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D425: ENHANCING ADDITIVE MANUFACTURING WITH KNOWLEDGE-BASED DESIGN TOOLS
Time:
Thursday, 23/May/2024:
10:45am - 12:30pm
Session Chair: Roland Lachmayer, Leibniz University Hannover, Germany
Location:Congress Hall Konavle
Presentations
A knowledge-driven, integrated design support tool for additive manufacturing
Claudius Ellsel, Rainer Stark
Technische Universität Berlin, Germany
Increasing adoption of additive manufacturing (AM) makes software support for design for additive manufacturing (DfAM) more relevant. This paper presents a novel, knowledge-driven design support tool for AM that leverages a central knowledge base to provide extensible and powerful DfAM support early in the development process. The approach was implemented using Python for the knowledge base and as a plugin for Siemens NX. It offers automated design checks, optimizations, and further information through an integrated Wiki. Evaluation confirms the feasibility and benefits of the approach.
Investigating designers’ preferred learning media to design for additive manufacturing
Martins Obi1, Patrick Pradel2, Matt Sinclair3, Richard Bibb4, Mark Evans2
1Coventry University, United Kingdom; 2Loughborough University, United Kingdom; 3Edinburgh Napier University, United Kingdom; 4Nottingham Trent University, United Kingdom
In this exploratory study, designers’ preferred learning media in learning to design for Additive Manufacturing was explored. Furthermore, by deploying an online survey questionnaire, factors such as years of experience, and the categories of products designed were explored to understand how they influence designers’ learning media with a response from 201 respondents. The results show that designers have learned how to design for AM through experimentation and present the first step towards developing an appropriate Design for Additive Manufacturing knowledge dissemination approach.
A proposal for guiding the selection of suitable DfAM support based on experiential knowledge
Pascal Schmitt, Lisa Siewert, Kilian Gericke
University of Rostock, Germany
Unlocking additive manufacturing's (AM) potential requires designer expertise. Design for additive manufacturing (DfAM) addresses this need but faces barriers, such as uncertainty in scope of integration, design support selection, result validation or time investment for incorporating design support. This paper proposes a framework aligning SCRUM (agile framework) to aid designers in overcoming those barriers. The goal is to pave the way for a better exchange between academia and industry and fostering iterative development of DfAM support tailored to designer needs.
A Bayesian expert system for additive manufacturing design assessment
Benedict Alexander Rogers, Neill Campbell, Mandeep Dhanda, Alexander James George Lunt, Elise Catherine Pegg, Vimal Dhokia
University of Bath, United Kingdom
Tools for analysing additive manufacturability often employ complex models that lack transparency; this impedes user understanding and has detrimental effects on the implementation of results. An expert system tool that transparently learns features for successful printing has been created. The tool uses accessible data from STL models and printer configurations to create explainable parameters and identify risks. Testing has shown good agreement to print behaviour and easy adaptability. The tool reduces the learning curves designers face in understanding design for additive manufacturing.
Providing a knowledge-based design catalog as an approach to support the development of design for additive manufacturing skills
Gregory-Jamie Tüzün, Daniel Roth, Matthias Kreimeyer
University of Stuttgart, Germany
Proficiency in design for additive manufacturing (DfAM) requires training and a lot of trial and error. To support the development of DfAM skills, we redesigned 47 design artifacts from case studies and derived tacit knowledge from successful and unsuccessful redesigns. All knowledge about these artifacts was then collected in a design catalog. In a workshop with a total of 48 graduates and students, 45 participants deemed the design catalog supportive. After evaluating their designs, we concluded that the use of a knowledge-based design catalog can develop and improve individual DfAM skills.
