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77 results for “additive manufacturing”
Residual stress in 316L stainless steel benchmark additively manufactured arches determined by neutron diffraction and snychtron X-ray diffraction
<p>Residual stress data recorded as part of the EASI-STRESS project. </p> <p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon and ID15A at ESRF). The data is for residual stress in a benchmark 316L stainless steel arch manufactured by laser powder bed fusion using a MetalFAB1 additive manufacturing machine. The arch is square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for two lines is presented: line 1 runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction). Line 2 runs along to the x axis at a depth of 1 mm into the arch. </p> <p>The stress-free reference used was a reference comb cut from an identically made arch.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 ´ 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</p>
As built and post heat treatment residual stresses in 316L stainless steel additively manufactured benchmark arches
<p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon). The data is for residual stress in a benchmark 316L stainless steel arches manufactured by laser powder bed fusion using MetalFAB1 and EOS M290 additive manufacturing machines. These arches are square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for part in both an as built and after a 700 °C 2 hour heat treatment are presented. The measurement line runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction).</p> <p>The stress-free reference used was a reference comb cut from an identically made arches. For heat treated samples, heat treated combs were used.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 x 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</p>
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br> journal = "International Journal of Solids and Structures",<br> year = "2023",<br> volume = "283",<br> pages = "112470",<br> doi = "10.1016/j.ijsolstr.2023.112470",<br> author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: https://doi.org/10.1016/j.ijsolstr.2023.112470</li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: https://doi.org/10.1016/j.polymertesting.2022.107556 (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” http://dx.doi.org/10.5281/zenodo.6136935 (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Data from: Fatigue crack propagation in AA5083 structures additively manufactured via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Fatigue crack propagation in AA5083 structures additively manufactured via multi-layer friction surfacing"</p>
Tailoring magnetic hysteresis of Fe-Ni additive manufactured permalloy via multiphysics-multiscale simulations: Temperature-dependent parameters, thermodynamic database, results, and utilities
<p>This dataset contains temperature-dependent parameters and thermodynamic database, supplementary data and utilities of the publication "Tailoring magnetic hysteresis of additive manufactured Fe-Ni permalloy via multiphysics-multiscale simulations of process-property relationships" (<a href="http://doi.org/10.1038/s41524-023-01058-9">Yang et al., 2023</a>).</p> <p>We performed non-isothermal phase-field simulations of SLS process of the Fe<sub>21.5</sub>Ni<sub>78.5</sub> permalloy and subsequential mesoscopic thermo-elasto-plastic calculations and nanoscopic chemical order-disorder (<span>\(\gamma/\gamma'\)</span>) transition simulations as well as micromagnetic hysteresis calculations on nanostructures. Temperature-dependent parameters are employed. We then investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe<sub>21.5</sub>Ni<sub>78.5 </sub>on beam power and scan speed.</p> <p>This dataset contains:</p> <ul> <li><em>feni_cac.tdb</em>: Thermodynamic database of the Fe-Ni binary system based on <a href="https://doi.org/10.1016/j.intermet.2010.02.026">Cacciamani et al., 2010</a></li> <li><em>average_values.csv</em>: Average quantities for creating the contours in Fig. 6a, 6b, 7a, 7b, 8a, and Supp. Fig. 10a, 10b.