Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizer
In this study, Mg-Ti-SiC composite powders with varied micron and nano silicon carbide (SiC) particle sizes were fabricated utilizing the ball milling technology at various milling times. The effect of reinforcement particles sizes and milling time on the morphology and microstructure of the magnesi...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823009882 |
| _version_ | 1797401770546167808 |
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| author | Hossein Ahmadian Tianfeng Zhou Mohamed Abd Elaziz Mohammed Azmi Al-Betar A.M. Sadoun I.M.R Najjar A.W. Abdallah A. Fathy Qian Yu |
| author_facet | Hossein Ahmadian Tianfeng Zhou Mohamed Abd Elaziz Mohammed Azmi Al-Betar A.M. Sadoun I.M.R Najjar A.W. Abdallah A. Fathy Qian Yu |
| author_sort | Hossein Ahmadian |
| collection | DOAJ |
| description | In this study, Mg-Ti-SiC composite powders with varied micron and nano silicon carbide (SiC) particle sizes were fabricated utilizing the ball milling technology at various milling times. The effect of reinforcement particles sizes and milling time on the morphology and microstructure of the magnesium composite powders was characterized. Then, we developed a machine-learning model based on Adaptive Neuro-fuzzy Inference System (ANFIS) modified with termite life cycle optimizer to predict the crystallite size of the produced composites. The average particles size in all composites including micron SiC (µSiC) and nano SiC (nSiC) always decreased with increasing milling time and SiC content, and the most optimal reduction in particle size was achieved after 16 h of milling for both configurations, which were 5.12 µm and 1.96 µm, respectively. Changing reinforcement particle size from micron to nano caused the peak intensities of Mg and Ti more decreased and phases Ti5Si3 and TiC were observed after milling for 16 h in ND composite powder. With increasing milling time in Mg-25 wt% Ti-5 wt% µSiC, the crystallite size decreased from 31 nm to 13.62 nm after 1 h and 32 h milled, respectively. The most optimum reduction in crystallite size occurred in the composite powders including nSiC, in which crystallite size decreased to 13.35 nm. The developed Machine learning model was able to predict the evolution of the crystallite size of the produce d composites with very good accuracy. |
| first_indexed | 2024-03-09T02:16:02Z |
| format | Article |
| id | doaj.art-9fb39c8a742c4645816e4d4bc23e8b12 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-09T02:16:02Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-9fb39c8a742c4645816e4d4bc23e8b122023-12-07T05:27:58ZengElsevierAlexandria Engineering Journal1110-01682023-12-0184285300Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizerHossein Ahmadian0Tianfeng Zhou1Mohamed Abd Elaziz2Mohammed Azmi Al-Betar3A.M. Sadoun4I.M.R Najjar5A.W. Abdallah6A. Fathy7Qian Yu8School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, People's Republic of ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, People's Republic of ChinaDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab EmiratesArtificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab EmiratesMechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 80204, Saudi ArabiaMechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 80204, Saudi Arabia; Corresponding authors.Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, EgyptDepartment of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt; Mechanical Department, Higher Technological Institute, Tenth of Ramadan City, EgyptSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, People's Republic of China; Corresponding authors.In this study, Mg-Ti-SiC composite powders with varied micron and nano silicon carbide (SiC) particle sizes were fabricated utilizing the ball milling technology at various milling times. The effect of reinforcement particles sizes and milling time on the morphology and microstructure of the magnesium composite powders was characterized. Then, we developed a machine-learning model based on Adaptive Neuro-fuzzy Inference System (ANFIS) modified with termite life cycle optimizer to predict the crystallite size of the produced composites. The average particles size in all composites including micron SiC (µSiC) and nano SiC (nSiC) always decreased with increasing milling time and SiC content, and the most optimal reduction in particle size was achieved after 16 h of milling for both configurations, which were 5.12 µm and 1.96 µm, respectively. Changing reinforcement particle size from micron to nano caused the peak intensities of Mg and Ti more decreased and phases Ti5Si3 and TiC were observed after milling for 16 h in ND composite powder. With increasing milling time in Mg-25 wt% Ti-5 wt% µSiC, the crystallite size decreased from 31 nm to 13.62 nm after 1 h and 32 h milled, respectively. The most optimum reduction in crystallite size occurred in the composite powders including nSiC, in which crystallite size decreased to 13.35 nm. The developed Machine learning model was able to predict the evolution of the crystallite size of the produce d composites with very good accuracy.http://www.sciencedirect.com/science/article/pii/S1110016823009882Machine learningTermite Life Cycle Optimizer (TLCO)Magnesium compositeSilicon carbideCrystallite sizeBall milling |
| spellingShingle | Hossein Ahmadian Tianfeng Zhou Mohamed Abd Elaziz Mohammed Azmi Al-Betar A.M. Sadoun I.M.R Najjar A.W. Abdallah A. Fathy Qian Yu Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizer Alexandria Engineering Journal Machine learning Termite Life Cycle Optimizer (TLCO) Magnesium composite Silicon carbide Crystallite size Ball milling |
| title | Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizer |
| title_full | Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizer |
| title_fullStr | Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizer |
| title_full_unstemmed | Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizer |
| title_short | Predicting crystallite size of Mg-Ti-SiC nanocomposites using an adaptive neuro-fuzzy inference system model modified by termite life cycle optimizer |
| title_sort | predicting crystallite size of mg ti sic nanocomposites using an adaptive neuro fuzzy inference system model modified by termite life cycle optimizer |
| topic | Machine learning Termite Life Cycle Optimizer (TLCO) Magnesium composite Silicon carbide Crystallite size Ball milling |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823009882 |
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