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.

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.

Planning and designing factory layouts are frequently performed within virtual environments, relying on inputs from various cross-disciplinary activities e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. The success of this process heavily relies on the expertise of the practitioners performing the task. Studies have shown that layout planning often hinges on the practitioners’ knowledge and interpretation of current rules and requirements. As there is significant variability in this knowledge and interpretation, there is a risk that decisions are made on incorrect grounds. Consequently, the layout planning process depends on individual proficiency. In alignment with Industry 4.0 and Industry 5.0 principles, there is a growing emphasis on providing practitioners involved in industrial development processes with efficient decision support tools. This paper presents a digital support function integrated into a virtual layout planning tool, developed to support practitioners in considering current rules and requirements for space claims in the layout planning process. This digital support function was evaluated by industry practitioners and stakeholders involved in the factory layout planning process. This initiative forms part of a broader strategy to provide advanced digital support to layout planners, enhancing objectivity and efficiency in the layout planning process while bridging cross-disciplinary gaps.

The emergence of collaborative robots in confined spaces marks a new era in manufacturing and automation. Robots designed to assist human operators in complex and physically demanding tasks are gaining popularity due to their ability to work alongside humans, minimising risks and maximising productivity. However, most applications are in open areas where operators have enough space to move out of the robot's trajectory in the case of an emergency. This paper presents a unique case study of Human-Robot Collaboration (HRC) inside a car body, a scenario that has yet to be extensively explored. This study utilises the NASA TLX method to evaluate the workload of human operators in an HRC application. The paper presents findings of various factors such as mental, physical, and temporal demands, as well as performance, effort, and frustration levels experienced by operators while performing HRC tasks within a car body. The experiences of the participants were explored in detail through semi-structured interviews. The study indicates that operators are willing to collaborate with robots within car bodies. However, ergonomic obstacles and difficulties still exist when robots are deployed in confined spaces like car bodies. A new concept called "Immersive Human-Robot Collaboration" is proposed in this paper, aiming to overcome the challenges and enable effective HRC in confined spaces.

This article provides a systematic literature review of computer vision applications in robotized wire harness assembly.

Wire harnesses are essential hardware for electronic systems in modern automotive vehicles. With a shift in the automotive industry towards electrification and autonomous driving, more and more automotive electronics are responsible for energy transmission and safety-critical functions such as maneuvering, driver assistance, and safety system. This paradigm shift places more demand on automotive wire harnesses from the safety perspective and stresses the greater importance of high-quality wire harness assembly in vehicles. However, most of the current operations of wire harness assembly are still performed manually by skilled workers, and some of the manual processes are problematic in terms of quality control and ergonomics. There is also a persistent demand in the industry to increase competitiveness and gain market share. Hence, assuring assembly quality while improving ergonomics and optimizing labor costs is desired. Robotized assembly, accomplished by robots or in human-robot collaboration, is a key enabler for fulfilling the increasingly demanding quality and safety as it enables more replicable, transparent, and comprehensible processes than completely manual operations. However, robotized assembly of wire harnesses is challenging in practical environments due to the flexibility of the deformable objects, though many preliminary automation solutions have been proposed under simplified industrial configurations. Previous research efforts have proposed the use of computer vision technology to facilitate robotized automation of wire harness assembly, enabling the robots to better perceive and manipulate the flexible wire harness. This article presents an overview of computer vision technology proposed for robotized wire harness assembly and derives research gaps that require further study to facilitate a more practical robotized assembly of wire harnesses.

Wire harnesses are vital for any modern automotive vehicle. They control the basic functions in a vehicle, for example, windshield wipers and critical functions such as sensors, cameras, and autopilot functions. Thus, the quality of wire harness assembly is highly important. Today, wire harnesses are usually assembled manually, which creates unergonomic and tedious working conditions for operators. Traditional and collaborative industrial robots have been identified as possible solutions to overcome challenges faced by operators in this type of assembly. The international research community has proposed many solutions for automating the assembly of wire harnesses in automotive vehicles but despite these solutions, the industry has not been able to adopt a method to automate this assembly process fully or partially. This paper presents a review of findings on robot-assisted wire harness assembly processes based on a systematic literature review. Specifically, the assembly of wire harnesses in Electric Vehicles (EVs). The state-of-the-art review focuses on solutions to improve unergonomic work situations and ensure the quality of assembly operations. Best practices and reasons for the lack of extensive implementation in automotive final assembly systems are described. Further, the paper presents suggestions based on success stories where the automation of the wire harness assembly in automotive vehicles has been realised by leveraging human-centred automation solutions. Based on the findings, this paper identifies the research for future study. The findings also indicate that there is already technology that can support the automation of wire harness assembly processes in EVs but it is crucial to identify the human aspects and the role of humans in the assembly of wire harness assembly process.

