Metal Additive Manufacturing Open Repository

<strong>LMD dataset</strong> This dataset gathers data from different parts of the Laser Metal Deposition process (Additive Manufacturing). The dataset covers not only the process data, but also the design, NDT (Non-Destructive Testing) and dimensional inspection. <br> <strong>Motivation</strong> The industrialisation of Additive Manufacturing (AM) requires a holistic data management and integrated automation. INTEGRADDE aims to develop an end-to-end Digital Manufacturing solution, enabling a cybersecured bidirectional dataflow for a seamless integration across the entire AM chain. 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. To achieve this aim, INTEGRADDE addresses following key innovations: Development of an intelligent data-driven AM pipeline. Combination of automatic topology optimisation algorithms for design, multi-scale process modelling, automated hardware-independent process planning, online control and distributed NDT for the manufacturing of certified metal parts. A self-adaptive control is adopted focused on the implementation of non-propagation of defects strategy. Moreover, Data Analytics will provide a continuous refinement by acquiring process knowledge to assist in the manufacturing of new metal components, improving right-first-time production by adopting a mass customization approach Cybersecurity ensures data integrity along the AM workflow, providing a novel manufacturing methodology for the certification of metal AM parts. INTEGRADDE implements a twofold deployment approach for the pilot lines: both in application-driven at five industrial end-users (steel, tooling, aeronautics, and construction) and open-pilot networks at RTOs already owning AM infrastructure (AIMEN, IREPA, CEA, WEST). This will allow a continuous validation and deployment of specific developments towards industrialization, boosting definitive uptake of AM in EU metalworking sector. <br> <strong>Authors</strong> Carlos Gonzalez-Val: Main contact ([e-mail]) Baltasar Lodeiro Marcos Diez <strong>Entities</strong> This dataset was collected under the INTEGRADDE project. Attributions: AIMEN: Process data collection and manufacturing of P1, P2, P3 and P4 CEA: Tomography analysis. DATAPIXEL: Dimensional inspection. <br> <strong>Structure</strong> The dataset follows this structure: Dataset [SAMPLE 1 NAME] README: metadata and information about the sample. Format: txt. Photo: a photo of the manufactured sample. Format: jpg. Design: a 3D design file of the piece before manufacturing (original design). Format: stl. Trajectories: the trajectories followed for the manufacturing. Format: gcode. Process data: data recorded from the process. Format hdf5. Tomography: data from a 3D tomographic reconstruction. Format: raw. Dimensional inspection: A comparison [SAMPLE 2 NAME] ... Further information and metadata is contained in each stage's subdirectory. Note that not all the samples contain all the stages. <br> <strong>Software</strong> To open the different files that conform the dataset, we recommend the following Open softwares: hdf5 -&gt; HDF5 Viewer: https://www.hdfgroup.org/downloads/hdfview/ stl/amf -&gt; Slic3r: https://slic3r.org / OpenJScad: https://openjscad.org/ stp -&gt; ShareCad: https://beta.sharecad.org/ gcode -&gt; Text editor / Slic3r: https://slic3r.org/ raw -&gt; ImageJ: https://imagej.net/