Papers by Author: Hong Yan Duan

Abstract: The fracture problems of medium carbon steel under extra-low cycle bend torsion fatigue loading were studied using artificial neural networks (ANN) in this paper. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and notch open angle, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Abstract: The gradient calcium phosphate bioceramic coating was produced on titanium alloy substrate by laser cladding. The microstructure, microhardness, fracture toughness, and residual stress of the tatanium-based gradient bioceramic composite coating were investigated. The results show that the microhardness gradually decreases with further depth increasing cross-section. The highest microhardness of the coating and the transition layer is 1544HV and 1160HV, respectively. The fracture toughness K_IC is 3.72±0.03 MPa·m^1/2 of bioceramic coating and 4.55±0.02 MPa·m^1/2 of the transition layer, which is closely resembles the human compact bone. Furthermore, the residual stress gradually decreases from the coating to substrate, which is 221MPa between ceramic layer and the transition layer and 108MPa between the transition layer and substrate. This distribution is conforms to gradient composition design, which reducing harm of the specimen deformation and cracking.

Abstract: The special clamp for fatigue of shaft under bending, torsion, and bending-torsion that used on the fatigue machine is designed and manufactured. The low-cycle fatigue experiments of shaft with annular notch under cantilever bending have been made. Through experiments and analysis, the effects of tip radius, depth and open angle of notch on low cycle fatigue life of shaft with annular notch under cantilever bending are obtained. The method and results will play an important role on the fatigue life prediction and anti-fatigue design.

Abstract: Artificial neural network (ANN) back-propagation model was developed to predict the thermal expansion behavior and internal residual strains in reinforced ceramic matrix composites (CMCS).The ANN training model has been used to predict the thermal expansion behavior and internal residual strains, exhibiting excellent comparison with the experimental results. It was concluded that predicted thermal expansion behavior and internal residual strains by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the neural network architecture is designed. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result shows that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Abstract: Artificial neural network (ANN) back-propagation model was developed to predict the fracture design parameters in reinforced ceramic matrix composites (CMCS).Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used.

Abstract: The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Abstract: The fracture problems of medium carbon steel under extra-low cycle axial fatigue loading were studied using artificial neural network in this paper. The training data were used in the formation of training set of artificial neural network. The artificial neural network model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. Training artificial neural network model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The artificial neural network model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The result show that the training model has good performance, and the experimental data and predicted data from artificial neural network are in good coherence.