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Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer

The impact of multi-walled carbon nanotubes (MWCNTs) on the development of the intermetallic compound (IMC) at the interface of the Sn5Sb/Cu solder joint was investigated. Reflow soldering was used to produce the samples, which were subsequently isothermally aged at different temperatures. The prese...

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Main Authors: Temitope T., Dele-Afolabi, Masoud, Ahmadipour, Mohamed Ariff, Azmah Hanim, A.A., Oyekanmi, M.N.M., Ansari, Sikiru, Surajudeen, Kumar, Niraj
Format: Article
Language:English
English
Published: Elsevier Ltd 2024
Online Access:http://psasir.upm.edu.my/id/eprint/105784/1/105784.pdf
http://psasir.upm.edu.my/id/eprint/105784/2/105784_Archive.pdf
http://psasir.upm.edu.my/id/eprint/105784/
https://www.sciencedirect.com/science/article/pii/S0925838823039877
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spelling my.upm.eprints.1057842024-09-12T09:10:45Z http://psasir.upm.edu.my/id/eprint/105784/ Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer Temitope T., Dele-Afolabi Masoud, Ahmadipour Mohamed Ariff, Azmah Hanim A.A., Oyekanmi M.N.M., Ansari Sikiru, Surajudeen Kumar, Niraj The impact of multi-walled carbon nanotubes (MWCNTs) on the development of the intermetallic compound (IMC) at the interface of the Sn5Sb/Cu solder joint was investigated. Reflow soldering was used to produce the samples, which were subsequently isothermally aged at different temperatures. The presence of MWCNTs in the Sn-5Sb solder alloy significantly prevented IMC formation at the interface and enhanced the shear strength, according to empirical observations, which were supported by the excellent properties of MWCNTs. An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. The AO-ELM model's input parameters included a number of significant variables, such as MWCNTs addition, aging temperature, and aging period that have an impact on the IMC thickness and the shear strength of the solder composite joints. In terms of the statistical accuracy measures, it was observed that the AO-ELM outperformed the traditional ANN and ELM models in predicting the IMC thickness and shear strength of MWCNTs-reinforced Sn5Sb/Cu composite solder joints. The novelty of the approach recommended stems from the accuracy attained by modifying hyper-parameters with AO that has been paired with the fast processing speed of ELM. Elsevier Ltd 2024 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/105784/1/105784.pdf text en http://psasir.upm.edu.my/id/eprint/105784/2/105784_Archive.pdf Temitope T., Dele-Afolabi and Masoud, Ahmadipour and Mohamed Ariff, Azmah Hanim and A.A., Oyekanmi and M.N.M., Ansari and Sikiru, Surajudeen and Kumar, Niraj (2024) Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer. Journal of Alloys and Compounds, 970. art. no. 172684. pp. 1-14. ISSN 0925-8388; ESSN: 1873-4669 https://www.sciencedirect.com/science/article/pii/S0925838823039877 10.1016/j.jallcom.2023.172684
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description The impact of multi-walled carbon nanotubes (MWCNTs) on the development of the intermetallic compound (IMC) at the interface of the Sn5Sb/Cu solder joint was investigated. Reflow soldering was used to produce the samples, which were subsequently isothermally aged at different temperatures. The presence of MWCNTs in the Sn-5Sb solder alloy significantly prevented IMC formation at the interface and enhanced the shear strength, according to empirical observations, which were supported by the excellent properties of MWCNTs. An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. The AO-ELM model's input parameters included a number of significant variables, such as MWCNTs addition, aging temperature, and aging period that have an impact on the IMC thickness and the shear strength of the solder composite joints. In terms of the statistical accuracy measures, it was observed that the AO-ELM outperformed the traditional ANN and ELM models in predicting the IMC thickness and shear strength of MWCNTs-reinforced Sn5Sb/Cu composite solder joints. The novelty of the approach recommended stems from the accuracy attained by modifying hyper-parameters with AO that has been paired with the fast processing speed of ELM.
format Article
author Temitope T., Dele-Afolabi
Masoud, Ahmadipour
Mohamed Ariff, Azmah Hanim
A.A., Oyekanmi
M.N.M., Ansari
Sikiru, Surajudeen
Kumar, Niraj
spellingShingle Temitope T., Dele-Afolabi
Masoud, Ahmadipour
Mohamed Ariff, Azmah Hanim
A.A., Oyekanmi
M.N.M., Ansari
Sikiru, Surajudeen
Kumar, Niraj
Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer
author_facet Temitope T., Dele-Afolabi
Masoud, Ahmadipour
Mohamed Ariff, Azmah Hanim
A.A., Oyekanmi
M.N.M., Ansari
Sikiru, Surajudeen
Kumar, Niraj
author_sort Temitope T., Dele-Afolabi
title Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer
title_short Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer
title_full Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer
title_fullStr Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer
title_full_unstemmed Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer
title_sort performance assessment of sn-based lead-free solder composite joints based on extreme learning machine model tuned by aquila optimizer
publisher Elsevier Ltd
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/105784/1/105784.pdf
http://psasir.upm.edu.my/id/eprint/105784/2/105784_Archive.pdf
http://psasir.upm.edu.my/id/eprint/105784/
https://www.sciencedirect.com/science/article/pii/S0925838823039877
_version_ 1811685930194436096
score 13.223943

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