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  • 51.
    Schütte, Simon
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Lokman, Anitawati Mohd
    Univ Teknol MARA UiTM, Malaysia.
    Marco-Almagro, Lluis
    Univ Politecn Cataluna, Spain.
    Ishihara, Shigekazu
    Hiroshima Int Univ, Japan.
    Yanagisawa, Hideyoshi
    Univ Tokyo, Japan.
    Yamanaka, Toshimasa
    Univ Tsukuba, Japan.
    Valverde, Nuno
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Coleman, Shirley
    Newcastle Univ, England.
    Kansei for the Digital Era2023In: INTERNATIONAL JOURNAL OF AFFECTIVE ENGINEERING, ISSN 2187-5413Article in journal (Refereed)
    Abstract [en]

    For over 40 years, Kansei-based research and development have been conducted in Japan and other East Asian countries and these decades of research have influenced Kansei interpretation. New methods and applications, including virtual reality and artificial intelligence, have emerged since the millennium, as the Kansei concept has spread throughout Europe and the rest of the world. This paper reviews past literature and industrial experience, offering a comprehensive understanding of Kansei, the underlying philosophy, and the methodology of Kansei Engineering from the approach of psychology and physiology, both qualitatively and quantitatively. The breadth of Kansei is described by examples, emerging from both industry and academia. Additionally, thematic mapping of the state-of-the-art as well as an outlook are derived from feedback obtained from structured interview of thirty-five of the most distinguished researchers in Kansei. The mapping provides insights into current trends and future directions. Kansei is unique because it includes the consideration of emotion in the design of products and services. The paper aims at becoming a reference for researchers, practitioners, and stakeholders across borders and cultures, looking for holistic perspectives on Kansei, Kansei Engineering, and implementation methods. The novelty of the paper resides in the unification of authors amongst pioneers from different parts of the world, spanning across diversified academic backgrounds, knowledge areas and industries.

  • 52.
    Schütte, Simon
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    MARCO-ALMAGRO, Lluis
    Univ Politecn Cataluna, Spain.
    Linking the Kansei Food Model to the General Affective Engineering Model: An Application on Chocolate Toffee Fillings2022In: INTERNATIONAL JOURNAL OF AFFECTIVE ENGINEERING, ISSN 2187-5413, Vol. 21, no 3, p. 219-227Article in journal (Refereed)
    Abstract [en]

    The methodology of Kansei Engineering can grasp consumer???s subjective affective impressions about a product and turn it into concrete product solutions. The Kansei Food model is a specialized model doing this for food products. The aim of this paper is to interlink it with the more general model on Affective Engineering as well as to validate the findings by applying them in a case study. The general Kansei Engineering model and the Kansei Food model were analyzed, and the key parts of both models merged to a hybrid model. This model is then applied in a study on a development project for chocolate toffee fillings for sport applications. The case yielded valid results and gave an input to a parallel food development process. In conclusion, the Kansei Food model fits together with the general Kansei model. Hence, standardization makes it possible to have a more detailed look on sequential steps. Also, it becomes possible to transfer tools from other branch models (e.g. from automotive industry) in food industry and vice versa.

  • 53.
    Sköldhed, Max
    Linköping University, Department of Management and Engineering, Product Realisation.
    En ljusare framtid för lantbruket: Användarcentrerad konceptutveckling inom ljussättning2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis is a human-centered concept development in lighting. The purpose of the work was to show the possibility of how lighting could be used and how lighting is applied to have the desired effect during night driving. The purpose of the customer was a user-friendly machine. The goal was to deliver knowledge in lighting to Väderstad regarding their agricultural machines. There were three product groups, all of which were carefully examined. Ideas in design-, user- and indication-lighting were presented.

     

    The question that was answered in the essay was: What are the most important lighting functions for farmers on seed drills from Väderstad with a focus on night driving?

     

    The methodology was taken from two engineering design processes and a lighting design process. Initially, it was problem finding and information gathering to gain a good understanding of the work and what was desired, then a creative phase with concept development that generated different ideas for possible solutions. This was followed by a mid-term meeting where concept selection was in focus. Finally, a lighting plan was created that became the appearance and function of the end result.

     

    The theory describes the technical possibility that LED lighting has created. With cheaper and more accessible technology, increases the number of users and more uses for light. This development is not only positive, but also brings light pollution that is negative for humans and animals. The physiology of the eye and how it comes about that the brain "sees" as well as visual changes that are linked to age are also described in the theory chapter. Finally, various forms of light environments and the risk of injury and accidents are described, including blinding and flicker.

     

    The end result was a concept of luminaires applied to the machine. Better lighting in the seed box and on platforms and steps is presented in rendered images on the Inspire 1200 C / S machine.

