The dataset presents detailed results in images for all 1600 composite structures from the testing set. These images display stress-strain curves from phase-field simulations (ground truth) and model-predicted stress-strain curves, along with their corresponding composite structural arrangements and MAEs.

Virtual uniaxial tensile test coupons were generated with stochastic Prepreg Platelet Molded Composite (PPMC) mesostructure. Progressive Failure Analysis was performed on Abaqus Standard using Continuum Damage Mechanics (CDM) and Cohesive Zone Modelling (CZM) methods.

The stochastic mesostructure information of each sample was processed such that the explicitly represented platelet geometry and fiber orientations from the Finite Element (FE) model were reduced to compact mesostructure descriptors in the form of in-plane distributions of second-order fiber orientation tensor components a11 and a12. The layerwise a11 and a12 distributions (high-resolution mesostructure descriptors) were further reduced to coarse mesostructure descriptors by locally averaging the a11 and a12 values through the thickness at each voxel location across the length and width of a coupon.

The macroscopic (effective) stress-strain data for each coupon were also preprocessed to include a) the strain at peak stress, b) terminal strain (corresponding to the simulation cut-off point at 10% load drop) and c) 40 stress values at prescribed fractions of the previously mentioned reference strains a and b.

Both the preprocessed (normalized) and raw (non-normalized) stress-strain data, as well as coarse and high-resolution mesostructure descriptors for 3400 unique virtual PPMC tensile test samples are included in the attachments. An accompanying dataset guide spreadsheet documents the file-naming convention, pre-processing applied on the raw data, material properties of each sample and dataset-wide summary statistics. An example python script is also provided to visualize the inputs (fiber orientation distribution at low and high resolution) and outputs (stress-strain curves) for individual samples.

Research Hypothesis: This dataset enables training of data-driven surrogate models to learn mappings between fiber orientation distributions and stress–strain response of PPMCs. In a related study by the authors (to be linked upon publication), a deep learning–based surrogate model trained on this dataset demonstrated that coarse mesostructural descriptors in the form of through-thickness–averaged fiber orientations can serve as effective structural representations for multiscale analysis of stress–strain response in PPMCs. Using only these coarse descriptors, tensile stiffness was predicted with a mean absolute percentage error (MAPE) below 3% while tensile strength and failure strain were predicted with a MAPE below 10%. These results highlight the potential of reduced-order structural descriptors to lower experimental characterization and computational modeling requirements for materials with complex, spatially heterogeneous subscale morphology.

Abstract A fragmentation model based on global load sharing (GLS) theory is developed to obtain stress-strain curves that describe the mechanical behavior of unidirectional composites. The model is named C N B + τ * because it is based on the Critical Number of Breaks model (CNB) and on the correction of the fiber matrix interfacial strength, τ *. Model allows both obtaining the ultimate tensile strength of CFRP and GFRP composites, and correcting the σ vs ε curve to match its peak point with the predicted strength, which is more accurate than the one obtained by previous GLS-based models. Our model is used to classify the mechanical response of the material according to the energetic contributions of two phenomena up to the failure: intact fibers (IF) and fragmentation (FM). Additionally, the influence of fiber content, V f, on the tensile strength, σ U, failure strain, ε U, and total strain energy, U T, is analyzed by means of novel mechanical-performance maps obtained by the model. The maps show a dissimilar behavior of σ U, ε U and U T with V f between GFRP and CFRP composites. The low influence of V f on the percent energetic contributions of IF and FM zones, as well as the larger energetic contribution of the FM zone, are common conclusions that can be addressed for both kinds of composites.

Stress-strain curves of uniaxial tension test subjected to quasi-static axial loads of RTV-2 material. Please refer to "E-Skin Development and Prototyping via Soft Tooling and Composites with Silicone Rubber and Carbon Nanotubes" on Materials (MDPI) for details.

The experimental samples are flame-retardant carbon fiber reinforced polymer (CFRP) materials used for lithium-ion battery enclosures and hydrogen storage tanks. The dataset contains stress–strain data for 22 different raw material formulations, with multiple experimental trials conducted for each formulation. The data files follow the naming format “a_b_200_shifted”, where a denotes the formulation number (with the corresponding mapping provided in a separate file) and b represents the trial index for that specific formulation.

