The creation of a neural network based capability profile to enable generative design and the manufacture of functional FDM parts

Mark Goudswaard*, Ben Hicks, Aydin Nassehi

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

In order to manufacture functional parts using filament deposition modelling (FDM), an understanding of the machine’s capabilities is necessary. Eliciting this understanding poses a significant challenge due to a lack of knowledge relating manufacturing process parameters to mechanical properties of the manufactured part. Prior work has proposed that this could be overcome through the creation of capability profiles for FDM machines. However, such an approach has yet to be implemented and incorporated into the overall design process. Correspondingly, the aim of this paper is two-fold and includes the creation of a comprehensive capability profile for FDM and the implementation of the profile and evaluation of its utility within a generative design methodology. To provide the foundations for the capability profile, this paper first reports an experimental testing programme to characterise the influence of five manufacturing parameters on a part’s ultimate tensile strength (UTS) and tensile modulus (E). This characterisation is used to train an artificial neural network (ANN). This ANN forms the basis of a capability profile that is shown to be able to represent the mechanical properties with RMSEP of 1.95 MPa for UTS and 0.82 GPa for E. To validate the capability profile, it is incorporated into a generative design methodology enabling its application to the design and manufacture of functional parts. The resulting methodology is used to create two load bearing components where it is shown to be able to generate parts with satisfactory performance in only a couple of iterations. The novelty of the reported work lies in demonstrating the practical application of capability profiles in the FDM design process and how, when combined with generative approaches, they can make effective design decisions in place of the user.

Original languageEnglish
Pages (from-to)2951-2968
Number of pages18
JournalInternational Journal of Advanced Manufacturing Technology
Volume113
Issue number9-10
DOIs
Publication statusPublished - 27 Apr 2021

Bibliographical note

Funding Information:
The work reported in this paper has been undertaken as part of the ProtoTwin project (improving the product development process through integrated revision control and twinning of digital-physical models during prototyping). The work was conducted at the University of Bristol in the Design and Manufacturing Futures Lab ( http://www.dmf-lab.co.uk ) and is funded by the Engineering and Physical Sciences Research Council (EPSRC), Grant reference EP/R032696/1.

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • Additive manufacture
  • Capability profiles
  • Design for additive manufacture
  • Generative design
  • Mechanical testing
  • Neural networks

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