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Artificial neural network modelling of aluminium/Al2O3/fly ash hybrid composites prepared by powder metallurgy
https://orcid.org/http://orcid.org/0000-0003-2001-3266
Industrial waste low density residue particles and ceramic particles are used as reinforcement materials in the aluminum matrix. The composites are prepared using powder metallurgy technique and the effects of sintering parameters on the mechanical properties are studied. The input parameters are impact pressure, sintering time, weight percent (wt%) of alumina is 10 and that of fly ash (FA) is 5. Maximum strength is observed for the composites sintered at 75 min and the hardness increased with the sintering time. The artificial neural network (ANN) model is developed and used to envisage the properties of the composite material. From the results, it is observed that the percentage error of the properties of the developed composites did not exceed 3.3%, which shows the accuracy of the developed ANN model. In addition flyash particles are added to Al composites the cost and weight was reduced.
Artificial neural network modelling of aluminium/Al2O3/fly ash hybrid composites prepared by powder metallurgy
https://orcid.org/http://orcid.org/0000-0003-2001-3266
Industrial waste low density residue particles and ceramic particles are used as reinforcement materials in the aluminum matrix. The composites are prepared using powder metallurgy technique and the effects of sintering parameters on the mechanical properties are studied. The input parameters are impact pressure, sintering time, weight percent (wt%) of alumina is 10 and that of fly ash (FA) is 5. Maximum strength is observed for the composites sintered at 75 min and the hardness increased with the sintering time. The artificial neural network (ANN) model is developed and used to envisage the properties of the composite material. From the results, it is observed that the percentage error of the properties of the developed composites did not exceed 3.3%, which shows the accuracy of the developed ANN model. In addition flyash particles are added to Al composites the cost and weight was reduced.
Artificial neural network modelling of aluminium/Al2O3/fly ash hybrid composites prepared by powder metallurgy
https://orcid.org/http://orcid.org/0000-0003-2001-3266
Industrial waste low density residue particles and ceramic particles are used as reinforcement materials in the aluminum matrix. The composites are prepared using powder metallurgy technique and the effects of sintering parameters on the mechanical properties are studied. The input parameters are impact pressure, sintering time, weight percent (wt%) of alumina is 10 and that of fly ash (FA) is 5. Maximum strength is observed for the composites sintered at 75 min and the hardness increased with the sintering time. The artificial neural network (ANN) model is developed and used to envisage the properties of the composite material. From the results, it is observed that the percentage error of the properties of the developed composites did not exceed 3.3%, which shows the accuracy of the developed ANN model. In addition flyash particles are added to Al composites the cost and weight was reduced.
Artificial neural network modelling of aluminium/Al2O3/fly ash hybrid composites prepared by powder metallurgy
Int J Interact Des Manuf
Reddy, Seelam Pichi (author) / Danda, Jagan Mohan Reddy (author) / Kolli, Murahari (author) / Yaramala, Anusha (author)
2025-01-01
9 pages
Article (Journal)
Electronic Resource
English
Aluminium , Alumina , Fly ash , Powder metallurgy , Artificial neural network Engineering , Materials Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
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