It can be argued that it is common that industrial workstations are “built” rather than purposefully designed with user and task requirements in mind. Afterwards, built-in problems typically need to be corrected, causing undesired costs and efforts. With the objective to assist workstation designers in avoiding most problems already in the design phase, a design support tool is being developed. The paper argues the need for such a tool and presents the fundamental tool functionality. Expected advantages are more efficient and ergonomic workstations and a more efficient design process with built-in learning and documentation.
Driver-vehicle interaction analyses are done to ensure a successful vehicle design from an ergonomics perspective. Digital Human Modelling (DHM) tools are often used to support such verifications, particularly at early stages of the product development process. When verifying that a vehicle design accommodates the diversity of users and tasks, a DHM tool needs to be able to represent postures and motions that are likely under certain conditions. This functionality is essential so that the tool user will obtain objective and repeatable simulation results. The DHM tool IMMA (Intelligently Moving Manikins) predicts postures and motions by using computational methods. This offers the possibility to generate postures and motions that are unique for the present design conditions. IMMA was originally developed for simulating manual assembly work, whereas the work presented here is a step towards utilizing the IMMA tool for occupant packaging and related tasks. The objective is a tool for virtual verification of driver-vehicle interaction that supports and automates the simulation work to a high degree. The prediction functionality in IMMA is based on the use of optimization algorithms where one important component is the consideration of comfort level. This paper reports results from an basic investigation of driving postures and available comfort models suitable in a driving context, and shows initial results of seated posture and motion prediction functionality in the IMMA tool.
Anthropometry, the study of human measurements, is central in the design of products and workplaces. This paper describes how Swedish anthropometric data is made available through a web page (www.antropometri.se) intended to be used by designers and engineers when developing new products and workplaces. With the anthropometric web resource it is possible to get mean and standard deviation values, and to calculate percentile values, for a number of anthropometric measurements. Further functionality on the web page enables simultaneous consideration of several anthropometric measurements. The web page also contains guidelines for how to use anthropometric data depending on the design task at hand.
Digital human modelling (DHM) systems are used to simulate production processes and analyse the human-machine interaction, particularly at early development stages. Consideration of anthropometric variation is central in DHM simulations due to the necessity of ensuring intended accommodation levels. This paper describes the process of how digital human models are created and defined within the IMMA software. The process begins with the definition of a number of key measurements, which acts as the basis for the definition of several boundary manikins using a confidence ellipsoid methodology. These manikins represents the appropriate confidence region and hence the anthropometric diversity. Key measurements are then entered into regression equations to define the complete set of measurements for each manikin. These measurements are based on the appropriate ISO-standard. Finally, measurements are used to define the size and alignment of each segment in the biomechanical model of the manikin. The manikins are then used to automatically simulate and analyse human-machine interaction.
In digital human modelling (DHM), ergonomics evaluations are typically done with few human models. However, humans vary a lot in sizes and shapes. Therefore, few manikins can rarely ensure accommodation of an entire target population. Different approaches exist on how to consider anthropometric diversity. This paper reviews current DHM tools and clarify problems and opportunities when working with anthropometric diversity. The aim is to suggest functionality for a state of the art DHM module and work process for considering anthropometric diversity. The study is done by an analysis of some of the current DHM systems and by interviews of personnel at car companies about their way of working with anthropometric diversity. The study confirmed that critical production simulations are often done in early development stages with only one or a few human models. The reason for this is claimed to be time consuming processes, both at the creation of the human model but mainly when correctly positioning the model in the CAD environment. The development of a new method and work process for considering anthropometric diversity is suggested. Necessary features for such a module are that it shall be easy to use and not require expert knowledge about the consideration of anthropometric diversity. It shall also be configurable and transparent, in a sense that it should be possible to work with own anthropometric data and ergonomics evaluation standards. The module has to be flexible and have different entrances depending on the type of anthropometric problem being analyzed. An improved work method is expected to lead to faster and more correct analyses.
