Högskolan i Skövde

This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.

Minimizing the non-machining time of CNC machines requires optimal positioning of cutting tools on indexes (stations) of CNC machine turret magazine. This work presents a genetic algorithm with a novel solution representation and genetic operators to find the best possible index positions while tool duplicates and tools life are taken in to account during the process. The tool allocation in a machining process of a crankshaft with 10 cutting operations, on a 45-index magazine, is optimized for the entire life of the tools on the magazine. The tool-indexing time is considerably reduced compared to the current index positions being used in an automotive factory. 

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices. 

Practical multi-objective optimization problems often involve several decision variables that influence the objective space in different ways. All variables may not be equally important in determining the trade-offs of the problem. Decision makers, who are usually only concerned with the objective space, have a hard time identifying such important variables and understanding how the variables impact their decisions and vice versa. Several graphical methods exist in the MCDM literature that can aid decision makers in visualizing and navigating high-dimensional objective spaces. However, visualization methods that can specifically reveal the relationship between decision and objective space have not been developed so far. We address this issue through a novel visualization technique called trend mining that enables a decision maker to quickly comprehend the effect of variables on the structure of the objective space and easily discover interesting variable trends. The method uses moving averages with different windows to calculate an interestingness score for each variable along predefined reference directions. These scores are presented to the user in the form of an interactive heatmap. We demonstrate the working of the method and its usefulness through a benchmark and two engineering problems.

Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. 

The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker's preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences. 

Most preference-based multi-objective evolutionary algorithms use reference points to articulate the decision maker's preferences. Since these algorithms typically converge to a sub-region of the Pareto-optimal front, the use of conventional performance measures (such as hypervolume and inverted generational distance) may lead to misleading results. Therefore, experimental studies in preference-based optimization often resort to using graphical methods to compare various algorithms. Though a few ad-hoc measures have been proposed in the literature, they either fail to generalize or involve parameters that are non-intuitive for a decision maker. In this paper, we propose a performance metric that is simple to implement, inexpensive to compute, and most importantly, does not involve any parameters. The so called expanding hypercube metric has been designed to extend the concepts of convergence and diversity to preference optimization. We demonstrate its effectiveness through constructed preference solution sets in two and three objectives. The proposed metric is then used to compare two popular reference-point based evolutionary algorithms on benchmark optimization problems up to 20 objectives.

In today’s global and aggressive market system, for manufacturing companies to remain competitive, they must manufacture high-quality products that can be produced at a low cost; they also must respond efficiently to customers’ predictable and unpredictable needs and demand variations. Increasingly shortened product lifecycles, as well as product customization degrees, lead to swift changes in the market that need to be supported by capable and flexible resources able to produce faster and deliver to the market in shorter periods while maintaining a high degree of cost-efficiency. To cope with all these challenges, the setup of production systems needs to shift toward Reconfigurable Manufacturing Systems (RMSs), making production capable of rapidly and economically changing its functionality and capacity to face uncertainties, such as unforeseen market variations and product changes. Despite the advantages of RMSs, designing and managing these systems to achieve a high-efficiency level is a complex and challenging task that requires optimization techniques. Simulation-based optimization (SBO) methods have been proven to improve complex manufacturing systems that are affected by predictable and unpredictable events. However, the use of SBO methods to tackle challenging RMS design and management processes is underdeveloped and rarely involves Multi-Objective Optimization (MOO). Only a few attempts have applied Simulation-Based Multi-Objective Optimization (SMO) to simultaneously deal with multiple conflictive objectives. Furthermore, due to the intrinsic complexity of RMSs, manufacturing organizations that embrace this type of system struggle with areas such as system configuration, number of resources, and task assignment. Therefore, this dissertation contributes to such areas by employing SMO to investigate the design and management of RMSs. The benefits for decision-makers have been demonstrated when SMO is employed toward RMS-related challenges. These benefits have been enhanced by combining SMO with knowledge discovery and Knowledge-Driven Optimization (KDO). This combination has contributed to current research practices proving to be an effective and supportive decision support tool for manufacturing organizations when dealing with RMS challenges.

In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.

