Transformation Temperature Predictions Through Computational Intelligence for NiTi-Based Shape Memory Alloys

Machine learning the lattice constant of cubic pyrochlore compounds

“…The introduction of the methodology follows previous studies 36‐59 . GPRs are nonparametric probabilistic models.…”

“…The model is highly stable and accurate that contributes to efficient and low‐cost lattice constant estimations and understandings of which based on ionic radii and electronegativities of both ternary and mixed pyrochlores. As a machine learning technique, 34,35 the GPR model has been used various areas of materials science and engineering to acquire important physicochemical parameters through rapid and robust predictions 36‐59 . This model could serve as a guideline for pyrochlore lattice design and might be adopted for lattice mismatch estimations in thin film configurations.…”

Analysis of Surface Roughness and Flank Wear Using the Taguchi Method in Milling of NiTi Shape Memory Alloy with Uncoated Tools

“…The Ra _opt (0.340 µm) and Vb _opt (0.231 mm) values estimated with the optimization process are given in Table 9. The confidence interval (CI) for Ra and Vb was calculated as specified in Equation (2) given in the previous section.…”

“…Based on the calculation in Equation (2), α = 0.05, F α:1,V2 = 4.747 (F ratio at 95% (in F table)), Ve Ra = 0.0017 (from Table 8), Ve Vb = 0.0068, r = 3, T exp = 18, T dof = 5 and n eff = 3 were found. Using Equations (2) and 3, CI values were calculated as CI Ra = ±0.073 and CI Vb = ±0.147. The Ra exp and Vb exp values obtained from the experimental study remained within the CI.…”

“…The use of smart materials is increasing in the biomedical, aerospace, and automotive industries. Shape memory alloys (SMAs), which are among smart materials, can return to their original form (shape or size) when subjected to a recall process between two transformation phases dependent on temperature or magnetic field [2].…”