Prediction of the abrasive wear behaviour of heat-treated aluminium-clay composites using an artificial neural network
| Autor: | S. A. Balogun, Adeyanju Sosimi, Johnson Olumuyiwa Agunsoye, D.E. Esezobor, A. A. Agbeleye |
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| Rok vydání: | 2018 |
| Předmět: |
Aluminium-clay composite
Work (thermodynamics) Materials science Artificial neural network heat treatment Composite number Treatment process Abrasive chemistry.chemical_element 02 engineering and technology 021001 nanoscience & nanotechnology wear rate 020303 mechanical engineering & transports 0203 mechanical engineering chemistry Aluminium Heat treated Composite material lcsh:Science (General) 0210 nano-technology artificial neural network performance lcsh:Q1-390 |
| Zdroj: | Journal of Taibah University for Science, Vol 12, Iss 2, Pp 235-240 (2018) |
| ISSN: | 1658-3655 |
| DOI: | 10.1080/16583655.2018.1451119 |
| Popis: | This work employs the T6 heat treatment process to aluminium-clay (Al-Clay) composite consisting of 15 wt% clay. The samples were solutionized at 500°C, 550°C and 600°C, and were quenched in air, oil and water. Selected samples of the heat-treated composite were subjected to wear tests using Denison T62 HS pin-on-disc wear-testing machine in accordance with ASTM: G99-05 standard. The effects of two different loads (4 and 10 N) and three sliding speeds (200, 500 and 1000 rpm) under dry sliding conditions were investigated. The potential of using back-propagation neural network with 4-10-1 architecture was explored to predict the wear rate of the heat-treated composites. The results show that the performance of Levenberg–Marquardt training algorithm is superior to all other algorithms used. The well-trained ANN system satisfactorily predicted the experimental results and can be handy for an optimum design and also an alternative technique to evaluate wear rate. |
| Databáze: | OpenAIRE |
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