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7 results for “Metal Additive manufacturing”
Dataset- Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)
<p>This repository collects all the data (Figures and Tables) presented in the review article "Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)"</p>
Metal Additive Manufacturing Open Repository
<p><strong>Metal Additive Manufacturing Open Repository</strong></p> <p>This dataset gathers data from different parts of Additive manufacturing processes (Laser metal deposition - LMD, and Wire-arc additive manufacturing - WAAM). The dataset covers not only the process data, but also the design, NDT (Non-Destructive Testing) and dimensional inspection.</p> <p><br> <strong>Motivation</strong></p> <p>The industrialisation of Additive Manufacturing (AM) requires a holistic data management and integrated automation. The presented dataset is part of an end-to-end Digital Manufacturing solution, enabling a cybersecured bidirectional dataflow for a seamless integration across the entire AM chain.</p> <p>The goal is to develop a new manufacturing methodology capable of ensuring the manufacturability, reliability and quality of a target metal component from initial product design via Direct Energy Deposition (DED) technologies, implementing a zero-defect manufacturing approach ensuring robustness, stability and repeatibility of the process.</p> <p>To that end, we present the Metal Additive Manufacturing Open Dataset, the first holistic dataset for AM manufacturing, covering all engineering stages from desing to validation. We hope that this dataset will be the first step for the development of new data pipelines aimed to optimize and improve the AM processes and to speed up their digital transformation.</p> <p><br> <strong>Authors</strong></p> <ul> <li>Carlos Gonzalez-Val: Main contact (carlos.gonzalez@aimen.es)</li> <li>Baltasar Lodeiro</li> <li>Marcos Diez</li> </ul> <p> </p> <p><strong>Entities</strong></p> <p>This dataset was collected under the INTEGRADDE project. Attributions:</p> <ul> <li>AIMEN: Process data collection and manufacturing of T-Coupons, CC-Coupons-AIMEN and Jet Engine.</li> <li>MX3D: Process data collection and manufacturing of CC-Coupons-MX3D and Plates.</li> <li>University of West: Process data collection and manufacturing of CC-Coupons-WEST.</li> <li>IREPA: Process data collection and manufacturing of CC-Coupons-IREPA.</li> <li>CEA: Tomography analysis.</li> <li>DATAPIXEL: Dimensional inspection.</li> </ul> <p><br> <strong>Structure</strong></p> <p>The dataset follows this structure:</p> <ul> <li>Dataset <ul> <li>[SAMPLE 1 NAME] <ul> <li>README: metadata and information about the sample. Format: txt.</li> <li>Photo: a photo of the manufactured sample. Format: jpg.</li> <li>Design: a 3D design file of the piece before manufacturing (original design). Format: stl.</li> <li>Trajectories: the trajectories followed for the manufacturing. Format: gcode.</li> <li>Process data: data recorded from the process. Format hdf5.</li> <li>Tomography: data from a 3D tomographic reconstruction. Format: raw.</li> <li>Dimensional inspection: A comparison</li> </ul> </li> <li>[SAMPLE 2 NAME] <ul> <li>...</li> </ul> </li> </ul> </li> </ul> <p>Further information and metadata is contained in each stage's subdirectory.</p> <p>Note that not all the samples contain all the stages.</p> <p><br> <strong>Software</strong></p> <p>To open the different files that conform the dataset, we recommend the following Open softwares:</p> <ul> <li> hdf5 -> HDF5 Viewer: https://www.hdfgroup.org/downloads/hdfview/</li> <li> stl/amf -> Slic3r: https://slic3r.org / OpenJScad: https://openjscad.org/</li> <li> stp -> ShareCad: https://beta.sharecad.org/</li> <li> gcode -> Text editor / Slic3r: https://slic3r.org/</li> <li> raw -> ImageJ: https://imagej.net/</li> </ul> <p>More information on how to open the files of the dataset can be found in the README.</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>
Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework
Open the record for dataset details and reuse information.
Mechanical properties on Metal Additive manufacturing
<p><strong>Mechanical properties on Metal Additive manufacturing</strong></p> <p><strong>Abstract</strong></p> <p>This dataset contains the results on the experimental procedure to gather data on the manufacturing of Additive Manufacturing (AM) pieces for Laser Metal Deposition (LMD) processes.To create this dataset, a set of 37 pieces were manufactured using the same material and procedure but different manufacturing parameters and strategies. The process was monitored and recorded, capturing thermal images and sensor readings on each point of the manufacturing. Each one of the pieces was divided in 4 coupons, that were tested to obtain mechanical properties of resistance and hardness.</p> <p> </p> <p><strong>Contents</strong></p> <p>This dataset contains data related to 37 coupons (named T1 - T37), including the monitoring data gathered during manufacturing, the process parameters, results of tensile testing, results of hardness testing and pictures of the coupons.</p> <p>The dataset is structured as follows:</p> <ul> <li>[Piece ID] <ul> <li>Hdf5 file</li> <li>Piece picture</li> <li>Piece crosssection</li> </ul> </li> <li>Testing <ul> <li>Parameters.csv: tabular data with process parameters and piece measurements as a direct result of the manufacturing</li> <li>Coupons.csv: tabular data with tensile and hardness metrics on the tested coupons for each piece</li> </ul> </li> <li>README: explicative document</li> </ul> <p> </p> <p><strong>Formats</strong></p> <p>The data is provided in the following formats:</p> <ol> <li>Monitoring: HDF5 file format. This format allows the recording of multimodal manufacturing data.</li> <li>Pictures: jpg format</li> <li>Parameters: csv file containing process parameters</li> <li>Coupons: csv file with the material testing results.</li> </ol> <p>More information on the contents and formats of the dataset is contained in the attached README document.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work has been funded under the "Red de Excelencia en Fabricación Aditiva" (READI) with the support of the Spanish Ministry of Science and Innovation and the Centre for the Development of Industrial Technology (CDTI), under the program Cervera (CER-20191020).</p> <p> </p>
Data for the strength-ductility enhancement of 316L stainless steel meta-crystal lattice of architected materials by harnessing the non-equilibrium solidification in metal additive manufacturing
<p>Raw (tabular) CALPHAD data for SLM produced 316L stainless steel.</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>
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