The purpose of this study is to analyze the use of Simulation-Based Multi-Objective Optimization (SMO) for Reconfigurable Manufacturing System Configuration Analysis (RMS-CA). In doing so, this study addresses the need for efficiently performing RMS-CA with respect to the limited time for decision-making in the industry, and investigates one of the salient problems of RMS-CA: determining the minimum number of machines necessary to satisfy the demand. The study adopts an NSGA II optimization algorithm and presents two contributions to existing literature. Firstly, the study proposes a series of steps for the use of SMO for RMS-CA and shows how to simultaneously maximize production throughput, minimize lead time, and buffer size. Secondly, the study presents a qualitative comparison with the prior work in RMS-CA and the proposed use of SMO; it discusses the advantages and challenges of using SMO and provides critical insight for production engineers and managers responsible for production system configuration.

Nowadays, manufacturing companies face an increasing number of challenges that can cause unpredictable market changes. These challenges are derived from a fiercely competitive market. These challenges create unforeseen variations and uncertainties, including new regional requirements or regulations, new technologies and materials, new market segments, increasing demand for new product features, etc. To cope with the challenges above, companies must reinvent themselves and design manufacturing systems that seek to produce quality products while responding to the changes faced. These capabilities are encompassed in Reconfigurable Manufacturing Systems (RMS), capable of dealing with uncertainties quickly and economically. The availability of RMS is a crucial factor in establishing the production capacity of a system that considers all events that could interrupt the planned production. The impact of the availability in RMS is influenced by the configuration of the systems, including the number of resources used. This paper presents a case study in which a simulation-based multi-objective optimization (SMO) method is used to find machines’ optimal task allocation and assignment to workstations under different scenarios of availability. It has been shown that considering the availability of the machines affects the optimal configuration, including the number of resources needed, such as machines and buffers. This study demonstrates the importance of the availability consideration during the design of RMS.

Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies’ success in the current fiercely competitive market. Despite the RMS’s advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers’ capacity, and minimizing the number of machines, are optimized simultaneously while considering the system’s stochastic behavior to achieve the desired system’s configuration.

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

Due to the nature of today's manufacturing industry, where enterprises are subjected to frequent changes and volatile markets, reconfigurable manufacturing systems (RMS) are crucial when addressing ramp-up and ramp-down scenarios derived from, among other challenges, increasingly shortened product lifecycles. Applying simulation-based optimization techniques to their designs under different production volume scenarios has become valuable when RMS becomes more complex. Apart from proposing the optimal solutions subject to various production volume changes, decision-makers can extract propositional knowledge to better understand the RMS design and support their decision-making through a knowledge discovery method by combining simulation-based optimization and data mining techniques. In particular, this study applies a novel flexible pattern mining algorithm to conduct post-optimality analysis on multi-dimensional, multi-objective optimization datasets from an industrial-inspired application to discover the rules regarding how the tasks are assigned to the workstations constitute reasonable solutions for scalable RMS. 

Most evolutionary many-objective optimization (EMaO) algorithms start with a description of a number of the predefined set of reference points on a unit simplex. So far, most studies have used the Das and Dennis's structured approach for generating well-spaced reference points. Due to the highly structured nature of the procedure, this method cannot produce an arbitrary number of points, which is desired in an EMaO application. Although a layer-wise implementation has been suggested, EMO researchers always felt the need for a more generic approach. Motivated by earlier studies, we introduce a metric for defining well-spaced points on a unit simplex and propose a number of viable methods for generating such a set. We compare the proposed methods on a variety of performance metrics such as hypervolume (HV), deviation in triangularized simplices, distance of the closest point pair, and variance of the geometric means to nearest neighbors in up to 15-D spaces. We show that an iterative improvement based on Riesz s-energy is able to effectively find an arbitrary number of well-spaced points even in higher-dimensional spaces. Reference points created using the proposed Riesz s-energy method for a number of standard combinations of objectives and reference points as well as a source code written in Python are available publicly at https://www.egr.msu.edu/coinlab/blankjul/uniform. © 1997-2012 IEEE.