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  • 54.
    Srikantha Dath, Adithya
    Linköping University, Department of Management and Engineering, Product Realisation.
    Optimization of a Floor Grinding Machine for Uniform Grinding Pattern2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Husqvarna Construction is one of the leading construction machinery manufacturers in the world. To stay in the forefront, investing in novel methods to model, test & and optimize machinery is crucial. The most important part of development and testing is to bridge the gap between desired and actual results. Model-based Simulation in testing plays a superior role in visualizing possibilities while cutting down the usage of resources.

    Floor Grinders are common in industrial and commercial settings to achieve desired floor results. Like every machinery, optimization towards achieving better results is a necessity. The purpose of this thesis is to develop a methodology to optimize Husqvarna Constructions’s floor-grinding machine through its grinding pattern and further study & gather data about the key indicators for an optimum grinding pattern. This is done by setting up a grinding pattern simulation of the PG 690 floor grinder on SIMGRIND (Husqvarna Construction’s own simulation application).

    A metric was developed to determine whether a grinding pattern is good, and by utilizing the metric as an optimization goal, the impact of different machine parameters on the grinding pattern was established. The grinding & travel speeds were viewed as ratios and it was observed that optimized patterns were attained at particular ratios. Another crucial factor that was studied was the impact of oscillations. Further, the impact of grinding head size on the grinding pattern was also studied.

    The investigation was limited to a simulation study since physical validation opened up several uncertainties beyond the scope of this work. At the end of this work, a few recommendations for developing physical validation setups are made, to test the results of the simulation.

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  • 55.
    Sundin, Erik
    et al.
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Backman, Björn
    Linköping University, Department of Management and Engineering, Product Realisation. Research Institutes of Sweden (RISE), Sweden.
    Johansen, Kerstin
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering. Department of Industrial Product development, Production and Design, Jönköping University, Jönköping, Sweden.
    Hochwallner, Martin
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering. Department of Forestry and Wood Technology, Linnaeus University, Växjö, Sweden.
    Landscheidt, Steffen
    Department of Forestry and Wood Technology, Linnaeus University, Växjö, Sweden.
    Shahbazi, Sasha
    Research Institutes of Sweden (RISE), Sweden.
    Automation Potential in the Remanufacturing of Electric and Electronic Equipment (EEE)2020In: Proceedings of the Swedish Production Symposium (SPS-20) / [ed] K. Säfsten and F. Elgh, Amsterdam, The Netherlands: IOS Press, 2020, Vol. 13, p. 285-296Conference paper (Refereed)
    Abstract [en]

    Remanufacturing is the industrial process of returning used products(cores) to a like-new or better condition. During this industrial process, the cores go through several process steps, e.g., inspection, disassembly, cleaning, reprocess (repairs), storage, reassembly and final testing. Manufacturing companies also see remanufacturing as a way to become more circular and sustainable in economic, environmental and social terms. Technological advancements within the robot industry have increased the possibilities for using more automation within there manufacturing industry, while recently, the remanufacturing of electric and electronic equipment (EEE) has grown around the world. This paper aims to identify the automation potentials of the remanufacturing of EEE. A multiple case study at four EEE remanufacturing companies was conducted to meet this aim. The case study, along with previous research, shows examples of EEE remanufacturing steps that are mainly performed manually. The results from this research show the possible automation potential for the process steps of cleaning, disassembly and reassembly at the four remanufacturing case companies.

  • 56.
    Svensson, Amanda
    Linköping University, Department of Management and Engineering, Product Realisation.
    Extending the Use of Design Automation Within 3D-Modelling of Tool Inserts: A project investigating the possibility of reusing and adapting an existing design automation in a similar situation.2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    About eighty percent of all engineering work done today is repetitive, and about ninety percent of all engineering work consist of modelling minor changes. By replacing all the repetitive human engineering work with computers, the engineers could instead focus on creating new products or improving the existing. This could make companies more competitive and increase sales. Using design automation to model small changes would also enable companies to produce small batches at a lower cost.

    This project is done in collaboration with Thule Group. One of Thule’s largest product categories contain roof racks. The company manufacture the roof racks at their warehouse in Sweden and all variant modelling of them are performed by their technicians and engineers. For every new car model that is released, a new variant of the attachment for the roof racks needs to be modelled. There are different types of brackets used in the attachments, whereas two of them are called Evo Clamp and Evo Flush. For each new car model, new tool inserts for the manufacturing of the brackets also needs to be modelled. In previous research, a design automation process of the tool inserts for Evo Clamp was created. This project aims to use the outcome from that research to create a new design automation process for tool inserts to create Evo Flush.

    To execute the project, the DRM framework was used. To gather information, a literature study and an empirical study were performed. Furthermore, the design automation was created using VB.NET and Solidworks. To evaluate the outcome of the project, three factors were set up to test the process by. The outcome from this project was also compared to the outcome from the Evo Clamp research.

    The results showed that it was difficult to reuse and adapt the previous research since the templates for the tool inserts of the two different brackets were modelled in two completely different ways. Therefore, the main conclusion from this project is that; if the intention is to automate a process, then this must be kept in mind when modelling the components and templates. To have concrete modelling guidelines seems to be even more important if the intention is to reuse code from one process when automating another.

