Optimizing Clutch Disc Friction Material with Neural Networks
Author Information
Author(s): Bălășoiu George, Munteniță Cristian, Amortila Valentin Tiberiu, Titire Larisa
Primary Institution: Dunarea de Jos University of Galati
Hypothesis
Can the chemical composition of clutch disc friction materials be optimized using artificial neural networks to improve their performance?
Conclusion
The study successfully optimized the chemical composition of clutch disc friction materials to enhance their tribological performance using an artificial neural network.
Supporting Evidence
- The study analyzed four different clutch disc friction materials from various manufacturers.
- Artificial neural networks were used to optimize the chemical composition based on tribological performance.
- Significant differences in chemical compositions were found to directly affect the performance of the discs.
Takeaway
This study looked at different materials used in car clutches and found a way to make them work better by using a computer program that learns from data.
Methodology
The study used scanning electron microscopy and energy-dispersive X-ray spectroscopy for material analysis, and a pin-on-disc tribometer for testing friction properties.
Limitations
The chemical compositions of the friction materials were not disclosed due to confidentiality reasons.
Digital Object Identifier (DOI)
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