Application of BP-GA Algorithm in Optimization of Process Parameters in Thin Strip Tandem Cold Rolling

2013 ◽  
Vol 774-776 ◽  
pp. 1042-1045
Author(s):  
Li Chen Wang ◽  
Ji Shun Song ◽  
Jian Zhang ◽  
Pan Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cold Rolling ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Rolling Force ◽  
Thin Strip ◽  
Tandem Cold Rolling ◽  
Optimization Of Process Parameters ◽  
Ga Algorithm

The process parameters of thin strip tandem cold rolling were optimized based on the BP neural network and the genetic algorithm with which the rolling energy consumption required was reduced and could contribute to the rolling force and the thickness control.

Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network

2021 ◽  
Author(s):  
Lei Gao ◽  
Feng Li ◽  
Peng Da Huo ◽  
Chao Li ◽  
Jie Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Bonding Strength ◽  
Prediction Accuracy ◽  
High Performance ◽  
Orthogonal Experiment ◽  
Optimization Method ◽  
Factors Affecting

Abstract As a widely recognized optimization method, BP neural network can provide scientific guidance for the formulation of reasonable process parameters. However, due to the randomness of its own weights and thresholds, the prediction accuracy remains to be further improved. The forming and manufacturing of heterogeneous welded sheet is a new extrusion connection method. There are many factors affecting the bonding quality, which brings trouble to the evaluation of bonding strength and quality. In this paper, orthogonal experiment, finite element simulation and process experiment were used to design and verify the key process parameters that affected the bonding strength of heterogeneous sheets. BP neural network and genetic algorithm neural network were used to predict the bonding strength. The results showed that the genetic algorithm neural network model has higher reliability, and the prediction accuracy was 99.5 %. Compared with the traditional BP neural network, the prediction accuracy was improved by 5.78 %, and the error was reduced to 0.5 %. It has good generalization ability, and provides a new way for intelligent reliability evaluation of high performance heterogeneous sheets extrusion manufacturing.

Rolling Force Prediction System of Cold Rolling Process Based on BP Neural Network

2013 ◽  
Vol 690-693 ◽  
pp. 2361-2365 ◽  
Author(s):  
Wei Teng ◽  
Guang Ming Wang
Keyword(s):  
Neural Network ◽  
Cold Rolling ◽  
Bp Neural Network ◽  
Rolling Force ◽  
Rolling Process ◽  
Prediction System ◽  
Force Prediction ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Generic Process

This paper took the example of rolling force prediction in the cold rolling process to describe the establishment and application of BP neural network prediction system. This system is a prediction model for generic process. Users can select different parameters to train the network structure according to their needs, and can calculate relative rolling force parameters based on the known structure. This system can provide very valuable process information for workers and researchers .

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