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    Final Report
  • 57. Order onlineBuy this publication >>
    Vidner, Olle
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    On Multi-Disciplinary Optimization in Engineer-to-Order Product Configuration2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Customized products are becoming increasingly common, and increasingly important for maintaining a competitive advantage in certain industries. Being able to quickly and accurately respond to unique customer requirements can provide a competitive edge or even be the only path to survival. In practice, configurators are commonly used to manage the customization process, gathering the customer’s requirements and suggesting feasible solutions to the customer’s problem.

    Fostering and maintaining a viable product customization offering is not easy. A particularly challenging category of products is one where an extensive engineering effort might be needed to even produce a reliable estimate of the product’s price. These products are usually referred to as engineer-to-order (ETO) products.

    Prior work has pointed out the potential of using optimization as part of configuration solutions for ETO products, but the literature is limited in its extent and does not clearly prescribe how to structure and approach such solutions.

    This thesis outlines a conceptual and technical architecture for implementing optimization-based configuration solutions. Reusable primitives for supporting the routines involved in this architecture are provided. These findings are verified through application and evaluation within two industrial case studies, also yielding important industrial needs to cover in the future research and development of the proposed framework. By examining three additional case studies, common issues in the development and deployment of design automation (DA) systems are identified.

    Successful implementation of the proposed framework for optimization-based configurators can lead to two main benefits. First, engineering configurator prototypes can be developed rapidly, to test the viability of configurator projects – a category of projects prone to expensive failures. Second, optimization-based configurators can be used to support rapid design space exploration in early product development stages, leading to enhanced product knowledge in a critical phase, and in turn, increased product value.

    List of papers
    1. Configuring Customized Products with Design Optimization and Value-Driven Design
    Open this publication in new window or tab >>Configuring Customized Products with Design Optimization and Value-Driven Design
    2021 (English)In: Proceedings of the Design Society, Cambridge University Press, 2021, Vol. 1, p. 741-750Conference paper, Published paper (Refereed)
    Abstract [en]

    In order to efficiently design and deliver customized products, it is crucial that the process of translating customer needs to engineering characteristics and into unique products is smooth and without any misinterpretations. The paper proposes a method that combines design optimization with value-driven design to support and automate configuration of customized products. The proposed framework is applied to a case example with spiral staircases, a product that is uniquely configured for each customer from a set of both standard and customized components; a process that is complex, iterative and error-prone. In the case example, the optimization and value-driven design models are used to automate and speed-up the process of delivering quotations and design proposals that could be judged based on both engineering characteristics as well as their added value, thereby increasing the knowledge at the sales stage. Finally, a multi-objective optimization algorithm is employed to generate a set of Pareto-optimal solutions that contain four clusters of solutions that dominate the baseline design. Hence the decision-maker is given a set of optimal solutions to choose from when balancing different economical and technical characteristics.

    Place, publisher, year, edition, pages
    Cambridge University Press, 2021
    National Category
    Other Mechanical Engineering
    Identifiers
    urn:nbn:se:liu:diva-181577 (URN)10.1017/pds.2021.74 (DOI)
    Conference
    23rd International Conference on Engineering Design (ICED)
    Available from: 2023-01-13 Created: 2023-01-13 Last updated: 2023-05-15
    2. Multidisciplinary Design Optimization of a Mobile Miner Using the OpenMDAO Platform
    Open this publication in new window or tab >>Multidisciplinary Design Optimization of a Mobile Miner Using the OpenMDAO Platform
    2021 (English)In: Proceedings of the Design Society, Cambridge University Press, 2021, Vol. 1, p. 2207-2216Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper proposes an optimization framework based on the OpenMDAO software library intended for engineer-to-order products and applies it to the conceptual design of a Mobile Miner. A Mobile Miner is a complex machine and a flexible alternative to Tunnel Boring Machines for small-scale tunneling and mining applications. The proposed framework is intended for use in early design and quotation stages with the objective to get fast estimates of important product characteristics, such as excavation rate and cutter lifetime. The ability to respond fast to customer requests is vital when offering customized products for specific applications and thereby to stay competitive on the global market. This is true for most engineer-to-order products and especially for mining equipment where each construction project is unique with different tunnel geometries and rock properties. The presented framework is applied to a specific use-case where the design of the miner's cutter wheel is in focus and a set of Pareto optimal designs are obtained. Furthermore, the framework extends the capabilities of OpenMDAO by including support for mixed-variable formulations and it supports an exploratory approach to design optimization.

