Design and experimental validation of self-supporting topologies for additive manufacturing

Fu, Yun-Fei; Rolfe, Bernard; Chiu, Louis N.S.; Wang, Yanan; Huang, Xiaodong; Ghabraie, Kazem

doi:10.1080/17452759.2019.1637023

Cited by 52 publications

(29 citation statements)

References 54 publications

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“…3D printing technologies (also known as additive manufacturing (AM)) have been developed for more than twenty years. This technology has become a competitive technique for manufacturing parts with complex structures that are difficult or even impossible to be produced via conventional manufacturing technologies [1][2][3][4]. Fused deposition modelling (FDM) is one of the most common AM technologies for manufacturing 3D parts.…”

Section: Introductionmentioning

confidence: 99%

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

Jiang¹,

Xu²,

Rui³

et al. 2021

E3S Web Conf.

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Additive manufacturing is becoming increasingly popular because of its unique advantages, especially fused deposition modelling (FDM) which has been widely used due to its simplicity and comparatively low price. However, in current FDM processes, it is difficult to fabricate parts with highly accurate dimensions. One of the reasons is due to the slicing process of 3D models. Current slicing software divides the parts into layers and then lines (paths) based on a fixed value. However, in a real printing process, the printed line width will change when the process parameters are set in different values. The various printed widths may result in inaccuracy of printed dimensions of parts if using a fixed value for slicing. In this paper, a mathematical model is proposed to predict the printed line width in different layer heights. Based on this model, a method is proposed for calculating the optimal width value for slicing 3D parts. In the future, the proposed mathematical model can be integrated into slicing software to slice 3D models for precision additive manufacturing.

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Section: Introductionmentioning

confidence: 99%

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

Jiang¹,

Xu²,

Rui³

et al. 2021

E3S Web Conf.

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“…Moreover, many available approaches have not been broadly and satisfactorily tested for manufacturability [589].…”

Section: Development Referencementioning

confidence: 99%

Topology Optimisation in Structural Steel Design for Additive Manufacturing

2021

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Topology Optimisation is a broad concept deemed to encapsulate different processes for computationally determining structural materials optimal layouts. Among such techniques, Discrete Optimisation has a consistent record in Civil and Structural Engineering. In contrast, the Optimisation of Continua recently emerged as a critical asset for fostering the employment of Additive Manufacturing, as one can observe in several other industrial fields. With the purpose of filling the need for a systematic review both on the Topology Optimisation recent applications in structural steel design and on its emerging advances that can be brought from other industrial fields, this article critically analyses scientific publications from the year 2015 to 2020. Over six hundred documents, including Research, Review and Conference articles, added to Research Projects and Patents, attained from different sources were found significant after eligibility verifications and therefore, herein depicted. The discussion focused on Topology Optimisation recent approaches, methods, and fields of application and deepened the analysis of structural steel design and design for Additive Manufacturing. Significant findings can be found in summarising the state-of-the-art in profuse tables, identifying the recent developments and research trends, as well as discussing the path for disseminating Topology Optimisation in steel construction.

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“…Additive manufacturing (AM) technologies (also known as rapid prototyping, 3D printing, solid freeform fabrication, etc.) have been developed for more than 30 years [ 1 , 2 , 3 , 4 , 5 , 6 ]. As AM has matured, it has been applied in many fields, including aerospace, medical, construction and aesthetic products [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review

Jiang

2020

Micromachines

206

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Additive manufacturing (AM) is the process of joining materials layer by layer to fabricate products based on 3D models. Due to the layer-by-layer nature of AM, parts with complex geometries, integrated assemblies, customized geometry or multifunctional designs can now be manufactured more easily than traditional subtractive manufacturing. Path planning in AM is an important step in the process of manufacturing products. The final fabricated qualities, properties, etc., will be different when using different path strategies, even using the same AM machine and process parameters. Currently, increasing research studies have been published on path planning strategies with different aims. Due to the rapid development of path planning in AM and various newly proposed strategies, there is a lack of comprehensive reviews on this topic. Therefore, this paper gives a comprehensive understanding of the current status and challenges of AM path planning. This paper reviews and discusses path planning strategies in three categories: improving printed qualities, saving materials/time and achieving objective printed properties. The main findings of this review include: new path planning strategies can be developed by combining some of the strategies in literature with better performance; a path planning platform can be developed to help select the most suitable path planning strategy with required properties; research on path planning considering energy consumption can be carried out in the future; a benchmark model for testing the performance of path planning strategies can be designed; the trade-off among different fabricated properties can be considered as a factor in future path planning design processes; and lastly, machine learning can be a powerful tool to further improve path planning strategies in the future.

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Design and experimental validation of self-supporting topologies for additive manufacturing

Cited by 52 publications

References 54 publications

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

Topology Optimisation in Structural Steel Design for Additive Manufacturing

Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review

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