Study on the Law of Short Fatigue Crack Using Genetic Algorithm-BP Neural Networks
The initiation and propagation of short crack is a complex process of nonlinear dynamics. The material field has been concerning of the law of short crack and put forward a number of models and predictive equations. But most of them have to meet the problems which are unclear physical parameters meaning, narrow range of applications etc. That is the reason why there is not yet widely recognized quantitative model of crack evolution so far. The introduction of genetic algorithm-BP neural networks could solve those problems by avoiding building the explicit model equation and directly extracting the potential rules from the data. In this paper, the short crack for low cycle is studied under complex stress at high temperature. The material adopted in the experiment is Q245R steel. The initiation, propagation and coalescence of short crack are observed. The comparisons between experiment results and neural network simulation results show that genetic algorithm-BP neural networks simulation method can predict the law of short crack with higher prediction accuracy.
Short fatigue crack behaviour in 316L stainless steel
Abstract Short crack growth mechanism and kinetics were studied under symmetrical push–pull straining in austenitic stainless 316L steel. Fatigue cracks initiated along strain localized areas and crack linking was the main mechanism of the early crack growth. Two different regimes in short crack growth rate were identified. Based on the experimental data, the quantitative description of crack propagation rates was obtained for short and long cracks. The integration of this equation can be used to predict the fatigue life or residual life and to evaluate the number of cycles necessary to reach a given crack length.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.