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Integrating digital image correlation in mechanical testing for the materials characterization of big area additive manufacturing feedstock
- Source :
- Additive Manufacturing. 26:129-137
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- To enable the advancement of large-scale additive manufacturing processes, it is necessary to establish and standardize methodologies to characterize the mechanical properties of printed test coupons. Due to the large size of the print beads, conventional test standards are inadequate. The focus of this study was to determine the feasibility of using Digital image correlation (DIC) technology as a key enabler for robust data collection of strain measurements of large 3D printed parts. To incorporate the DIC measurements, a novel method was developed to prepare large 20% (by wt.) glass filled ABS test coupons for adequate contrast. Through this technique, Poisson's ratio and elastic modulus were measured and stress strain curves were generated. The data produced by DIC correlated well with failure analysis performed on spent test coupons. Additionally, fracture surface analysis of the specimens revealed poor adhesion among the ABS matrix and glass fibers. This matrix/fiber debonding demonstrated the need for improved printing parameters to maximize tensile strength. Finally, critical length analysis of the fibers revealed them to be dimensionally inadequate.
- Subjects :
- 0209 industrial biotechnology
Digital image correlation
Materials science
Stress–strain curve
Glass fiber
Biomedical Engineering
02 engineering and technology
021001 nanoscience & nanotechnology
Industrial and Manufacturing Engineering
Characterization (materials science)
020901 industrial engineering & automation
Ultimate tensile strength
Fracture (geology)
General Materials Science
Fiber
Composite material
0210 nano-technology
Engineering (miscellaneous)
Elastic modulus
Subjects
Details
- ISSN :
- 22148604
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- Additive Manufacturing
- Accession number :
- edsair.doi...........ee7db7c506af8588d9a7b30e889d462a
- Full Text :
- https://doi.org/10.1016/j.addma.2018.12.016