This dataset contains weld pool and keyhole geometry data from 73 high-fidelity Computational Fluid Dynamics (CFD) simulations of Laser Beam Welding (LBW) in 316L stainless steel, covering laser powers of 4,000–8,000 W, welding velocities of 0.2–0.4 m/s, and different circular beam profiles. The repository includes simulation geometry outputs, process parameter inputs, laser beam intensity distributions, and CBC click patterns, as well as Python code for data loading, surrogate modeling, and visualization. A README file with further details on the dataset structure and usage is also provided.
", "funding": [ { "award": { "acronym": "Lasers4MaaS", "id": "00k4n6c32::101178719", "identifiers": [ { "identifier": "https://cordis.europa.eu/projects/101178719", "scheme": "url" } ], "number": "101178719", "title": { "en": "Laser-as-a-Service Digital Platform with Dynamic Beam Shaping for Acceleration of Smart, Decentralised and Sustainable Factory of the Future" } }, "funder": { "id": "00k4n6c32", "name": "European Commission" } } ], "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2026-05-27", "publisher": "Zenodo", "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } }, { "description": { "en": "" }, "id": "agpl-3.0-or-later", "props": { "scheme": "spdx", "url": "https://www.gnu.org/licenses/agpl.txt" }, "title": { "en": "GNU Affero General Public License v3.0 or later" } } ], "subjects": [ { "subject": "Laser Beam Welding" }, { "subject": "Weld Pool Geometry" }, { "subject": "Keyhole Geometry" }, { "subject": "Computational Fluid Dynamics" }, { "subject": "Surrogate Modeling" }, { "subject": "Coherent Beam Combining" }, { "subject": "Process Parameter Prediction" } ], "title": "Weld Pool and Keyhole Geometry Dataset from High-Fidelity CFD Simulations of Laser Beam Welding", "version": "1.0" }, "parent": { "access": { "owned_by": { "user": "1652112" }, "settings": { "accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": false, "secret_link_expiration": 0 } }, "communities": { "default": "7913e9d2-d4d9-4161-8bb9-3a6577b5ff24", "entries": [ { "access": { "member_policy": "closed", "members_visibility": "public", "record_submission_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2026-05-16T15:34:44.311660+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "7913e9d2-d4d9-4161-8bb9-3a6577b5ff24", "links": {}, "metadata": { "curation_policy": "The lasers4MaaS project policy will be to produce data freely available without embargo period whenever possible. In addition, partners will be committed to generate and deliver valuable and high-quality datasets. The whole Data Management Plan (DMP) and its procedures are aligned with data quality assurance.
", "description": "Laser-as-a-Service Digital Platform with Dynamic Beam Shaping for Acceleration of Smart, Decentralised and Sustainable Factory of the Future", "funding": [ { "award": { "id": "00k4n6c32::101178719" }, "funder": { "id": "00k4n6c32" } } ], "organizations": [ { "id": "01a77tt86" } ], "page": "In the quest for on-demand, smart, sustainable, and decentralised manufacturing, digital servitisation offers high-quality, flexible production with quick turnaround times. However, this approach faces a hurdle: the lack of comprehensive methodologies. The EU-funded Lasers4MaaS project is stepping in to change this. Building upon the advancement of dynamic laser beam shaping to an 'all-in-one' laser welding tool with immense potential for servitisation, Lasers4MaaS introduces a six-point strategy to reconfigure, connect, control, predict, improve, and ensure compliance in manufacturing. With demonstrations in sectors like automotive, aerospace, food packaging, and renewable energy, this project aligns closely with Green Deal goals. A consortium of academic and industrial partners will bridge technology readiness levels and advancing standardisation.
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