    Place, publisher, year, edition, pages
    Cambridge University Press, 2021
    Keywords
    Optimisation, Multi- / Cross- / Trans-disciplinary processes, Large-scale engineering systems, Mobile Miners
    National Category
    Other Mechanical Engineering
    Identifiers
    urn:nbn:se:liu:diva-181578 (URN)10.1017/pds.2021.482 (DOI)
    Conference
    23rd International Conference on Engineering Design (ICED), Gothenburg, Sweden, 16th - 20th August, 2021
    Available from: 2023-01-13 Created: 2023-01-13 Last updated: 2023-05-15Bibliographically approved
    3. Design automation systems for the product development process: Reflections from Five Industrial Case Studies
    Open this publication in new window or tab >>Design automation systems for the product development process: Reflections from Five Industrial Case Studies
    2022 (English)In: Proceedings of the Design Society, Cambridge University Press, 2022, Vol. 2, p. 2533-2542Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents five industrial cases where design automation (DA) systems supported by design optimization has been developed, and aims to summarize the lesson learned and identify needs for future development of such projects. By mapping the challenges during development and deployment of the systems, common issues were found in technical areas such as model integration and organizational areas such as knowledge transfer. The latter can be seen as a two-layered design paradox; one for the product that the DA system is developed for, and one for the development of the DA system.

    Place, publisher, year, edition, pages
    Cambridge University Press, 2022
    National Category
    Mechanical Engineering
    Identifiers
    urn:nbn:se:liu:diva-193622 (URN)10.1017/pds.2022.256 (DOI)
    Conference
    17th International Design Conference (DESIGN2022), May 23-26, 2022
    Available from: 2023-05-09 Created: 2023-05-09 Last updated: 2023-05-16Bibliographically approved
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  • 58.
    Vidner, Olle
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Pettersson, Robert
    Epiroc Rock Drills AB.
    Persson, Johan
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Management and Engineering, Product Realisation.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Multidisciplinary Design Optimization of a Mobile Miner Using the OpenMDAO Platform2021In: Proceedings of the Design Society, Cambridge University Press, 2021, Vol. 1, p. 2207-2216Conference paper (Refereed)
    Abstract [en]

    This paper proposes an optimization framework based on the OpenMDAO software library intended for engineer-to-order products and applies it to the conceptual design of a Mobile Miner. A Mobile Miner is a complex machine and a flexible alternative to Tunnel Boring Machines for small-scale tunneling and mining applications. The proposed framework is intended for use in early design and quotation stages with the objective to get fast estimates of important product characteristics, such as excavation rate and cutter lifetime. The ability to respond fast to customer requests is vital when offering customized products for specific applications and thereby to stay competitive on the global market. This is true for most engineer-to-order products and especially for mining equipment where each construction project is unique with different tunnel geometries and rock properties. The presented framework is applied to a specific use-case where the design of the miner's cutter wheel is in focus and a set of Pareto optimal designs are obtained. Furthermore, the framework extends the capabilities of OpenMDAO by including support for mixed-variable formulations and it supports an exploratory approach to design optimization.

  • 59.
    Vidner, Olle
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Wehlin, Camilla
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Persson, Johan
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Management and Engineering, Product Realisation.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Configuring Customized Products with Design Optimization and Value-Driven Design2021In: Proceedings of the Design Society, Cambridge University Press, 2021, Vol. 1, p. 741-750Conference paper (Refereed)
    Abstract [en]

    In order to efficiently design and deliver customized products, it is crucial that the process of translating customer needs to engineering characteristics and into unique products is smooth and without any misinterpretations. The paper proposes a method that combines design optimization with value-driven design to support and automate configuration of customized products. The proposed framework is applied to a case example with spiral staircases, a product that is uniquely configured for each customer from a set of both standard and customized components; a process that is complex, iterative and error-prone. In the case example, the optimization and value-driven design models are used to automate and speed-up the process of delivering quotations and design proposals that could be judged based on both engineering characteristics as well as their added value, thereby increasing the knowledge at the sales stage. Finally, a multi-objective optimization algorithm is employed to generate a set of Pareto-optimal solutions that contain four clusters of solutions that dominate the baseline design. Hence the decision-maker is given a set of optimal solutions to choose from when balancing different economical and technical characteristics.

  • 60.
    Vidner, Olle
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Wehlin, Camilla
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Wiberg, Anton
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Design automation systems for the product development process: Reflections from Five Industrial Case Studies2022In: Proceedings of the Design Society, Cambridge University Press, 2022, Vol. 2, p. 2533-2542Conference paper (Refereed)
    Abstract [en]

    This paper presents five industrial cases where design automation (DA) systems supported by design optimization has been developed, and aims to summarize the lesson learned and identify needs for future development of such projects. By mapping the challenges during development and deployment of the systems, common issues were found in technical areas such as model integration and organizational areas such as knowledge transfer. The latter can be seen as a two-layered design paradox; one for the product that the DA system is developed for, and one for the development of the DA system.

  • 61. Order onlineBuy this publication >>
    Villena Toro, Javier
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Machine Learning In Design Engineering and Manufacturing2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Artificial intelligence (AI) has made significant strides in various fields, challenging conventional notions of computer capabilities. However, while data science research primarily concentrates on refining AI models, there are numerous challenges associated with integrating AI into industrial applications.

