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.
Highlights
Additive manufacturing (AM) technologies afford a wide range of benefits over traditional manufacturing techniques
The reviewed literature has highlighted research gaps shaping the need to (i) conduct comprehensive testing on a single printer, (ii) incorporate parameters previously omitted from extant capability profiles for filament deposition modelling (FDM), (iii) validate the behaviour of a capability profile by applying it in the creation of functional components and (iv) implement the capability profile within a generative design approach
Using the settings outlined in the previous section, four neural networks were generated as potential capability profiles for FDM
Summary
Additive manufacturing (AM) technologies afford a wide range of benefits over traditional manufacturing techniques These include facilitating increased design freedoms [1], reducing manufacturing costs [2, 3] and minimising both part weight and waste during production [4]. To contextualise the work carried in this paper, this section will consider three areas: material testing for FDM, capability profiling and generative design in the context of additive manufacturing. In doing this, it will clarify the four research gaps that the paper will address. Parts are shown to be strongest when the raster angle is in the direction of the applied load and increased raster width increases part strength [11, 13, 18,19,20] and a negative air gap between rasters is found to increase part strength [11, 18, 19]
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