When evaluating human-machine interaction it is central to consider anthropometric diversity to ensure intended accommodation levels. A well-known method is the use of boundary cases where manikins with extreme but likely measurement combinations are derived by mathematical treatment of anthropometric data. The supposition by that method is that the use of these manikins will facilitate accommodation of the expected part of the total, less extreme, population. In literature sources there are differences in how many and in what way these manikins should be defined. A similar field to the boundary case method is the use of experimental design in where relationships between affecting factors of a process is studied by a systematic approach. This paper examines the possibilities to adopt methodology used in experimental design to define a group of manikins. Different experimental designs were adopted to be used together with a confidence region and its axes. The result from the study shows that it is possible to adapt the methodology of experimental design when creating groups of manikins. The size of these groups of manikins depends heavily on the number of key measurements but also on the type of chosen experimental design.
Boundary manikins, the concept of creating statistically extreme cases to accommodate a big part of the less extreme population has been known for decades. Despite this, many ergonomics simulations are done with few human models. This fact can be explained by the time consuming processes when working with many manikins in current digital human modelling (DHM) tools, but may also be a result of difficulty to understand how these manikins are calculated and defined. This paper focuses on the method of defining boundary manikins and how that functionality can be integrated into a DHM tool. Examples of boundary case methods in the literature often use Principal Component Analysis (PCA) which makes it possible to reduce the dimensions of the problem without much loss of the variance of the analysed data. Using PCA often demands some extent of manual analysis at the critical stage of reducing dimensions. This paper will explain a similar methodology for ceating boundary manikins from any number of variables, i.e. anthropometric variables chosen as key measurements. This method of creating a group of manikins is intended to be used in an automatic simulation feature in the IMMA software being developed in the associated research project. By using the method, a confidence region in the standardized space is created from eigenvectors and scaled eigenvalues of a correlation matrix. Boundary manikins are chosen at the ends of the axes of the enclosing confidence region, and one manikin of mean values is also added to the group of manikins. In the method presented here, the number of manikins created depends directly on the number of variables, which lead to the fact that the decision making of which key measurements to consider has to be done carefully to not create an overwhelming number of manikins. In comparison with one method using PCA, the method presented in this paper creates more manikins with a bigger difference in the max and min values of the chosen key measurements. If a limited number of cases are of crucial interest, then using PCA to reduce the dimensions of the problem is a good method to use. But if it is possible to create automated simulations the limitation of the number of manikins might not be so important. This will, though, depend heavily on the speed of the automated simulations.
When evaluating human-machine interaction in a virtual environment using Digital human modelling (DHM) it is important to ensure that the predicted motions lie within the range of behavioural diversity for different people within a population. This paper presents a study in which a comparison is made between motions predicted by the DHM tool IMMA (Intelligently Moving Manikin) and motions from real humans stored in a motion database. Results show similar motions but the predicted motions were in total statistically significantly different compared to the motions performed by real persons. The differences are most likely due to the balance function and joint constraints that the IMMA tool uses for predicting motions. Differences can also be due to other factors, aside of body size, such as age, gender or strength that affects the movement behaviour.
This paper study and clarify problems, needs and opportunities when working with anthropometric diversity in digital human modelling (DHM) systems. A comparison between product development and production development in Swedish automotive industry is made. Interviews with DHM users and ergonomics specialists about their way of working with anthropometric diversity confirmed that simulations are often done with only one or a few human models. The reason for this is claimed to be time consuming processes, both at the creation of the human model but mainly when correctly positioning the model in the CAD environment.
In this paper we present a framework for digital human modelling using discrete mechanics and optimal control. Discrete mechanics is particularly well suited for modelling the dynamics of constrained mechanical systems, which is almost always the case when considering complex human models interacting with the environment. We demonstrate that, by using recently developed recursive dynamics algorithms, we are able to efficiently use discrete mechanics in direct optimal control methods to plan for complex motions. Besides a proper mechanical model, an appropriate objective function is paramount to achieve realistic motions as a solution to an optimal control problem. Hence, several different objective functions, such as for example minimum time or minimum applied torque over the joints, are compared, and the resulting motions are analyzed and evaluated. To further improve the model, we include basic muscular models for the muscles of the shoulder, arm and wrist, and examine how this affects the motions.
