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Nambiar, S., Ananno, A. A., Titus, H., Wiberg, A. & Tarkian, M. (2024). Multidisciplinary Automation in Design of Turbine Vane Cooling Channels. International Journal of Turbomachinery, Propulsion and Power, 9(1), Article ID 7.
Open this publication in new window or tab >>Multidisciplinary Automation in Design of Turbine Vane Cooling Channels
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2024 (English)In: International Journal of Turbomachinery, Propulsion and Power, ISSN 2504-186X, Vol. 9, no 1, article id 7Article in journal (Refereed) Published
Abstract [en]

In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
multidisciplinary automation, design automation, mesh automation, knowledge-based engineering, turbine vane cooling design
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-201145 (URN)10.3390/ijtpp9010007 (DOI)001192494000001 ()
Funder
Vinnova, 2020-04251
Note

Funding: VINNOVA

Available from: 2024-02-23 Created: 2024-02-23 Last updated: 2024-05-21Bibliographically approved
Wiberg, A., Persson, J. & Ölvander, J. (2023). A Design Automation Framework Supporting Design for Additive Manufacturing. In: Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2023: . Paper presented at ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2023). Boston, Massachusetts: ASME Press
Open this publication in new window or tab >>A Design Automation Framework Supporting Design for Additive Manufacturing
2023 (English)In: 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, Published 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. 

Place, publisher, year, edition, pages
Boston, Massachusetts: ASME Press, 2023
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-199168 (URN)
Conference
ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2023)
Available from: 2023-11-13 Created: 2023-11-13 Last updated: 2023-11-13
Nambiar, S., Wiberg, A. & Tarkian, M. (2023). Automation of unstructured production environment by applying reinforcement learning. Frontiers in Manufacturing Technology, 3
Open this publication in new window or tab >>Automation of unstructured production environment by applying reinforcement learning
2023 (English)In: Frontiers in Manufacturing Technology, E-ISSN 2813-0359, Vol. 3Article in journal (Refereed) Published
Abstract [en]

Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
Reinforcement Learning, Unity Game Engine, Mobile Robot, Mannequin, Production Environment
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-195616 (URN)10.3389/fmtec.2023.1154263 (DOI)
Funder
Vinnova, 2020-05173
Available from: 2023-06-22 Created: 2023-06-22 Last updated: 2024-05-21Bibliographically approved
Villena Toro, J., Wiberg, A. & Tarkian, M. (2023). Optical character recognition on engineering drawings to achieve automation in production quality control. Frontiers in Manufacturing Technology, 3
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
Vidner, O., Wehlin, C. & Wiberg, A. (2022). Design automation systems for the product development process: Reflections from Five Industrial Case Studies. In: Proceedings of the Design Society: . Paper presented at 17th International Design Conference (DESIGN2022), May 23-26, 2022 (pp. 2533-2542). Cambridge University Press, 2
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
Wiberg, A., Persson, J. & Ölvander, J. (2021). An optimisation framework for designs for additive manufacturing combining design, manufacturing and post-processing. Rapid prototyping journal, 27(11), 90-105
Open this publication in new window or tab >>An optimisation framework for designs for additive manufacturing combining design, manufacturing and post-processing
2021 (English)In: Rapid prototyping journal, ISSN 1355-2546, E-ISSN 1758-7670, Vol. 27, no 11, p. 90-105Article in journal (Refereed) Published
Abstract [en]

Purpose - The purpose of this paper is to present a Design for Additive Manufacturing (DfAM) methodology that connects several methods, from geometrical design to post-process selection, into a common optimisation framework.

Design/methodology/approach - A design methodology is formulated and tested in a case study. The outcome of the case study is analysed by comparing the obtained results with alternative designs achieved by using other design methods. The design process in the case study and the potential of the method to be used in different settings are also discussed. Finally, the work is concluded by stating the main contribution of the paper and highlighting where further research is needed.

