A review on benchmark artifacts for evaluating the geometrical performance of additive manufacturing processes

Rebaioli, Lara; Fassi, Irene

doi:10.1007/s00170-017-0570-0

Cited by 120 publications

(56 citation statements)

References 47 publications

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“…However, there are some limitations and problems that need to be considered and addressed when using AM, dimensional accuracy [16,17], process and geometric repeatability [18][19][20], and material defects [21][22][23] being the primary considerations. While all are important considerations, the most essential from the perspective of engineering design are the dimensional accuracy and repeatability of the final parts.…”

Section: Introductionmentioning

confidence: 99%

Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

Messimer

Pereira

Patterson

et al. 2019

JMMP

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This paper describes the collection of a large dataset (6930 measurements) on dimensional error in the fused deposition modeling (FDM) additive manufacturing process for full-density parts. Three different print orientations were studied, as well as seven raster angles ( 0 ∘ , 15 ∘ , 30 ∘ , 45 ∘ , 60 ∘ , 75 ∘ , and 90 ∘ ) for the rectilinear infill pattern. All measurements were replicated ten times on ten different samples to ensure a comprehensive dataset. Eleven polymer materials were considered: acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), high-temperature PLA, wood-composite PLA, carbon-fiber-composite PLA, copper-composite PLA, aluminum-composite PLA, high-impact polystyrene (HIPS), polyethylene terephthalate glycol-enhanced (PETG), polycarbonate, and synthetic polyamide (nylon). The samples were ASTM-standard impact-testing samples, since this geometry allows the measurement of error on three different scales; the nominal dimensions were 3 . 25 mm thick, 63 . 5 mm long, and 12 . 7 mm wide. This dataset is intended to give engineers and product designers a basis for judging the accuracy and repeatability of the FDM process for use in manufacturing of end-user products.

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Section: Introductionmentioning

confidence: 99%

Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

Messimer

Pereira

Patterson

et al. 2019

JMMP

View full text Add to dashboard Cite

show abstract

“…The authors stated that test artifacts in general are composed of a series of generic geometric features to evaluate dimensional accuracy and other parameters such as surface roughness, mechanical properties, or manufacturing time and costs. However, given that these types of test artifacts contain generic geometric features, they are often not representative of all of the geometric features of a specific product [26]. Moreover, they often lead to the utilization of a potentially unnecessarily large number of test artifacts [27].…”

Section: Design Of Test Artifacts For Am Of Space Componentsmentioning

confidence: 99%

Constraint Replacement-Based Design for Additive Manufacturing of Satellite Components: Ensuring Design Manufacturability through Tailored Test Artefacts

et al. 2019

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Additive manufacturing (AM) is becoming increasingly attractive for aerospace companies due to the fact of its increased ability to allow design freedom and reduce weight. Despite these benefits, AM comes with manufacturing constraints that limit design freedom and reduce the possibility of achieving advanced geometries that can be produced in a cost-efficient manner. To exploit the design freedom offered by AM while ensuring product manufacturability, a model-based design for an additive manufacturing (DfAM) method is presented. The method is based on the premise that lessons learned from testing and prototyping activities can be systematically captured and organized to support early design activities. To enable this outcome, the DfAM method extends a representation often used in early design, a function–means model, with the introduction of a new model construct—manufacturing constraints (Cm). The method was applied to the redesign, manufacturing, and testing of a flow connector for satellite applications. The results of this application—as well as the reflections of industrial practitioners—point to the benefits of the DfAM method in establishing a systematic, cost-efficient way of challenging the general AM design guidelines found in the literature and a means to redefine and update manufacturing constraints for specific design problems.

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“…However, there are some limitations and problems that need to be considered and addressed when using AM, dimensional accuracy [13,14], process and geometric repeatability [15][16][17], and material defects [18][19][20] being the primary considerations. While all of these are important to examine, the most important from the perspective of engineering design are the dimensional accuracy and repeatability of the final parts.…”

Section: Of 27mentioning

confidence: 99%

Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

Messimer¹,

Pereira²,

Patterson³

et al. 2018

Preprint

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This report describes the collection of a large dataset (6930 measurement) on dimensional error in the fused deposition modeling (FDM) additive manufacturing process for full-density parts. Three different print orientations were studied, as well as seven raster angles (0 • , 15 • , 30 • , 45 • , 60 • , 75 • , and 90 • ) for the rectilinear infill pattern. All measurements were replicated ten times on ten different samples to ensure a comprehensive dataset. Eleven polymer materials were considered: acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), high-temperature PLA, wood-composite PLA, carbon-fiber-composite PLA, copper-composite PLA, aluminum-composite PLA, high-impact polystyrene (HIPS), polyethylene terephthalate glycol-enhanced (PETG), polycarbonate, and synthetic polyamide (nylon). The samples were ASTM-standard impact testing samples, since this geometry allows the measurement of error on three different scales; the nominal dimensions were 3.25mm thick, 63.5mm long, and 12.7mm wide. This dataset is intended to give engineers and product designers a benchmark for judging the accuracy and repeatability of the FDM process for use in manufacturing of end-user products. Dataset: Attached with manuscriptDataset License: CC-BY

show abstract

A review on benchmark artifacts for evaluating the geometrical performance of additive manufacturing processes

Cited by 120 publications

References 47 publications

Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

Constraint Replacement-Based Design for Additive Manufacturing of Satellite Components: Ensuring Design Manufacturability through Tailored Test Artefacts

Full-Density Fused Deposition Modeling Dimensional Error as a Function of Raster Angle and Build Orientation: Large Dataset for Eleven Materials

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