This paper presents the development of body-shape-data-based digital human models, i.e. manikins, for ergonomics simulations. In digital human modeling (DHM) tools, it is important that the generated manikin models are accurate and representative for different body sizes and shapes as well as being able to scale and move during motion simulations. The developed DHM models described in this paper are based on body scan data from the CAESAR anthropometric survey. The described development process consists of six steps and includes alignment of body scans, fitting of template mesh through homologous body modeling, statistical prediction of body shape, joint centre prediction, adjustment of posture to T-pose, and, finally, generation of a relation between predicted mesh and manikin mesh. The implemented method can be used to create any type of manikin size that can be directly used in a simulation. To evaluate the results, a comparison was done of original body scans and statistically predicted meshes generated in an intermediary step, as well as the resulting DHM manikins. The accuracy of the statistically predicted meshes are relatively good, even though differences can be seen, mostly related to postural differences and differences around smaller areas with distinct shapes. The biggest differences between the final manikin models and the original scans can be found in the shoulder and abdominal areas, in addition to the significantly different initial posture that the manikin models have. To further improve and evaluate the generated manikin models, additional body scan data sets that include more diverse postures would be useful. DHM tool functionality could also be improved to enable evaluation of the accuracy of the generated manikin models, possibly resulting in DHM tools that are more compliant with standard documents. At the same time, standard documents might need to be updated in some aspects to include more three-dimensional accuracy analysis.

This paper presents the development of a software module and a graphical user interface which aims to support the definition of anthropometry of manikins in a digital human modelling (DHM) tool. The module is developed from user interviews and literature studies, as well as mathematical methods for anthropometric diversity consideration. The module has functionality to create both single manikins and manikin families, where it is possible to combine or analyse different population datasets simultaneously. The developed module and its interface have been evaluated via focus group interviews and usability tests by DHM tool users. Results from the studies show that the developed module and its interface has relevant functionality, fits well into industrial work processes, and is easy to use. The study also identifies possibilities to further increase usability.

Augmented reality smart glasses (ARSG) are an emerging technology that has the potential to revolutionize how operators interact with information in cyber-physical systems. However, augmented reality is currently not widespread in industrial assembly. The aim of this paper is to investigate and map ARSG in manufacturing from the perspectives of the operator, of manufacturing engineering, and of its technological maturity. This mapping provides an overview of topics relevant to enabling the implementation of ARSG in a manufacturing system, thus facilitating future exploration of the three perspectives. This investigation was done using a meta-analysis of literature reviews of applications of augmented reality in industrial manufacturing. The meta-analysis categorized previously identified topics within augmented reality in industrial manufacturing and mapped those to the scope of the three perspectives.

The aim of this paper is to give an overview of the current knowledge and future challenges of augmented reality smart glasses (ARSG) for use by industrial operators. This is accomplished through a survey of the operator perspective of ARSG for industrial application, aiming for faster implementation of ARSG for operators in manufacturing. The survey considers the categories assembly instructions, human factors, design, support, and training from the operator perspective to provide insights for efficient use of ARSG in production. The main findings include a lack of standards in the design of assembly instructions, the field of view of ARSG are limited, and the guidelines for designing instructions focus on presenting context-relevant information and limiting the disturbance of reality. Furthermore, operator task routine is becoming more difficult to achieve and testing has mainly been with non-operator testers and overly simplified tasks. Future challenges identified from the review include: longitudinal user-tests of ARSG, a deeper evaluation of how to distribute the weight of ARSG, further improvement of the sensors and visual recognition to facilitate better interaction, and task complexity is likely to increase.

Augmented reality smart glasses (ARSG) have been identified as relevant support tools for the Operator 4.0 paradigm. Although ARSG are starting to be used in industry, their use is not yet widespread. A previously developed online tool based on a framework for evaluating ARSG as assembly operator support is iteratively improved in this paper with expanded functionality. The added functionality consists of practical recommendations for implementing ARSG in production. These recommendations were produced with the help of five focus groups of industrial representatives working in production. The recommendations were evaluated using case studies at three different companies. The recommendations were found to be detailed and a good support for the process of considering ARSG integration into production. The companies overall found the tool and its recommendations to be relevant and correct for their cases.

Augmented reality smart glasses (ARSG) have been successfully used as operator support in production. However, their use is not yet widespread, likely in part due to a lack of knowledge about how to integrate ARSG into production. This lack of knowledge can also make it hard to estimate whether this is a worthwhile investment. Our solution is to provide an online evaluation tool to help production planners estimate the likelihood that ARSG will be worth the investment cost in specific production cases. Based on a strawman design, multiple design iterations were followed by a pilot test performed by participants from different manufacturing companies involved in planning production for operators. A Likert scale survey was used to evaluate the tool. The results show a slightly positive evaluation of the tool with suggestions for improvement, including widening the scope and granularity of the tool. Future works include further iterations and case studies.

Most of the recently proposed evolutionary many-objective optimization (EMO) algorithms start with a number of predefined reference points on a unit simplex. These algorithms use reference points to create reference directions in the original objective space and attempt to find a single representative near Pareto-optimal point around each direction. So far, most studies have used Das and Dennis’s structured approach for generating a uniformly distributed set of reference points on the unit simplex. Due to the highly structured nature of the procedure, this method does not scale well with an increasing number of objectives. In higher dimensions, most created points lie on the boundary of the unit simplex except for a few interior exceptions. Although a level-wise implementation of Das and Dennis’s approach has been suggested, EMO researchers always felt the need for a more generic approach in which any arbitrary number of uniformly distributed reference points can be created easily at the start of an EMO run. In this paper, we discuss a number of methods for generating such points and demonstrate their ability to distribute points uniformly in 3 to 15-dimensional objective spaces.

Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency.

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. 

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.

Lean is and will still be one of the most popular management philosophies in the Industry 4.0 context and simulation is one of its key technologies. Many authors discuss about the benefits of combining Lean and simulation to better support decision makers in system design and improvement. However, there is a lack of reviews in the domain. Therefore, this paper presents a four-stage comprehensive review and analysis of existing literature on their combination. The aim is to identify the state of the art, existing methods and frameworks for combining Lean and simulation, while also identifying key research perspectives and challenges. The main trends identified are the increased interest in the combination of Lean and simulation in the Industry 4.0 context and in their combination with optimisation, Six Sigma, as well as sustainability. The number of articles in these areas is likely to continue to grow. On the other hand, we highlight six gaps found in the literature regarding the combination of Lean and simulation, which may induce new research opportunities. Existing technical, organisational, as well as people and culture related challenges on the combination of Lean and simulation are also discussed.

In the design process of products and production systems, the activity to systematically evaluate initial alternative design concepts is an important step. The digital human modeling (DHM) tools include several different types of assessment methods in order to evaluate product and production systems. Despite this, and due to the fact that a DHM tool in essence is a computer-supported design and analysis tool, none of the DHM tools provide the functionality to, in a systematic way, use the results generated in the DHM tool to compare design concepts between each other. The aim of this paper is to illustrate how a systematic concept evaluation method is integrated in a DHM tool, and to exemplify how it can be used to systematically assess design alternatives. Pugh´s method was integrated into the IPS software with LUA scripting to systematically compare design concepts. Four workstation layout concepts were generated by four engineers. The four concepts were systematically evaluated with two methods focusing on human well-being and two methods focusing on system performance and cost. The result is very promising. The demonstrator illustrates that it is possible to perform a systematic concept evaluation based on human well-being, overall system performance, and other parameters, where some of the data is automatically provided by the DHM tool and other data manually. The demonstrator can also be used to evaluate only one design concept, where it provides the software user and the decision maker with an objective and visible overview of the success of the design proposal from the perspective of several evaluation methods.