A comprehensive dataset of the material data of unidirectional carbon fiber reinforced composites and their consitutents, namely the fiber and the matrix. The dataset includes text files acquired through mechanical testing, Python files for calculations, post processing and plotting, Excel files of results and a SEM (scanning electron microscopy) image of the composite sample.

For the material chatacterization data and methods, a journal publication is prepared.

Part of the data analysis included in this repository has been done on X-ray CT (computed tomography) scans of three samples (A, B and C) of the same unidirectional carbon fiber reinforced composites. Three pultruded profiles were used in this dataset, and each X-ray scanned sample is cut from one of these profiles. The base material of the profiles was the same, but the pultrusion process parameters were varied.

Please visit the following links for accessing these data:

sample A: https://doi.org/10.5281/zenodo.18364215

sample B: https://doi.org/10.5281/zenodo.18370051

sample C: https://doi.org/10.5281/zenodo.18364338

The first word of the files (composite, fiber or matrix) indicate the material they refer to.

For the composite material:

The X-ray scans are in h5 format. To extract them in tiff images, please use the script: composite_h5_file_to_tiff.py

For the optional image equalization of the tiff images, please use the script: composite_tiff_images_equalization.py

For the cropping of the tiff images, please use the script: composite_tiff_images_crop.py

For the changing the tiff images into a nifti volume format, please use the script: composite_tiff_images_to_nifti.py

For the determination of the fiber orientation through the structure tensor analysis, please use the script: composite_STanalysis.py (along with the M1_TomoHandling.py and M2_Alignment.py). The code was developed by Ole V. Ferguson and was later modified by Pinelopi Mageira to enable the extraction and storage of the fiber orientation data in an NPZ (numpy.savez) format.
The original implementation is available at:
https://gitlab.windenergy.dtu.dk/olen/structure-tensor-3d-fe-mapping/-/tree/79dff869ea6825748861018c90c4fc1900ca80fd
Users of this code should cite the following work:
O. V. Ferguson and L. P. Mikkelsen. Three-dimensional finite element modeling of anisotropic materials using X-ray computed micro-tomography data[Formula presented]. Software Impacts, vol. 17, 2023, doi: 10.1016/j.simpa.2023.100523.
The structure tensor analysis scripts are under the MIT license.

Please remember to adjust the file and folder names in the scripts above according to the sample you are examining.

composite_sem_grey_denoised_resampled.tiff: The SEM image of the composite sample. For the analysis scripts of the SEM image, please visit http://doi.org/10.5281/zenodo.7537379 . In case of usage, please give reference to:
Mikkelsen, L.P.; Fæster, S.; Dahl, V.A. Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites. Data Brief 2023, 48. doi: 10.1016/j.dib.2023.109058.
The SEM image was captured by Wenbo Sui.

composite_data_fiber_denity.xlsx: Excel file containing the results of the fiber density calculation, obtained using the burn-off technique on composite samples. It includes the fiber and matrix volume and weight contents, as well as the porosity volume content and other experimentally obtained quantities.

composite_JO*.txt: Text files that include the results of the compression testing of composite samples. Three groups of samples were tested (JOAC1, JOBC1 and JOCC1), with each group cut from one of the three pultruded profiles that the X-ray CT scanned samples were scanned from. This means that group JOAC1 corresponded to X-ray scanned sample A, JOBC1 to sample B and JOCC1 to sample C.

composite_graph_stress_strain.py: Python script that uses as input the composite_JO*.txt files to plot the compressive stress-strain behaviour of the tested samples.

composite_graph_fiber_orientation_distribution_histograms.py: Python script that uses as input a npz file (created by the composite_structure_tensor.py script and containing the fiber angles), calculates the mean elevation angle and the mean absolute elevation angle and plots the fiber orientation as a histogram. There is the option of two npz files serving as input, and subsequently two histograms plotted on the same figure, for comparative purposes.