</li> <li><em>mesostructures.zip</em>: Containing resampled mesostructures from SLS single scan simulations (final timestep) with associated temperature, stress, and strain evolution. Nodal values are explained in Table 1. Naming pattern is <ul> <li>SLS-TEP__<power>-<scan_speed>__.e</li> </ul> </li> <li><em>parameters.zip</em>: Containing temperature-dependent parameters for performing SLS simulations and thermo-elasto-plastic calculations with fine (1K) temperature increments. The same temperature-dependent parameters with coarse temperature increments are already listed as Supp. Table 1, 2.</li> <li><em>sampled_point_data.zip</em>: Containing mechanical quantities on sampled points and corresponding results of nanoscopic <span>\(\gamma'\)</span> phase fraction (<span>\(\Psi_{\gamma'}\)</span>) and magnetic coercivity <span>\(H_\mathrm{c}\)</span>. Naming pattern is <ul> <li>mech__<power>-<scan_speed>__.csv</li> <li>Psi__<power>-<scan_speed>__.csv</li> <li>Hc__<power>-<scan_speed>__.csv</li> </ul> </li> <li><em>utilities.zip</em>: Containing Python utilities to perform calculations of free energy density and related thermodynamic quantities, extracting parameters from <em>feni_cac.tdb. </em><br><strong>Notice: </strong><a href="https://pycalphad.org/docs/latest/">pyCALPHAD</a> (ver 0.8.4) is requested for performing the Python utilities.</li> </ul> <p>Table 1. Nodal values in an exodus file Nodal value name Symbol Meaning Unit T <span>\(T\)</span> Normalized Temperature by <span>\(T_\mathrm{M}\)</span> - c <span>\(\rho\)</span> Substance order parameter - pb <span>\(\xi\)</span> Fusion zone indicator - eps (eps_11, eps_12, eps_13, eps_22, eps_23, eps_33) <span>\({\varepsilon}\)</span> Strain - epsp (epsp_11, epsp_12, epsp_13, epsp_22, epsp_23, epsp_33) <span>\({\varepsilon}_\mathrm{pl}\)</span> Plastic Strain - peeq <span>\(p_\mathrm{e}\)</span> Accumulated plastic strain - sigma (sigma_11, sigma_12, sigma_13, sigma_22, sigma_23, sigma_33) <span>\({\sigma}\)</span> Stress MPa vonmises <span>\(\sigma_\mathrm{e}\)</span> von Mises stress MPa u (u_X, u_Y, u_Z) <span>\(\mathbf{u}\)</span> Displacement μm</p> <p> </p> <p><strong>Notice</strong>: The raw transient outputs are not cured in this dataset due to the vast file size. Please contact the authors to acquire related files/utilities.</p>
Dataset for Additive Manufacturing of Porous Biominerals
<p>This dataset contains results of rheology, SEM, polarized microscopic and X-ray tomography pictures,XRD results and mechanical testing. There are also results of EDX mapping and 2D scanning synchrotron XRD maps.</p>
Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing
<p>This repository maintains data and code associated with our accepted paper in IISE Transactions titled "Statistical and Dynamical model of Surface Morphology Evolution during Polishing in Additive Manufacturing". To briefly summarize,</p> <p><strong>1. Polishing_stagewise_data.zip</strong> - Contains height values measured at 32 different locations on the 3D printed sample using an optical profilometer prior to polishing (Stage 0) and post every stage of polishing (Stages 1 to 6). Please refer to the following paper for experimentation details and process parameters: "<em>Jin, S., A. Iquebal, S. Bukkapatnam, A. Gaynor, and Y. Ding (2019, 10). A gaussian process model-guided surface polishing process in additive manufacturing. Journal of Manufacturing Science and Engineering 142, 1–17.</em>"</p> <p><strong>2. Initial_surface_generation.m</strong> - Script containing the Initial surface generation algorithm using the random circle packing algorithm. This file generates the surface asperity distribution and their graph connectivity of a 3D printed sample prior to polishing (Figure 4(b) in paper). One such realization is stored and compared with experimental data (Refer #3).</p> <p><strong>3. Stage0_fitted_data.mat</strong> - .mat file containing data pertaining to height measures of the 3D printed sample prior to polishing and generated initial surface (simulation) which is statistically similar to the actual data.</p> <p><strong>4. Parameter_fitting_Polishing.m</strong> - Script containing the model capturing polishing dynamics with network formation, evaluated at each stage of polishing. This file generates the Bearing Area Curves of the initial surface simulated after each stage of polishing and compares with experimental data (Figures 3, 5, 6, 7 and 8 in paper). (The script makes use of other functions defined in #5).</p> <p><strong>5. surface_roughness.m, graph_evolution.m, solve_for_d.m, KLDiv.m</strong> and <strong>Gen_hurst.m</strong> - Matlab scripts containing functions that are called within the main script (Parameter_fitting_Polishing.m)</p> <p><strong>6. Simulated_Annealing.zip</strong> - Zip file containing files related to Simulated Annealing Algorithm. Please read the <strong>README_Simulated_Annealing.txt</strong> for instructions to reproduce the optimized parameter solutions.</p> <p><strong>7. pub_fig.m</strong> - Script containing the formatting options for plots and figures.</p>