Factory layouts are frequently planned and designed in virtual environments, based on the experience of the layout planner. This planning and design process depends on information from several cross-disciplinary activities performed by several functions and experts, e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. Additionally, the layout planner also needs to consider applicable rules and regulations. This experience-based and manual approach to plan and design factory layouts, considering a multitude of inputs and parameters, is a cumbersome iterative process with a high risk of human error and faulty inputs and updates. The general trend in industry is to automate and assist users with their tasks and activities, deriving from concepts such as Industry 4.0 and Industry 5.0. This paper presents and demonstrates how digital support for rules and regulations can assist layout planners in factory layout work. The objective is to support the layout planner in accounting for area/volume reservations required to comply with rules and regulations for workers and equipment in the factory layout. This is a step in a wider initiative to provide enhanced digital support to layout planners, making the layout planning and design process more objective and efficient, and bridge gaps between cross-disciplinary planning and design activities.

The planning and design process of manufacturing factory layouts is commonly performed using digital tools, enabling engineers to define and test proposals in virtual environments before implementing them physically. However, this approach often relies on the experience of the engineers involved and input from various cross-disciplinary functions, leading to a time-consuming and subjective process with a high risk of human error. To address these challenges, new tools and methods are needed. The Industry 5.0 initiative aims to further automate and assist human tasks, reinforcing the human-centric perspective when making decisions that influence production environments and working conditions. This includes improving the layout planning process by making it more objective, efficient, and capable of considering multiple objectives simultaneously. This research presents a demonstrator solution for layout planning using digital support, incorporating a virtual multi-objective optimization approach to consider safety regulations, area boundaries, workers’ well-being, and walking distance. The demonstrator provides a cross-disciplinary and transparent approach to layout planning for an assembly station in the context of battery production. The demonstrator solution illustrates how layout planning can become a cross-disciplinary and transparent activity while being automated to a higher degree, providing results that support decision-making and balance cross-disciplinary requirements.

The factory layout is frequently planned in virtual environments, based on the experience of software tool users.This planning process is cumbersome and iterative to collect the necessary information, with a high risk of faulty inputs and updates.The digital twin concept has been introduced in order to speed up information sharing within a company; it relies on connectivity.However, the concept is often misunderstood as just a 3D model of a virtual object, not including connectivity.The aim of this paper is to present an extended virtual and physical engineering communication framework including four concepts: digital model, digital pre-runner, digital shadow, and digital twin.The four concepts are demonstrated and described in order to facilitate understanding how data exchange between virtual and physical objects can work in the future and having up-to date virtual environments enables simulating, analysing, and improving on more realistic and accurate datasets.

Simulation using virtual models is used widely in industries because it enables efficient creation, testing, and optimization of the design of products and production systems in virtual worlds. Simulation is also used in the design of workstations to assess worker well-being by using digital human modelling (DHM) tools. DHM tools typically include musculoskeletal risk assessment methods, such as RULA, REBA, OWAS, and NIOSH Lifting Equation, that can be used to study, analyse, and evaluate the risk of work-related musculoskeletal disorders of different design solutions in a proactive manner. However, most musculoskeletal risk assessment methods implemented in DHM tools are in essence made to assess static instances only. Also, the methods are typically made to support manual observations of the work rather than by algorithms in a software. This means that, when simulating full work sequences to evaluate manikins’ well-being, using these methods becomes problematic in terms of the legitimacy of the evaluation results. In addition to that, to consider objectives in optimizations they should be measurable with real numbers, which most of musculoskeletal risk assessment methods cannot provide when simulating full work sequences. In this study, we implemented the musculoskeletal risk assessment method OWAS in a digital tool connected to the DHM tool IPS IMMA. We applied the Lundqvist index on top of the OWAS whole body risk category score