For the fibers:

fiber_*mm_gauge_length_data_results.xlsx: Three Excel files including the results of the single fiber testing, without compliance. Each file corresponds to one gauge length (30mm, 50mm and 70 mm). The first sheet of each file is titled 'Results', with quantities such as max stress and fiber diameter, for all the tested single fibers. The rest of the sheets correspond to each single fiber tested, and describe the tensile curves. These tests on different gauge length were carried out for the determination of the single fiber testing machine.

fiber_50mm_gauge_length_data_results_with_compliance: The Excel file of the results of the single fibers whose gauge length was 50mm, with the compliance of the machine considered on the calculations.

fiber_apparent_fiber_modulus_gauge_length.py: Python script for plotting the graph of the reciprocal of the apparent fiber modulus (1 / E*) compared to the reciprocal of each fiber gauge length examined.

fiber_graph_diameter_histogram.py: Python script for plotting the fiber diameter histogram.

fiber_graph_stress_strain_50mm.py: Python script for plotting the individual stress-strain curves of the 50mm gauge length single fiber data.

fiber_graph_tension_compression_stress_strain.py: Pyhton script for the back-calculation of the compressive stress-strain curves of the single fibers. Inputs from the composite and the matrix are needed for applying the rule of mixtures.

fiber_graph_fitted_stress_strain_all_calculation_mean_median_stress.py: Python script for calculating and plotting the fitting stress-strain curves, with each fit corresponding to each gauge length's single fiber data. It also calculates the mean and median failure stresses for each gauge length data.

fiber_tangent_modulus.py: Python script for calculating and ploting the tangent modulus, both regarding the tension (from experimental data) and the compression (from the back-calculated data).

fiber_graph_weibull.py: Python script for the Weibull plot of the failure stress of the single carbon fibers tested, for each examined gauge length.

fiber_calculation_weibull_failure_probability.py: Python script for the calculation of the fibers failure probability. It uses as input the fiber_*mm_gauge_length_data_results.xlsx files, and exports an Excel file with the results, named as fiber_weibull_failure_probability.xlsx, with each sheet corresponding to a gauge length.

fiber_graph_failure_stress_gauge_length.py: Python script for plotting the characteristic, median and mean failure stress of the single carbon fibers for each gauge length.

fiber_table_weibull_results.xlsx: Excel file with the Weibull parameters calculated from the above scripts gathered.

For the matrix:

matrix_KEB*_tension.sta.txt: Text files that include the results of the tensile testing of matrix samples.

matrix_KEA*_shear.sta.txt: Text files that include the results of the shear testing of matrix samples.

matrix_graph_tensile_stress_strain.py: Python script that uses as input the matrix_KEB*_tension.sta.txt files to plot the tensile stress-strain behaviour of the tested matrix samples.

matrix_graph_shear_stress_strain.py: Python script that uses as input the matrix_KEA*_shear.sta.txt files to plot the shear stress-strain behaviour of the tested matrix samples.

matrix_graph_von_mises: Python script for the calculation and plotting of the matrix von Mises stress. It uses as input the matrix_KE*.txt files.

matrix_data_stress_strain_for_ramberg_osgood.xlsx: The Excel that serves as input for the calculation of the Ramberg Osgood material model parameters. It includes the values of the strain and the stress, extracted from the tensile stress-strain results.

matrix_calculation_curve_fitting_ramberg_osgood.py: Python script for the calculation of the Ramberg Osgood parameters. The matrix_data_stress_strain_for_ramberg_osgood.xlsx serves as input.

This dataset has been used in the following publications:

RTV-2-based nanocomposite filled with different concentrations of SWCNTs. Please review the publication "E-Skin Development and Prototyping via Soft Tooling and Composites with Silicone Rubber and Carbon Nanotubes" on Materials (MDPI) for further details

This data set contains the finite element generated data necessary to validate the generalized stress-strain curves. It supports the paper: Generalized stress-strain curves for IBII tests on isotropic and orthotropic materials F. Pierron, L. Fletcher Journal of the Dynamic Behaviour of Materials, 2019 DOI: 10.1007/s40870-019-00197-9

explaining Stress vs. strain behavior of carbon composite with a nano mat of PAN-derived carbon fiber at the top of assembly.

