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.
Computation Method of Processing Time Based on BP Neural Network and Genetic Algorithm
Looking-up standard processing time table is a commonly used and important determination method of processing time. However, the large error in nonstandard nodes brings adverse effect on its accuracy. In view of the problem, a computation method of processing time based on back propagation neural network (BPNN) and genetic algorithm (GA) is proposed. Several key technologies of BPNN based on Matlab, including computation of the number of neurons in hidden layer, determination of training algorithm, and affecting factors of generalization ability, are researched in depth. In order to improve the training efficiency of BPNN, GA is used to optimize its connection weights and thresholds. The encoding method, selection operation, crossover, and mutation operation of GA are discussed in detail. The higher computation precision and faster operation speed of the proposed method is demonstrated through application cases.
An Ultrasensitive Analytical Method Based on Chemiluminescence Sensing Electrochemical Parallel Catalytic Reaction Event
An ultrasensitive electrochemiluminescence (ECL) method on the combination of electrochemical parallel catalytic reaction and chemiluminesence signal sensing was proposed for improving ECL analytical characteristics using vanadate(V) as a representative. Vanadate(V) could be electrochemically reduced to generate vanadate(II) which could be chemically oxidized by potassium periodate to regenerate vanadate(V) and give parallel catalytic wave effect. Then, the reduced product of potassium periodate could react with butyl-rhodamine B to emit a sensitive chemiluminescence signal. The chemiluminescence intensity was correlative with vanadate(V) concentration. The investigation on the electrochemical reaction rate constant (k0) confirmed that the speed of electrochemical reaction was faster than that of the subsequent chemiluminescence reaction. The possibility of the combination of electrochemical parallel catalytic reaction with chemiluminescence signal sensing was proved. The similar ECL behaviors could be observed at zirconia nanowires-Nafion modified electrode. Because of the separation and enrichment effect of the modified electrode on vanadate(V), the selectivity and sensitivity was further improved greatly. Based on these findings, a new concept on the combination of electrochemical parallel catalytic reaction and chemiluminesence signal sensing was proposed and an ultrasensitive ECL method for the determination of vanadate(V) was developed at zirconia nanowires-Nafion modified electrode. Under the optimum experimental conditions, the ECL intensity was linear with the concentration of vanadate(V) in the range of 2.0×10−12 mol/L–2.0×10−10 mol/L. The detection limit was 8.0×10−13 mol/L, which was more than 6 orders of magnitude lower than that observed by electrochemical current transduction for electrochemical parallel catalytic reaction at zirconia nanowires-Nafion modified electrode.