The Hot Pressing Process Optimization of Ti(C, N) Matrix Nano-Composite Ceramic Die Materials with the Back Propagation Neural Network and Immune Genetic Algorithm

2013 ◽  
Vol 652-654 ◽  
pp. 2086-2092
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Hui Fa Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fracture Toughness ◽  
Flexural Strength ◽  
Hot Pressing ◽  
Back Propagation ◽  
Composite Ceramic ◽  
Back Propagation Neural Network ◽  
Immune Genetic Algorithm ◽  
Nano Composite

The two hybrid algorithms of back propagation neural network and immune genetic algorithm were used in the optimum design of the hot pressing parameters of Ti(C, N) matrix nano-composite ceramic die material. The BP algorithm could set up the relationship well between the hot pressing parameters and single mechanical property. Compared with the experimental value, the relative error of fracture toughness, hardness and flexural strength is only 4.65%, 0.23% and 4.05%, respectively. After analyzed the predicted results, the best predicted results were the sintering temperature was about 1455°C and the holding time was 11 min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

Effect of Hot Pressing Technology on the Microstructure and Mechanical Properties of ZrO2 Nano-Composite Ceramic Tool and Die Material

2011 ◽  
Vol 686 ◽  
pp. 396-400
Author(s):  
Ming Dong Yi ◽  
Chong Hai Xu ◽  
Jing Jie Zhang ◽  
Zhen Yu Jiang
Keyword(s):  
Mechanical Properties ◽  
Fracture Toughness ◽  
Flexural Strength ◽  
Fracture Mode ◽  
Hot Pressing ◽  
Composite Ceramic ◽  
Ceramic Tool ◽  
Nano Composite ◽  
Die Material ◽  
Pressing Technology

A new nano-composite ceramic tool and die material was prepared by vacuum hot pressing technique. The effect of hot pressing technology on the microstructure and mechanical properties of ZrO2nano-composite ceramic tool and die material was investigated systemically, and the ceramic tool and die material with good mechanical properties was fabricated successfully. Results show that, the highest flexural strength, fracture toughness and hardness of ZrO2nano-composite ceramic tool and die material reaches 1055 MPa, 10.57 MPa∙m1/2 and 13.59 GPa, respectively by means of the vacuum hot pressing technique at 1430 °C for 60min at 35MPa. The flexural strength and fracture toughness has been improved greatly by the optimization of hot pressing technology. In the materials, the optimum sinter process could ensure the t-ZrO2stabilized till room temperature that can enhance the toughening effect of ZrO2. The microstructure of ZrO2nano-composite ceramic tool and die materials were improved by the optimization of hot pressing technology, and the fracture mode is the typical mixed trans/inter-granular fracture mode.

A Father-Keeping Immune Genetic Algorithm Based Neural Network Model for Properties Prediction of Carbon Fiber

2013 ◽  
Vol 699 ◽  
pp. 921-925
Author(s):  
Jia Jia Chen ◽  
Yong Sheng Ding ◽  
Kuang Rong Hao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Carbon Fiber ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Experiment Data ◽  
Immune Genetic Algorithm ◽  
Manufacture Process ◽  
Forecasting Method ◽  
Search Precision

Aiming to guide the manufacture process of carbon fiber and obtain high properties productions, we propose a hybrid algorithm named father-keeping immune genetic algorithm based on back propagation neural network (FKIGA-BP) as a properties prediction model. The present study also compares it with BP neural network forecasting method. It shows better search precision and convergence efficiency. The prediction results are consistent with the practical experiment data.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

2016 ◽  
Vol 7 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Search ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Information Need ◽  
Data Set ◽  
User Query ◽  
User Queries

In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

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