ArticleMaterials (Basel, Switzerland)2021
Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron.Materials (Basel, Switzerland) · 2023Article
- Tribological Aspects of Sheet Titanium Forming.Materials (Basel, Switzerland) · 2023Article
- Investigation of Surface Roughness in Incremental Sheet Forming of Conical Drawpieces from Pure Titanium Sheets.Materials (Basel, Switzerland) · 2022Article
- Finite Element Simplifications and Simulation Reliability in Single Point Incremental Forming.Materials (Basel, Switzerland) · 2022Article
- Single-Point Incremental Forming of Titanium and Titanium Alloy Sheets.Materials (Basel, Switzerland) · 2021Review
- Mechanical Properties and Residual Stress Measurements of Grade IV Titanium and Ti-6Al-4V and Ti-13Nb-13Zr Titanium Alloys after Laser Treatment.Materials (Basel, Switzerland) · 2021Article
- Full-Field Temperature Measurement of Stainless Steel Specimens Subjected to Uniaxial Tensile Loading at Various Strain Rates.Materials (Basel, Switzerland) · 2021Article
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3 authors.
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Abstract
This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: the quasi-Newton, Levenberg-Marquardt and back propagation. The values of root mean square (RMS) error and determination coefficient were adopted as evaluation criteria for ANNs. The minimum value of the RMS error for the training set (RMS = 0.0982) and the validation set (RMS = 0.1493) with the highest value of correlation coefficient (
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