This book of proceedings contains papers accepted for the 6th International Digital Human Modeling Symposium (DHM2020), hosted by the University of Skövde in Sweden, and held at the ASSAR Industrial Innovation Arena in Skövde, as well as online, August 31 – September 2, 2020. The proceedings of DHM2020 consists of 43 papers subdivided into six parts, reflecting the topics addressed at the symposium. Part 1 is entitled Anthropometry. It contains papers on the collection and processing of anthropometric data, and on the development of methods for how to use anthropometric data in DHM settings, e.g. in the design of truck interiors and protective equipment. Also included in this part are methods for handling 3D scan data, skewed data, and how to generate full body shapes with a limited number of measures. Part 2 is entitled Behaviour and Biomechanical Modeling. It contains papers on cognitive modeling of roadside human interactions, and on physical musculoskeletal modelling of jaw motions. Modelling of hand-eye strategies and vision behaviour are covered, representing areas in the intersection of cognitive and physical modelling. Also presented are modelling technologies, including optimal control and neural networks. Part 3 is entitled Human Motion Data Collection and Modeling. It contains papers on reach and grasp modelling, as well as posture stability and hand trajectories. This part also includes papers on how to gather motion data with 3D textiles and smart clothing, and how to store motion data in databases. Part 4 is entitled Human-Product Interaction Modeling. It contains papers on how vehicle drivers interact with automotive interiors. Seat interaction for vehicle drivers and pilots is presented, as well as papers on models for human-seat foam interaction. Also included in this part is modelling of exoskeleton as a human support. Part 5 is entitled Industry and User Perspectives. It contains papers on both industry, health, and medical sector perspectives. Examples are given on applications of DHM software and associated technologies. Future needs and identified gaps are discussed. Several papers focus on usability of DHM software, both on desktop and in VR. Also included in this part is gamification of DHM. Part 6 is entitled Production Planning and Ergonomics Evaluation. It contains papers on DHM as an ergonomics evaluation tool. Gender perspectives on DHM are presented, as well as a case from the maritime sector. The development of a multi-objective approach for DHM simulation and evaluation is presented. DHM simulations are compared with motion capture data. Also included in this part are DHM tools with VR functionality, combined with motion capture and AI technologies.

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.

IPS IMMA (Industrial Path Solutions - Intelligently Moving Manikins) is a digital human modeling tool developed in close cooperation between academia and industry in Sweden. The academic consortium behind the software consists of expertise within applied mathematics, ergonomics, and engineering. The development of IMMA was initiated from the vehicle industries’ need of an effective, efficient, objective, and user-friendly software for verification of manufacturing ergonomics. The ‘Industrial path solutions - intelligently moving manikins’ chapter consists of two main sections: the first about the commercially available tool, and the second about current or recent research projects developing the software further. Commercial IPS IMMA is presented by describing the biomechanical model and appearance, anthropometrics module, motion prediction, instruction language, and ergonomics evaluation. The research projects focus on dynamic motions simulation, muscle modelling and application areas such as human-robot collaboration, occupant packaging, and layout planning.

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.

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.

The realisation of a successful product requires collaboration between developers andproducers, taking account of stakeholder value, reinforcing the contribution of industry tosociety and enhancing the wellbeing of workers while respecting planetary boundaries.Founded in 2006, the Swedish Production Academy (SPA) aims to drive and developproduction research and education and to increase cooperation within the production area.SPA initiated and hosts the conference Swedish Production Symposium. This specialissue is based on invited papers from the 10th Swedish Production Symposium(SPS2022), held in Skövde, Sweden, from 26–29 April 2022. The overall theme forSPS2022 was ‘Industry 5.0 transformation – towards a sustainable, human-centric, andresilient production’.As stated by the European Commission the vision of Industry 5.0 recognises societalgoals. It goes beyond a techno-economic vision, industrial value chains and growthaiming for the industry to become a resilient provider of prosperity, respecting ourplanets boundaries, and placing the industrial worker, her well-being, at the centre of theproduction process.In this special issue, we set out to explore the transition to a resilient, sustainable andhuman centric industry. The first paper explores the need for a joint strategical vision thatinclude technology (selection, development, and implementation), organisation(structure, agility, management, stakeholder collaborations, work environment) andpeople (skills and competences, participation, innovation and creative collaborativeculture, and change readiness), to achieve a resilient and sustainable production systemeffectively and efficiently. The second paper discusses how reconfigurable manufacturingsystems can enable sustainable manufacturing and circularity, achieving highresponsiveness and cost efficiency. The third paper, a synthesis of universal workplacedesign in assembly, explores how human assembly workplaces can be designed in abetter way in regard to inclusion of diverse worker populations. The fourth paperdiscusses different meanings of digital transformation in manufacturing industry fromboth a theoretical and industrial perspective. The fifth paper explores challenges to designa product service system at an SME as an approach to support transition to Industry 5.0.The concluding paper in this special issue discusses a knowledge extraction platform forreproducible decision support based on data from multi-objective experiments.The organiser of SPS2022 has found these six outstanding papers to perfectly alignwith the theme ‘Industry 5.0 transformation’ and express their gratitude to theEditor-in-Chief of IJMR for accepting them for publication in this special issue.

