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77 results for “additive manufacturing”
Supplementary Data of the Manuscript Titled: Tunable Mechanical Properties of Thermoplastic Foams Produced by Additive Manufacturing
<p>This document presents the supplementary data of the manuscript titled "Tunable Mechanical Properties of Thermoplastic Foams Produced by Additive Manufacturing". At the moment of uploading this document to Zenodo, the main manuscript is still under review in Express Polymer Letters. As the journal format does not allow supplementary documents, we chose to provide the material here.</p>
Controlling grain structure in metallic additive manufacturing using a simple, inexpensive process control system
<p>Raw data associated with a paper submission.<br> "Hardness Variation in Inconel 718 Produced by Laser Directed Energy Deposition" submitted to Nature Communications.</p> <p>Contained are all the raw data used in the analysis, as well as csv's of any data plotted in graphs.</p> <p>Raw coaxial images captured during printing of various processing parameters<br> EBSD scans (.crc and .oip) of all disucssed samples, as well as matlab (MTEX) files of the EBSD map subsets<br> Raw Hardness Data<br> Position/Power logs from the build</p> <p>Samples built were:<br> Control samples - 3, 6, 10 hatches wide, with No control, Power control and Velocity control<br> Varying Thickness samples - 1, 2, 3, 4, 6, 8 hatches wide, all with constant parameters</p>
Bruker NMR data set for journal article: 3.4. Understanding the Microstructure Connectivity in Photopolymerizable Aluminum-Phosphate-Silicate Sol−Gel Hybrid Materials for Additive Manufacturing
<p>Solid state fast MAS 1H data for hybrid polymerizable compounds. </p>
Marginal Bone Level Around Titanium Implants Produced by Additive Manufacture
ClinicalTrials.gov study NCT05627037. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of CAD/CAM Onlays Fabricated by Subtractive Versus Additive Digital Manufacturing Techniques.
ClinicalTrials.gov study NCT05943782. IPD Sharing: NO. Countries: 0. Publications: 0.
Evaluation of Clinical Wear and Surface Roughness of Partial Restorations Produced by Additive and Subtractive Manufacturing Methods: A Split-Mouth Randomized Controlled Clinical Trial
ClinicalTrials.gov study NCT07057401. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparative Study Between Injection Molding and Additive Manufacturing Complete Denture
ClinicalTrials.gov study NCT06520280. IPD Sharing: NO. Countries: 1. Publications: 0.
Efficacy and Safety of Additive Manufacturing Personalized Titanium Mesh in Guided Bone Regeneration
ClinicalTrials.gov study NCT06692244. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Additive Versus Subtractive Manufacturing Techniques of Custom-Made Plates for the Fixation of Interforaminal Mandibular Fractures
ClinicalTrials.gov study NCT07263633. IPD Sharing: NO. Countries: 1. Publications: 0.
Additively Versus Subtractively Manufactured Implant Supported Fixed Dental Prostheses
ClinicalTrials.gov study NCT07090863. IPD Sharing: YES. Countries: 2. Publications: 0.
Experimentation of Sensorized Pseudoelastic Orthoses Produced by Additive Manufacturing
ClinicalTrials.gov study NCT04328857. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Additive Manufacture in the Production of Orthotic Insoles
ClinicalTrials.gov study NCT02895139. IPD Sharing: NO. Countries: 1. Publications: 0.
