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Lind, A., Iriondo Pascual, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2024). Multi-objective optimisation of a logistics area in the context of factory layout planning. Production & Manufacturing Research, 12(1), Article ID 2323484.
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2024 (English)In: Production & Manufacturing Research, ISSN 2169-3277, Vol. 12, no 1, article id 2323484Article in journal (Refereed) Published
Abstract [en]

The manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2024
Keywords
factory layout, logistics area, multi-objective optimisation, simulation
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design; Virtual Production Development (VPD); VF-KDO
Identifiers
urn:nbn:se:his:diva-23640 (URN)10.1080/21693277.2024.2323484 (DOI)
Funder
Knowledge Foundation, 20200044Knowledge Foundation, 2018-0011
Note

CC BY 4.0

CONTACT Andreas Lind andreas.lind@his.se Global Industrial Development, Scania CV AB, Södertälje, Sweden

The authors appreciatively thank the support of Scania CV AB, the research school Smart Industry Sweden (20200044) and the research project Virtual Factories with Knowledge-Driven Optimisation (2018-0011) funded by the Knowledge Foundation via the University of Skövde. With this support the research was made possible.

The work was supported by the Stiftelsen för Kunskaps- och Kompetensutveckling [20200044]; Stiftelsen för Kunskaps- och Kompetensutveckling [2018-0011].

Available from: 2024-02-29 Created: 2024-02-29 Last updated: 2024-02-29Bibliographically approved
Schmitt, T., Viklund, P., Sjölander, M., Hanson, L., Amouzgar, K. & Urenda Moris, M. (2023). Augmented reality for machine monitoring in industrial manufacturing: framework and application development. Paper presented at 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023 Cape Town 24 October 2023 through 26 October 2023. Procedia CIRP, 1327-1332
Open this publication in new window or tab >>Augmented reality for machine monitoring in industrial manufacturing: framework and application development
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2023 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, p. 1327-1332Article in journal (Refereed) Published
Abstract [en]

Enhancing data visualization on the shop floor provides support for dealing with the increasing complexity of production and the need for progressing towards emerging goals like energy efficiency. It enables personnel to make informed decisions based on real-time data displayed on user-friendly interfaces. Augmented reality (AR) technology provides a promising solution to this problem by allowing for the visualization of data in a more immersive and interactive way. The aim of this study is to present a framework to visualize live and historic data about energy consumption in AR, using Power BI and Unity, and discuss the applications' capabilities. The study demonstrated that both Power BI and Unity can effectively visualize near-real-time machine data with the aid of appropriate data pipelines. While both applications have their respective strengths and limitations, they can support informed decision-making and proactive measures to improve energy utilization. Additional research is needed to examine the correlation between energy consumption and production dynamics, as well as to assess the user-friendliness of the data presentation for effective decision-making support. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
augmented reality, data pipelines, energy efficiency, user interface, Visualization
National Category
Computer Sciences Computer Systems Other Engineering and Technologies not elsewhere specified
Research subject
User Centred Product Design
Identifiers
urn:nbn:se:his:diva-23628 (URN)10.1016/j.procir.2023.09.171 (DOI)2-s2.0-85184584712 (Scopus ID)
Conference
56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023 Cape Town 24 October 2023 through 26 October 2023
Projects
EXPLAIN
Note

CC BY-NC-ND 4.0 DEED

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

Correspondence Address: T. Schmitt; Scania CV AB, Smart Factory Lab, Södertälje, Verkstadsvägen 17, 151 38, Sweden; email: thomas.schmitt@scania.com

This paper is produced as part of the EXPLAIN project, which is partly funded by the Swedish research and development agency Vinnova.

