scholarly journals Research on Rolling Force of Hot Rolling Strip Based on Improved Genetic Algorithm

2015 ◽  
Author(s):  
Xingdong Li
Keyword(s):  
Genetic Algorithm ◽  
Hot Rolling ◽  
Rolling Force ◽  
Improved Genetic Algorithm ◽  
Rolling Strip

An Investigation of the Effect of Speeds of Work Rolls on Rolling Strip

1995 ◽  
Vol 117 (3) ◽  
pp. 341-346 ◽  
Author(s):  
Zone-Ching Lin ◽  
Y. C. Cheng
Keyword(s):  
Hot Rolling ◽  
Rolling Force ◽  
Large Strain ◽  
Work Roll ◽  
Rolling Process ◽  
Analysis Model ◽  
Hot Rolling Process ◽  
Rolling Strip ◽  
Work Rolls ◽  
Thermoelastic Plastic

The paper is an investigation of strip curvature caused by the different speeds between the upper work roll and the lower work roll in the rolling process for an aluminum strip. At the same time, we analyzed the variations in the temperature field and strain field, and used a method of speeds variation of the upper and lower work rolls to calibrate the deformation curvature caused by the coolant condition in the hot rolling process. Based on the large deformation-large strain theory, and by means of the Updated Lagrangean Formulation (ULF) and increment theory, a coupled thermoelastic-plastic analysis model for hot rolling process is thus constructed. At the same time the finite difference method was also used to solve the transient heat transfer equation. Finally, the numerical analysis method developed in this study was employed to analyze the changes in the aluminum strip’s temperature and other changes during rolling. In addition, the average rolling force obtained from the simulation was compared with that from the experiments. It verified that the model in this study is reasonable.

Hot Rolling Batch Planning Problem Model Based on Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 2042-2046
Author(s):  
Hai Tao Li ◽  
Su Jian Li ◽  
Di Wu ◽  
Fang Han ◽  
Fang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hot Rolling ◽  
Production Scheduling ◽  
Steel Production ◽  
Planning Problem ◽  
Planning Model ◽  
Improved Genetic Algorithm ◽  
Problem Model ◽  
Iron And Steel ◽  
Batch Planning

To solve the hot rolling batch planning problem in production scheduling of iron and steel enterprises, a hot rolling batch planning model is formulated based on multiple travelling salesmen problem(MTSP) model. The objective is to minimize the total limit penalty value of adjacent stripped steels in width, thickness and hardness. The main constraints include jumps in width, thickness and hardness between adjacent stripped steels, which are essential for steel production process. An improved genetic algorithm is designed to solve the model. A simulation example shows the reasonability of the model and validity of the algorithm.

The Optimal Allocation of Finishing Train in Steel Rolling Based on Improved Genetic Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 720-724
Author(s):  
Hong Xia Liu ◽  
Xin Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Empirical Distribution ◽  
Rolling Force ◽  
Optimization Methods ◽  
Improved Genetic Algorithm ◽  
Load Curve ◽  
Distribution Method ◽  
Steel Rolling ◽  
Mutation Operators

The central issue of finishing train is that we should distribute the thickness of each exit with reason and determine the rolling force and relative convexity. The optimization methods currently used are empirical distribution method and the load curve method, but they both have drawbacks. To solve those problems we established a mathematical model of the finishing train and introduced an improved Genetic Algorithm. In this algorithm we used real number encoding, selection operator of a roulette and elitist selection and then improved crossover and mutation operators. The results show that the model and algorithm is feasible and could ensure the optimal effect and convergence speed. The products meet the production requirements.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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