Today, manufacturing factory layout setups are most often manually designed and kept up-to-date during their lifecycle with computer-aided design software’s, so that analyses, verification simulations, and decisions can continuously be done. This manual approach is a cumbersome iterative process to collect the necessary information, with a high risk of faulty inputs and updates. Often the virtual descriptions do not match the physical version of the factory setup. This research presents a digital twin solution where physical equipment is connected to a virtual representation of the same equipment and automatically updates the virtual environment with the spatial position of the physical equipment and a proposed way to evaluate it. The physical equipment either has inbuilt sensors or has been equipped with external wireless sensors to track the spatial position. The metadata are distributed via Node-RED (a tool to visualize Internet of Things) to the simulation software Industrial Path Solutions, where the virtual equipment is repositioned based on data from the physical equipment. The result shows that it is possible to send spatial position information from a physical equipment and update the corresponding virtual description of the equipment in its virtual environment. The accuracy of the updates has been evaluated with manual measurements. Hence, the virtual environment of the factory setup, i.e., the digital twin, updates automatically based on the data sent by the physical equipment. With an up-to-date virtual environment, more accurate simulations and optimizations of the factory setup can be achieved. Examples of such possibilities are to evaluate ergonomic conditions or to optimize robot paths for robot cells in realistic and up-to-date virtual environments. Also, one could imagine making virtual reality visits to hazardous or sensitive factory environments in a safe way or studying things that otherwise would be hard or impossible in the real world.

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.

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.

The vehicle industry is a common user of Digital Human Modeling Software (DHMS) tools, both for product development and production process development. One of three major risk factors interacting and contributing to the level of risk for an assembly worker is working posture (the two others are time and force). To achieve a working posture that is likely to happen in reality when using a DHMS tool is therefore crucial. Realistic posturing is required to obtain accurate quantitative assessments of human performance, as it has been shown that the performance models are sensitive to the postural condition. The need for faster and more consistent human simulation tools has motivated most commercial digital human modeling tool suppliers to implement various posture / motion wizards. However, these wizards are designed for certain types of tasks (e.g. ingress, egress or driving postures), and can not be utilized as a general solution for obtaining correct assembly/working postures. This paper shows examples of where working postures, achieved through manual use of DHMS tools, and discrete motions, achieved through use of a motion wizard, often considerably differ from working postures compiled during VR/AR sessions. This way of working (using VR/AR) is used to achieve more realistic working postures and a further development of the DHMS tools and working procedures should lead to faster and more consistent human simulation tools. The paper also justifies several research projects, which purposes are to decrease the gaps between wanted and existing functionalities.

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.

Digital human modelling (DHM) allows ergonomic risk assessment to be performed at early stages of design and development. Such assessment is typically based on observational methods, which do not take advantage of the potential of DHM tools to provide precise posture and motion data. This paper describes and illustrates an alternative assessment approach employing DHM tools, inspired by risk assessment based on direct measurements. A literature survey established a reference database of epidemiological associations between exposure and wrist-related disorders. This approach is illustrated by a DHM simulation of a car assembly task. Wrist posture and motion were simulated and compared to the database, predicting the prevalence of work-related musculoskeletal disorders on the basis of direct measurements.

High-quality input data are a necessity for successful discrete event simulation DES applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simulation models. Unfortunately, this research stream rests on the assumption that the collected data are already of high quality, and there is a lack of in-depth understanding of simulation data quality problems from a practitioners' perspective. Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a practical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simulation. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practical guidelines that can support manufacturing companies in improving data quality in DES.

Manufacturing companies continuously capture shop floor information using sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the nonbottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of the data-driven method. The main prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.

This paper introduces a Streamlined Modeling and Decision Support (StreaMod) approach in which input data management, simulation model generation/update and simulation-based optimization are synergistically integrated into a largely automated process. The aim of this automated process is to support decision making related to the optimal maintenance resources allocation that could improve the performance of production/logistic systems.