Passive Morphological Adaptation for Obstacle Avoidance in a Self-Growing Robot Produced by Additive Manufacturing
<p>Dataset acquired for the obstacle negotiation experiments. The dataset collects the forces obtained by the growing robot when facing obstacles at different inclinations.</p> <p>You an find the related publication on https://doi.org/10.1089/soro.2019.0025</p>
Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts Data and Scripts
<p>The repository contains the data corresponding to the Paper "Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts".</p> <p>Random30, Random50, Random100, Similiar10, Similar30 and Similar50.zip contain the data sets (obj Files).</p> <p>R30_physical_images.zip and sim50_physical_images.zip contain the photos made from the physical components which are used for the evaluation.</p>
Dataset: The effect of a keyhole defect on strain localisation in an additive manufactured titanium alloy
<p><strong>This is the dataset used in the following publication: </strong></p> <div> <div> <div> <p>S. Cao, R. Thomas, A.D. Smith, P. Zhang, L. Meng, H. Liu, J. Guo, J. Donoghue, D. Lunt, The effect of a keyhole defect on strain localisation in an additive manufactured titanium alloy, Journal of Materials Research and Technology, https://doi.org/10.1016/j.jmrt.2024.11.237</p> </div> </div> </div> <p><strong>Contained in this dataset are:</strong></p> <p>A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC and EBSD data for two regions in an SLM Ti64 sample, one around a keyhole defect and one ~1mm away in the bulk.</p> <p>Please use the 'master' version of DefDAP: <a href="https://github.com/MechMicroMan/DefDAP/tree/51074e158b0131c69358ddf7eee319e41cf582ca">https://github.com/MechMicroMan/DefDAP/</a></p> <p><strong>Publication abstract:</strong></p> <p>The influence of a keyhole defect on local deformation behaviour in additive manufactured Ti-6Al-4V was investigated by comparing it to a representative bulk region without a defect. High resolution digital image correlation (HRDIC) was used to measure the differences in strain localisation at the microstructural length-scale. A nanoscale speckle pattern was used to allow small changes in strain to be detected and resolved within a single individual lamella and at pre-existing crack locations around the defect. Strain localisation was observed around the defect and formed well below the macroscopic yield stress. In contrast, minimal deformation was found in the bulk at this stress level. Following further deformation into the plastic regime, the strain localisation around the keyhole became more heterogenous with a distinct strain field. A large amount of strain localisation and <c+a> slip was observed either side of the defect normal to the loading direction compared to relatively little in the regions close to the defect in line with the loading direction. This HRDIC observation was consistent with finite element analysis of the expected strain fields around the defect both below and above the yield point. Furthermore, micro-cracks were observed in αp/αp and αp/βt interfaces in both regions with the more pronounced strain fields around the defect leading to an increased number of long micro-cracks than in the bulk. The formation mechanisms of micro-cracks have been discussed, emphasising the role of localised strain caused by the defect.</p> <p> </p>
Dataset for paper entitled, 'Tailoring equiaxed β-grain structures in Ti-6Al-4V coaxial electron beam wire additive manufacturing'
