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/<strong>LMD数据集</strong> 本数据集收录了激光金属沉积（Laser Metal Deposition, LMD）增材制造（Additive Manufacturing, AM）全流程各环节的数据。数据集不仅包含工艺过程数据，还涵盖了产品设计、无损检测（Non-Destructive Testing, NDT）以及尺寸检测相关数据。 <strong>研发动因</strong> 增材制造（AM）的工业化落地亟需全域化数据管理与集成化自动化技术支撑。INTEGRADDE项目旨在开发端到端数字化制造解决方案，构建具备网络安全保障的双向数据流，实现增材制造全产业链的无缝整合。项目目标是研发全新制造方法论，依托直接能量沉积（Direct Energy Deposition, DED）技术，从初始产品设计阶段起，保障目标金属构件的可制造性、可靠性与最终质量，并推行零缺陷制造理念，确保工艺过程具备鲁棒性、稳定性与可重复性。为达成上述目标，INTEGRADDE项目聚焦以下核心创新点：构建数据驱动的智能化增材制造全流程管线；融合设计环节自动拓扑优化算法、多尺度工艺建模、硬件无关自动化工艺规划、在线控制与分布式无损检测技术，实现认证合格金属构件的制造；采用以缺陷不扩展策略为核心的自适应控制方案；此外，通过数据分析持续挖掘工艺知识，辅助新型金属构件的制造，依托大规模定制模式提升一次合格生产效率；网络安全技术保障增材制造全工作流的数据完整性，为金属增材制造构件的认证提供全新制造方法论。INTEGRADDE项目为试点产线采用双重部署模式：一方面面向钢铁、模具制造、航空航天与建筑五大领域的工业终端用户开展应用导向式部署；另一方面依托已具备增材制造基础设施的研究与技术组织（RTOs）搭建开放试点网络，合作方包括AIMEN、IREPA、CEA与WEST。该模式可针对工业化落地需求持续验证并部署专项研发成果，推动增材制造技术在欧盟金属加工行业的最终普及。 <strong>作者信息</strong> Carlos Gonzalez-Val：主要联系人（邮箱：[e-mail]）；Baltasar Lodeiro；Marcos Diez <strong>资助与贡献单位</strong> 本数据集由INTEGRADDE项目资助采集。贡献说明：AIMEN负责工艺数据采集以及P1、P2、P3、P4试样的制造；CEA负责层析成像分析；DATAPIXEL负责尺寸检测工作。 <strong>数据集结构</strong> 本数据集采用如下结构：数据集根目录下按试样命名，每个试样目录包含： - README：试样元数据与相关说明，格式为txt； - Photo：已制造试样的实物照片，格式为jpg； - Design：制造前的工件3D设计文件（原始设计），格式为stl； - Trajectories：加工所用的运动轨迹文件，格式为gcode； - Process data：工艺过程采集数据，格式为hdf5； - Tomography：3D层析重建数据，格式为raw； - Dimensional inspection：尺寸检测相关数据。 后续将以[SAMPLE 2 NAME]等形式依次列出其他试样。各环节的子目录中均包含详细说明与元数据。请注意，并非所有试样都包含上述全部环节。 <strong>配套软件</strong> 若需查看数据集中的各类文件，推荐使用以下开源软件： - hdf5格式文件：HDF5 Viewer，下载地址：https://www.hdfgroup.org/downloads/hdfview/ - stl/amf格式文件：Slic3r、OpenJScad，下载地址分别为：https://slic3r.org、https://openjscad.org/ - stp格式文件：ShareCad，下载地址：https://beta.sharecad.org/ - gcode格式文件：文本编辑器或Slic3r，下载地址：https://slic3r.org/ - raw格式文件：ImageJ，下载地址：https://imagej.net/