Abstract
Additive manufacturing (AM) is expected to generate huge economic revenue by 2025; however, this will only be realised by overcoming the barriers that are preventing its increased adoption to end-use parts. Design for AM (DfAM) is recognised as a multi-faceted problem, exasperated by constraints to creativity, knowledge propagation, insufficiencies in education and a fragmented software pipeline. This study proposes a novel approach to increase the creativity in DfAM. Through comparison between DfAM and in utero human development, the unutilised potential of design through the time domain was identified. Therefore, the aim of the research is to develop a computer-aided manufacturing (CAM) programme to demonstrate design through the time domain, known as Temporal DfAM (TDfAM). This was achieved through a bespoke MATLAB code which applies a linear function to a process parameter, discretised across the additive build. TDfAM was demonstrated through the variation of extrusion speed combined with the infill angle, through the axial and in-plane directions. It is widely accepted in the literature that AM processing parameters change the properties of AM materials. Thus, the application of the TDfAM approach offers the engineer increased creative scope and control, whilst inherently upskilling knowledge, in the design of AM materials.
Highlights
Additive manufacturing (AM) offers huge benefits to industry and healthcare through reduced material costs, weight reduction, customisation and reduction of the time-to-market for products
This study proposes to overcome the issue of psychological inertia, in how engineers think about Design for AM (DfAM), through innovation generated by reflecting between these comparable yet contrasting knowledge domains
This study proposes to achieve this through a paradigm shift in the perception of design, by reflecting the synergy of the spatial and temporal development of the form and the function in the growing foetus through DfAM
Summary
Additive manufacturing (AM) offers huge benefits to industry and healthcare through reduced material costs, weight reduction, customisation and reduction of the time-to-market for products. The current design framework spans to various interfaces and utilises different file formats to encode the topology data. Different spatial data formats include parametric computer-aided design (CAD) representations, discretised Finite Element Analysis (FEA) meshes and the tessellated stereolithography (STL) files, compatible with most AM computer-aided manufacturing (CAM) software. This “software-hopping” leads to inefficient transformations between parametric, tessellated and meshed datasets. Commercial interests have been dominated by design for function using topology optimisation, for example, OptiStruct [14] This approach utilises a discretised interface to optimise the geometry of a part for a specific design criteria subject to manufacturing parameters. The review by Pradel et al [16] crucially demonstrates how previous literature into DfAM maps onto a framework which defines the distinction between research aimed at different stages of the design and manufacture interface
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