Fabrication Of Hybird Gas Turbine Disk Material System By Additive Manufacturing Project
<p>AM offers the potential to be revolutionary for GRC hybrid disk concept as it: i) bypasses joining through direct deposition or &ldquo;building&rdquo; of the PX bore alloy on a SX rim and ii) circumvents interface heating issues that may cause microstructural degradation during welding. Our objective is to demonstrate the feasibility of the high payoff AM hydrid disk concept described through test specimens built up using electron beam melting (EBM).</p>
IAWAS - Development of Alternating Current Plasma Transfer Arc (ACPTA) for Additive Manufacturing
<p>In the framework of the CleanSky project IAWAS, these data have been gather for the Work Package 3 on process development. </p> <p>These files provide the experimental data gathered during the preliminary development of ACPTA for aluminium lithium wire + arc additive manufacturing. The results anlysis is provided in the deliverable 3.4. </p> <ul> <li>The excel files are the experimental data sheet</li> <li>The AMV compressed folders gather the arc voltage and current raw measurements</li> <li>The AMV data are analysed and the results are provided by the excel file Characterisation of the ACPTA arcs for different process targets</li> </ul>
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: "<i>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.</i>"</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 them 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> - A 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>
HybridCAD++: Expanded Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models
<p>The<strong> <em>HybridCAD++</em> </strong>dataset is a significantly expanded version of the <strong><em>HybridCAD</em></strong> dataset, offering a larger volume of CAD models and a more comprehensive set of hybrid additive-subtractive manufacturing features. This dataset includes additional feature classes, bringing the total to <strong>36</strong>, and contains over <strong>161,000 samples</strong>—making it a unique and robust resource for machine learning applications in hybrid manufacturing feature recognition.</p> <h3>Key Differences from HybridCAD</h3> <ul> <li><strong>Increased Dataset Volume</strong>: <em>HybridCAD++</em> features a total of 161,441 CAD models, significantly larger than the original <em>HybridCAD</em> dataset.</li> <li><strong>Expanded Feature Classes</strong>: This dataset includes 36 feature labels, with newly added classes. This increase in feature variety enhances the dataset's applicability to complex hybrid manufacturing scenarios.</li> </ul> <h3>Dataset Composition</h3> <p>The dataset includes three primary components:</p> <ol> <li> <p><strong>STEP Files</strong>:</p> <ul> <li>Each CAD model is stored in STEP format and includes labeled B-Rep faces for hybrid manufacturing feature recognition.</li> <li>The CAD models were generated programmatically using PythonOCC, ensuring consistent quality and scalability.</li> </ul> </li> <li> <p><strong>Feature Labels</strong>:</p> <ul> <li><strong>File</strong>: <code>feature_labels.txt</code></li> <li>Contains label IDs for each hybrid additive-subtractive feature across B-Rep faces in each CAD model.</li> <li>With 36 unique feature classes, this file allows precise mapping of CAD model faces to specific hybrid features.</li> </ul> <ul> <li> </li> </ul> </li> <li> <p><strong>Hierarchical B-Rep Graphs</strong>:</p> <ul> <li>Stored in HDF5 format, these graphs provide structured access to B-Rep data, as detailed in <code>h5_structure.txt</code>.</li> </ul> </li> </ol> <h3>Dataset Splits</h3> <p>The dataset is divided into three subsets, structured for effective model training and evaluation:</p> <ul> <li><strong>Training Set</strong>: 113,008 samples (70%)</li> <li><strong>Validation Set</strong>: 32,288 samples (20%)</li> <li><strong>Testing Set</strong>: 16,145 samples (10%)</li> </ul> <h3>Full Feature Label List</h3> <p>This comprehensive list includes both subtractive and additive manufacturing features, with added classes for more intricate hybrid manufacturing applications:</p> <p> </p> <p>Label Feature<br>0 Chamfer<br>1 Through hole<br>2 Triangular passage<br>3 Rectangular