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Design Automation for Additive Manufacturing: A Multi-Disciplinary Optimization Approach
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
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: urn:nbn:se:liu:diva-180998DOI: 10.3384/9789179291082ISBN: 9789179291075 (print)ISBN: 9789179291082 (electronic)OAI: oai:DiVA.org:liu-180998DiVA, id: diva2:1611401
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
List of papers
1. Design for additive manufacturing: a review of available design methods and software
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
2. Design for Additive Manufacturing using a Master Model approach
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
3. An optimisation framework for designs for additive manufacturing combining design, manufacturing and post-processing
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

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Wiberg, Anton

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