The aim of the present case study is to present and evaluate a computer-based standardized procedure to order, perform and document virtual ergonomic analyses. Results showed that the use of the new working methodology increased the number of factors considered during analysis. Participants indicated that the proposed methodology, including task analysis and use of manikin families, would increase the reliability of the results. The increase in numbers of factors considered during analysis and the improved reliability of the results is also likely to reduce the number of iterations needed in the design process to make products meet established requirements, therefore reducing total development time.
Developers, reviewers and users of human simulation tools claim that the use of these tools may reduce development time and development cost. However, before these benefits will be fully visible, there are some barriers to overcome. The aims of this case study are to identify which departments at Saab Automobile use some sort of human simulation tool today, and to identify the information flow and procedure when the tool is used. Four departments crash safety, packaging, production planning and vehicle ergonomics were identified as direct users of human simulation tools. The tools used were finite element with crash dummy representation, SAE human model, Safework and Ramsis. Communications between human simulation tool users are limited. Communications are done through the project management. The crash safety and packaging departments have formal descriptions of the human simulation process, whereas production planning and vehicle ergonomics have no formal process descriptions. To gain from the benefits of human simulation tools, Saab Automobile needs to adapt them to the organization and the organization to the tools. Integration of a working methodology is essential for effective and efficient use in the other human simulation departments where this is currently lacking.
The purpose of the research project described in this paper is to improve the efficiency of product development processes by exchanging knowledge and experiences about user centred design methods and technologies between the two branches: vehicle and health care industries. The health care industry can benefit from visualisation and simulation tools that include computer manikins, a physical representation of the human, and the vehicle industry can benefit from manikins having personal characteristics, which has proven to be successful in the health care industry.
Effective simulation of manual assembly operations considering ergonomic load and clearance demands requires detailed modeling of human body kinematics and motions, as well as a tight coupling to powerful algorithms for collision-free path planning. The focus in this paper is a unified solution that automatically creates assembly motions for manikins taking kinematic constraints, balance, contact forces, collision avoidance and comfort into account. The manikin used in this work has 162 degrees of freedom - six exterior fictitious joints determine the position of the lower lumbar and the remaining ones are interior joints. The inverse kinematic problem leads to an underdetermined system allowing us to pick a solution that maximizes a scalar valued comfort function. The comfort function offers a generic way to give preference to certain poses while avoiding others, typically by considering joint limits, forces and moments on joints, and magnitude of contact forces. In order to avoid collisions, poses close to collision are penalized. The method is implemented and demonstrated on two challenging assembly operations taken from the automotive industry.
Simulating manual assembly operations considering ergonomic load and clearance demands requires detailed modeling of human body kinematics and motions, as well as a tight coupling to powerful algorithms for collision-free path planning. The focus in this paper is kinematics including balance and contact forces, and ergonomically preferable motions in free space. A typical manikin has more than 100 degrees of freedom. To describe operations and facilitate motion generation, the manikin is equipped with coordinate frames attached to end-effectors like hands and feet. The inverse kinematic problem is to find joint values such that the position and orientation of hands and feet matches certain target frames during an assembly motion. This inverse problem leads to an underdetermined system of equations since the number of joints exceeds the end-effectors' constraints. Due to this redundancy there exist a set of solutions, allowing us to consider ergonomics aspects and maximizing comfort when choosing one solution.The most common approach to handle both forward and inverse kinematics is building a hierarchy of joints and links where one root must be defined. A popular place to define the root is in a body part, e.g. in a foot. This leads to a two-step procedure; (i) one level determining when to re-root when moving the root part, (ii) then the Penrose pseudoinverse is used to match the end-effectors' constraints.In this paper we propose using a fixed exterior root by introducing six additional parameters positioning the lower lumbar - three rotations and three translations. This makes it possible to reposition the manikin without a series of re-rooting operations. Another important aspect is to keep the manikin, affected by internal and external forces and moments, in balance. However, by utilizing the exterior root and its added degrees of freedom it is possible to solve the balance, positioning, contact force and comfort problems simultaneously in a unified way. A manikin was implemented, and two test cases demonstrate the applicability of the presented method.
