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

• Published in: International journal of advanced manufacturing technology Vol. 113; no. 9-10; pp. 2951 - 2968
• Main Authors: Goudswaard, Mark, Hicks, Ben, Nassehi, Aydin
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2021; Springer Nature B.V
• Subjects: Artificial neural networks; CAE) and Design; Computer-Aided Engineering (CAD; Design engineering; Engineering; Industrial and Production Engineering; Load bearing components; Mechanical Engineering; Mechanical properties; Media Management; Modulus of elasticity; Neural networks; Original Article; Process parameters; Rapid prototyping; Ultimate tensile strength; Mechanical testing; Additive manufacture; Generative design; Capability profiles; Neural networks; Design for additive manufacture
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-021-06770-8

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.