Nanoindentation mapping defects filtration for heterogeneous materials using generative adversarial networks

Journal Article · Sat Jul 09 00:00:00 EDT 2022 · Materials Characterization

DOI:https://doi.org/10.1016/j.matchar.2022.112107· OSTI ID:1885532

Bianco, Giuseppe ^[1]; Paul, Tanaji ^[1]; Nisar, Ambreen ^[1]; Hamrani, Abderrachid ^[1]; Boesl, Benjamin ^[1]; Agarwal, Arvind ^[1]

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1. Florida International Univ. (FIU), Miami, FL (United States)

Advanced composite materials with multiple phases and heterogeneous microstructure necessitate spatial mapping characterization of elastic modulus to develop constitutive relations and overall mechanical response. Such modulus mapping can be obtained using the nanoindentation technique, where the indenter tip raster over the selected microstructure region. Typically, a surface preparation procedure is done in the specimens to ensure proper contact between the indenter tip and sample surface. However, a near-perfect surface finish is unachievable in heterogeneous materials, primarily with ceramic reinforcements, due to the differential material removal rate during polishing. Thus, the nanoindenter records localized erroneous measurements due to differences in surface roughness and corresponding force response. This study establishes a novel deep learning-based strategy to rectify incorrect experimental spatial measurements acquire during nanoindentation modulus mapping. Here, the integrated bicubic interpolation and generative adversarial networks (GANs) model was trained using 14 ceramic and 18 metallic data sets, each comprising 65,536 measurements. The developed algorithm was validated against experimental measurements on four unknown specimens. The standard deviation in measured elastic modulus reduces by ~50% in ceramics and ~72% in metallic samples. This computational framework proposes a novel approach to reducing uncertainty in materials’ properties using state-of-the-art computer vision techniques.

• Research Organization: Florida International Univ. (FIU), Miami, FL (United States); Kansas City Nuclear Security Campus (KCNSC), Kansas City, MO (United States)
• Sponsoring Organization: US Army Research Office (ARO); USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003865
• OSTI ID: 1885532
• Alternate ID(s): OSTI ID: 1877943; OSTI ID: 1958100
• Report Number(s): NSC-614-4564; W911NF1910097
• Journal Information: Materials Characterization, Journal Name: Materials Characterization Vol. 191; ISSN 1044-5803
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
Elastic Modulus mapping
Erroneous measurements filter
Generative Adversarial Networks (GANs)
Heterogeneous materials
Nanoindentation