Current product and production development tends to become more complex where principal design decisions are made in very early development phases when product data only exist in virtual formats. To support this virtual product realisation process there exist a number of tools and technologies. Considering ergonomics and human factors in an increasingly complex process with often complex tools requires competent people able to handle multidisciplinary development challenges in a proactive manner. To answer the need for educational programs to cover these issues the School of Engineering Science at University of Skövde has developed a new master (second cycle) program Virtual Ergonomics and Design. The aim with the program is to give students and future product and production developers, necessary knowledge and skills to effectively use virtual tools for analysis, development, and verification of ergonomics and integrate ergonomics and user aspects into the product realisation process. This is achieved through a number of courses that partly forms a core within the subject Virtual product realisation but also provides in-depth knowledge in ergonomics. Students will in a possible future role as design or production engineers have a great influence on ergonomics in manufacturing departments but also better perception of ergonomics, higher motivation and knowledge of support tools and methods for ergonomics integration.
The higher level of product variation in the automotive industry leads to an increasing workload for the assembler that has to search, fetch and assemble all the variants. This puts high demands on the information that is given to the assembler to fulfil the assembly task. This paper describes the impact of information overload and sources, and their influence on the assembler. Through observations conducted in the Swedish automotive industry, the study has shown that the assembly personnel perceive the kit as structured information and that structured kits are able to present distinct information at a certain place to the assembler, which in turn reduces the searching, resulting in decreased cognitive workload.
In manual assembly, a strategy to meet the goal of efficient production is the increased use of kitting as a material supply principle. Even though kitting is already implemented in industry, there are still uncertainties regarding the effects of introducing kits, particularly from a human factors perspective.
This paper presents initial steps in the development of a method to be used for the evaluation of kitting. This from an information source point of view and for studying effects related to productivity and quality. The methodology is projected to act as a foundation for how to carry out a subsequent comprehensive case study. The purpose of the case study is to explore how kitting affects the cognitive workload compared to the ordinary material rack combined with part numbers used in the current manufacturing industry. This is done by measuring productivity; time spent on assembling a product, and quality; number of assembly errors. One step in the methodology development process, which is described in this paper, was to conduct a pilot study, primarily to test the methodology related to the selection of measurement parameters, as well as for getting experiences from running the methodology with real test subjects.
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.
Digital human modelling (DHM) systems can be used to simulate production processes and analyse the human-machine interaction, particularly at early design stages. The human-machine interaction is affected and limited by factors or characteristics belonging to the human user and the machine or product but also the surrounding environment. DHM systems consider in most cases only physical user capabilities and with focus on consideration of body size related anthropometric diversity. However, the human-machine interaction is not only affected by the size and proportions of a user but for example also the user´s muscle strength and range of motion (ROM). This paper describes a study where diversity in strength and ROM, together with diversity in body size, is implemented in the process of creating data for a group of human arm models. A literature study was done to investigate the diversity of strength and ROM and the correlation between such measurements and body size data. The results from the literature study showed that there is little correlation between body size, strength and ROM. The study also showed that there are few published studies where body size, strength and ROM have been tested at the same time. From the literature study, generic correlation coefficients between body size, strength and ROM were synthesized. Using these correlation coefficients and Principal Component Analysis, data for a group of 14 female arm models with varying body size, strength and ROM were calculated. The results show that it is possible to introduce additional variables such as strength and ROM, but also that data of the correlation between body size and other types of anthropometric measurements are scarce. New measurement studies are important to decrease the uncertainties when predicting correlation coefficients between body size, strength and ROM variables.