passage<br>4 6-sided passage<br>5 Triangular through slot<br>6 Rectangular through slot<br>7 Circular through slot<br>8 Rectangular through step<br>9 2-sided through step<br>10 Slanted through step<br>11 O-ring<br>12 Blind hole<br>13 Triangular pocket<br>14 Rectangular pocket<br>15 6-sided pocket<br>16 Circular end pocket<br>17 Rectangular blind slot<br>18 Vertical circular end blind slot<br>19 Horizontal circular end blind slot<br>20 Triangular blind step<br>21 Circular blind step<br>22 Rectangular blind step<br>23 Round<br>24 Extrude cylinder<br>25 Extrude rectangle<br>26 Extrude triangle<br>27 Extrude hexagon<br>28 Extrude pentagon<br>29 Elliptical/Oval blind hole<br>30 Elliptical/Oval through hole<br>31 Slot hole<br>32 Obround boss<br>33 5-sided passage<br>34 5-sided pocket<br>35 Cylinder with hole<br>36 Stock</p>
HybridCAD: A Comprehensive Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models
<p>The <em>HybridCAD</em> dataset is a novel resource tailored for hybrid additive-subtractive feature recognition in Computer-Aided Design (CAD) models, uniquely combining features from both manufacturing processes. Building on the <em>MFCAD</em> and <em>MFCAD++</em> datasets, <em>HybridCAD</em> introduces additive manufacturing features alongside traditional subtractive ones, enabling the exploration and development of machine learning models for more complex, hybrid manufacturing applications. This dataset is especially suited for automatic feature recognition (AFR) research and model training in hybrid manufacturing contexts.</p> <h3>Dataset Composition</h3> <p>The dataset consists of 8,938 Boundary Representation (B-Rep) CAD models distributed into three main directories:</p> <ol> <li> <p><strong>STEP Files</strong>:</p> <ul> <li>Contains CAD models in STEP format, each representing a distinct part with hybrid manufacturing features.</li> <li>Generated using PythonOCC CAD software, these CAD files serve as the foundation for feature recognition tasks.</li> </ul> </li> <li> <p><strong>Feature Labels</strong>:</p> <ul> <li><strong>File</strong>: <code>feature_labels.txt</code></li> <li>Provides unique label IDs for each B-Rep face in every CAD model, denoting the manufacturing feature name it belongs to.</li> <li>Each CAD model face is labelled according to one of 29 hybrid additive-subtractive features, allowing for accurate and detailed feature recognition.</li> </ul> <ul> <li> </li> </ul> </li> <li> <p><strong>Hierarchical B-Rep Graphs</strong>:</p> <ul> <li>Stored in HDF5 format for structured access to B-Rep data.</li> <li>Detailed structural information is available in <code>h5_structure.txt</code>, explaining the hierarchical arrangement of B-Rep graphs.</li> </ul> </li> </ol> <h3>Dataset Splits</h3> <p>The dataset is split into training, validation, and testing sets as follows:</p> <ul> <li><strong>Training Set</strong>: 6,256 samples (70%)</li> <li><strong>Validation Set</strong>: 1,342 samples (15%)</li> <li><strong>Testing Set</strong>: 1,340 samples (15%)</li> </ul> <h3>Hybrid Manufacturing Features</h3> <p><em>HybridCAD</em> includes a diverse range of additive and subtractive manufacturing features, expanding beyond the subtractive-only features of <em>MFCAD++</em>. This inclusion enables the exploration of hybrid manufacturing processes and the recognition of a broader feature set in CAD models. The complete feature list includes:</p> <p>Label Feature<br>0 Chamfer<br>1 Through hole<br>2 Triangular passage<br>3 Rectangular passage<br>4 6-sided passage<br>5 Triangular through slot<br>6 Rectangular through slot<br>7 Circular through slot<br>8 Rectangular through step<br>9 2-sided through step<br>10 Slanted through step<br>11 O-ring<br>12 Blind hole<br>13 Triangular pocket<br>14 Rectangular pocket<br>15 6-sided pocket<br>16 Circular end pocket<br>17 Rectangular blind slot<br>18 Vertical circular end blind slot<br>19 Horizontal circular end blind slot<br>20 Triangular blind step<br>21 Circular blind step<br>22 Rectangular blind step<br>23 Round<br>24 Extrude cylinder<br>25 Extrude rectangle<br>26 Extrude triangle<br>27 Extrude hexagon<br>28 Extrude pentagon<br>29 Stock</p>
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OpenNeuro
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