In the digitalisation era, overlaying digital, contextualised information on top of the physical world is essential for an efficient operation. Mixed reality (MR) is a technology designed for this purpose, and it is considered one of the critical drivers of Industry 4.0. This technology has proven to have multiple benefits in the manufacturing area, including improving flexibility, efficacy, and efficiency. Among the challenges that prevent the big-scale implementation of this technology, there is the authoring challenge, which we address by answering the following research questions: (1) “how can we fasten MR authoring in a manufacturing context?” and (2) “how can we reduce the deployment time of industrial MR experiences?”. This paper presents an experiment performed in collaboration with Volvo within the remanufacturing of truck engines. MR seems to be more valuable for remanufacturing than for many other applications in the manufacturing industry, and the authoring challenge appears to be accentuated. In this experiment, product lifecycle management (PLM) tools are used along with internet of things (IoT) platforms and MR devices. This joint system is designed to keep the information up-to-date and ready to be used when needed. Having all the necessary data cascading from the PLM platform to the MR device using IoT prevents information silos and improves the system’s overall reliability. Results from the experiment show how the interconnection of information systems can significantly reduce development and deployment time. Experiment findings include a considerable increment in the complexity of the overall IT system, the need for substantial investment in it, and the necessity of having highly qualified IT staff. The main contribution of this paper is a systematic approach to the design of industrial MR experiences.

Observational ergonomic evaluation methods have inherent subjectivity. Observers’ assessment results might differ even with the same dataset. While motion capture (MOCAP) systems have improved the speed and the accuracy of motiondata gathering, the algorithms used to compute assessments seem to rely on predefined conditions to perform them. Moreover, the authoring of these conditions is not always clear. Making use of artificial intelligence (AI), along with MOCAP systems, computerized ergonomic assessments can become more alike to human observation and improve over time, given proper training datasets. AI can assist ergonomic experts with posture detection, useful when using methods that require posture definition, such as Ovako Working Posture Assessment System (OWAS). This study aims to prove the usefulness of an AI model when performing ergonomic assessments and to prove the benefits of having a specialized database for current and future AI training. Several algorithms are trained, using Xsens MVN MOCAP datasets, and their performance within a use case is compared. AI algorithms can provide accurate posture predictions. The developed approach aspires to provide with guidelines to perform AI-assisted ergonomic assessment based on observation of multiple workers.

Simulation software is used in industry to simulate production as it allows predicting behaviours, calculate times and plan production already at virtual stages of the production development process. There is also software to simulate humans working in production, commonly called digital human modelling (DHM) tools. When humans are simulated, ergonomics evaluations can be carried out in order to assess whether workstation designs offer appropriate ergonomic conditions for the worker. However, these human simulations are usually carried out by human factors engineers or ergonomists, with the purpose of validating workstations, without integrating these simulations with those performed in production by production engineers. Due to this, simulations performed to predict production are usually done separately from human simulations performed to evaluate ergonomics. This can lead to suboptimal solutions when the factory is optimized to improve productivity and ergonomics. This research proposal contains a frame of reference, literature review, research questions, proposed approach, motivation, expected results, philosophical paradigm, research methodology, method, challenges and planning, founded in the hypothesis that more optimal solutions for workstation design, layout and line balancing can be obtained in simulations by optimizing productivity and ergonomic factors at the same time instead of improving them separately. Hence, the aim is to carry out research in the development of a multi-objective optimization method of productivity and ergonomic factors, and to implement the method into a simulation tool in order to test and communicate the method. From an academic perspective, the overall objective is to contribute to knowledge and publish findings in the academic community, eventually leading to a PhD thesis. From an application perspective, the overall objective is to contribute to the development of efficient methods for how to find successful designs of productive and ergonomic workstations.

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.

Nowadays, different technologies and software for ergonomics evaluations are gaining greater relevance in the field of ergonomics and production development. The tools allow users such as ergonomists and engineers to perform assessments of ergonomic conditions of work, both related to work simulated in digital human modelling (DHM) tools or based on recordings of work performed by real operators. Regardless of approach, there are many dimensions of data that needs to be processed and presented to the users.