    Knowledge-Based Engineering, with its potential to streamline the production cycle by reusing engineering knowledge and intent, emerges as a promising avenue for AI in the industry. When engineering knowledge is effectively processed and categorized, neural networks naturally emerge as potent tools for automation.

    This thesis presents three case studies that demonstrate the practicality of supervised learning, particularly in the domain of neural networks, to address manufacturing automation challenges. These case studies span various stages of the manufacturing system, encompassing engineering design, production planning, and quality control phases. The first application employs supervised learning to automate the generation of engineering drawings, while the third employs optical character recognition to expedite the quality control process for complex engineering drawings. The second application centers on the estimation of fixturing clamps for welding operations in automobile parts.

    In summary, this thesis strives to make a meaningful contribution to the field of design engineering and manufacturing by examining the potential of AI in enhancing processes and addressing automation hurdles. By presenting case studies that showcase the utility of machine learning models in production settings, this thesis aims to stimulate further research in this evolving field.

    List of papers
    1. Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study
    Open this publication in new window or tab >>Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study
    2022 (English)In: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering ConferenceAugust 14–17, 2022St. Louis, Missouri, USA: Volume 3B: 48th Design Automation Conference (DAC), St. Louis, MO, USA, 2022, Vol. BConference paper, Published paper (Refereed)
    Abstract [en]

    This paper describes a methodology for automation of measurements in Computer-Aided Design (CAD) software by enabling the use of supervised learning algorithms. The paper presents a proof of concept of how dimensions are placed automatically in the drawing at predicted positions. The framework consists of two trained neural networks and a rule-based system. Four steps compound the methodology. 1. Create a data set of labeled images for training a pre-built convolutional neural network (YOLOv5) using CAD automatic procedures. 2. Train the model to make predictions on 2D drawing imagery, identifying their relevant features. 3. Reuse the information extracted from YOLOv5 in a new neural network to produce measurement data. The output of this model is a matrix containing measurement location and size data. 4. Convert the final data output into actual measurements of an unseen geometry using a rule-based system for automatic dimension generation. Although the rule-based system is highly dependent on the problem and the CAD software, both supervised learning models exhibit high performance and reusability. Future work aims to make the framework suitable for more complex products. The methodology presented is promising and shows potential for minimizing human resources in repetitive CAD work, particularly in the task of creating engineering drawings.

    Place, publisher, year, edition, pages
    St. Louis, MO, USA: , 2022
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-196468 (URN)10.1115/DETC2022-88971 (DOI)978-0-7918-8623-6 (ISBN)
    Conference
    ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
    Projects
    iProd
    Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-12-13
    2. Application of optimized convolutional neural network to fixture layout in automotive parts
    Open this publication in new window or tab >>Application of optimized convolutional neural network to fixture layout in automotive parts
    2023 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed) Epub ahead of print
    Abstract [en]

    Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (approximate to 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.

    Place, publisher, year, edition, pages
    SPRINGER LONDON LTD, 2023
    Keywords
    Design automation; Machine learning; Fixtures; CNN; Hyperparameter tuning; EfficientNet
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-192681 (URN)10.1007/s00170-023-10995-0 (DOI)000938262100003 ()
    Note

    Funding Agencies|Linkping University; Vinnova-FFI (Fordonsstrategisk forskning ochinnovation) [2020-02974]

    Available from: 2023-03-29 Created: 2023-03-29 Last updated: 2023-12-13
    3. Optical character recognition on engineering drawings to achieve automation in production quality control
    Open this publication in new window or tab >>Optical character recognition on engineering drawings to achieve automation in production quality control
    2023 (English)In: Frontiers in Manufacturing Technology, E-ISSN 2813-0359, Vol. 3Article in journal (Refereed) Published
    Abstract [en]

    Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.

    Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.

    Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.

    Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.

    Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

    Place, publisher, year, edition, pages
    Frontiers Media S.A., 2023
    Keywords
    optical character recognition, image segmentation, object detection, engineering drawings, quality control, keras-ocr
    National Category
    Engineering and Technology Production Engineering, Human Work Science and Ergonomics
    Identifiers
    urn:nbn:se:liu:diva-195416 (URN)10.3389/fmtec.2023.1154132 (DOI)
    Funder
    Vinnova, 2021-02481
    Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2023-12-13Bibliographically approved
    4. Model Architecture Exploration Using Chatgpt for Specific Manufacturing Applications
    Open this publication in new window or tab >>Model Architecture Exploration Using Chatgpt for Specific Manufacturing Applications
    2023 (English)In: ASME IDETC-CIE, 2023, Vol. 2Conference paper, Published paper (Refereed)
    Abstract [en]

    Selecting an appropriate machine learning model architecture for manufacturing tasks requires expertise in both computer science and manufacturing. However, integrating state-of-the-art machine learning models and manufacturing processes is often challenging due to the distance between these fields. OpenAI’s popular language model, ChatGPT, has the potential to bridge this gap.

