The Optimization of Cutting Parameters of Cast Iron Based on the Genetic Algorithm

2012 ◽  
Vol 424-425 ◽  
pp. 1102-1106
Author(s):  
Hao Jin Yang ◽  
Xu Hong Guo ◽  
Dong Dong Wan ◽  
Ting Ting Chen
Keyword(s):  
Genetic Algorithm ◽  
Cast Iron ◽  
Production Efficiency ◽  
Cutting Parameters ◽  
Optimum Combination ◽  
Algorithm Analysis ◽  
Process Data ◽  
Optimization Of Cutting Parameters ◽  
The Mathematical Model ◽  
New Situation

Data timeliness of cutting process data is so intenser under the new situation than ever .It’s especially necessary to propose the physical model on which the optimization of cutting parameters is based. In this paper the mathematical model is established by the analysis of the data measured from the cast iron experiments, then use MATLAB genetic algorithm analysis to calculate the optimum combination of cutting parameters. The results show that the optimum combination of cutting parameters could improve the production efficiency in practice.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

The Optimization of Precision Milling Parameters for Titanium Based on Genetic Algorithm

2012 ◽  
Vol 472-475 ◽  
pp. 1078-1081
Author(s):  
Wen Sheng Zhang ◽  
Hong Xiang Wang ◽  
Hong Du ◽  
Tao Xu
Keyword(s):  
Genetic Algorithm ◽  
Production Efficiency ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Cutting Parameters ◽  
Function Optimization ◽  
Cnc Machining ◽  
Production Costs ◽  
Milling Parameters ◽  
Low Efficiency

The genetic algorithm converges faster compared with the traditional optimization algorithm, the global optimal solution can be quickly obtained and it is very effective for multi-peak function optimization. A milling process parameter optimization model is established for titanium based on genetic algorithm in this paper, the relevant constraints is considered and the optional titanium milling parameters is achieved based on the targets of maximum production efficiency and minimum cost, utilizing MATLAB optimization software to program, the best combination of cutting parameters is got finally. Experimental results show that the cutting efficiency and production costs are significantly improved with the optimized cutting parameters, so that the defects of low efficiency in CNC machining resulting in relying on experienced cutting parameters is overcome.

Research on Optimization of Cutting Parameters Based on Genetic Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4640-4645 ◽  
Author(s):  
Shu Ren Zhang ◽  
Xue Guang Li ◽  
Jun Wang ◽  
Hui Wei Wang
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Surface Quality ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Experiment Data ◽  
Optimization Of Cutting Parameters ◽  
Working Efficiency

Three elements of Cutting dosages have a great effect on parts surface quality and working efficiency, the best organization of cutting three elements for parts surface quality must be found before machining, in order to surface roughness and machining cost of parts, multi-objective optimization model is established in this paper, model is solved by using genetic algorithm. Based on the BP neural network of three layers, forecasting model of surface roughness is established. According to existing experiment data and optimized cutting dosages, analysis and prediction of surface roughness is done. Machining experiment is done by using optimized data. The experiment result verifies feasibility of this optimistic method and prediction method of surface roughness.

Parameters Optimization and Experimental Analysis in High Speed Machining

2012 ◽  
Vol 591-593 ◽  
pp. 480-483
Author(s):  
Huan Lin ◽  
Dong Qiang Gao ◽  
Zhong Yan Li ◽  
Jiang Miao Yi
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Production Efficiency ◽  
Cutting Parameters ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
High Speed Machining ◽  
Before And After ◽  
Processing Parameters Optimization ◽  
Cutting Parameters Optimization

First of all, in the cutting parameters optimization, according to the different processing conditions, optimization variables selection is different, including production efficiency the objective function and the constraint conditions of the machine tool. And then using genetic algorithm to build a high-speed processing parameters optimization model. The mathematical model explores the best solution through the software Matlab, and gets the optimal combination between the parameters of each cutting; high speed machining cutting parameters provides the reference for the choice of the user. Through the optimization of the comparison of the before and after that, using genetic algorithm cutting parameters optimization, mach inability got obvious improvement, in order to ensure the quality of processing also achieve the maximization of the production efficiency.

An Optimization Model on Cutting Parameters of Material Processing for High Productivity

2016 ◽  
Vol 723 ◽  
pp. 214-219
Author(s):  
Mei Xia Yuan ◽  
Xi Bo Wang ◽  
Li Jiao ◽  
Shao Nan Liu
Keyword(s):  
Material Processing ◽  
Optimization Model ◽  
Production Efficiency ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Complex Method ◽  
High Productivity ◽  
Model Based ◽  
Constraint Model ◽  
Optimization Of Cutting Parameters

The selection of cutting parameters of material processing such as cutting speed, feed and cutting depth affects product productivity. The optimization of cutting parameters achieves the purpose of raise the productivity. Based on the research of the cutting theory, the turning constraint model and the optimization mathematical model of cutting parameters are established for highest productivity. The optimization process of the complex method is studied. The cutting parameters of the material 310s are optimized by the optimization model based on complex method. The optimized cutting time is shorter 2.06s than the one before the optimization. The production efficiency increases by 15.45%. The results show that the optimization model based on the complex method can improve the productivity of material processing.

Design Parameters Optimization of Cutting Head of Cantilever Roadheader Based on Genetic Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 193-196 ◽  
Author(s):  
Gang Liu ◽  
De Sen Mu ◽  
Shi Xi Duan ◽  
De Chao Song
Keyword(s):  
Genetic Algorithm ◽  
Kinetic Parameters ◽  
Optimization Design ◽  
Production Efficiency ◽  
Rock Strength ◽  
Cutting Parameters ◽  
Parameters Optimization ◽  
Design Parameters ◽  
Cutting Head ◽  
Cutting Parameters Optimization

For hard rocks whose protodyakonov scale of hardness f are greater than 8, research on the optimization design of cutting head of cantilever roadheader. Optimization model of the cutting head was established, in this model, the objective function was established based on dust mount, energy consumption and production efficiency. The design variables contain cutting head structural and kinetic parameters, the constraints are determined in accordance with working condition and practical experience. Cutting head parameters are optimized with parameters change related to rock strength, using genetic algorithm in Matlab7.0. A variety of cutting parameters optimization results related to rock strength are of great significance for the structural design of the cutting head and selection of kinetic parameters.

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