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-26Bibliographically approved
Lind, A., Hanson, L., Högberg, D., Lämkull, D., Mårtensson, P. & Syberfeldt, A. (2023). Digital support for rules and regulations when planning and designing factory layouts. Procedia CIRP, 120, 1445-1450
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2023 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 120, p. 1445-1450Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier, 2023
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design; Virtual Production Development (VPD); VF-KDO
Identifiers
urn:nbn:se:his:diva-23532 (URN)10.1016/j.procir.2023.09.191 (DOI)2-s2.0-85184599288 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY-NC-ND 4.0 DEED

Corresponding author: E-mail address: andreas.lind@scania.com

The authors appreciatively thank the support from Scania CV AB, the research school Smart Industry Sweden, and the VF-KDO (Virtual Factories with Knowledge-Driven Optimization) project funded by the Knowledge Foundation in Sweden; this support made the research possible.

Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-02-22Bibliographically approved
Lind, A., Hanson, L., Högberg, D., Lämkull, D. & Syberfeldt, A. (2023). Extending and demonstrating an engineering communication framework utilising the digital twin concept in a context of factory layouts. International Journal of Services Operations and Informatics, 12(3), 201-224
Open this publication in new window or tab >>Extending and demonstrating an engineering communication framework utilising the digital twin concept in a context of factory layouts
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2023 (English)In: International Journal of Services Operations and Informatics, ISSN 1741-539X, E-ISSN 1741-5403, Vol. 12, no 3, p. 201-224Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
InderScience Publishers, 2023
Keywords
digital model, digital pre-runner, digital shadow, digital twin, factory layout
National Category
Production Engineering, Human Work Science and Ergonomics Other Computer and Information Science Information Systems Media and Communication Technology
Research subject
User Centred Product Design; Virtual Production Development (VPD); VF-KDO
Identifiers
urn:nbn:se:his:diva-22481 (URN)10.1504/IJSOI.2023.132345 (DOI)2-s2.0-85166580963 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

This paper is a revised and expanded version of a paper entitled ‘Evaluating a digital twin concept for an automatic up-to-date factory layout setup’ presented at 10th Swedish Production Symposium (SPS2022), Skövde, Sweden, 26–29 April, 2022.

The authors gratefully thank the support of Scania CV AB, the Research School Smart Industry Sweden, and the VF-KDO Project (Virtual Factories with Knowledge-Driven Optimization) funded by the Knowledge Foundation in Sweden; this support made the research possible.

Available from: 2023-05-02 Created: 2023-05-02 Last updated: 2024-02-22Bibliographically approved
Nourmohammadi, A., Ng, A. H. C., Fathi, M., Vollebregt, J. & Hanson, L. (2023). Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling. CIRP - Journal of Manufacturing Science and Technology, 47, 71-85
Open this publication in new window or tab >>Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling
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2023 (English)In: CIRP - Journal of Manufacturing Science and Technology, ISSN 1755-5817, E-ISSN 1878-0016, Vol. 47, p. 71-85Article in journal (Refereed) Published
Abstract [en]

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Mixed-model assembly line balancing, Digital human modeling, Musculoskeletal risks, Multi-objective optimization, Mathematical mode, lNSGA-II
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Virtual Production Development (VPD); User Centred Product Design; VF-KDO
Identifiers
urn:nbn:se:his:diva-23240 (URN)10.1016/j.cirpj.2023.09.002 (DOI)001082058800001 ()2-s2.0-85171985258 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Corresponding author. E-mail address: amir.nourmohammadi@his.se (A. Nourmohammadi).

This study is supported by the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the VF-KDO, ACCURATE 4.0, and PREFER Projects. The authors highly appreciate the valuable collaborations with the experts from the industrial partner of this research.

Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-11-06Bibliographically approved
Zhu, X., Björkman, M., Maki, A., Hanson, L. & Mårtensson, P. (2023). Surface Defect Detection with Limited Training Data: A Case Study on Crown Wheel Surface Inspection. Paper presented at 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023 Cape Town 24 October 2023 through 26 October 2023. Procedia CIRP, 120, 1333-1338
Open this publication in new window or tab >>Surface Defect Detection with Limited Training Data: A Case Study on Crown Wheel Surface Inspection
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2023 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 120, p. 1333-1338Article in journal (Refereed) Published
Abstract [en]

