“…Most of the studies [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] employed directional solidification systems to allow microstructural and tensile data to be assessed. After solidifying the casting, transverse (perpendicular to the solidification direction) and longitudinal samples (at various sections from the cooled bottom of the castings) were removed of alloy casting for the metallographic procedure using optical microscopy.…”
In this study, an extensive data set was based on existing literature records in order to enable the suitability of several predictive models, from Multiple Linear Regression (MLR) to Neural Networks (NN). The main objective was to, through regression analyses, generate model computations to correlate tensile properties (UTS- Ultimate Tensile Strength, YTS – Yield Tensile Strength and EF – Elongation-to-Fracture) to a given alloy composition and microstructural spacing. This investigation led to positive results, as the highest accuracies of the trained modules (in 80% of the database) were found to be above ~82% (UTS and EF) and a maximum of ~98% (YTS), when analyzing the results to a test data set. Later, these models were used to define trends for possible next solder alloy commercial compositions. Overall, using the standard model’s setup, the Random Forest and Decision Tree models showed the highest accuracy results, with 0.958 for YTS as opposed to 0.907 for MLR. Moreover, Multilayer Perceptron (MLP)-optimized models yielded the best results for each variable, with the highest increases in accuracy associated with the YTS and EF. The present contribution might imply an important milestone towards alloy design research based on data science guidelines to unlock the full potential of former experiments and their extensive set of results.
“…Most of the studies [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] employed directional solidification systems to allow microstructural and tensile data to be assessed. After solidifying the casting, transverse (perpendicular to the solidification direction) and longitudinal samples (at various sections from the cooled bottom of the castings) were removed of alloy casting for the metallographic procedure using optical microscopy.…”
In this study, an extensive data set was based on existing literature records in order to enable the suitability of several predictive models, from Multiple Linear Regression (MLR) to Neural Networks (NN). The main objective was to, through regression analyses, generate model computations to correlate tensile properties (UTS- Ultimate Tensile Strength, YTS – Yield Tensile Strength and EF – Elongation-to-Fracture) to a given alloy composition and microstructural spacing. This investigation led to positive results, as the highest accuracies of the trained modules (in 80% of the database) were found to be above ~82% (UTS and EF) and a maximum of ~98% (YTS), when analyzing the results to a test data set. Later, these models were used to define trends for possible next solder alloy commercial compositions. Overall, using the standard model’s setup, the Random Forest and Decision Tree models showed the highest accuracy results, with 0.958 for YTS as opposed to 0.907 for MLR. Moreover, Multilayer Perceptron (MLP)-optimized models yielded the best results for each variable, with the highest increases in accuracy associated with the YTS and EF. The present contribution might imply an important milestone towards alloy design research based on data science guidelines to unlock the full potential of former experiments and their extensive set of results.
“…It had been reported that filling with Zn can change the dendrite organization and form finer eutectic in the matrix [ 31 , 32 ]. Ramos et al found that the addition of 0.1–0.7% Zn as an alloying element in SAC305 solder can reduce subcooling [ 33 , 34 ]. The formation of IMC in large Ag 3 Sn plates was inhibited.…”
“…The most frequently studied lead-free solder alloys are binary and ternary Sn-based solder alloy compositions. Ag (Shalaby et al, 2018;Gumaan, 2020), Cu (Shalaby et al, 2017;Sokolov et al, 2017), Zn (Ding et al, 2018;Ramos et al, 2020) and Bi are some of the common alloying elements used in such compositions.…”
Purpose
This paper aims to review recent reports on mechanical properties of Sn-Bi and Sn-Bi-X solders (where X is an additional alloying element), in terms of the tensile properties, hardness and shear strength. Then, the effects of alloying in Sn-Bi solder are compared in terms of the discussed mechanical properties. The fracture morphologies of tensile shear tested solders are also reviewed to correlate the microstructural changes with mechanical properties of Sn-Bi-X solder alloys.
Design/methodology/approach
A brief introduction on Sn-Bi solder and reasons to enhance the mechanical properties of Sn-Bi solder. The latest reports on Sn-Bi and Sn-Bi-X solders are combined in the form of tables and figures for each section. The presented data are discussed by comparing the testing method, technical setup, specimen dimension and alloying element weight percentage, which affect the mechanical properties of Sn-Bi solder.
Findings
The addition of alloying elements could enhance the tensile properties, hardness and/or shear strength of Sn-Bi solder for low-temperature solder application. Different weight percentage alloying elements affect differently on Sn-Bi solder mechanical properties.
Originality/value
This paper provides a compilation of latest report on tensile properties, hardness, shear strength and deformation of Sn-Bi and Sn-Bi-X solders and the latest trends and in-depth understanding of the effect of alloying elements in Sn-Bi solder mechanical properties.
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