A Bottom-up Design Framework for CAD Tools to Support Design for Additive Manufacturing

Goguelin, Steven; Flynn, Joseph M.; Dhokia, Vimal

doi:10.1007/978-3-319-32098-4_35

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Once the geometry of a given component is represented by a triangular mesh (STL file), identifying facets that overhang beyond a critical angle is trivial. The critical overhanging angle was set at 45° as this is a common guideline for all AM manufacturing processes (Goguelin, Flynn, and Dhokia 2016). It is therefore tempting to use the number of overhanging facets as a metric to measure the optimality of a particular build orientation, as given in (1).…”

Section: Optimisation Challenges and Evaluation Criteriamentioning

confidence: 99%

Bayesian optimisation of part orientation in additive manufacturing

Goguelin

Dhokia

Flynn

2021

International Journal of Computer Integrated Manufacturing

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Additive manufacturing (AM) remains slow in terms of volumetric processing rates. Minimising support for overhanging faces is an effective method of reducing material wastage and postprocessing cost. Mindful design can remove much of this support; however, well-selected build orientations are still essential. Searching all feasible orientations is inefficient due to the large number of faces in many mesh files. Nevertheless, support structure generation forms a critical part of the AM process planning stage. This research uses novel combinations of proxy evaluation criteria models for support estimation and optimisation methods to minimise support structure. The number of overhanging facets, overall support structure length and a new estimate for the volume of support structure are used in place of a precise calculation of support quantity. These proxies are used within three different optimisation schemes: grid search, random search and Bayesian optimisations (BO). BO is found to out-perform random and grid search techniques, with grid search having the worst performance in most cases, requiring up to 17-times fewer optimisation iterations. The overall length of support is the most effective proxy model, even outperforming the volume of support estimation, and is shown to perform within 3.5% of results benchmarked against commercial software.

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Section: Optimisation Challenges and Evaluation Criteriamentioning

confidence: 99%

Bayesian optimisation of part orientation in additive manufacturing

Goguelin

Dhokia

Flynn

2021

International Journal of Computer Integrated Manufacturing

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“…However, as the hybrid prototyping technique in this paper is focussing on FDM printing, the DfAM review will only consider those relevant to FDM printing. There are two areas in DfAM that the designer must understand: process considerations and geometric considerations (Goguelin, Flynn, & Dhokia, 2016). The process considerations include the capability of the printers (size, accuracy, speed), material properties (strength, temperature, part anisotropy), and the print settings (layer height, infill percentage, wall thickness, support material etc).…”

Section: Design For Additive Manufacturementioning

confidence: 99%

Hybrid Prototyping: Pure Theory or a Practical Solution to Accelerating Prototyping Tasks?

Mathias¹,

Hicks²,

Snider³

2019

Proc. Int. Conf. Eng. Des.

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Physical prototyping is critical activity in the produce development process, but the cost and time required to produce prototypes hinders it use in the design process. Hybrid prototyping through coupling LEGO and FDM printing is presented as an approach to address these issues. After establishing the separate design rules for FDM printing and LEGO, this paper created new set of rules called Design for Fabrication (DfF) for hybrid prototyping. These cover the three main considerations (Technical, Process, and Design) that the designer and process planning must include to practically implement LEGO and FDM hybrid prototyping. The DfF rules were considered in a prototype of a computer mouse. While the fabrication time was not reduced as expected, it showed that the rules could be practically implemented in a real-world example. Additional considerations were identified that are to be included in the DfF rules.Further work is required to realise the predicted step-change reduction in fabrication time. The first approach is to leverage multiple printers to parallelise the printing. The second is to reduce fidelity while maintaining high fidelity in key regions of interest.

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“…To overcome this problem, software tools can be used to accompany the existing DfAM guidelines. This allows them to be applied in a more interactive and context-sensitive way, as partly described by Goguelin et al (Goguelin et al, 2016). In a previous publication, the available functionality of DfAM software support tools has been analyzed (in scientific literature, as well as in commercial software).…”

Section: Introductionmentioning

confidence: 99%

A knowledge-driven, integrated design support tool for additive manufacturing

Ellsel,

Stark

2024

Proc. Des. Soc.

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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.

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A Bottom-up Design Framework for CAD Tools to Support Design for Additive Manufacturing

Cited by 4 publications

References 11 publications

Bayesian optimisation of part orientation in additive manufacturing

Bayesian optimisation of part orientation in additive manufacturing

Hybrid Prototyping: Pure Theory or a Practical Solution to Accelerating Prototyping Tasks?

A knowledge-driven, integrated design support tool for additive manufacturing

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