Archive · dataset · 2026
Dataset 3 - Proyecto SPAREMETAL
Listed in RIUMA
README – SPAREMETAL DATASET 3 Title: Experimental and predictive data from sustainable machining of light alloys (SPAREMETAL project)
Description
Project
PID2021-125988OB-I00 Sistema experto para la mejora de la integridad superficial en el mecanizado sostenible de aleaciones ligeras Institution: University of Malaga (Universidad de Málaga) Principal Investigator: Lorenzo Sevilla Hurtado (ORCID: 0000-0002-2236-5807) Contributors: Francisco Javier Trujillo Vilches Carolina Bermudo Gamboa Sergio Martín Béjar Manuel Herrera Fernández Tobias Andersson Daniel Svensson Yezika Sánchez Hernández Description: This dataset contains experimental and processed data obtained during dry machining processes of light alloys (UNS A97075 aluminum alloy and Ti6Al4V titanium alloy).
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The data were collected within the SPAREMETAL research project and include both online monitoring data and offline characterization results. This is dataset 3 (there are 4 in total for this project), which analyzes the effect of cutting parameters on slenderness tests. Cutting forces, Temperature, Macrogeometrical deviations, Surface Roughness and Energy consumption were analysed.
Data include: - Online monitoring data: cutting forces, temperature, vibrations, energy consumption - Offline measurements: surface roughness (2D/3D), microhardness, geometrical deviations - Derived datasets: aggregated databases linking input machining parameters and output variables - Predictive models: artificial neural networks (ANN) developed in MATLAB/Simulink Folder structure: 01. RAW DATA: Folder containing the raw data for the output variables; 02.
PROCESSED DATA: Folder with the processed databases of the output variables; 03. MODELS: Developed mathematical models for the output variables as a function of the input variables; 04. Documentation: Documentation associated with the Project; File formats: - CSV, XLSX, FEL, DWD, ATS, DXD (data) - MAT, M, SLX, MALAPP, MLX, EXE (models) - PDF, DOCX (documentation) - PNG, TIFF, JPG (images) Methodology: The dataset was generated through controlled machining experiments using CNC turning equipment.
Input parameters include cutting speed, feed rate and depth of cut. Output variables were measured using dynamometers, thermographic cameras, accelerometers and optical metrology systems. Units: All variables are expressed in SI units unless otherwise specified in metadata files.
Reuse: Data can be reused for: - Validation of machining models - Development of predictive algorithms - Comparative studies in manufacturing engineering License: Creative Commons Attribution 4.0 (CC BY 4.0) Funding: Spanish State Research Agency (AEI) – Projects of Knowledge Generation 2021 Contact: Lorenzo Sevilla Hurtado University of Malaga Email: (lsevilla@uma.es) Date of publication: 2026 Version: v2.0
Links
Where it is published
- RIUMA record hdl.handle.net/10630/46490 ↗
landing page · from riuma uma es
- DOI doi.org/10.24310/riuma.46490 ↗
DOI / persistent id · from riuma uma es
Catalogue records · 1
- OAI-PMH record riuma.uma.es/rest/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3… ↗
metadata API · from riuma uma es
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Machine learning
- Inferred from text
- Image 65%
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| RIUMA | oai:riuma.uma.es:10630/46490 | 19 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · riuma uma es | connector:riuma_uma_es@1.0.0 | |
| concepts[field].anzsrc:group:4611 | mapping · riuma uma es | vocabulary-mapper@1.0.0 | keywords['Machine learning'] |
| concepts[field].local:field:computer-science-ai | mapping · riuma uma es | connector:riuma_uma_es@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · riuma uma es | connector:riuma_uma_es@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · riuma uma es | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · riuma uma es | connector:riuma_uma_es@1.0.0 | /metadata/dc/description |
| license | source · riuma uma es | connector:riuma_uma_es@1.0.0 | /metadata/dc/rights |
| publication_date | source · riuma uma es | connector:riuma_uma_es@1.0.0 | |
| title | source · riuma uma es | connector:riuma_uma_es@1.0.0 | /metadata/dc/title |