This dataset contains results of tensile (tension) testing performed on epoxy resin specimens reinforced with oil shale ash (OSA), which is an industrial by-product explored as a sustainable additive for improving concrete performance and reducing environmental impact. The tests were conducted to investigate the effect of OSA addition on the stress-strain behavior of the composite material. The data include measured strain and corresponding stress values (in Pa) for a particular material composition and sample number given below. Epoxy Resin: 0% Oil Shale Ash (by weight) - Material 1 Samples 1-3 Epoxy Resin: 10% Oil Shale Ash (by weight) - Material 2 Samples 1-7 Epoxy Resin: 20% Oil Shale Ash (by weight) - Material 3 Samples 1-7 Epoxy Resin: 30% Oil Shale Ash (by weight) - Material 4 Samples 1-7 Epoxy Resin: 40% Oil Shale Ash (by weight) - Material 5 Samples 1-7 Epoxy Resin: 50% Oil Shale Ash (by weight) - Material 6 Samples 1-4

Abstract For studying the stress-strain state at singular points and their neighborhoods new concept is proposed. A singular point is identified with an elementary volume that has a characteristic size of the real body representative volume. This makes it possible to set and study the restrictions at that point. It is shown that problems with singular points turn out to be ambiguous, their formulation depends on the combination of the material and geometric parameters of the investigated body. Number of constraints in a singular point is redundant compared to the usual point of the boundary (it makes singular point unique, exclusive). This circumstance determines the non-classical problem formulation for bodies containing singular points. The formulation of a non-classical problem is given, the uniqueness of its solution is proved (under the condition of existence), the algorithm of the iterative-analytical decision method is described. Restrictions on the state parameters at the composite wedge vertex, one generatrix of which is in non-friction contact with a rigid surface are studied under temperature and strength loading. The proposed approach allows to identify critical combinations of material and geometric parameters that define the singularity of stress and strain fields close to singular representative volumes. The constraints on load components needed to solution existence are established. An example of a numerical analysis of the state parameters at the wedge vertex and its neighborhood is considered. Solutions built on the basis of a new concept, directly in a singular point, and its small neighborhood differ significantly from the solutions made with asymptotic methods. Beyond a small neighborhood of a singular point the solutions obtained on the basis of different concepts coincide.

Tensile test raw data.Tensile test was done to examine the effect of nanoclay (with different processing: NEC or EC) on the mechanical properties of Nylon 12.The attached data are for Neat Nylon12, 3%NEC+Nylon12, 3�+Nylon12, 5%NEC+Nylon12, and 5�+Nylon12. It includes: (Force, displacement) and (stress-strain) raw data. The conditions for my data are always normal (room temperature).

Raw data regarding stress/strain and conductivity performance. (ZIP)

This is the dataset for the research paper "Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator"

This dataset contains results of compressive strength tests conducted on several concrete mixtures reinforced with oil shale ash (OSA), which is an industrial by-product explored as a sustainable additive for improving concrete performance and reducing environmental impact. The data include averaged compression strength values (in MPa) for different material variants tested under controlled laboratory conditions at Riga Technical University. Each entry represents a test result for a particular material composition and sample number given below. Concrete A: 0% Oil Shale Ash (by weight) - Material 1 Sample 1-8 Concrete A: 25% Oil Shale Ash (by weight) - Material 2 Sample 1-4 Concrete A: 30% Oil Shale Ash (by weight) - Material 3 Sample 1-3 Concrete B: 0% Oil Shale Ash (by weight) - Material 4 Sample 1-4 Concrete B: 10% Oil Shale Ash (by weight) - Material 5 Sample 1-5 Concrete B: 25% Oil Shale Ash (by weight) - Material 6 Sample 1-4 Concrete B: 30% Oil Shale Ash (by weight) - Material 7 Sample 1-5 Concrete B: 35% Oil Shale Ash (by weight) - Material 8 Sample 1-3 Concrete C: 0% Oil Shale Ash (by weight) - Material 9 Sample 1-6 Concrete C: 10% Oil Shale Ash (by weight) - Material 10 Sample 1-7 Concrete C: 15% Oil Shale Ash (by weight) - Material 11 Sample 1-7 Concrete C: 20% Oil Shale Ash (by weight) - Material 12 Sample 1-7 Concrete C: 25% Oil Shale Ash (by weight) - Material 13 Sample 1-7 Concrete C: 30% Oil Shale Ash (by weight) - Material 14 Sample 1-7 Concrete C: 35% Oil Shale Ash (by weight) - Material 15 Sample 1-7

Due to the high axial Young's moduli as well as high aspect ratio, it follows those CNTs, irrespective of whether they are multi or single-walled nanotubes exhibit potential, excellent mechanical reinforcing fillers in polymer composites. Shows the stress value around 342 MPa with R2 is equal around 0.9 versus maximum strain value is 0.85 mm.

