script for AFF evaluations. The integration of these methods improved the precision and reliability of ergonomics assessments by replacing subjective estimates with direct measurements. The findings demonstrate the potential of combining motion capture with DHM tools to enhance ergonomics evaluation and support decisionmaking in workstation design and automation.

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.

Multiple levels of classification naturally occur in many domains. Several multi-level modeling approaches account for this, and a subset of them attempt to provide their users with sanity-checking mechanisms in order to guard them against conceptually ill-formed models. Historically, the respective multi-level well-formedness schemes have either been overly restrictive or too lax. Orthogonal Ontological Classification has been proposed as a foundation for sound multi-level modeling that combines the selectivity of strict schemes with the flexibility afforded by laxer schemes. In this article, we present the second iteration of a formalization of Orthogonal Ontological Classification, which we empirically validated to demonstrate some of its hitherto only postulated claims using an implementation in ConceptBase. We discuss the expressiveness of the formal language used, ConceptBase’s evaluation efficiency, and the usability of our realization based on a digital twin example model.

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.

Augmented reality smart glasses (ARSG) have been available as a commercial product since 2015. Many potential usage areas have been identified, including industrial use. The needs from industry have evolved, with more emphasis being put on sustainability. While ARSG can help improve efficiency and sustainability, there are also similarly associated costs to their implementation and use. This paper aims to present a process for how to choose ARSG for specific use cases as assembly operator support while considering the sustainability of their implementation. A narrative review of the literature was made to identify the current understanding of the environmental impact of ARSG, as well as what has been considered in regards to ARSG being integrated into a manufacturing environment. The analysis of the literature resulted in a proposed decision process. The decision process serves as a baseline for how to guide the decision of whether ARSG could be a suitable solution and, if so, what aspects to consider in the choosing of the ARSG model. Future work includes collaboration with industry to further improve the decision process based on empirical input. 

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.