PREDICTIVE MODELING OF SURFACE ROUGHNESS AND CUTTING PARAMETERS OPTIMIZATION IN ULTRA-PRECISION TURNING BASED ON GENETIC ALGORITHM

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.215-216.193 ◽

2012 ◽

Vol 215-216 ◽

pp. 193-196 ◽

Cited By ~ 1

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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Cutting Parameters Optimization of Thin-Walled Workpiece Based on Deflection Control

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.407-408.448 ◽

2009 ◽

Vol 407-408 ◽

pp. 448-451

Author(s):

Wei Fang Chen ◽

Jiu Hua Xu ◽

Zheng Hua Shen ◽

Hua Chen

Keyword(s):

Genetic Algorithm ◽

Finite Element Method ◽

Finite Element ◽

Iterative Method ◽

Cutting Parameters ◽

Optimization Method ◽

Parameters Optimization ◽

Prototype System ◽

Thin Walled ◽

Cutting Parameters Optimization

Thin-walled workpiece is prone to produce deformation in the process of machining because of cutting and clamping forces. In this paper, a model of cutting parameters optimization is proposed to control the deflection. The influence of deflection on nominal milling depth is taken into account and the machining deflection is computed by iterative method. Based on the optimization model, a prototype system is developed to optimize the cutting parameters for a thin-walled workpiece with the genetic algorithm and finite element method. Finally, a simulation example is used to demonstrate the feasibility of the cutting parameters optimization method. The simulation result can be further employed into practical machining situation.

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Optimization of Cutting Conditions in Ultra-Precision Turning Based on Mixed Genetic-Simulated Annealing Algorithm

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.315-316.617 ◽

2006 ◽

Vol 315-316 ◽

pp. 617-622

Author(s):

Ze Sheng Lu ◽

Ming Hai Wang

Keyword(s):

Genetic Algorithm ