ArticlePolymers2024
Optimisation of Clutch Disc Friction Material Using a Multi-Layer Perceptron Artificial Neural Network.
Article in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents an analysis of four clutch disc friction materials (from different manufacturers) used in manual transmissions. Scanning electron microscopy and energy-dispersive X-ray spectroscopy were employed for the microstructural and chemical characterisation of the friction materials. To reveal the tribological properties of the selected clutch discs, three measurements of the friction coefficient between the material and the cast iron disc were conducted. The findings were employed to construct an artificial neural network using Easy NN software (V 14), with the objective of optimising the friction material. The chemical composition of the friction materials was employed as the input data, whereas the minimum, maximum, and average values of the friction coefficient, as well as the temperature generated during friction, were utilised as the output data. To assess the efficacy of the neural network, the correlation between the importance of input data and their sensitivity to output data was examined. It was determined that the model with three hidden layers exhibited a notable correlation between the six most influential chemical elements and their sensitivity. Based on this neural model, the chemical composition of the friction disc materials was optimised using the "Query" mode, aiming to minimise discrepancies in friction coefficients and temperature development.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.