Dataset Information

Dataset of non-crimp fabric reinforced composites for an X-ray computer tomography aided engineering process.

ABSTRACT: This data in brief article describes a dataset used for an X-ray computer tomography aided engineering process consisting of X-ray computer tomography data and finite element models of non-crimp fabric glass fibre reinforced composites. Additional scanning electron microscope images are provided for the validation of the fibre volume fraction. The specimens consist of 4 layers of unidirectional bundles each supported by off-axis backing bundles with an average orientation on ±80° The finite element models, which were created solely on the image data, simulate the tensile stiffness of the samples. The data can be used as a benchmark dataset to apply different segmentation algorithms on the X-ray computer tomography data. It can be further used to run the models using different finite element solvers.

SUBMITTER: Auenhammer RM

PROVIDER: S-EPMC7689367 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Dataset of non-crimp fabric reinforced composites for an X-ray computer tomography aided engineering process.

Auenhammer Robert M RM Mikkelsen Lars P LP Asp Leif E LE Blinzler Brina J BJ

Data in brief 20201110

This data in brief article describes a dataset used for an X-ray computer tomography aided engineering process consisting of X-ray computer tomography data and finite element models of non-crimp fabric glass fibre reinforced composites. Additional scanning electron microscope images are provided for the validation of the fibre volume fraction. The specimens consist of 4 layers of unidirectional bundles each supported by off-axis backing bundles with an average orientation on ±80° The finite elem ...[more]

PMID: 33294516

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Project description:Ferrocement is a cost-effective construction material used in the low-cost constructions. It is produced with the combination of cement mortar with closely spaced wire mesh known as chicken wire mesh. Ferrocement process eliminates coarse aggregates when compared to reinforced concrete thus makes the process simple. This paper deals with the influence of various characteristics of warp knitted fabric on the flexural properties of ferrocement composites. Ferrocement composites have a wide range of applications in the construction industry and it has some limitations due to the durability issues. Among the various durability issues, corrosion is one of the main issues to be addressed to enhance the long-term service life of the ferrocement composites. The idea of using non-metallic mesh to eliminate the corrosion problem is discussed in this paper. In this experiment, warp knitted fabric reinforced ferrocement composites were produced using polypropylene warp knitted fabrics. This paper deals with the flexural properties of ferrocement composites made of warp knitted fabric coated with expoxy. This paper deals with the flexural properties of ferrocement composites made of warp knitted fabric coated with expoxy. These composites were analyzed for their flexural strength, energy absorption and ductile property. The variables in the experiment are filament thickness, warp knitted structure and number of layers in the composites. Experimental results proved that the replacement of chicken mesh wire by warp knitted fabrics has an impact in the flexural properties of the composites and the effect of variables in the experiment set up has been analyzed. There is an imporvement of 200% is observed in the first crack load and 120% improvement in the ultimate load of the warp knit fabric reinforced composite compared to control sample. Experimental results proved that there is an increase in flexural strength of ferrocement composites made up with warp knitted fabrics. Microstructure studies like SEM and EDX on ferrocement laminates confirmed good bonding between the mortar mix and warp knitted fabrics.

Project description:BackgroundIn India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation.ResultsWe generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08).ConclusionOur study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

Dataset Information

Dataset of non-crimp fabric reinforced composites for an X-ray computer tomography aided engineering process.

Publications

Dataset of non-crimp fabric reinforced composites for an X-ray computer tomography aided engineering process.

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