ArticleMaterials (Basel, Switzerland)2023
Surface Roughness Prediction of Titanium Alloy during Abrasive Belt Grinding Based on an Improved Radial Basis Function (RBF) Neural Network.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Investigation of Cutting Forces and Temperature in Face Milling of Wood-Plastic Composite Using Radial Basis Function Neural Network.Materials (Basel, Switzerland) · 2025Article
- Prediction of Mechanical Properties and Stress-Strain Relation of Closed-Cell Aluminium Foam Under Compression Using Neural Network Models.Materials (Basel, Switzerland) · 2025Article
- Innovative Solutions in the Design of Microfinishing Attachments for Surface Finishing with Abrasive Films.Micromachines · 2025Article
- Carbon Texture Formation on the Surface of Titanium Alloy Grade 5 (Ti-6Al-4V) During Finishing with Abrasive Films.Molecules (Basel, Switzerland) · 2025Article
- Unit Load of Abrasive Grains in the Machining Zone During Microfinishing with Abrasive Films.Materials (Basel, Switzerland) · 2024Article
- Evaluation of Surface Finishing Efficiency of Titanium Alloy Grade 5 (Ti-6Al-4V) After Superfinishing Using Abrasive Films.Materials (Basel, Switzerland) · 2024Article
- Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression.Sensors (Basel, Switzerland) · 2024Article
- Article
- Morphology of Microchips in the Surface Finishing Process Utilizing Abrasive Films.Materials (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
Titanium alloys have become an indispensable material for all walks of life because of their excellent strength and corrosion resistance. However, grinding titanium alloy is exceedingly challenging due to its pronounced material characteristics. Therefore, it is crucial to create a theoretical roughness prediction model, serving to modify the machining parameters in real time. To forecast the surface roughness of titanium alloy grinding, an improved radial basis function neural network model based on particle swarm optimization combined with the grey wolf optimization method (GWO-PSO-RBF) was developed in this study. The results demonstrate that the improved neural network developed in this research outperforms the classical models in terms of all prediction parameters, with a model-fitting R
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