    This paper proposes guidelines and questions to explore model architecture options and extract valuable information from ChatGPT’s natural language processing capabilities. While ChatGPT is a powerful tool, it is important to verify any answers obtained against reliable sources before making any decisions. The guidelines compose a flowchart with four queries to give ChatGPT enough context and exisiting input data information. ChatGPT suggestions will be directed towards input processing, output, and architecture proposals. The last query produces keywords based on the chat for a background study on the topic.

    A manufacturing case study was conducted to demonstrate the effectiveness of these guidelines. The study involved creating a model to forecast fixturing locations for welding processes in the automotive sector. After conducting four separate interviews with ChatGPT, the authors discuss the selection of architecture based on ChatGPT suggestions and contrast it with previous literature.

    The proposed guidelines are expected to be useful in a variety of manufacturing contexts, as they offer a structured approach to exploring model architecture options using ChatGPT’s capabilities, ultimately leading to new and innovative applications of machine learning in this field.

    Keywords
    fixture layout, machine learning, ChatGPT, manufacturing planning, model exploration
    National Category
    Production Engineering, Human Work Science and Ergonomics
    Identifiers
    urn:nbn:se:liu:diva-199611 (URN)10.1115/DETC2023-116483 (DOI)978-0-7918-8729-5 (ISBN)
    Conference
    IDETC-CIE 43rd Computers and Information in Engineering
    Funder
    Vinnova, 2020-02974
    Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13
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  • 62.
    Villena Toro, Javier
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Tarkian, Mehdi
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Automated and Customized CAD Drawings by Utilizing Machine Learning Algorithms: A Case Study2022In: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering ConferenceAugust 14–17, 2022St. Louis, Missouri, USA: Volume 3B: 48th Design Automation Conference (DAC), St. Louis, MO, USA, 2022, Vol. BConference paper (Refereed)
    Abstract [en]

    This paper describes a methodology for automation of measurements in Computer-Aided Design (CAD) software by enabling the use of supervised learning algorithms. The paper presents a proof of concept of how dimensions are placed automatically in the drawing at predicted positions. The framework consists of two trained neural networks and a rule-based system. Four steps compound the methodology. 1. Create a data set of labeled images for training a pre-built convolutional neural network (YOLOv5) using CAD automatic procedures. 2. Train the model to make predictions on 2D drawing imagery, identifying their relevant features. 3. Reuse the information extracted from YOLOv5 in a new neural network to produce measurement data. The output of this model is a matrix containing measurement location and size data. 4. Convert the final data output into actual measurements of an unseen geometry using a rule-based system for automatic dimension generation. Although the rule-based system is highly dependent on the problem and the CAD software, both supervised learning models exhibit high performance and reusability. Future work aims to make the framework suitable for more complex products. The methodology presented is promising and shows potential for minimizing human resources in repetitive CAD work, particularly in the task of creating engineering drawings.

  • 63.
    Villena Toro, Javier
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Tarkian, Mehdi
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Model Architecture Exploration Using Chatgpt for Specific Manufacturing Applications2023In: ASME IDETC-CIE, 2023, Vol. 2Conference paper (Refereed)
    Abstract [en]

    Selecting an appropriate machine learning model architecture for manufacturing tasks requires expertise in both computer science and manufacturing. However, integrating state-of-the-art machine learning models and manufacturing processes is often challenging due to the distance between these fields. OpenAI’s popular language model, ChatGPT, has the potential to bridge this gap.

    This paper proposes guidelines and questions to explore model architecture options and extract valuable information from ChatGPT’s natural language processing capabilities. While ChatGPT is a powerful tool, it is important to verify any answers obtained against reliable sources before making any decisions. The guidelines compose a flowchart with four queries to give ChatGPT enough context and exisiting input data information. ChatGPT suggestions will be directed towards input processing, output, and architecture proposals. The last query produces keywords based on the chat for a background study on the topic.

    A manufacturing case study was conducted to demonstrate the effectiveness of these guidelines. The study involved creating a model to forecast fixturing locations for welding processes in the automotive sector. After conducting four separate interviews with ChatGPT, the authors discuss the selection of architecture based on ChatGPT suggestions and contrast it with previous literature.

    The proposed guidelines are expected to be useful in a variety of manufacturing contexts, as they offer a structured approach to exploring model architecture options using ChatGPT’s capabilities, ultimately leading to new and innovative applications of machine learning in this field.

  • 64.
    Villena Toro, Javier
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Wiberg, Anton
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Tarkian, Mehdi
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Application of optimized convolutional neural network to fixture layout in automotive parts2023In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed)
    Abstract [en]

    Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (approximate to 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.

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    fulltext
  • 65.
    Villena Toro, Javier
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Wiberg, Anton
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Tarkian, Mehdi
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Optical character recognition on engineering drawings to achieve automation in production quality control2023In: Frontiers in Manufacturing Technology, E-ISSN 2813-0359, Vol. 3Article in journal (Refereed)
    Abstract [en]

    Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.

    Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.

    Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.

    Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.

    Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

    Download full text (pdf)
    fulltext
  • 66.
    Waagaard, Morgan
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Department of Management and Engineering, Machine Design.
    Persson, Johan
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Department of Management and Engineering, Machine Design.
    Additive Manufacturing Applications for Suspension Systems: Part selection, concept development, and design2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This project was conducted as a case study at Öhlins Racing AB, a manufacturer of suspension systems for automotive applications. Öhlins usually manufacture their components by traditional methods such as forging, casting, and machining. The project aimed to investigate how applicable Additive Manufacturing (AM) is to manufacture products for suspension systems to add value to suspension system components. For this, a proof of concept was designed and manufactured. The thesis was conducted at Öhlins in Upplands Väsby via the consultant firm Combitech. 

    A product catalog was searched, screened, and one part was selected. The selected part was used as a benchmark when a new part was designed for AM, using methods including Topology Optimization (TO) and Design for Additive Manufacturing (DfAM). Product requirements for the chosen part were to reduce weight, add functions, or add value in other ways. 

    Methods used throughout the project were based on traditional product development and DfAM, and consisted of three steps: Product Screening, Concept Development, and Part Design. The re-designed part is ready to be manufactured in titanium by L-PBF at Amexci in Karlskoga. 

    The thesis result shows that at least one of Öhlin's components in their product portfolio is suitable to be chosen, re-designed, and manufactured by AM. It is also shown that value can be added to the product by increased performance, in this case mainly by weight reduction. The finished product is a fork bottom, designed with hollow structures, and is ready to print by L-PBF in a titanium alloy. 

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    fulltext
  • 67.
    Wehlin, Camilla
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Vidner, Olle
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Poot, Leon
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Tarkian, Mehdi
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Integrating Sales, Design and Production: A Configuration System for Automation in Mass Customization2021In: Proceedings of ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2021): Volume 3B: 47th Design Automation Conference (DAC), New York: The American Society of Mechanical Engineers , 2021, article id V03BT03A042Conference paper (Refereed)
    Abstract [en]

    Companies manufacturing customized engineer-to-order (ETO) products are decelerated by repetitive work, misinterpretations and uncoordinated processes which prohibits the achievement of mass customization. Being able to deliver customized product with low costs and fast delivery times, the concept of mass customization, is a prerequisite for maintained competitiveness with the demands from the market today. This paper presents a product configuration system (PCS) for customized products using design automation enabled by knowledge-based engineering (KBE) and enterprise-wide optimization (EWO). With this approach, the process from sales to delivery of customized products can be extensively rationalized. The PCS consists of two modules. The first being a configurator for use in the sales quotation stage. Here, customer requirements are captured, and used to generate alternatives feasible for the customer context. Thereby, correct quotations can be generated at the sales instance. The second module is the enterprise-wide configurator where accepted orders are concurrently optimized for their detailed and final design, considering the current state of the production and concurrent sales cases in the company. In other terms, instead of adapting the supply chain according to the design of the products in the order entry, the design of the products in the order entry are adapted according to the state of the supply chain. Thereby, resources can be efficiently utilized to the benefit of both the customer and the company, with reduced costs and delivery times. An implementation of the PCS in a case concerning spiral staircases, an ETO product, has shown potential of substantially reducing resources and errors and enable a reliable process supporting achievement of mass customization.

    Download full text (pdf)
    fulltext
  • 68.
    Wennersten, Karin
    et al.
    Linköping University, Department of Management and Engineering, Engineering Materials. Linköping University, Faculty of Science & Engineering.
    Xu, Jinghao
    Linköping University, Department of Management and Engineering, Engineering Materials. Linköping University, Faculty of Science & Engineering.
    Armakavicius, Nerijus
    TekSiC AB, S-58330 Linkoping, Sweden.
    Wiberg, Anton
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Nadali Najafabadi, Hossein
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, Faculty of Science & Engineering.
    Moverare, Johan
    Linköping University, Department of Management and Engineering, Engineering Materials. Linköping University, Faculty of Science & Engineering.
    Feasibility of Melting NbC Using Electron Beam Powder Bed Fusion2024In: Advanced Engineering Materials, ISSN 1438-1656, E-ISSN 1527-2648Article in journal (Refereed)
    Abstract [en]

    High melting point materials such as ceramics and metal carbides are in general difficult to manufacture due to their physical properties, which imposes the need for new manufacturing methods where electron beam powder bed fusion (EB-PBF) seems promising. Most materials that have been successfully printed with EB-PBF are metals and metal alloys with good electrical conductivity, whereas dielectric materials such as ceramics are generally difficult to print. Catastrophic problems such as smoking and spattering can occur during the EB-PBF processing owing to inappropriate physical properties such as lack of electrical, and thermal conductivity and high melting point, which are challenging to overcome by process optimization. Due to these difficulties, a limited level of understanding has been achieved regarding melting ceramics and refractory alloys. Herein, three different substrates of niobium carbide (NbC) are melted using EB-PBF. The established process parameter window shows a good correlation between EB-PBF process parameters, surface, and melt characteristics, which can be used as a baseline for a printing process. Melting NbC is proven feasible using EB-PBF; the work also points out challenges related to arc trips and spattering, as well as future investigations necessary to create a stable printing process. Additive manufacturing offer new ways of manufacturing ceramics and metal carbides otherwise hard to produce. This study presents one of the first attempts at melting niobium carbide using electron beam powder bed fusion by identifying process window and investigating how the different process parameters affect the melt characteristics, as well as identifying potential issues regarding printing metal carbides.image (c) 2024 WILEY-VCH GmbH