The users may have a range of different expectations and purposes from reading the data. Examples of situations are to: judge and compare different design solutions; analyse data in relation to anthropometric differences among subjects; investigate different body regions; assess data based on different time perspectives; and to perform assessments according to different types of ergonomics evaluation methods. The range of different expectations and purposes from reading the data increases the complexity of creating an interface that considers all the necessary tools and functions that the users require, while at the same time offer high usability.

This paper focuses on the structural design of a flexible and intuitive interface for an ergonomics evaluation software that possesses the required tools and functions to analyse work situations from different perspectives, where the data input can be either from DHM tools or from real operators while performing work. 

OCCUPATIONAL APPLICATIONS

Worker well-being and overall system performance are important elements in the design of production lines. However, studies of industry practice show that current design tools are unable to consider concurrently both productivity aspects (e.g., line balancing and cycle time) and worker well-being related aspects (e.g., the risk of musculoskeletal disorders). Current practice also fails to account for anthropometric diversity in the workforce and does not use the potential of multi-objective simulation-based optimization techniques. Accordingly, a framework consisting of a workflow and a digital tool was designed to assist in the proactive design of workstations to accommodate worker well-being and productivity. This framework uses state-of-the-art optimization techniques to make it easier and quicker for designers to find successful workplace design solutions. A case study to demonstrate the framework is provided

TECHNICAL ABSTRACT

Rationale: Simulation technologies are used widely in industry as they enable efficient creation, testing, and optimization of the design of products and production systems in virtual worlds. Simulations of productivity and ergonomics help companies to find optimized solutions that maintain profitability, output, quality, and worker well-being. However, these two types of simulations are typically carried out using separate tools, by persons with different roles, with different objectives. Silo effects can result, leading to slow development processes and suboptimal solutions.

Purpose: This research is related to the realization of a framework that enables the concurrent optimization of worker well-being and productivity. The framework demonstrates how digital human modeling can contribute to Ergonomics 4.0 and support a human factors centered approach in Industry 4.0. The framework also facilitates consideration of anthropometric diversity in the user group.

Methods: Design and creation methodology was used to create a framework that was applied to a case study, formulated together with industry partners, to demonstrate the functionality of the noted framework.

Results: The framework workflow has three parts: (1) Problem definition and creation of the optimization model; (2) Optimization process; and (3) Presentation and selection of results. The case study shows how the framework was used to find a workstation design optimized for both productivity and worker well-being for a diverse group of workers.

Conclusions: The framework presented allows for multi-objective optimizations of both worker well-being and productivity and was successfully applied in a welding gun use case.

Taking a holistic perspective is central in production development, aiming to optimize ergonomics and overall production system performance. Hence, there is a need for tools and methods that can support production companies to identify the production system alternatives that are optimal regarding both ergonomics and production efficiency. The paper presents a devised case study where multi-objective optimization is applied, as a step to towards the development of such an optimization tool. The overall objective in the case study is to find the best order in which an operator performs manual tasks during a workday, considering ergonomics and production system efficiency simultaneously. More specifically, reducing the risk of injury from lifting tasks and improving the throughput are selected as the two optimization objectives. An optimization tool is developed, which communicates with a digital human modelling tool to simulate work tasks and assess ergonomics. 

Simulation software is used in the production development process to simulate production and predict behaviors, calculate times, and plan production in advance. Digital human modeling (DHM) software is used to simulate humans working in production and assess whether workstation designs offer appropriate ergonomic conditions for the workers. However, these human simulations are usually carried out by human factors engineers or ergonomists, whereas the production simulations are carried out by production engineers. Lack of integration of these two simulations can lead to suboptimal solutions when the factory is not optimized to improve both productivity and ergonomics. To tackle this problem, a platform has been developed that connects production flow simulation software data and DHM software data and integrates them in a generic software for data treatment and visualization. Production flow simulation software data and DHM software data are organized in a hierarchical structure that allows synchronization between the production data and the ergonomic data on the target simulation software. The platform is generic and can be connected to any production flow simulation software and any DHM software by creating specific links for each software. The platform requires only the models of the production line, workstations, and tasks in order to perform the simulations in the target simulation software and collect the simulation results to present the results to the user of the platform. 

