This study explores the effect of adding 3wt.% Y to pure magnesium (Mg)
on its mechanical behavior under cyclic torsional loadings at room
temperature. The research examines deformation and cracking modes in
both pure Mg and Mg-3Y samples. Deformation modes are monitored using
quasi-in-situ EBSD observations coupled with slip trace analysis. The
findings reveal that basal slip dominates the cyclic deformation
throughout the fatigue life of the pure Mg sample, while both basal and
pyramidal slip dominate the cyclic deformation in the Mg-3Y sample.
Intergranular cracking is the primary cracking mode for both samples
under cyclic torsional loadings. Basal and pyramidal slip PSB cracking
serves as a primary transgranular cracking mode in the pure Mg and Mg-3Y
samples, respectively. The study also investigates the underlying
mechanism governing the activity of various deformation modes, cracking
modes, and mechanical behavior.
The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction.
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