Data · dataset · 2022
Machine-Learning based root stress calculation model for gears with a progressive curved path of contact
Listed in Teesside University Research Data Repository
The study aims to investigate the possibility of employing Machine-Learning models in the design of non-involute gears.
Description
Such a model would be useful for design calculations of non-standard gears, where there are no available guidelines. Multiple models for numerical prediction were tested, i.e. Linear Regression, Support Vector Machine, K-nearest neighbour, Neural Network, AdaBoost, and Random Forest.
The aim is to create a surrogate model to the final element analysis simulations, from which the data for training was collected. The models were firstly validated with the N-fold cross-validation. Further validation was done with new simulations.
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The results from the simulations and the models were in good agreement. The best-performing ones were Random Forest and AdaBoost. Based on the validation results a Machine-Learning constructed model for calculation of root stress in gears with a progressive curved path of contact is proposed.
The model can be used as an alternative to simulations for determining the root stress in real-time, and is able to calculate the stress for gears with different number of teeth, widths, modules, paths of contact, materials, and loads. Therefore, many combinations of gear geometries can be analysed and the most suitable can be chosen.
Links
Where it is published
- DOI doi.org/10.17632/sp3hcpmczn.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Machine learning · Mechanical engineering
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/sp3hcpmczn.1 | 8 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4017 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Mechanical Engineering'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |