Microstructural characterization of additively manufactured multi-directional preforms and composites via X-ray micro-computed tomography

Quan, Zhenzhen; Larimore, Zachary; Qin, Xiaohong; Yu, Jianyong; Mirotznik, Mark S.; Byun, Joon‐Hyung; Oh, Youngseok; Chou, Tsu‐Wei

doi:10.1016/j.compscitech.2016.05.015

Cited by 55 publications

(28 citation statements)

References 40 publications

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“…Additionally, X-ray microtomography has been used to investigate micro cracking in carbon-fiber and glass-fiber composites [14], and for determining material composition based on density differences [43]. Similarly, micro-CT has been utilized to investigate the internal structure of other hybrid composite materials in the literature such as woven composite laminates with carbon nanotubes [44], chopped carbon-reinforced tapes [45], chopped fiber reinforced foams [46], short carbon-reinforced ABS (acrylonitrile butadiene styrene) preforms [3], uncompressed relaxed fibers [41], dentin-dentin bonding systems and composite systems [15] and stitched carbon epoxy composites [16]. The permeability and impregnation of the fiber reinforcements were quantified using the Micro-CT for a non-crimp carbon textile in [17] and an out-of-autoclave carbon/toughened epoxy prepreg in [18].…”

Section: State Of the Art In Micro-ct Of Woven Fabric Compositesmentioning

confidence: 99%

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A Review of Current Challenges and Case Study toward Optimizing Micro-Computed X-Ray Tomography of Carbon Fabric Composites

et al. 2020

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X-ray computed tomography provides qualitative and quantitative structural and compositional information for a broad range of materials. Yet, its contribution to the field of advanced composites such as carbon fiber reinforced polymers is still limited by factors such as low imaging contrast, due to scarce X-ray attenuation features. This article, through a review of the state of the art, followed by an example case study on Micro-computed tomography (CT) analysis of low X-ray absorptive dry and prepreg carbon woven fabric composites, aims to highlight and address some challenges as well as best practices on performing scans that can capture key features of the material. In the case study, utilizing an Xradia Micro-CT-400, important aspects such as obtaining sufficient contrast, an examination of thin samples, sample size/resolution issues, and image-based modeling are discussed. The outcome of an optimized workflow in Micro-CT of composite fabrics can assist in further research efforts such as the generation of surface or volume meshes for the numerical modeling of underlying deformation mechanisms during their manufacturing processes.

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Section: State Of the Art In Micro-ct Of Woven Fabric Compositesmentioning

confidence: 99%

“…Unique insights can be obtained from Micro-CT, regarding processing or in-service degradation. At the same time, different types of Micro-CT scanners can offer detailed information across the length scales, from the whole part to laminates sections, and from yarns to the individual filament level [3].…”

Section: Introductionmentioning

confidence: 99%

A Review of Current Challenges and Case Study toward Optimizing Micro-Computed X-Ray Tomography of Carbon Fabric Composites

et al. 2020

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“…We refer the reader to [17,18] for more details on finite element modeling of 3D images obtained by Xray computed tomography. An example of X-ray computed tomography applied to manufactured parts can be found in [19]. The ideal geometries involved in the proposed workflow were obtained by using computer-aided-design (CAD).…”

Section: Complexitymentioning

confidence: 99%

Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests

et al. 2018

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In this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommend a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator, is proposed to assess the accuracy of this output. This article details simple algorithmic choices that allowed a realistic mechanical modeling via computer vision.

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“…While limitations in the technique's application to additively fabricated structures are becoming increasingly understood 32 , few other techniques currently provide the breadth needed to provide reasonable interrogation across the many applications, sizes and material systems being investigated. As such, micro-computed tomography continues to emerge as one of the more favorable techniques for post-build inspection of additively manufactured parts and products 33,34 .…”

Section: Introductionmentioning

confidence: 99%

Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

Madison

Underwood

Swiler

et al. 2018

AIP Conference Proceedings

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bhjared@sandia.gov, f) jmrodel@sandia.gov, g) bsalzbr@sandia.govAbstract. The intrinsic relation between structure and performance is a foundational tenant of most all materials science investigations. While the specific form of this relation is dictated by material system, processing route and performance metric of interest, it is widely agreed that appropriate characterization of a material allows for greater accuracy in understanding and/or predicting material response. However, in the context of additive manufacturing, prior models and expectations of material performance must be revisited as performance often diverges from traditional values, even among well explored material systems. This work utilizes micro-computed tomography to quantify porosity and lack of fusion defects in an additively manufactured stainless steel and relates these metrics to performance across a statistically significant population using high-throughput mechanical testing. The degree to which performance in additively manufactured stainless steel can and cannot be correlated to detectable porosity will be presented and suggestions for performing similar experiments will be provided.

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Microstructural characterization of additively manufactured multi-directional preforms and composites via X-ray micro-computed tomography

Cited by 55 publications

References 40 publications

A Review of Current Challenges and Case Study toward Optimizing Micro-Computed X-Ray Tomography of Carbon Fabric Composites

A Review of Current Challenges and Case Study toward Optimizing Micro-Computed X-Ray Tomography of Carbon Fabric Composites

Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests

Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

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