Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study
In present work, Aluminium matrix composites reinforced with x wt.% SiC (x = 5, 7.5 and 10) microparticles were synthesised by powder metallurgy route. The microhardness (VHN) of the Al/SiC composites were investigated using Response Surface Methodology (RSM) and artificial neural network (ANN) appr...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/86787/ http://psasir.upm.edu.my/id/eprint/86787/1/Modelling%20and%20optimization%20of%20hardness%20behaviour%20of%20sintered%20%281%29.pdf |
| _version_ | 1848860287331991552 |
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| author | Alam, Mohammad Azad Yaa, Hamdan H. Azeem, Mohammad Hussain, Patthi Salit, Mohd Sapuan Khan, Rehan Arifc, Sajjad Ansari, Akhter Husain |
| author_facet | Alam, Mohammad Azad Yaa, Hamdan H. Azeem, Mohammad Hussain, Patthi Salit, Mohd Sapuan Khan, Rehan Arifc, Sajjad Ansari, Akhter Husain |
| author_sort | Alam, Mohammad Azad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In present work, Aluminium matrix composites reinforced with x wt.% SiC (x = 5, 7.5 and 10) microparticles were synthesised by powder metallurgy route. The microhardness (VHN) of the Al/SiC composites were investigated using Response Surface Methodology (RSM) and artificial neural network (ANN) approach. Scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDS), Elemental mapping and Optical microscopy were done for the microstructural investigations. The X-ray diffraction (XRD) analysis was done for received powders and composites samples for phase recognition and existence of reinforcement particles (SiC) in the synthesised sintered composites. The design of experiments based on RSM was utilised following the central composite design method. Empirical models have been developed by considering variance analysis (ANOVA), to establish relationships among the control factors and the response variables. A feed-forward back-propagation neural network (FF-BPNN) was used to determine the qualitative characteristics of the process, and the accuracy of the BPNN system was attributed with mathematical models based on RSM model. The ANN model predicted surface hardness values are near the experimental findings. It is established that the developed models can be used to predict the hardness of the surface within the investigation range. The composite with reinforcement 7.5% revealed higher sintered density and Vickers microhardness due to the uniform distribution of filler particles in the Al matrix featuring no pores. The results indicate overall higher accuracy in the ANN method than RSM model. |
| first_indexed | 2025-11-15T12:42:50Z |
| format | Article |
| id | upm-86787 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:42:50Z |
| publishDate | 2020 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-867872021-11-16T06:37:16Z http://psasir.upm.edu.my/id/eprint/86787/ Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study Alam, Mohammad Azad Yaa, Hamdan H. Azeem, Mohammad Hussain, Patthi Salit, Mohd Sapuan Khan, Rehan Arifc, Sajjad Ansari, Akhter Husain In present work, Aluminium matrix composites reinforced with x wt.% SiC (x = 5, 7.5 and 10) microparticles were synthesised by powder metallurgy route. The microhardness (VHN) of the Al/SiC composites were investigated using Response Surface Methodology (RSM) and artificial neural network (ANN) approach. Scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDS), Elemental mapping and Optical microscopy were done for the microstructural investigations. The X-ray diffraction (XRD) analysis was done for received powders and composites samples for phase recognition and existence of reinforcement particles (SiC) in the synthesised sintered composites. The design of experiments based on RSM was utilised following the central composite design method. Empirical models have been developed by considering variance analysis (ANOVA), to establish relationships among the control factors and the response variables. A feed-forward back-propagation neural network (FF-BPNN) was used to determine the qualitative characteristics of the process, and the accuracy of the BPNN system was attributed with mathematical models based on RSM model. The ANN model predicted surface hardness values are near the experimental findings. It is established that the developed models can be used to predict the hardness of the surface within the investigation range. The composite with reinforcement 7.5% revealed higher sintered density and Vickers microhardness due to the uniform distribution of filler particles in the Al matrix featuring no pores. The results indicate overall higher accuracy in the ANN method than RSM model. Elsevier 2020-11 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/86787/1/Modelling%20and%20optimization%20of%20hardness%20behaviour%20of%20sintered%20%281%29.pdf Alam, Mohammad Azad and Yaa, Hamdan H. and Azeem, Mohammad and Hussain, Patthi and Salit, Mohd Sapuan and Khan, Rehan and Arifc, Sajjad and Ansari, Akhter Husain (2020) Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study. Journal of Materials Research and Technology, 9 (6). 14036 - 14050. ISSN 2238-7854; ESSN: 2214-0697 https://www.sciencedirect.com/science/article/pii/S223878542031810X 10.1016/j.jmrt.2020.09.087 |
| spellingShingle | Alam, Mohammad Azad Yaa, Hamdan H. Azeem, Mohammad Hussain, Patthi Salit, Mohd Sapuan Khan, Rehan Arifc, Sajjad Ansari, Akhter Husain Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study |
| title | Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study |
| title_full | Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study |
| title_fullStr | Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study |
| title_full_unstemmed | Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study |
| title_short | Modelling and optimization of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study |
| title_sort | modelling and optimization of hardness behaviour of sintered al/sic composites using rsm and ann: a comparative study |
| url | http://psasir.upm.edu.my/id/eprint/86787/ http://psasir.upm.edu.my/id/eprint/86787/ http://psasir.upm.edu.my/id/eprint/86787/ http://psasir.upm.edu.my/id/eprint/86787/1/Modelling%20and%20optimization%20of%20hardness%20behaviour%20of%20sintered%20%281%29.pdf |