The Sn-Ag-Cu (SAC) solder alloys with a low Ag (Ag < 3 wt.%) content have attracted great attention owing to their low cost, increased ability in bulk compliance, and plastic energy dissipation. However, some of their mechanical properties are generally lower than the SAC alloys with a higher Ag content. Adding alloying elements is an effective approach for improving the mechanical properties of the SAC alloys. In this study, the effect of Bi, Sb, and Ti on Sn-1 wt.%Ag-0.5 wt.%Cu (SAC105) solder alloys was investigated. The SAC solders with four compositions: SAC105-1 wt.%Bi, SAC105-1 wt.%Sb, SAC105-1 wt.%Bi-1 wt.%Sb, SAC105-1 wt.%Bi-1 wt.%Sb-0.4 wt.%Ti were prepared. The microstructure and phase compositions were characterized using electron scanning microscopy, and X-ray diffraction. The thermal properties and wettability were also examined. Uniaxial tensile tests and nano-indentation tests were conducted to evaluate the mechanical properties. The results show that adding Bi or Sb could increase the strength of SAC105 alloys mainly due to the solid solution strengthening effect. The creep resistance of SAC105 alloys was also improved with the additions of Bi and Sb. The co-additions of Bi and Sb into SAC105 alloys exhibit an enhanced creep resistance than that calculated by the theoretical calculation. The further addition of Ti into SAC105-1Bi-1Sb alloys demonstrated a much-improved creep resistance, which could be attributed to the synergistic effects of both solid solution strengthening and the precipitation hardening effects.
The attainment of both high strength and high ductility is always the goal for structure materials, because the two properties generally are mutually competing, called strength-ductility trade-off. Nowadays, the data-driven paradigm combined with expert domain knowledge provides the state-of-the-art methodology to design and discovery for structure materials with high strength and high ductility. To enhance both strength and ductility, a joint feature is proposed here to be the product of strength multiplying ductility. The strategy of “divide and conquer” is developed to solve the contradictory problem, that material experimental data of mechanical behaviors are, in general, small in size and big in noise, while the design space is huge, by a newly developed data preprocessing algorithm, named the Tree-Classifier for Gaussian Process Regression (TCGPR). The TCGPR effectively divides an original dataset in a huge design space into three appropriate sub-domains and then three Machine Learning (ML) models conquer the three sub-domains, achieving significantly improved prediction accuracy and generality. After that the Bayesian sampling is applied to design next experiments by balancing exploitation and exploration. Finally, the experiment results confirm the ML predictions, exhibiting novel lead-free solder alloys with high strength high ductility. Various material characterizations were also conducted to explore the mechanism of high strength and high ductility of the alloys.
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