  • 69.
    Wever, Renee
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Opening-up the power of innovation: or: what we can learn from Pippi, Willy Wonka & Tintin2023In: / [ed] NVC, 2023Conference paper (Other academic)
  • 70.
    Wiberg, Anton
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Persson, Johan
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    Ölvander, Johan
    Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
    A Design Automation Framework Supporting Design for Additive Manufacturing2023In: Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2023, Boston, Massachusetts: ASME Press, 2023Conference paper (Refereed)
    Abstract [en]

    This scientific paper introduces a Design Automation (DA) framework that streamlines the Design for Additive Manufacturing (DfAM) process. The framework is designed to simplify the creation and evaluation of different design options by automating geometry creation using high-level CAD templates and setting up and connecting Computer-Aided Engineering (CAE) models to perform functional and manufacturing evaluations. By considering manufacturing constraints early in the design process, the framework aims to investigate various design alternatives and facilitate design changes late in the development process without additional manual work. This framework provides a comprehensive view of the entire DfAM process, integrating everything from functional requirements to manufacturing evaluation and preparation into the same design automation framework. To demonstrate the usefulness of the framework, the authors used it to design a hydraulic pump. Compared to the original design, the design created with the proposed framework reduces pressure drop by more than 50% and reduces the pump's weight by 35%. Furthermore, on an assembly level, the framework consolidates four components into two and eliminates two sealings. In summary, the Design Automation framework introduced in this paper simplifies the DfAM process by enabling automation of geometry creation and the setup and connection of CAE models. The framework facilitates the exploration of different design alternatives early in the process, considering manufacturing constraints, and enables design changes later in the development process without manual work. The benefits of the framework are illustrated through the design of a hydraulic pump, where it achieved significant improvements in performance, weight, and assembly complexity. 

  • 71.
    Wittwång, Arvid
    et al.
    Linköping University, Department of Management and Engineering, Product Realisation.
    Storck, Markus
    Linköping University, Department of Management and Engineering, Product Realisation.
    Användarcentrerad konceptutveckling: Förbättrad användarupplevelse genom välgrundade designändringar i Husqvarnas batteridrivna motorsågar2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    With Husqvarna as the case company, this master’s thesis explores the desired features of a battery-powered chainsaw and how they can be achieved. This research is done in light of Husqvarna’s implementation of a centrifugal clutch in the battery-powered model 542i XP – at launch during the autumn of 2023, the first of its kind. The research is carried out through qualitative methods, with data from interviews held at multiple of Husqvarna’s departments as a central part. This data is extended through interviews with resellers and professional users, to map the terrain of the appreciated and problematic characteristics of battery-powered chainsaws. As a brand-new product, the aforementioned Husqvarna 542i XP is not available in stores at the time of writing this report. Therefore, experience-focused research is emphasized in-house at Husqvarna, with employees familiar with the product from the development phase. Complementary data is collected through practical testing by the authors of this report, and tests of prototypes, in collaboration with professionals.

    The collected empirical data is categorized and grouped, combine with theory and technical data, and distilled to a design criteria list. A function/means tree is created on this foundation, and used alongside the methods to generate concepts to meet the requests and demands. Concepts are created on an overarching level, with more weight put on concepts focused on details, evaluated through weighted and comparative methods. Three final concepts are chosen for further refinement.

    The concept FrivarvD with its idea of delayed inertia to enhance the starting behaviour is considered unjustifiably complex. However, the concepts Tomgång and Propeller show great potential. The former is proposed as an added, optional mode, excelling through rapid response to the throttle and continuous cooling. The latter is a compliant mechanism version of a centrifugal clutch. The duo also addresses the desired and well-known start and stop behaviours. A transition from a sensor-less electric motor to a more exact and sensor-controlled version is suggested as an enhancement to the two concepts.

    This master’s thesis results in a summary of the distinguished problems and insights of preferable properties of using a battery-powered chainsaw. Further, the tried-and-true centrifugal clutch has an important role in preventing sudden stops, but is evidently only one of many solutions to address the identified criteria. However, it is reliable and highly realistic. Finally, the authors of this report recommend Husqvarna to upgrade the motor, and realize the concept Tomgång to achieve an improved user-experience for batterypowered chainsaws

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    Användarcentrerad konceptutveckling – Förbättrad användarupplevelse genom välgrundade designändringar i Husqvarnas batteridrivna motorsågar
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