Bending deformation prediction in a welded square thin-walled aluminum alloy tube structure using an artificial neural network

2021 ◽  
Author(s):  
Chunbiao Wu ◽  
Chao Wang ◽  
Jae-Woong Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Deformation Prediction ◽  
Bending Deformation ◽  
Alloy Tube ◽  
Thin Walled ◽  
Artificial Neural ◽  
Aluminum Alloy Tube ◽  
Tube Structure

Deformation Prediction Based on BP Artificial Neural Network of Milling Thin-Walled Aluminum Alloy Parts

2014 ◽  
Vol 687-691 ◽  
pp. 492-495
Author(s):  
Hua Bing Ouyang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Prediction Method ◽  
Aviation Industry ◽  
Deformation Prediction ◽  
Bp Artificial Neural Network ◽  
Thin Walled ◽  
Artificial Neural

Thin-walled aluminum alloy parts are widely used in the aviation industry. In order to predict the deformation of milling aluminum alloy 7075-T7451 thin-walled parts, a deformation prediction method based on BP artificial neural network is presented. Firstly, the orthogonal experiment is designed to acquire the experimental data. Secondly, the BP neural network model of deformation prediction based on the experimental data is established. The comparison of the simulated values with the experimental results is performed to validate the proposed model. Lastly, the result shows that the proposed deformation prediction model is reasonable and can be used to predict the milling deformation.

Applications of Artificial Neural Network and Genetic Algorithm on Copper Tube Drawing

2012 ◽  
Vol 557-559 ◽  
pp. 2039-2044
Author(s):  
Yan Min Meng ◽  
Jin Song Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Copper Alloy ◽  
Orthogonal Experiment ◽  
Development Trend ◽  
Database Integration ◽  
Knowledge Reasoning ◽  
Alloy Tube ◽  
Artificial Neural

The processing of copper alloy tube is a typical processing technology with kinds of varieties, specifications and procedures. Its technology design is not only very alternative, but also has the development trend of integration, intelligence and automation. Research is based on the drawing technology after the cast & roll procedure of copper alloy tube. The expert database system of tube process was developed with the methods of knowledge reasoning, orthogonal experiment design, artificial neural network, genetic algorithm, numerical simulation, CAD parameter optimization and database integration. Thus, the intelligent design of floating plug drawing procedure was accomplished.

Predicting Fatigue Life of Pre-Corroded LC4 Aluminum Alloy by Artificial Neural Network

2010 ◽  
Vol 118-120 ◽  
pp. 221-225 ◽  
Author(s):  
Cheng Long Xu ◽  
Sheng Li Lv ◽  
Zhen Guo Wang ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Fatigue Life ◽  
Network Architecture ◽  
Fatigue Tests ◽  
Data Sets ◽  
Absolute Relative Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this work was to predict the fatigue life of pre-corroded LC4 aluminum alloy by applying artificial neural network (ANN). Specimens were exposed to the same corrosive environment for 24h, 48h, and 72h. Fatigue tests were conducted under different stress levels. The existing experimental data sets were used for training and testing the construction of proposed network. A suitable network architecture (2-15-1) was proposed with good performance in this study. For evaluating the method efficiency, the experimental results have been compared to values predicted by ANN. The maximum absolute relative error for predicted values does not exceed 5%. Therefore it can be concluded that using neural networks to predict the fatigue life of LC4 is feasible and reliable.

Displacement Prediction for Soil Nailing Based on ANN

2013 ◽  
Vol 353-356 ◽  
pp. 614-618
Author(s):  
Lang Gao ◽  
Zhao Wen Tang ◽  
Quan Zhong Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Nailing ◽  
Deep Excavation ◽  
Engineering Project ◽  
Deformation Prediction ◽  
Displacement Prediction ◽  
Artificial Neural ◽  
Excavation Support ◽  
Artificial Neural Network Ann

Soil nailing has become an important excavation support system for its good performance and cost-effectiveness. It is complicated to predict deformation of soil nailing during excavating. The Artificial Neural Network (ANN) is developed very quickly these years, which can be applied in diverse applications such as complex non-linear function mapping, pattern recognition, image processing and so on, and has been widely used in many fields, including geotechnical engineering. In this paper, the artificial neural network is applied for deformation prediction for soil nailing in deep excavation. The time series neural networks-based model for predicting deformation is presented and used in an engineering project. The results predicted by the model and those observed in the field are compared. It is shown that the artificial neural network-based method is effective in predicting the displacement of soil nailing during excavation.

Artificial Neural Network for Predicting the Flow Behaviors of Hot Compressed 2124-T851 Aluminum Alloy

2010 ◽  
Vol 146-147 ◽  
pp. 720-723
Author(s):  
Yong Cheng Lin ◽  
Xiao Min Chen ◽  
Yu Chi Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Flow Stress ◽  
Strain Rate ◽  
Learning Algorithm ◽  
Ann Model ◽  
Wide Range ◽  
Feed Forward Network ◽  
Artificial Neural

The compressive deformation experiments of 2124-T851 aluminum alloy were carried out over a wide range of temperature and strain rate. An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the flow behaviors of hot compressed 2124-T851 aluminum alloy and working conditions. The input parameters of the model consist of strain rate, forming temperature and deformation degree whereas flow stress is the output. A three layer feed-forward network with 15 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. Good performance of the ANN model is achieved. The predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.

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