In digital human modelling (DHM) systems consideration of anthropometry is central. Important functionality in DHM tools is the regression model, i.e. the possibility to predict a complete set of measurements based on a number of defined independent anthropometric variables. The accuracy of a regression model is measured by how well the model predicts dependent variables based on independent variables, i.e. known key anthropometric measurements. In literature, existing regression models often use stature and/or body weight as independent variables in so-called flat regressions models which can produce estimations with large errors when there are low correlations between the independent and dependent variables. This paper suggests a conditional regression model that utilise all known measurements as independent variables when predicting each unknown dependent variable. The conditional regression model is compared to a flat regression model, using stature and weight as independent variables, and a hierarchical regression model that uses geometric and statistical relationships between body measurements to create specific linear regression equations in a hierarchical structure. The accuracy of the models is assessed by evaluating the coefficient of determination, R2 and the root-mean-square deviation (RMSD). The results from the study show that using a conditional regression model that makes use of all known variables to predict the values of unknown measurements is advantageous compared to the flat and hierarchical regression models. Both the conditional linear regression model and the hierarchical regression model have the advantage that when more measurements are included the models will give a better prediction of the unknown measurements compared to the flat regression model based on stature and weight. A conditional linear regression model has the additional advantage that any measurement can be used as independent variable. This gives the possibility to only include measurements that have a direct connection to the design dimensions being sought. Utilising the conditional regression model would create digital manikins with enhanced accuracy that would produce more realistic and accurate simulations and evaluations when using DHM tools for the design of products and workplaces.
This paper describes and evaluates the boundary case methodology for the simultaneous consideration of variance for a number of selected anthropometric variables. The methodology includes the calculation of key dimension values for extreme but likely anthropometric measurement combinations. This data can be applied when utilising digital human modelling (DHM) tools for proactive design work and entered as input data when representative manikins are defined. The mathematical procedure is clearly described and exemplified to demonstrate how to use the methodology in design work. The outcome of the method is illustrated and compared using several different cases where the number of measurements is varied and where principal component analysis (PCA) is used to reduce the number of dimensions in one case. The paper demonstrates that the proposed boundary case method is advantageous compared to approaches based on the use of univariate percentile data in design.
Some anthropometric measurements, such as body weight often show a positively skewed distribution. Different types of transformations can be applied when handling skewed data in order to make the data more normally distributed. This paper presents and visualises how square root, log normal and, multiplicative inverse transformations can affect the data when creating boundary confidence ellipses. The paper also shows the difference of created manikin families, i.e. groups of manikin cases, when using transformed distributions or not, for three populations with different skewness. The results from the study show that transforming skewed distributions when generating confidence ellipses and boundary cases is appropriate to more accurately consider this type of diversity and correctly describe the shape of the actual skewed distribution. Transforming the data to create accurate boundary confidence regions is thought to be advantageous, as this would create digital manikins with enhanced accuracy that would produce more realistic and accurate simulations and evaluations when using DHM tools for the design of products and workplaces.
This paper describes a study where diverse anthropometric data is included in the process of generating data for a group of virtual test persons. Data on body size, strength and ROM were either collected on an individual level or predicted and synthesized and then used in cluster analyses to generate six unique virtual test persons. Results show that the method is able to generate detailed virtual test persons which enables more realistic and accurate simulations, as strength and ROM data is included into the motion prediction algorithms used to generate motions.
This paper presents and evaluates an adaptive linear regression model for the prediction of unknown anthropometric data based on a flexible set of known predictive data. The method is based on conditional regression and includes use of principal component analysis to reduce effects of multicollinearity between the predictive variables. Results from the study show that the proposed adaptive regression model produces more accurate predictions compared to a flat regression model based on stature and weight, and also compared to a hierarchical regression model, that uses geometric and statistical relationships between body measurements to create specific linear regression equations in a hierarchical structure. An additional evaluation shows that the accuracy of the adaptive regression model increases logarithmically with the sample size. Apart from the sample size, the accuracy of the regression model is affected by the number of, and on which measurements that are, variables in the predictive dataset.
