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

• Main Authors: Dele-Afolabi T.T., Ahmadipour M., Azmah Hanim M.A., Oyekanmi A.A., Ansari M.N.M., Sikiru S., Kumar N.
• Other Authors: 56225674500
• Format: Article
• Published: Elsevier Ltd 2025
• Subjects: Forecasting; Knowledge acquisition; Lead-free solders; Machine learning; Multiwalled carbon nanotubes (MWCN); Optimization; Soldered joints; Soldering; Aquila optimizer; Extreme learning machine; Intermetallic compound layer; Learning machines; Machine modelling; Multi-walled-carbon-nanotubes; Optimizers; Performance assessment; Shears strength; Sn-based solders; Tin alloys

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. ? 2023 Elsevier B.V.