Voxel-based support structures for additive manufacture of topologically optimal geometries

Leary, Martin; Mazur, Maciej; Watson, Marcus; Boileau, Etienne; Brandt, Milan

doi:10.1007/s00170-019-03964-z

Cited by 25 publications

(8 citation statements)

References 55 publications

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“…Thus, the developed framework should also suggest suitable alternative AM processes or machines for printing a given artifact. Moreover, designing an efficient support structures is a significant issue in the AM processes that is based on the consideration of a range of influencing factors such as the removal process or the limits of the artifact's geometry [35]. In this context, the manufacturability framework should also incorporate a component for support generation in order to achieve better industrial acceptance and therefore usage.…”

Section: B Discussionmentioning

confidence: 99%

“…The voxel-based representation uses voxels (cubes aligned to the cartesian coordinate system) to represent the shape of the object. Leary et al proposed a voxel-based Cellular automata approach for the generation of AM support structures cite [35]. Tedia and Williams [7] developed a framework for performing a manufacturability analysis of parts to be manufactured using a voxel-based representation schema.…”

Section: ) Three Dimensional Object Model Analysismentioning

confidence: 99%

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Knowledge-Driven Manufacturability Analysis for Additive Manufacturing

Mayerhofer¹,

Lepuschitz²,

Hoebert³

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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Additive Manufacturing (AM) evolved recently from a rapid prototyping process to a standard manufacturing tool. Nevertheless, it is still not a widely used method due to different process-related challenges. In recent years printer technologies and possible printable materials emerged but there are still challenging demands on the printing process. Hence, it is of vital importance to inspect the manufacturability of the designed parts. This work focuses on the not yet widely researched ceramic printing with the Lithography-based Ceramic Manufacturing (LCM) processes. It presents a knowledge-driven framework able to automatically examine geometric properties of a part and compare it to AM guidelines. As a knowledge base, an ontology is used which contains information about the capabilities of AM processes, printers and materials. The manufacturability system uses triangle-based mesh processing algorithms to recognize features and check the guidelines necessary for LCM. The evaluation shows the feasibility of manufacturability analysis with the developed framework and its limitations. INDEX TERMSAdditive manufacturing, computer-aided design, manufacturability analysis, knowledge representation, ontology.

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Section: B Discussionmentioning

confidence: 99%

Section: ) Three Dimensional Object Model Analysismentioning

confidence: 99%

Knowledge-Driven Manufacturability Analysis for Additive Manufacturing

Mayerhofer¹,

Lepuschitz²,

Hoebert³

et al. 2021

IEEE Open J. Ind. Electron. Soc.

View full text Add to dashboard Cite

show abstract

“…Developing to be an autonomous process (McPherson and Zhou, 2018), AM allows for extensive topology optimization in the design phase (Leary et al , 2019). Structures which were extremely challenging or impossible to manufacture in the past can now be built using AM (Yap and Yeong, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fused filament fabrication of nylon 6/66 copolymer: parametric study comparing full factorial and Taguchi design of experiments

Rashed

Kafi

Simons

et al. 2022

RPJ

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Purpose Process parameters in Fused Filament Fabrication (FFF) can affect mechanical and surface properties of printed parts. Numerous studies have reported parametric studies of various materials using full factorial and Taguchi design of experiments (DoEs). However, a comparison between the two are not well-established in literature. The purpose of this study is to compare full factorial and Taguchi DoEs to determine the effects of FFF process parameters on mechanical and surface properties of Nylon 6/66 copolymer. In addition, perform in-depth failure mechanism analysis to understand why the process parameters affect the responses. Design/methodology/approach A full factorial DoE was used to determine the effects of FFF process parameters, such as infill density, infill pattern, layer height and raster angle on responses, such as compressive strength, impact strength, surface roughness and manufacturing time of Nylon 6/66. Micro-computed tomography was used to analyse the impact test samples before and after impact and scanning electron microscope was used to understand the failure mechanism of infill and top layers. Differential scanning calorimetry (DSC) scans of infill and top layers were then taken to determine if a variation in crystallinity existed in different regions of the build. Findings Analysis of variance and main effects plots reveal that infill density has the greatest effect on mechanical and surface properties while manufacturing time is most affected by layer height for the polymer used. A 20% reduction in infill increased impact strength by 19% on average, X-ray images of some of the samples before and after impact tests are presented to understand the reason behind the difference. Moreover, DSC revealed a difference in the degree of crystallinity between the infill and top layers for 80% infill density samples. In addition, Taguchi DoE is realized to be a more efficient technique to determine optimum process parameters for responses that vary linearly as it reduces experimental effort significantly while providing mostly accurate results. Originality/value To the author’s knowledge, no published paper has reported a comparison between predictive DoE method with full factorial DoE to verify their accuracy in determining the effects of FFF process parameters on properties of printed parts. Also, a theory was developed based on DSC results that as the infill is printed faster, it cools slowly compared to the top layers, and hence the infill is in a less crystalline state when compared to the top layers. This increased the ductility of the infill (of 80% infill samples) and thus improved impact absorption.

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“…Testing results revealed that the distortion of the sample was well controlled with this new support structure. Leary et al [17] used voxel-based cellular automata method as fundamental to generate support structures in the printing process. He found that with these CA, it was possible to apply topology optimization geometries in the AM process.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process

Xia

Han

2022

Materials

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Selective laser melting (SLM) process was optimized in this work using multi-objectives genetic algorithm. Process parameters involved in the printing process have an obvious impact on the quality of the printed parts. As the relationship between process parameters and the quality of different parts are complex, it is quite essential to study the effect of process parameter combination. In this work, the impact of four main process parameters, including defocusing amount, laser power, scan speed and layer thickness, were studied on overhanging surface quality of the parts with different inner structures. A multiple-factor and multiple-level experiment was conducted to establish a prediction model using regression analysis while multi-objective genetic algorithm was also employed here to improve the overhanging surface quality of parts with different inner shapes accordingly. The optimized process parameter combination was also used to print inner structure parts and compared with the prediction results to verify the model we have obtained before. The prediction results revealed that sinking distance and roughness value of the overhanging surface on a square-shape inner structure can reduce to 0.017 mm and 9.0 μm under the optimal process parameters combination, while the sinking distance and roughness value of the overhanging surface on a circle-shape inner structure can decrease to 0.014 mm and 10.7 μm under the optimal process parameters combination respectively. The testing results showed that the error rates of the prediction results were all within 10% in spite of random powder bonding in the printing process, which further proved the reliability of the previous results.

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Voxel-based support structures for additive manufacture of topologically optimal geometries

Cited by 25 publications

References 55 publications

Knowledge-Driven Manufacturability Analysis for Additive Manufacturing

Knowledge-Driven Manufacturability Analysis for Additive Manufacturing

Fused filament fabrication of nylon 6/66 copolymer: parametric study comparing full factorial and Taguchi design of experiments

A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process

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