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.

Simulations of future production systems enable engineers to find effective and efficient design solutions with fewer physical prototypes and fewer reconstructions. This can save development time and money and be more sustainable. Better design solutions can be found by linking simulations to multiobjective optimization methods to optimize multiple design objectives. When production systems involve manual work, humans and human activity should be included in the simulation. This can be done using digital human modeling (DHM) software which simulates humans and human activities and can be used to evaluate ergonomic conditions. This paper addresses challenges related to including existing ergonomics evaluation methods in the optimization framework. This challenge arises because ergonomics evaluation methods are typically developed to enable people to investigate ergonomic conditions by observing real work situations. The methods are rarely developed to be used by computer algorithms to draw conclusions about ergonomic conditions. This paper investigates how to adapt ergonomics evaluation methods to implement the results as objectives in the optimization framework. This paper presents a use case of optimizing a workstation using two different approaches: 1) an observational ergonomics evaluation method, and 2) a direct measurement method. Both approaches optimized two objectives: the average ergonomics results, and the 90th percentile ergonomics results.

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.

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 modeling (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, analyze, 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 become 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. This gave us an integer of the time-based ergonomic load for a specific simulation sequence, enabling us to qualitatively compare different design solutions. Using this approach, we performed an optimization in a welding gun workstation to improve the design of the workstation. The results show that using time-based musculoskeletal risk assessment methods as objective functions in optimizations in DHM tools can provide valuable decision support in finding solutions for workstation designs that consider worker well-being.

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.

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology. 

The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker's preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.

Production systems are evolving rapidly, thanks to key Industry 4.0 technologies such as production simulation, digital twins, internet-of-things, artificial intelligence, and big data analytics. The combination of these technologies can be used to meet the long-term enterprise goals of profitability, sustainability, and stability by increasing the throughput and reducing production costs. Owing to digitization, manufacturing companies can now explore operational level data to track the performance of systems making processes more transparent and efficient. This untapped granular data can be leveraged to better understand the system and identify constraining activities or resources that determine the system’s throughput. In this paper, we propose a data-driven methodology that exploits the technique of data integration, process mining, and analytics based on factory physics to identify constrained resources, also known as bottlenecks. To test the proposed methodology, a case study was performed on an industrial scenario were a discrete event simulation model is built and validated to run future what-if analyses and optimization scenarios. The proposed methodology is easy to implement and can be generalized to any other organization that captures event data.

Posture/motion prediction is the basis of the human motion simulations that make up the core of many digital human modeling (DHM) tools and methods. With the goal of producing realistic postures and motions, a common element of posture/motion prediction methods involves applying some set of constraints to biomechanical models of humans on the positions and orientations of specified body parts. While many formulations of biomechanical constraints may produce valid predictions, they must overcome the challenges posed by the highly redundant nature of human biomechanical systems. DHM researchers and developers typically focus on optimization formulations to facilitate the identification and selection of valid solutions. While these approaches produce optimal behavior according to some, e.g., ergonomic, optimization criteria, these solutions require considerable computational power and appear vastly different from how humans produce motion. In this paper, we take a different approach and consider the Forward and Backward Reaching Inverse Kinematics (FABRIK) solver developed in the context of computer graphics for rigged character animation. This approach identifies postures quickly and efficiently, often requiring a fraction of the computation time involved in optimization-based methods. Critically, the FABRIK solver identifies posture predictions based on a lightweight heuristic approach. Specifically, the solver works in joint position space and identifies solutions according to a minimal joint displacement principle. We apply the FABRIK solver to a seven-degree of freedom human arm model during a reaching task from an initial to an end target location, fixing the shoulder position and providing the end effector (index fingertip) position and orientation from each frame of the motion capture data. In this preliminary study, predicted postures are compared to experimental data from a single human subject. Overall the predicted postures were very near the recorded data, with an average RMSE of 1.67°. Although more validation is necessary, we believe that the FABRIK solver has great potential for producing realistic human posture/motion in real-time, with applications in the area of DHM.