Findings - The proposed method is implemented in a novel framework which is applied to a physical component in the case study. The component is a structural aircraft part that was designed to minimise weight while respecting several static and fatigue structural load cases. An addition goal is to minimise the manufacturing cost. Designs optimised for manufacturing by two different AM machines (EOS M400 and Arcam Q20+), with and without post-processing (centrifugal finishing) are considered. The designs achieved in this study show a significant reduction in both weight and cost compared to one AM manufactured geometry designed using more conventional methods and one design milled in aluminium.

Originality/value - The method in this paper allows for the holistic design and optimisation of components while considering manufacturability, cost and component functionality. Within the same framework, designs optimised for different setups of AM machines and post-processing can be automatically evaluated without any additional manual work.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2021
Keywords
Additive Manufacturing, Design for Additive Manufacturing, Optimisation, Multidisciplinary Design Optimisation, Computer aided design
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-181000 (URN)10.1108/RPJ-02-2021-0041 (DOI)000714110000001 ()
Note

Funding: European Unions Horizon 2020 research and innovation programme [738002]

Available from: 2021-11-15 Created: 2021-11-15 Last updated: 2021-12-06Bibliographically approved
Wiberg, A. (2021). Design Automation for Additive Manufacturing: A Multi-Disciplinary Optimization Approach. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Design Automation for Additive Manufacturing: A Multi-Disciplinary Optimization Approach
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Additive manufacturing (AM) is a group of manufacturing methods which have attracted rapidly increasing interest in academia and industry during the last years. AM's main benefits are manufacturing of complex shapes and small-scale manufacturing, without the additional cost of traditional manufacturing methods. Creating complex geometries that fully leverage the potential of AM requires time, knowledge, and design skills. Design for additive manufacturing (DfAM) is a vast area that includes methods and tools that aim to overcome the challenges of AM and support the development of new components and products.

Design automation and optimization are two terms often mentioned as potential methods to support the DfAM process. In a broad definition, design automation (DA) refers to reusable computer tools developed to aid the design engineering process. The general idea with DA is to create flexible design processes where different solutions can be explored without an increase in manual work. Together with methods for design optimization, DA has shown the potential to support the DfAM process.

This work focuses on how DA technologies can support the development of components manufactured by AM. By analyzing the current state of the art, today's DfAM process is mapped, and the potential for automation is explored. The work contributes to the field by presenting a holistic DA framework that bridges function, design, AM setup, and post-processing. A master model is used to span the different phases of the design process and utilize combined optimization of geometry and manufacturing setup. The proposed method is refined in an iterative process where details are solved, and computer tools supporting the process are developed. Application cases from the aerospace sector and the fluid power industry are used to evaluate and demonstrate the developed methods and computer support.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2021. p. 62
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2188
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-180998 (URN)10.3384/9789179291082 (DOI)9789179291075 (ISBN)9789179291082 (ISBN)
Public defence
2021-12-14, ACAS, A Building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2021-11-15 Created: 2021-11-15 Last updated: 2021-11-15Bibliographically approved
Wiberg, A. & Andersson (Ölvander), J. (2020). Design for Additive Manufacturing using a Master Model approach. In: PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2019, VOL 2A: . Paper presented at Proceedings of the ASME 2019, International Design Engineering Technical Conferences, and Computers and Information in Engineering Conference IDETC/CIE2019, Anaheim, CA, USA, August 18 – 21, 2019. ASME Press
Open this publication in new window or tab >>Design for Additive Manufacturing using a Master Model approach
2020 (English)In: PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2019, VOL 2A, ASME Press, 2020Conference paper, Published paper (Refereed)
Abstract [en]

The introduction of Additive Manufacturing opens up possibilities for creating lighter, better and customized products. However, to take advantage of the possibilities of Additive Manufacturing, the design engineer is challenged. In this paper, a general design process for the creation of complex products is proposed and evaluated. The proposed method aims to aid a design process in which Topology Optimization (TO) is used for concept development, and the result is then interpreted into a Master Model (MM) supporting design evaluations during detailed design. At the same time as the MM is created, information regarding manufacturing is saved in a database. This makes it possible to automatically generate and export models for manufacturing or CAE analyses. A tool that uses Knowledge-Based Engineering (KBE) to realize the presented methodology has been developed. The tool is specialized for the creation of structural components that connect to other components in an assembly. A case study, part of an aircraft door, has been used for evaluation of the tool. The study shows that the repetitive work when interpreting the topology-optimized design could be reduced. The result comes in the form of a parametric CAD model which allows fast changes and the coupled database enables the export of models for various purposes.

Place, publisher, year, edition, pages
ASME Press, 2020
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-160905 (URN)10.1115/DETC2019-97915 (DOI)000518726800038 ()978-0-7918-5918-6 (ISBN)
Conference
Proceedings of the ASME 2019, International Design Engineering Technical Conferences, and Computers and Information in Engineering Conference IDETC/CIE2019, Anaheim, CA, USA, August 18 – 21, 2019
Note

Funding agencies:  AddMan project, is part of the Clean Sky 2 Joint Undertaking under the European Unions Horizon 2020 research and innovation programme [738002]

Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2024-01-26Bibliographically approved
Persson, J. & Wiberg, A. (2020). Using Boundary Condition and Topology Optimization to Design an Airplane Component. In: AIAA Scitech 2020 Forum: . Paper presented at AIAA Scitech 2020 Forum (pp. 547).
Open this publication in new window or tab >>Using Boundary Condition and Topology Optimization to Design an Airplane Component
2020 (English)In: AIAA Scitech 2020 Forum, 2020, p. 547-Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates a method that can be used to combine topology optimization with optimization of the boundary conditions. The method utilizes design of experiments and surrogate models to model how the boundary conditions affect the potential mass of the component. A demonstration of the method is made by applying it to design an airplane component and comparing the result to other approaches. The best design is then manufactured using additive manufacturing to verify that it is feasible.

National Category
Aerospace Engineering
Identifiers
urn:nbn:se:liu:diva-169016 (URN)10.2514/6.2020-0547 (DOI)
Conference
AIAA Scitech 2020 Forum
Available from: 2020-09-04 Created: 2020-09-04 Last updated: 2021-09-20
Wiberg, A., Persson, J. & Ölvander, J. (2019). Design for additive manufacturing: a review of available design methods and software. Rapid prototyping journal, 25(6), 1080-1094
Open this publication in new window or tab >>Design for additive manufacturing: a review of available design methods and software
2019 (English)In: Rapid prototyping journal, ISSN 1355-2546, E-ISSN 1758-7670, Vol. 25, no 6, p. 15p. 1080-1094Article, review/survey (Refereed) Published
Abstract [en]

Purpose

This paper aims to review recent research in design for additive manufacturing (DfAM), including additive manufacturing (AM) terminology, trends, methods, classification of DfAM methods and software. The focus is on the design engineer’s role in the DfAM process and includes which design methods and tools exist to aid the design process. This includes methods, guidelines and software to achieve design optimization and in further steps to increase the level of design automation for metal AM techniques. The research has a special interest in structural optimization and the coupling between topology optimization and AM.

Design/methodology/approach

The method used in the review consists of six rounds in which literature was sequentially collected, sorted and removed. Full presentation of the method used could be found in the paper.

Findings

Existing DfAM research has been divided into three main groups – component, part and process design – and based on the review of existing DfAM methods, a proposal for a DfAM process has been compiled. Design support suitable for use by design engineers is linked to each step in the compiled DfAM process. Finally, the review suggests a possible new DfAM process that allows a higher degree of design automation than today’s process. Furthermore, research areas that need to be further developed to achieve this framework are pointed out.

Originality/value

The review maps existing research in design for additive manufacturing and compiles a proposed design method. For each step in the proposed method, existing methods and software are coupled. This type of overall methodology with connecting methods and software did not exist before. The work also contributes with a discussion regarding future design process and automation.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019. p. 15
Keywords
Additive manufacturing, Design automation, Design for additive manufacturing, Design optimization, Knowledge-based engineering
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-160357 (URN)10.1108/RPJ-10-2018-0262 (DOI)000482449200011 ()2-s2.0-85070356872 (Scopus ID)
Note

Funding agencies: European Union [738002]

Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2021-11-15Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7210-0209

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