This paper presents the development of an adaptive linear regression model for synthesizing of missing anthropometric population data based on a flexible set of known predictive data. The method is based on a conditional regression model and includes use of principal component analysis, to reduce effects of multicollinearity between selected predictive measurements, and incorporation of a stochastic component, using the partial correlation coefficients between predicted measurements. In addition, skewness of the distributions of the dependent variables is considered when incorporating the stochastic components. Results from the study show that the proposed regression models for synthesizing population data give valid results with small errors of the compared percentile values. However, higher accuracy was not achieved when the number of measurements used as independent variables was increased compared to using only stature and weight as independent variables. This indicates problems with multicollinearity that principal component regression were not able to overcome. Descriptive statistics such as mean and standard deviation values together with correlation coefficients is sufficient to perform the conditional regression procedure. However, to incorporate a stochastic component when using principal component regression requires raw data on an individual level.
Relevance to industry
When developing products, workplaces or systems, it is of great importance to consider the anthropometric diversity of the intended users. The proposed regression model offers a procedure that gives valid results, maintains the correlation between the measurements that are predicted and is adaptable regarding which, and number of, predictive measurements that are selected.
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.
This paper describes a study where diversity in body size, strength and joint range of motion, together with diversity in other capability measurements, is included in the process of generating data for a group of test cases using cluster analysis. Descriptive statistics and correlation data was acquired for 15 variables for different age groups and both sexes. Based on this data, a population of 10,000 individuals was synthesised using correlated random numbers. The synthesised data was used in cluster analyses where three different clustering algorithms were applied and evaluated; hierarchical clustering, k-means clustering and Gaussian mixture distribution clustering. Results from the study show that the three clustering algorithms produce groups of test cases with different characteristics, where the hierarchical and k-means algorithm give the most diverse results and where the Gaussian mixture distribution gives results that are in between the first two.
When considering vehicle interior ergonomics in the automotive design and development process, it is important to be able to realistically predict the initial, more static, seated body postures of the vehicle occupants. This paper demonstrates how published statistical posture prediction models can be implemented into a digital human modelling (DHM) tool to evaluate and improve the overall posture prediction functionality in the tool. The posture prediction functionality uses two different posture prediction models in a sequence, in addition to the DHM tool´s functionality to optimize postures. The developed posture prediction functionality is demonstrated and visualized with a group of 30 digital human models, so called manikins, by using accurate car geometry in two different use case scenarios where the sizes of the adjustment ranges for the steering wheel and seat are altered. The results illustrate that it is possible to implement previously published posture prediction models in a DHM tool. The results also indicate that, depending on how the implemented functionality is used, different results will be obtained. Having access to a digital tool that can predict and visualize likely future vehicle occupants’ postures, for a family of manikins, enables designers and developers to consider and evaluate the human-product interaction and fit, in a consistent and transparent manner. © 2020, Springer Nature Switzerland AG.
This paper identifies and describes product development activities where ergonomics issues could be considered and illustrates how that could be done through a number of different approaches. The study is divided into two parts where an interview study is done to identify where in a product development process consideration of ergonomics issues are or could be done. The second part of the study includes an observation, motion capture and simulation study of current manufacturing operations to evaluate and compare three different assessment approaches; observational based ergonomics evaluation, usages of motion capture data and DHM simulation and evaluation. The results shows the importance of consideration of ergonomics in early development phases and that the ergonomics assessment process is integrated in the overall product and production development process.
Lean analyses and following corrections of workstations are typically performed reactively, i.e. solving problems that already exist. However, there are benefits of enhanced proactivity related to the consideration of lean and human factors, as this would reduce the need for updating workstations. The approach presented here utilises a company specific, reactive lean evaluation methodology, but applied proactively, in the workstation design phase. Results gave that many assessment items in fact can be proactively addressed. This way, ergonomic and lean workstations that support quality, performance and wellbeing for a diversity of workers, can be built right the first time.
