Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy
In the study, Al7075-TiC composites were synthesized by using a novel dual step blending process followed by cold pressing and sintering. The effect of ball milling time on the microstructure of the synthesized composite powder was characterized using X-ray diffraction measurements (XRD), scanning e...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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MDPI
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100391/ |
| _version_ | 1848863312496820224 |
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| author | Alam, Mohammad Azad Ya, Hamdan H. Mohammad Azeem Mohammad Yusuf Soomro, Imtiaz Ali Masood, Faisal Shozib, Imtiaz Ahmed Salit, M. Sapuan Akhter, Javed |
| author_facet | Alam, Mohammad Azad Ya, Hamdan H. Mohammad Azeem Mohammad Yusuf Soomro, Imtiaz Ali Masood, Faisal Shozib, Imtiaz Ahmed Salit, M. Sapuan Akhter, Javed |
| author_sort | Alam, Mohammad Azad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In the study, Al7075-TiC composites were synthesized by using a novel dual step blending process followed by cold pressing and sintering. The effect of ball milling time on the microstructure of the synthesized composite powder was characterized using X-ray diffraction measurements (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). Subsequently, the integrated effects of the two-stage mechanical alloying process were investigated on the crystallite size and lattice strain. The crystallite size and lattice strain of blended samples were calculated using the Scherrer method. The prediction of the crystallite size and lattice strain of synthesized composite powders was conducted by an artificial neural network technique. The results of the mixed powder revealed that the particle size and crystallite size improved with increasing milling time. The particle size of the 3 h-milled composites was 463 nm, and it reduces to 225 nm after 7 h of milling time. The microhardness of the produced composites was significantly improved with milling time. Furthermore, an artificial neuron network (ANN) model was developed to predict the crystallite size and lattice strain of the synthesized composites. The ANN model provides an accurate model for the prediction of lattice parameters of the composites. |
| first_indexed | 2025-11-15T13:30:55Z |
| format | Article |
| id | upm-100391 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:30:55Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1003912023-12-26T04:29:49Z http://psasir.upm.edu.my/id/eprint/100391/ Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy Alam, Mohammad Azad Ya, Hamdan H. Mohammad Azeem Mohammad Yusuf Soomro, Imtiaz Ali Masood, Faisal Shozib, Imtiaz Ahmed Salit, M. Sapuan Akhter, Javed In the study, Al7075-TiC composites were synthesized by using a novel dual step blending process followed by cold pressing and sintering. The effect of ball milling time on the microstructure of the synthesized composite powder was characterized using X-ray diffraction measurements (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). Subsequently, the integrated effects of the two-stage mechanical alloying process were investigated on the crystallite size and lattice strain. The crystallite size and lattice strain of blended samples were calculated using the Scherrer method. The prediction of the crystallite size and lattice strain of synthesized composite powders was conducted by an artificial neural network technique. The results of the mixed powder revealed that the particle size and crystallite size improved with increasing milling time. The particle size of the 3 h-milled composites was 463 nm, and it reduces to 225 nm after 7 h of milling time. The microhardness of the produced composites was significantly improved with milling time. Furthermore, an artificial neuron network (ANN) model was developed to predict the crystallite size and lattice strain of the synthesized composites. The ANN model provides an accurate model for the prediction of lattice parameters of the composites. MDPI 2022-03-10 Article PeerReviewed Alam, Mohammad Azad and Ya, Hamdan H. and Mohammad Azeem and Mohammad Yusuf and Soomro, Imtiaz Ali and Masood, Faisal and Shozib, Imtiaz Ahmed and Salit, M. Sapuan and Akhter, Javed (2022) Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy. Crystals, 12 (3). art. no. 372. pp. 1-20. ISSN 2073-4352 https://www.mdpi.com/2073-4352/12/3/372 10.3390/cryst12030372 |
| spellingShingle | Alam, Mohammad Azad Ya, Hamdan H. Mohammad Azeem Mohammad Yusuf Soomro, Imtiaz Ali Masood, Faisal Shozib, Imtiaz Ahmed Salit, M. Sapuan Akhter, Javed Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy |
| title | Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy |
| title_full | Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy |
| title_fullStr | Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy |
| title_full_unstemmed | Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy |
| title_short | Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy |
| title_sort | artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of al7075-tic composites fabricated by powder metallurgy |
| url | http://psasir.upm.edu.my/id/eprint/100391/ http://psasir.upm.edu.my/id/eprint/100391/ http://psasir.upm.edu.my/id/eprint/100391/ |