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Microalloyed steels have emerged to replace conventional plain-carbon steels to achieve longer wheel life on Chinese railroads. In this work, with the aim of preventing spalling, a mechanism that consists of ratcheting and shakedown theory correlated with steel properties is systematically investigated. Mechanical and ratcheting tests were carried out for microalloyed wheel steel to which vanadium was added in the range of 0–0.15 wt.% and the results were compared with that obtained for conventional plain-carbon wheel steel. The microstructure and precipitation were characterized via microscopy. As a result, the grain size was not obviously refined, and the pearlite lamellar spacing decreased from 148 nm to 131 nm in microalloyed wheel steel. Moreover, an increase in the number of vanadium carbide precipitates was observed, which were mainly dispersed and uneven, and precipitated in the pro-eutectoid ferrite region, in contrast to the observation of lower precipitation in the pearlite. It has been found that vanadium addition can lead to an increase in yield strength by precipitation strengthening, with no reduction or increase in tensile strength, elongation or hardness. The ratcheting strain rate for microalloyed wheel steel was determined to be lower than that for plain-carbon wheel steel via asymmetrical cyclic stressing tests. An increase in the pro-eutectoid ferrite content leads to beneficial wear, which can diminish spalling and surface-initiated RCF.

This article considers a new improved balanced joint progressive type-II censoring scheme based on two different populations, where the lifetime distributions of two populations follow the generalized inverted exponential distribution with different shape parameters but a common scale parameter. The maximum likelihood estimates of all unknown parameters are obtained and their asymptotic confidence intervals are constructed by the observed Fisher information matrix. Furthermore, the existence and uniqueness of solutions are proved. In the Bayesian framework, the common scale parameter follows an independent Gamma prior and the different shape parameters jointly follow a Beta-Gamma prior. Based on whether the order restriction is imposed on the shape parameters, the Bayesian estimates of all parameters concerning the squared error loss function along with the associated highest posterior density credible intervals are derived by using the importance sampling technique. Then, we use Monte Carlo simulations to study the performance of the various estimators and a real dataset is discussed to illustrate all of the estimation techniques. Finally, we seek an optimum censoring scheme through different optimality criteria.