To systematically investigate the aperture effect in MI-SiCf/SiC composites, open-hole tensile (OHT) specimens (labeled K1–K5) with central hole diameters (D) of 1 mm, 2 mm, 3 mm, 6 mm, and 9 mm were designed. The OHT specimens were of straight strip shape, with a uniform width (W) of 18 mm (i.e., W/D ranging from 18 to 2) and a length (L) of 68 mm. To protect the gripping area and ensure effective clamping by the wedge grips of the testing machine, glass‑fiber‑reinforced composite tabs were bonded at both ends of each specimen. The tabs had a length of 20 mm and a thickness (t) of 1.5 mm. Additionally, an unnotched standard tensile specimen (labeled K0) was prepared to obtain the intrinsic material properties. All specimens were fabricated by laser cutting from composite plates.Open‑hole tensile tests were conducted under ambient conditions of room temperature (23±2 °C) and relative humidity (50±5)%, using an Instron 5982 universal testing machine (50 kN capacity, load accuracy ±1% FS). Specimens were clamped with hydraulic flat‑action grips. Deformation measurements combined a contact extensometer (Instron 3442AVG, 10 mm gauge length, mounted on the specimen side) with a full‑field digital image correlation (DIC) system (VIC‑3D, single‑camera 2D mode). Prior to testing, the specimen gauge section was spray‑coated with matte paint to create a random speckle pattern for DIC analysis. The DIC acquisition frequency was set to 5 Hz.To monitor damage initiation and evolution in real time, an acoustic emission (AE) monitoring system (2CHS PCI‑2) was employed. AE sensors were fixed with silicone grease couplant near the central hole on the back side of the specimen. Acquisition parameters were set to a gain of 40 dB and a sampling rate of 1 MHz. Damage onset was defined as the point where the AE event rate consistently exceeded 50 events/s.The testing procedure was as follows: after specimen installation, a pre‑load of 10 N was applied to zero the DIC and AE systems. Subsequently, under displacement‑controlled loading at a rate of 0.2 mm/min, the test machine’s load‑displacement signal, extensometer strain data, DIC images, and AE parameters (event count, energy, peak frequency, etc.) were synchronously recorded. Loading continued until the load dropped to 80% of the peak load.Fig. 4(a) and (b) compare the nominal open‑hole tensile stress–strain curves and net‑section tensile stress–strain curves, respectively, for the unnotched (K0) specimen and the open‑hole specimens with different hole diameters (K1–K5). The strain data were all obtained from extensometer measurements.Fig. 6(a) illustrates the influence of hole diameter (D) on the open‑hole tensile strength (SOHT) and the net‑section strength (SNS). Fig. 6(b) shows the effect of hole diameter on tensile modulus parameters.Fig. 7 presents the evolution curves of AE peak frequency versus normalized cumulative energy for the unnotched specimen. Fig. S2–S6 display the AE peak frequency versus normalized cumulative energy curves analyzed using the same method for the five open‑hole specimens with different hole diameters.Fig. 10(c)–14(c) show the distribution of S11 along the line connecting the hole edge to the specimen edge, as obtained from finite element simulations.Fig. S7 shows the strain variation along the line from the hole edge to the specimen edge for the five open‑hole specimens (K1–K5) at different stress levels, extracted from DIC strain contour maps.

Stress and strain data for both pure carbon fibre layer-to-layer 3D woven composite and a fibre-hybrid carbon fibre and polypropylene layer-to-layer 3D woven composite for both warp and weft samples. The data was obtained following ASTM D3039 and used to obtain values of Young's Modulus, tensile strength and maximum tensile strain for both materials in the warp and weft direction.

In this research, we bridge theory and experiment by developing an integrated framework that couples in situ chemical strain measurements with mechanics modeling to quantify stress and strain energy evolution during ion insertion/extraction.

