Datasets and models for monitoring ultra-short pulse laser texturing using spectro-temporal data

Dataset

DOI

This repository contains spectrometric data acquired during laser femtosecond texturing campaigns on 316L steel samples. Deep learning models have been trained on this data to enable the estimation of hue or in situ texturing conditions. This repository therefore contains four datasets, two models, and the code needed to load the datasets and models and reproduce our results.

Python, 3.10.0

Tensorflow, 2.9.0

Identifier
DOI	https://doi.org/10.57745/D2NV6Z
Metadata Access	https://entrepot.recherche.data.gouv.fr/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.57745/D2NV6Z

Provenance
Creator	Mosser, Loic ; Renaud, Pierre ; Lecler, Sylvain ; Barbé, Laurent ; Geiskopf, François ; Mermet, Frédéric; Romano, Jean-Michel
Publisher	Recherche Data Gouv
Contributor	Lecler, Sylvain; Jesse Schiffler; Baptiste De Azevedo; Laboratoire ICube; IREPA Laser; Institut National des Sciences Appliquées de Strasbourg; Entrepôt Recherche Data Gouv
Publication Year	2025
Funding Reference	Agence nationale de la recherche ANR-22-LCV2-0006 ; Agence nationale de la recherche ANR-10-EQPX-44-01 ; Agence nationale de la recherche ANR-21-ESRE-0015
Rights	etalab 2.0; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/etalab-2.0.html
OpenAccess	true
Contact	Lecler, Sylvain (ICube UMR7357 ; Université de Strasbourg, INSA Strasbourg, CNRS ; France)

Representation
Resource Type	Dataset
Format	application/zip; text/x-python; text/markdown
Size	2132092935; 14423; 14485; 177363520; 11335
Version	1.0
Discipline	Computer Science; Engineering Sciences; Physics; Construction Engineering and Architecture; Engineering; Natural Sciences
Spatial Coverage	Pôle API, Illkirch-Graffenstaden