Design for AM: Contributions from surface finish, part geometry and part positioning

Castelão, A.; Soares, Bruno; Machado, Carla M.; Leite, Marco; Mourão, António

doi:10.1016/j.procir.2019.04.247

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…A more recent study by Hallmann et al (2019) developed a method that allows the study of both design and process parameters simultaneously while considering the machine in use. Furthermore, Castelão et al (2019) evaluated the surface finish and dimensional deviations of parts printed with FDM when considering multiple parameters and using design of experiments coupled with the response surface methodology.…”

Section: Additive Manufacturing Process and Parametersmentioning

confidence: 99%

A bibliometric analysis of research in design for additive manufacturing

Ugonna

Pradel

Sinclair

et al. 2022

RPJ

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Purpose The purpose of this paper is to understand how Design for Additive manufacturing Knowledge has been developing and its significance to both academia and industry. Design/methodology/approach In this paper, the authors use a bibliometric approach to analyse publications from January 2010 to December 2020 to explore the subject areas, publication outlets, most active authors, geographical distribution of scholarly outputs, collaboration and co-citations at both institutional and geographical levels and outcomes from keywords analysis. Findings The findings reveal that most knowledge has been developed in DfAM methods, rules and guidelines. This may suggest that designers are trying to learn new ways of harnessing the freedom offered by AM. Furthermore, more knowledge is needed to understand how to tackle the inherent limitations of AM processes. Moreover, DfAM knowledge has thus far been developed mostly by authors in a small number of institutional and geographical clusters, potentially limiting diverse perspectives and synergies from international collaboration which are essential for global knowledge development, for improvement of the quality of DfAM research and for its wider dissemination. Originality/value A concise structure of DfAM knowledge areas upon which the bibliometric analysis was conducted has been developed. Furthermore, areas where research is concentrated and those that require further knowledge development are revealed.

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Section: Additive Manufacturing Process and Parametersmentioning

confidence: 99%

A bibliometric analysis of research in design for additive manufacturing

Ugonna

Pradel

Sinclair

et al. 2022

RPJ

View full text Add to dashboard Cite

show abstract

“…Overall, there is no consistency in the artefacts used, with huge variation in both size and shape. Some of the most widely used artefacts used are the dog bone [ 31 , 32 , 33 , 34 ] and the cube [ 10 , 35 , 36 ]. This lack of consistency has a significant impact on the comparability of results across the different studies.…”

Section: Experimental Studies Investigating Printing Parameter Optimi...mentioning

confidence: 99%

“…NGH can also influence the defects listed: Circularity, Cylindricity, Porosity, and Surface Roughness. ↑ [ 10 , 22 , 25 , 32 , 33 , 37 , 46 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ] ↓ [ 12 , 26 , 27 , 31 , 34 , 35 , 36 , 44 , 45 , 53 , 54 , 62 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , ...…”

Section: Printing Parameters and Their Impactmentioning

confidence: 99%

“…BO can also influence the defects listed: Anisotropy, Circularity, Geometrical accuracy, Porosity, Surface Roughness, and Warping. ↑ [ 10 , 33 , 46 , 71 , 79 , 81 , 100 , 129 , 130 ] ↓ [ 22 , 32 , 35 , 44 , 45 , 60 , 66 , 69 , 70 , 72 , 78 , 85 , 87 , 96 , 101 , 110 , 131 , 132 , 133 , 134 , 135 ] Infill pattern (IP) The internal architecture of the part, which affects mechanical properties, build time, printing cost, weight, and density. IP can also influence the defects listed: Geometrical accuracy and Surface roughness.…”

Section: Printing Parameters and Their Impactmentioning

confidence: 99%

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How generalisable are material extrusion additive manufacturing parameter optimisation studies? A systematic review

Golab

Massey

Moultrie

2022

Heliyon

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“…Inspite of the tremendous advantage offered by AM techniques in addressing design complexities in a wide range of materials ranging from metallic materials to Functionally Graded Materials (FGM) [2], [12], the primary challenge still remains in obtaining 'real, robust and functional' objects of engineering interest [3], [13], [14]. Moreover, the other challenges include: (i) size limitations [1], [15], (ii) quality consistency [16], (iii) scaling issues [17], [18] and (iv) high material cost [16], [18], [19].…”

Section: Introductionmentioning

confidence: 99%