<p>Dataset for paper entitled, 'Tailoring equiaxed β-grain structures in Ti-6Al-4V coaxial electron beam wire additive manufacturing'. Abstract: High-deposition-rate, directed-energy-deposition additive manufacturing (DED-AM) processes typically produce Ti-6Al-4V (Ti64) components with coarse columnar β-grain structures that lead to undesirable mechanical anisotropy, as well as a fine heterogeneous lamellar transformation microstructure, which is very different to that seen standard wrought products. This arises because of the intrinsic lack of constitutional undercooling at the solidification front, and the subsequent high cooling rates and rapid thermal cycling experienced by the deposited material. In this work, the more refined primary β-grain solidification structures and textures seen in components built with the novel coaxial electron beam wire DED AM (CEWAM) process have been characterised in detail, for the first time, with the aim of investigating the potential for this technology to directly replicate the β-annealed damage-tolerant microstructure used in large Ti64 aerospace forgings. Due to its different lower energy density solidification conditions, it has been confirmed, by electron backscatter diffraction (EBSD) analysis and β-grain reconstruction in three orthogonal cross-sections, that the CEWAM process changes the melt conditions to promote β-grain nucleation ahead of the solidification front, which can result in a highly refined, equiaxed, β-grain structure. However, the conditions for refinement were marginal and a mixed grain structure was commonly observed in thicker sections. Additionally, the subsequent grain-growth stability during β-annealing was investigated. It is shown that an equivalent microstructure can be achieved to that seen in a standard β-forged component, by grain structure homogenisation and slow cooling through the β transus, to promote α colony nucleation, allowing direct part substitution. This was made possible by the refined primary β-grain structure achieved during deposition with the CEWAM solidification conditions which, importantly, are also shown to lead to a weaker texture than in a typical forging.</p> <p>Paper doi: https://doi.org/10.1016/j.mtla.2021.101202</p>
Dataset for paper entitled, 'Isomorphic grain inoculation in Ti-6Al-4V during additive manufacturing'
<p>Dataset for paper entitled, 'Isomorphic grain inoculation in Ti-6Al-4V during additive manufacturing'. Abstract: The potential for using isomorphic inoculation (ISI) to grain refine titanium alloys in additive manufacturing was investigated by adding TiAlNb particles to Ti-64 during building test samples. A surviving particle was identified and its crystallographic relationship with the matrix studied by transmission Kikuchi diffraction. The particle and bulk matrix grain were shown to have the same crystallographic orientation, demonstrating that the ISI mechanism of solidification bypasses the nucleation step in favour of direct epitaxial growth.</p> <p>Paper doi: https://doi.org/10.1016/j.mlblux.2020.100057</p>
Dataset for paper entitled, 'The potential for grain refinement of Wire-Arc Additive Manufactured (WAAM) Ti-6Al-4V by ZrN and TiN inoculation'
<p>Dataset for paper entitled, 'The potential for grain refinement of Wire-Arc Additive Manufactured (WAAM) Ti-6Al-4V by ZrN and TiN inoculation'. Abstract: Wire-Arc Additive Manufacturing (WAAM) of large near-net-shape titanium components has the potential to reduce costs and lead-time in many industrial sectors including aerospace. However, with titanium alloys, such as Ti-6Al-4V, standard WAAM processing conditions result in solidification microstructures comprising large cm- scale, <001> fibre textured, columnar β grains, which are detrimental to mechanical performance. In order to reduce the size of the solidified β-grains, as well as refine their columnar morphology and randomise their texture, two cubic nitride phases, TiN and ZrN were investigated as potential grain refining inoculants. To avoid the cost of manufacturing new wire, experimental trials were performed using powder adhered to the surface of the deposited tracks. With TiN particle additions, the β grain size was successfully reduced and modified from columnar to equiaxed grains, with an average size of 300 µm, while ZrN powder was shown to be ineffective at low addition levels studied. Clusters of TiN particles were found to be responsible for nucleating multiple β Ti grains. By utilizing the Burgers orientation relationship, EBSD investigation showed that a Kurdjumov-Sachs orientation relationship could be demonstrated between the refined primary β grains and TiN particles.</p> <p>Paper doi: https://doi.org/10.1016/j.addma.2021.101928</p>
Dataset for paper entitled 'Microstructure transition gradients in titanium dissimilar alloy (Ti-5Al-5V-5Mo-3Cr/Ti-6Al-4V) tailored wire-arc additively manufactured components'
