Benchmarking of Rapid Prototyping Techniques in Terms of Dimensional Accuracy and Surface Finish

Ippolito, R.; Iuliano, Luca; Gatto, Andrea

doi:10.1016/s0007-8506(07)62296-3

Cited by 153 publications

(59 citation statements)

References 3 publications

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“…This benchmark part focuses on the overall performance of the AM systems. Ippolito et al (1995) worked on the development, manufacture and test of a benchmarking model in order to investigate dimensional accuracy and surface finish of various AM techniques such as SL, selective laser sintering (SLS), FDM, laminated object manufacturing (LOM) and solid ground curing (SGC). Jayaram et al (1994) aimed to lay the groundwork for the development of standards to measure various performance factors such as repeatability, warpage, curl, creep, shrinkage and tensile strength in a quantitative way; test parts were designed for studying SL, SLS, LOM and FDM techniques.…”

Section: Geometric Benchmarking Modelmentioning

confidence: 99%

Towards a standard experimental protocol for open source additive manufacturing

Sanchez

Boudaoud

Muller

et al. 2014

Virtual and Physical Prototyping

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The technological development of open source three-dimensional (3D) printers is creating more affordable Additive Manufacturing (AM) machines for society in different applications. For this reason, the machines' capability should be evaluated in order to establish minimum standards of performance. This paper deals with the development, manufacture and testing of a geometrical benchmarking model (GBM) in order to evaluate the geometrical accuracy performance of open source 3D printers. The methodology is demonstrated with a case study based on fused deposition modelling (FDM). The case study positions the evaluated machine according to ANSI-ISO's International Tolerance (IT) Grades. Furthermore, root-mean-square deviation (RMSD) value is employed as an accuracy estimator, while Taguchi tools are employed to determine the control factors with the highest accuracy for the fabrication of the GBM.

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Section: Geometric Benchmarking Modelmentioning

confidence: 99%

Towards a standard experimental protocol for open source additive manufacturing

Sanchez

Boudaoud

Muller

et al. 2014

Virtual and Physical Prototyping

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“…For this reason, surface roughness is a key issue in RP (Ippolito et al, 1995;Reeves and Cobb, 1997;Mahesh et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Representation of surface roughness in fused deposition modeling

Ahn

Kweon

Kwon

et al. 2009

Journal of Materials Processing Technology

358

172

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“…The accuracy and geometrical fidelity of 3D-printed objects is researched in many works and for over 20 years [11,12] due to the necessity to produce objects that match their digital models closely for the use as prototypes (Rapid Prototyping, (RP) [13,14]), consumer products (Rapid Manufacturing, (RM) [15]) or tools (Rapid Tooling, (RT) [16]). …”

Section: Accuracymentioning

confidence: 99%

Scanned Image Data from 3D-Printed Specimens Using Fused Deposition Modeling

Baumann

Eichhoff

Roller

2017

Data

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Abstract:This dataset provides high-resolution 2D scans of 3D printed test objects (dog-bone), derived from EN ISO 527-2:2012. The specimens are scanned in resolutions from 600 dpi to 4800 dpi utilising a Konica-Minolta bizHub 42 and Canon LiDE 210 scanner. The specimens are created to research the influence of the infill-pattern orientation; The print orientation on the geometrical fidelity and the structural strength. The specimens are printed on a MakerBot Replicator 2X 3D-printer using yellow (ABS 1.75 mm Yellow, REC, Moscow, Russia) and purple ABS plastic (ABS 1.75 mm Pink Lion&Fox, Hamburg, Germany). The dataset consists of at least one scan per specimen with the measured dimensional characteristics. For this, software is created and described within this work. Specimens from this dataset are either scanned on blank white paper or on white paper with blue millimetre marking. The printing experiment contains a number of failed prints. Specimens that did not fulfil the expected geometry are scanned separately and are of lower quality due to the inability to scan objects with a non-flat surface. For a number of specimens printed sensor data is acquired during the printing process. This dataset consists of 193 specimen scans in PNG format of 127 objects with unadjusted raw graphical data and a corresponding, annotated post-processed image. Annotated data includes the detected object, its geometrical characteristics and file information. Computer extracted geometrical information is supplied for the images where automated geometrical feature extraction is possible.

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Benchmarking of Rapid Prototyping Techniques in Terms of Dimensional Accuracy and Surface Finish

Cited by 153 publications

References 3 publications

Towards a standard experimental protocol for open source additive manufacturing

Towards a standard experimental protocol for open source additive manufacturing

Representation of surface roughness in fused deposition modeling

Scanned Image Data from 3D-Printed Specimens Using Fused Deposition Modeling

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