This paper presents an approach to automatic surface defect detection by a deep learning-based object detection method, particularly in challenging scenarios where defects are rare, i.e., with limited training data. We base our approach on an object detection model YOLOv8, preceded by a few steps: 1) filtering out irrelevant information, 2) enhancing the visibility of defects, namely brightness contrast, and 3) increasing the diversity of the training data through data augmentation. We evaluated the method in an industrial case study of crown wheel surface inspection in detecting Unclean Gear as well as Deburring defects, resulting in promising performances. With the combination of the three preprocessing steps, we improved the detection accuracy by 22.2% and 37.5% respectively while detecting those two defects. We believe that the proposed approach is also adaptable to various applications of surface defect detection in other industrial environments as the employed techniques, such as image segmentation, are available off the shelf. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Automatic Quality Inspection, Computer Vision, Deep Learning, Image Processing, Surface Defect Detection
National Category
Computer Vision and Robotics (Autonomous Systems) Remote Sensing Robotics
Research subject
User Centred Product Design
Identifiers
urn:nbn:se:his:diva-23627 (URN)10.1016/j.procir.2023.09.172 (DOI)2-s2.0-85184602644 (Scopus ID)
Conference
56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023 Cape Town 24 October 2023 through 26 October 2023
Funder
Knut and Alice Wallenberg Foundation
Note

CC BY-NC-ND 4.0 DEED

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

Correspondence Address: X. Zhu; Scania CV AB (publ), Södertälje, SE-151 87, Sweden; email: xiaomeng.zhu@scania.com

This work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-26Bibliographically approved
Zhu, X., Bilal, T., Mårtensson, P., Hanson, L., Björkman, M. & Maki, A. (2023). Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset. In: Proceedings, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: Vancouver, Canada 18 – 22 June 2023. Paper presented at 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Vancouver, Canada 18 – 22 June 2023 (pp. 4454-4463). IEEE
Open this publication in new window or tab >>Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset
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2023 (English)In: Proceedings, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: Vancouver, Canada 18 – 22 June 2023, IEEE, 2023, p. 4454-4463Conference paper, Published paper (Refereed)
Abstract [en]

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset † and code ‡ are publicly available. 

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, ISSN 2160-7508, E-ISSN 2160-7516
Keywords
Benchmarking, Computer vision, Deep neural networks, Internet protocols, Five state, Industrial parts, Industrial use case, Performance, Post-processing, Randomisation, Real-world image, State of the art, Synthetic data, Synthetic datasets, Classification (of information)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
User Centred Product Design
Identifiers
urn:nbn:se:his:diva-23236 (URN)10.1109/CVPRW59228.2023.00468 (DOI)2-s2.0-85170821045 (Scopus ID)979-8-3503-0249-3 (ISBN)979-8-3503-0250-9 (ISBN)
Conference
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Vancouver, Canada 18 – 22 June 2023
Funder
Knut and Alice Wallenberg FoundationSwedish Research Council, 2018-05973
Note

© 2023 IEEE.

This work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973, as well as by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre. 

Available from: 2023-09-21 Created: 2023-09-21 Last updated: 2023-10-10Bibliographically approved
Lind, A., Elango, V., Hanson, L., Högberg, D., Lämkull, D., Mårtensson, P. & Syberfeldt, A. (2023). Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory. Systems, 11(8), Article ID 395.
Open this publication in new window or tab >>Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory
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2023 (English)In: Systems, E-ISSN 2079-8954, Vol. 11, no 8, article id 395Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
multi-objective, optimization, simulation, Industry 5.0, factory layout
National Category
Production Engineering, Human Work Science and Ergonomics Robotics
Research subject
Virtual Production Development (VPD); User Centred Product Design; VF-KDO
Identifiers
urn:nbn:se:his:diva-23075 (URN)10.3390/systems11080395 (DOI)001056657200001 ()2-s2.0-85169108939 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Correspondence: andreas.lind@scania.com

This research was funded by Scania CB AB and the Knowledge Foundation via the University of Skövde, the research project Virtual Factories with Knowledge-Driven Optimization (2018-0011), and the industrial graduate school Smart Industry Sweden (20200044).