<p>Dataset for paper entitled 'Microstructure transition gradients in titanium dissimilar alloy (Ti-5Al-5V-5Mo-3Cr/Ti-6Al-4V) tailored wire-arc additively manufactured components'. doi: <a href="https://doi.org/10.1016/j.matchar.2021.111577">https://doi.org/10.1016/j.matchar.2021.111577</a></p>
A case study of the dimensional effects in an additive manufacturing process with multiple operations
<p>This dataset provides a case study for the dimension changes of the prints in an additive manufacturing process with multiple operations. The operations are Stereolithography (SLA) printing and its two post-processing processes (washing and post-curing). The dataset contains the measured dimensions of the prints after each operation, which allows the modeling of each operation individually.<br> </p>
Wear resistance of an additively manufactured high-carbon martensitic stainless steel
<p>Dataset supporting the study:</p> <p>'Wear resistance of an additively manufactured high-carbon martensitic stainless steel', E. Iakovakis et al., 2022</p> <p>The dataset contains the hardness, the friction values, the x,y,z coordinates from profilometry measurements for the wear rate calculation and the generation of the surface profile map of the track and the hardness data to generate the cross-sectional hardness map of the wear tracks of an additive manufactured (EBM-processed) high-carbon martensitic stainless steel (Vibenite®350). Most specifically, it incudes the following:</p> <ul> <li>Hardness measurement file (Hardness vibenite350 HV5.csv): The .csv file reports the hardness for 30 measurements.</li> <li>Reciprocating tests files (Vibenite 350 cof1/2/3 alumina 3N/10N.csv): The .csv files report the testing parameters and the friction forces of the reciprocating dry wear tests.</li> <li>Profilometry measurements files (Vibenite 350 profilometry1/2/3 3N/10N.dat and Vibenite 350 profilometry data map_5umscan at 3N/10N): The .dat files report the x,y,z coordinates of the wear track used for the calculation of the Vibenite®350 and for the generation of the surface profile maps.</li> <li>Hardness data in the cross-section of the wear track files (Vibenite 350 hardness data map at 3N/10N.spe): The .spe files report the hardness values measured in the cross-section of the wear track and used to generate the hardness maps.</li> <li>SEM-EDX analysis of the wear track at 10N</li> </ul> <p>The dataset also includes tribological data for an additive manufactured (EBM-processed) high carbon martensitic tool steel (Vibenite®150) which is used to compare the wear rate data of the EBM-processed martensitic stainless steel. Most specifically, it incudes the following:</p> <ul> <li>Reciprocating tests files (Vibenite 150 cof1/2/3 alumina 3N/10N.csv): The .csv files report the testing parameters and the friction forces of the reciprocating dry wear tests (CoF-sliding distance plot included).</li> <li>Profilometry measurements files (Vibenite 150 profilometry1/2/3 3N/10N.dat ): The .dat files report the x,y,z coordinates of the wear track used for the calculation of the wear rate for Vibenite®150 and for the generation of the surface profile maps (included maps for 3 N and 10N).</li> </ul>
Datasets and Code for "A Gaussian process model-guided surface polishing process in additive manufacturing"
<p>These are the datasets and computer code for reproducing the results in Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol. 142(1), pp. 011003.1–011003.12.</p>
Data for 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'
<p>All data supporting the paper 'In-Situ EBSD Study of Austenitisation in a Wire-Arc Additively Manufactured High-Strength Steel'. Includes gifs (movies) of the high temperature in-situ EBSD experiments and the scripts used for analysis.</p>
Backscatter tuned laser absorption spectroscopy in additive manufacturing
<p>Raw data accompanying our paper </p> <p><strong>Backscatter absorption spectroscopy for process monitoring in powder bed fusion</strong></p> <p> </p>
Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing
<p>The Excel file is structured to provide a comprehensive overview of the design of experiments (DoE) for the research project titled <em>"Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing"</em>, conducted under the BALSAM project. This research specifically contributes to deliverables D1.2 and D3.1.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.