High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al30Cu17.5Fe17.5Cr17.5Mn17.5 alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.

The activation energy to create a phase transformation or for the reaction to move to the next stage in the milling process can be calculated from the slop of the DSC plot, obtained at the various heating rates for mechanically activated Al-Ni alloy systems by using Kissinger's equation. The mechanically activated material has been called “the driven material” as it creates new phases or intermetallic compounds of AlNi in Al-Ni alloy systems. The reaction time for phase transformation by milling can be calculated using the activation energy obtained from the above mentioned method and from the real required energy. The real required energy (activation energy) could be calculated by subtracting the loss energy from the total input energy (calculated input energy from electric motor). The loss energy and real required energy divided by the reaction time are considered the “metabolic energy” and “the effective input energy”, respectively. The milling time for phase transformation at other Al-Co alloy systems from the calculated data of Al-Ni systems can be predicted accordingly.

The purpose of this paper is to examine the effects on reliability of equipment or product which spends a great deal of its time in the dormant condition. Many systems experienced periods of dormancy throughout their life cycle, such as periods of operati

This study was intended to estimate the axial deformation of joint between pavement modules in the rapid-constructible modular pavement system, and to investigate the applicability of two-phase composites for a joint material, which was composed of cement paste, epoxy, or polyurethane as a matrix and sand as particles.

The aspects of Omega phase prediction are briefly reviewed, and Swanson's Model and Pierce's Model are presented. The equations for the Omega phase prediction and the most probable coefficients of the propagating equations are derived on the base of Pierce's Model by the least square method. The coefficients are calculated from the data which are the phase differences between the pairs of the Station A (Aldra, Norway), C(Haiku, Hawaii), and D(La Mour, North Dakota) observed at Busan Harbor of the South Coast of Korea in June and September, 1976. It is clearly shown that the standard deviations of the observed lane values at Busan Harbor are as followed: 1. June, 1976. Pair (A-C) : 0.1446 Pair (C-D) : 0.2598 2.September, 1976. Pair (A-D) : 0.3958 Pafr (C-D) : 0.3278 As a conclusion of the above investigation, it is shown that the Omega phase velocity can be predicted by the method, proposed in this paper, of analyzing the diurnal and seasonal variations of the Omege phase velocity except SID, PCD and AZD. If more observed data are employed, more exact Omega phase velocity is expected to be obtained.