Data analysis of high-cycle fatigue testing on piston aluminum-silicon alloys under various conditions: Wear, lubrication, corrosion, nano-particles, heat-treating, and stress

Analyzing experimental data from reciprocating wear testing on piston aluminum alloys, with and without clay nano-particle reinforcement

“…Finally, the R 2 value demonstrates the fitness quality of the nonlinear equation of experimental data. More details could be found in the literature [15 , 16] .…”

Data analysis of striation spacing, lifetime, and crack length in crankshaft ductile cast iron under cyclic bending loading through high-cycle fatigue regime

“…These results are the summation of squares, the degree of freedom (df), the mean square, the F-value, and the P-value. Notably, a value lower than 0.05 for the P-value (equal to 95% of the confidence level) means the parameter is effective on the output and its influence is significant [6] .…”

Effect of Training Data Ratio and Normalizing on Fatigue Lifetime Prediction of Aluminum Alloys with Machine Learning

“…This segment is based on the experimental dataset described by Azadi and Parast (8). The goal was to evaluate how different ISO 1143 standard specimens perform in corrosion fatigue, pure fatigue, and fretting fatigue tests with varying inputs in this work (8). Furthermore, these specimens were manufactured using a commercially known alloy called AlSi12CuNiMg, which has widespread application in the automotive manufacturing industry.…”