In our study, we investigated the effects of the silver-coated graphene (Ag@GNS) content on the microstructural and mechanical properties of Ti-13Nb-13Zr (TC26) based composites and explored their fracture mechanisms. The results showed that with an increase in Ag@GNS content, the in situ-generated TiC aggregated in the grain boundaries, which led to a decrease in α grain size and an increase in the dislocation density. Meanwhile, the density, 0.2% yield strength and ultimate tensile strength exhibited an initial increase followed by a decrease. When the Ag@GNS content achieved 0.5 wt.%, the TC26 based composites demonstrated the best mechanical performance with the hardness of 387.87 HV0.1, 0.2% yield strength of 950.54 MPa and tensile strength of 1169.46 MPa, respectively, where the elongation maintained 6.49%. Moreover, the elastic modulus of 0.5Ag@GNS/TC26 composite was 28.30 GPa, which meets the requirements of the elastic modulus of human implants. The tensile strength of the composites was affected by the contents of the reinforcing phases, TiC and Ti3Ag at the interface. Both excessive and insufficient reinforcing phases deteriorate tensile strength. Therefore, these uploaded figures come from the results of above study. Figure 1 shows the Schematic diagram of Ag@GNS/TC26 composites prepared by SLM. Figure 2 shows the SEM images of titanium matrix composite powders with different Ag@GNS contents. (a) 0.3 wt.% Ag@GNS, (b) 0.5 wt.% Ag@GNS, (c) 0.7 wt.% Ag@GNS, (d) 0.9 wt.% Ag@GNSFigure 3 shows the XRD patterns of titanium matrix composites with different Ag@GNS contents. (a) XRD patterns of titanium matrix composites with different Ag@GNS contents, (b) Enlarged view of areas 37°-42°Figure 4 shows the OM images of titanium matrix composites with different Ag@GNS contents. (a) 0.3Ag@GNS/TC26 composite, (b) 0.5Ag@GNS/TC26 composite, (c) 0.7Ag@GNS/TC26 composite, (d) 0.9Ag@GNS/TC26 compositeFigure 5 shows the SEM images of titanium matrix composites with different Ag@GNS content. (a-c) 0.3Ag@GNS/TC26 composite, (d-f) 0.5Ag@GNS/TC26 composite, (g-i) 0.7Ag@GNS/TC26 composite, (j-l) 0.9Ag@GNS/TC26 compositeFigure 6 shows the Measured density of titanium matrix composites with different Ag@GNS contents.Figure 7 shows the TEM, HRTEM and GPA images of 0.5Ag@GNS/TC26 composite. (a) Bright-field TEM image of 0.5Ag@GNS/TC26 composite, (b) Enlarged view of the yellow box in Fig. 7(a), (c) HRTEM image in yellow box in Fig. 7(b), (d) corresponding GPA image in Fig. 7(c).Figure 8 shows the Contrast, inverse polarity plots and grain size histograms of titanium matrix composites with different Ag@GNS contents. (a1-a3) 0.3Ag@GNS/TC26 composite, (b1-b3) 0.5Ag@GNS/TC26 composite, (c1-c3) 0.7Ag@GNS/TC26 composite, (d1-d3) 0.9Ag@GNS/TC26 composite.Figure 9 shows the KAM diagrams of titanium matrix composites with different Ag@GNS contents. (a1, a2) 0.3Ag@GNS/TC26 composite, (b1, b2) 0.5Ag@GNS/TC26 composite, (c1, c2) 0.7Ag@GNS/TC26 composite, (d1, d2) 0.9Ag@GNS/TC26 compositeFigure 10 shows the High-angle and low-angle grain boundaries distribution of titanium matrix composites with different Ag@GNS content. (a) 0.3Ag@GNS/TC26 composite, (b) 0.5Ag@GNS/TC26 composite, (c) 0.7Ag@GNS/TC26 composite, (d) 0.9 Ag@GNS/TC26 compositeFigure 11 shows the Parametric properties of composites. (a) hardness diagram, (b) stress-strain curve diagram, (c) reported properties of titanium matrix composites [9, 34, 43-47]Figure 12 shows the Fracture morphology of titanium matrix composites with different Ag@GNS contents. (a) 0.3Ag@GNS/TC26 composite, (b) 0.5Ag@GNS/TC26 composite, (c) 0.7Ag@GNS/TC26 composite, (d) 0.9 Ag@GNS/TC26 compositeFigure 13 shows the Schematic diagram of the tensile process of titanium matrix composites with different Ag@GNS content.