Available from: 2023-08-04 Created: 2023-08-04 Last updated: 2024-02-22Bibliographically approved
Flores-García, E., Jeong, Y., Wiktorsson, M., Kwak, D. H., Woo, J. H., Schmitt, T. & Hanson, L. (2022). Characterizing Digital Dashboards for Smart Production Logistics. In: Duck Young Kim; Gregor von Cieminski; David Romero (Ed.), Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action: IFIP WG 5.7 International Conference, APMS 2022, Gyeongju, South Korea, September 25–29, 2022, Proceedings, Part II. Paper presented at International Conference on Advances in Production Management Systems, APMS 2022, Gyeongju, South Korea, September 25–29, 2022 (pp. 521-528). Cham: Springer Nature Switzerland AG
Open this publication in new window or tab >>Characterizing Digital Dashboards for Smart Production Logistics
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2022 (English)In: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action: IFIP WG 5.7 International Conference, APMS 2022, Gyeongju, South Korea, September 25–29, 2022, Proceedings, Part II / [ed] Duck Young Kim; Gregor von Cieminski; David Romero, Cham: Springer Nature Switzerland AG , 2022, p. 521-528Conference paper, Published paper (Refereed)
Abstract [en]

Developing digital dashboards (DD) that support staff in monitoring, identifying anomalies, and facilitating corrective actions are decisive for achieving the benefits of Smart Production Logistics (SPL). However, existing literature about SPL has not sufficiently investigated the characteristics of DD allowing staff to enhance operational performance. This conceptual study identifies the characteristics of DD in SPL for enhancing operational performance of material handling. The study presents preliminary findings from an ongoing laboratory development, and identifies six characteristics of DD. These include monitoring, analysis, prediction, identification, recommendation, and control. The study discusses the implications of these characteristics when applied to energy consumption, makespan, on-time delivery, and status for material handling. The study proposes the prototype of a DD in a laboratory environment involving Autonomous Mobile Robots. 

Place, publisher, year, edition, pages
Cham: Springer Nature Switzerland AG, 2022
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 664
Keywords
Energy utilization, Mobile robots, Navigation, Autonomous Mobile Robot, Conceptual study, Corrective actions, Digital dashboard, Laboratory development, Material handling, Operational performance, Production logistics, Smart production logistic, Support staff, Materials handling, Autonomous mobile robots, Digital dashboards, Smart production logistics
National Category
Production Engineering, Human Work Science and Ergonomics Environmental Management Other Civil Engineering Other Mechanical Engineering Robotics
Research subject
User Centred Product Design
Identifiers
urn:nbn:se:his:diva-21947 (URN)10.1007/978-3-031-16411-8_60 (DOI)000869729400060 ()2-s2.0-85138813083 (Scopus ID)978-3-031-16410-1 (ISBN)978-3-031-16411-8 (ISBN)
Conference
International Conference on Advances in Production Management Systems, APMS 2022, Gyeongju, South Korea, September 25–29, 2022
Funder
Vinnova
Note

© 2022, IFIP International Federation for Information Processing.

© 2022 Springer Nature Switzerland AG. Part of Springer Nature.

The authors would like to acknowledge the support of Swedish Innovation Agency (VINNOVA), and its funding program Produktion2030. This study is part of the Explainable and Learning Production & Logistics by Artificial Intelligence (EXPLAIN) project.

Available from: 2022-10-13 Created: 2022-10-13 Last updated: 2023-07-11Bibliographically approved
Hanson, L., Högberg, D., Brolin, E., Billing, E., Iriondo Pascual, A. & Lamb, M. (2022). Current Trends in Research and Application of Digital Human Modeling. In: Nancy L. Black; W. Patrick Neumann; Ian Noy (Ed.), Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021): Volume V: Methods & Approaches. Paper presented at 21st Congress of the International Ergonomics Association (IEA 2021), 13-18 June (pp. 358-366). Cham: Springer
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2022 (English)In: Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021): Volume V: Methods & Approaches / [ed] Nancy L. Black; W. Patrick Neumann; Ian Noy, Cham: Springer, 2022, p. 358-366Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer, 2022
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 223
Keywords
Digital Human Modeling, Trends, Research, Development, Application
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design; Interaction Lab (ILAB); VF-KDO
Identifiers
urn:nbn:se:his:diva-19959 (URN)10.1007/978-3-030-74614-8_44 (DOI)2-s2.0-85111461730 (Scopus ID)978-3-030-74613-1 (ISBN)978-3-030-74614-8 (ISBN)
Conference
21st Congress of the International Ergonomics Association (IEA 2021), 13-18 June
Funder
Knowledge Foundation, 20180167Vinnova, 2018-05026Knowledge Foundation, 20200003
Note