Modern manufacturing information systems allow fast distribution of, and access to, information. One of the main purposes with an information system within manual assembly is to improve product quality, i.e. to ensure that assembly errors are as few as possible. Not only must an information system contain the right information, it must also provide it at the right time and in the right place. The paper highlights some of the concerns related to the design and use of information systems in manual assembly. The paper describes a study that focuses on the correlation between active information seeking behaviour and assembly errors. The results are founded on both quantitative and qualitative methods. The study indicates that by using simplified information carriers, with certain characteristics, the assembly personnel more easily could interpret the information, could to a higher degree be prompted (triggered) about product variants and could also be able to prepare physically and mentally for approaching products arriving along the assembly line. These conditions had positive influence on quality, i.e. gave a reduction of assembly errors.
In a modern manufacturing environment, data and information are a vital part of the manufacturing process and in particular for supporting the value adding activities. Modern manufacturing information systems allow fast distribution of, and access to, data and information. However, the technical improvements of manufacturing information systems do not always create the benefits that were expected from them. This paper discusses this problem in the context of manual assembly tasks. Attention, interpretation and decision-making are important drivers for how well the assembly tasks are performed - the acting. In other words, the acting is governed by how and when the attention of the assembly operator is caught, how easily the information can be interpreted, and to what extent the information is useful for decision making. The aim with the work is to find and present why data and information provided on the shop floor often fails to prevent quality problems; not seldom this data and information actually causes the problems. This paper focuses on one of the core issues related to assembly data and information, namely “active attention” and how this is triggered. If active information seeking behaviour is not present on the assembly shop floor, then the probability for a quality problem increases.
Simulation can support the design of an ergonomic workplace by enabling early assessment of ergonomic conditions in a virtual environment. An important feature is the possibility to study alternative solutions or the effect of improvements from an ergonomics perspective. To be able to conduct an efficient and reliable evaluation in a virtual environment, an objective analysis method is essential. Such an analysis method should be integrated in the simulation software, and support a company’s everyday ergonomics work process. In order to gain from existing ergonomics knowledge within accompany, the possibility to implement such wisdom in the current simulation software becomes important.
This paper presents an implementation work done with the purpose of integrating an established ergonomics work process into a virtual environment. It describes the benefits of an ergonomics work process where simulation and evaluation at early stages of a design process are key factors. The paper will also describe the integration process, i.e. the technical issues as well as the change in work methods.
Simulation can support the design of an ergonomic workplace by enabling early assessment of ergonomic conditions in a virtual environment. An important feature is the possibility to study alternative solutions or the effect of improvements from an ergonomics perspective. To be able to conduct an efficient and reliable evaluation in a virtual environment, an objective analysis method is essential. Such an analysis method should be integrated in the simulation software, and support a company's everyday ergonomics work process. In order to gain from existing ergonomics knowledge within a company, the possibility to implement such wisdom in the current simulation software becomes important. This paper presents an implementation work done with the purpose of integrating an established ergonomics work process into a virtual environment. It describes the benefits of an ergonomics work process where simulation and evaluation at early stages of a design process are key factors. The paper will also describe the integration process, i.e., the technical issues as well as the change in work methods.
A work method for product and production system development that includes virtual methods for ergonomics analysis is presented and argued.The proposed work method is described and illustrated with an example,which the authors believe shows how a virtual work method can contribute to a better workplace design, and thereby, if utilised, would have prevented some of the design flaws that existed in the actual final product design in the example. This paper will also present the outcome, gain, and setbacks that are connected to the use of virtual work analysis methods within a design process.
Anthropometric data are often described in terms of percentiles and too often digital human models are synthesised from such data using a single percentile value for all body dimensions. The poor correlation between body dimensions means that products may be evaluated against models of humans that do not exist. Alternative digital approaches try to minimise this difficulty using pre-defined families of manikins to represent human diversity, whereas in the real world carefully selected real people take part in 'fitting trials'. HADRIAN is a digital human modeling system which uses discrete data sets for individuals rather than statistical populations. A task description language is used to execute the evaluative capabilities of the underlying SAMMIE human modelling system as though a 'real' fitting trial was being conducted. The approach is described with a focus on the elderly and disabled and their potential exclusion from public transport systems.