The Aluminum Brazing Pieces of Mechanical Performance Based on GRNN Neural Network

2013 ◽  
Vol 455 ◽  
pp. 154-158
Author(s):  
Xing Hua Liang ◽  
Lin Shi ◽  
Yu Si Liu ◽  
Xiao Ming Hua
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Aluminum Alloy ◽  
Mechanical Performance ◽  
Orthogonal Experiment ◽  
Approximation Error ◽  
Orthogonal Test ◽  
Generalized Regression Neural Network ◽  
Absolute Value ◽  
Material Preparation

Aluminum and aluminum alloy were widely used in various industrial fields,the key to application is the reliable welding. In this thesis,LF21 aluminum alloy brazing materials were designed and prepared by Orthogonal experiment,and mechanical properties of the brazing specimen was tested. Based on the GRNN(Generalized Regression Neural Network),Nonlinear relationship modle of brazing material preparation parameters and mechanical properties of the weldment was established.The results show that the model has better stability.When smooth factor value is 0.1,the network approximation error and prediction error absolute value is 0.01%.It can be realized that an nonliner mapping between the aluminum alloy brazing preparation parameters and solder joint tensil strenth based on the Orthogonal test and can do better prediction of the weld mechanical properties,according to brazing material preparation parameters.

scholarly journals Modeling and Composition Design of Low-Alloy Steel’s Mechanical Properties Based on Neural Networks and Genetic Algorithms

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5316
Author(s):  
Zhenlong Zhu ◽  
Yilong Liang ◽  
Jianghe Zou
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Genetic Algorithm ◽  
Alloy Steel ◽  
Deep Neural Network ◽  
Treatment Process ◽  
Mechanical Performance ◽  
Low Alloy Steel ◽  
Forward Selection

Accurately improving the mechanical properties of low-alloy steel by changing the alloying elements and heat treatment processes is of interest. There is a mutual relationship between the mechanical properties and process components, and the mechanism for this relationship is complicated. The forward selection-deep neural network and genetic algorithm (FS-DNN&GA) composition design model constructed in this paper is a combination of a neural network and genetic algorithm, where the model trained by the neural network is transferred to the genetic algorithm. The FS-DNN&GA model is trained with the American Society of Metals (ASM) Alloy Center Database to design the composition and heat treatment process of alloy steel. First, with the forward selection (FS) method, influencing factors—C, Si, Mn, Cr, quenching temperature, and tempering temperature—are screened and recombined to be the input of different mechanical performance prediction models. Second, the forward selection-deep neural network (FS-DNN) mechanical prediction model is constructed to analyze the FS-DNN model through experimental data to best predict the mechanical performance. Finally, the FS-DNN trained model is brought into the genetic algorithm to construct the FS-DNN&GA model, and the FS-DNN&GA model outputs the corresponding chemical composition and process when the mechanical performance increases or decreases. The experimental results show that the FS-DNN model has high accuracy in predicting the mechanical properties of 50 furnaces of low-alloy steel. The tensile strength mean absolute error (MAE) is 11.7 MPa, and the yield strength MAE is 13.46 MPa. According to the chemical composition and heat treatment process designed by the FS-DNN&GA model, five furnaces of Alloy1–Alloy5 low-alloy steel were smelted, and tensile tests were performed on these five low-alloy steels. The results show that the mechanical properties of the designed alloy steel are completely within the design range, providing useful guidance for the future development of new alloy steel.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

KAISER ALUMINUM ALLOY 7049

Alloy Digest ◽  
1999 ◽  
Vol 48 (12) ◽  
Keyword(s):  
Mechanical Properties ◽  
Shear Strength ◽  
Aluminum Alloy ◽  
Physical Properties ◽  
Surface Treatment ◽  
Stress Corrosion Cracking ◽  
Heat Treating ◽  
Kaiser Aluminum ◽  
Resistance To Stress ◽  
Structural Parts

Abstract Kaiser Aluminum Alloy 7049 has high mechanical properties and good machinability. The alloy offers a resistance to stress-corrosion cracking and is typically used in aircraft structural parts. This datasheet provides information on composition, physical properties, hardness, tensile properties, and shear strength as well as fatigue. It also includes information on forming, heat treating, machining, and surface treatment. Filing Code: AL-365. Producer or source: Tennalum, A Division of Kaiser Aluminum.

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