Support slimming for single material based additive manufacturing

Hu, Kailun; Jin, Shuangshuang; Wang, Charlie C. L.

doi:10.1016/j.cad.2015.03.001

Cited by 146 publications

(81 citation statements)

References 28 publications

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“…Some research works still contribute in this particular area to improve the spatial distribution of airy support material [75,122].…”

Section: Optimisation In Additive Manufacturingmentioning

confidence: 99%

“…In addition, none-optimised support deposition affects finishing state, material consumption, fabrication time, etc. [75]. Strategies exist to reduce the dependence of AM to the presence of a support material by operating smart or slimming support generation [75,76].…”

Section: Introductionmentioning

confidence: 99%

“…[75]. Strategies exist to reduce the dependence of AM to the presence of a support material by operating smart or slimming support generation [75,76]. For some strategies, the part orientation is continuously adapted during the processing [57].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Challenges of additive manufacturing technologies from an optimisation perspective

Guessasma

Zhang

Zhu

et al. 2015

Int. J. Simul. Multisci. Des. Optim.

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-Three-dimensional printing offers varied possibilities of design that can be bridged to optimisation tools. In this review paper, a critical opinion on optimal design is delivered to show limits, benefits and ways of improvement in additive manufacturing. This review emphasises on design constrains related to additive manufacturing and differences that may appear between virtual and real design. These differences are explored based on 3D imaging techniques that are intended to show defect related processing. Guidelines of safe use of the term ''optimal design'' are derived based on 3D structural information.

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“…Some research works still contribute in this particular area to improve the spatial distribution of airy support material [75,122].…”

Section: Optimisation In Additive Manufacturingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Challenges of additive manufacturing technologies from an optimisation perspective

Guessasma

Zhang

Zhu

et al. 2015

Int. J. Simul. Multisci. Des. Optim.

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show abstract

“…Hu et al [100] proposed an orientation-driven shape optimizer to reduce the supporting structures used in single material-based AM (in particular FDM and selective laser sintering (SLS)). Their method can meet the overhang angle constraint through modifying a given topology by changing the angles and shapes of features, thus, reducing supports.…”

Section: Othersmentioning

confidence: 99%

Support Structures for Additive Manufacturing: A Review

Jiang

Stringer

2018

JMMP

301

154

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Additive manufacturing (AM) has developed rapidly since its inception in the 1980s. AM is perceived as an environmentally friendly and sustainable technology and has already gained a lot of attention globally. The potential freedom of design offered by AM is, however, often limited when printing complex geometries due to an inability to support the stresses inherent within the manufacturing process. Additional support structures are often needed, which leads to material, time and energy waste. Research in support structures is, therefore, of great importance for the future and further improvement of additive manufacturing. This paper aims to review the varied research that has been performed in the area of support structures. Fifty-seven publications regarding support structure optimization are selected and categorized into six groups for discussion. A framework is established in which future research into support structures can be pursued and standardized. By providing a comprehensive review and discussion on support structures, AM can be further improved and developed in terms of support waste in the future, thus, making AM a more sustainable technology.

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“…This qualifier is due to the often-overlooked manufacturability restrictions of AM, which largely go unaccounted for in current commercially available topology optimization algorithms. Sometimes termed "restrictive DfAM" [11], manufacturability constraints for AM include a system's minimum manufacturable feature size and hole size [12], geometric accuracy and repeatability [13], anisotropic material properties due to part orientation [14,15], and concerns over support material usage and removal [16]. While such constraints do appear in current topology optimization research (e.g., self-supporting angle considerations used in [17,18] and minimum feature size considerations used in [8]), they further increase the complexity of implementing such design algorithms in practice.…”

Section: Advances In Design For Additive Manufacturing Feedback Toolsmentioning

confidence: 99%

Predicting Part Mass, Required Support Material, and Build Time via Autoencoded Voxel Patterns

McComb¹,

Meisel²,

Simpson³

et al. 2018

Preprint

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Additive Manufacturing (AM) allows designers to create intricate geometries that were once too complex or expensive to achieve through traditional manufacturing processes. Currently, Design for Additive Manufacturing (DfAM) is restricted to experts in the field, and novices may overlook potentially transformational design potential enabled by AM. This project aims to make DfAM accessible to a broader audience through deep learning, enabling designers of all skill levels to leverage unique AM geometries when creating new designs. To demonstrate such an approach, a database of files was acquired from industry-sponsored AM challenges focused on lightweight design. These files were converted to a voxelized format, which provides more robust information for machine learning applications. Next, an autoencoder was constructed to a low-dimensional representation of the part designs. Finally, that autoencoder was used to construct a deep neural network capable of predicting various DfAM attributes. This work demonstrates a novel foray towards a more extensive DfAM support system that supports designers at all experience levels.

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Support slimming for single material based additive manufacturing

Cited by 146 publications

References 28 publications

Challenges of additive manufacturing technologies from an optimisation perspective

Challenges of additive manufacturing technologies from an optimisation perspective

Support Structures for Additive Manufacturing: A Review

Predicting Part Mass, Required Support Material, and Build Time via Autoencoded Voxel Patterns

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