© 2022

Available from: 2021-06-22 Created: 2021-06-22 Last updated: 2023-08-16Bibliographically approved
Projects
Development and evaluation of health preventive working glove [2015-04309_Vinnova]; University of SkövdeSynergy Virtual Ergonomics (SVE) [20180167]; University of Skövde; Publications
Iriondo Pascual, A. (2023). Simulation-based multi-objective optimization of productivity and worker well-being. (Doctoral dissertation). Skövde: University of SkövdeHanson, L., Högberg, D., Brolin, E., Billing, E., Iriondo Pascual, A. & Lamb, M. (2022). Current Trends in Research and Application of Digital Human Modeling. In: Nancy L. Black; W. Patrick Neumann; Ian Noy (Ed.), Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021): Volume V: Methods & Approaches. Paper presented at 21st Congress of the International Ergonomics Association (IEA 2021), 13-18 June (pp. 358-366). Cham: SpringerGarcia Rivera, F., Högberg, D., Lamb, M. & Perez Luque, E. (2022). DHM supported assessment of the effects of using an exoskeleton during work. International Journal of Human Factors Modelling and Simulation, 7(3/4), 231-246Marshall, R., Brolin, E., Summerskill, S. & Högberg, D. (2022). Digital Human Modelling: Inclusive Design and the Ageing Population (1ed.). In: Sofia Scataglini; Silvia Imbesi; Gonçalo Marques (Ed.), Internet of Things for Human-Centered Design: Application to Elderly Healthcare (pp. 73-96). Singapore: Springer NatureIriondo Pascual, A., Lind, A., Högberg, D., Syberfeldt, A. & Hanson, L. (2022). Enabling Concurrent Multi-Objective Optimization of Worker Well-Being and Productivity in DHM Tools. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 404-414). Amsterdam; Berlin; Washington, DC: IOS PressIriondo Pascual, A., Smedberg, H., Högberg, D., Syberfeldt, A. & Lämkull, D. (2022). Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity. Sustainability, 14(9), Article ID 4894. Lamb, M., Brundin, M., Perez Luque, E. & Billing, E. (2022). Eye-Tracking Beyond Peripersonal Space in Virtual Reality: Validation and Best Practices. Frontiers in Virtual Reality, 3, Article ID 864653. Hanson, L., Högberg, D., Iriondo Pascual, A., Brolin, A., Brolin, E. & Lebram, M. (2022). Integrating Physical Load Exposure Calculations and Recommendations in Digitalized Ergonomics Assessment Processes. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 233-239). Amsterdam; Berlin; Washington, DC: IOS PressIriondo Pascual, A., Högberg, D., Syberfeldt, A., Brolin, E., Perez Luque, E., Hanson, L. & Lämkull, D. (2022). Multi-objective Optimization of Ergonomics and Productivity by Using an Optimization Framework. In: Nancy L. Black; W. Patrick Neumann; Ian Noy (Ed.), Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021): Volume V: Methods & Approaches. Paper presented at 21st Congress of the International Ergonomics Association (IEA 2021), 13-18 June, 2021 (pp. 374-378). Cham: SpringerGarcía Rivera, F., Lamb, M., Högberg, D. & Brolin, A. (2022). The Schematization of XR Technologies in the Context of Collaborative Design. In: Amos H. C. Ng; Anna Syberfeldt; Dan Högberg; Magnus Holm (Ed.), SPS2022: Proceedings of the 10th Swedish Production Symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), Skövde, April 26–29 2022 (pp. 520-529). Amsterdam; Berlin; Washington, DC: IOS Press
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7232-9353

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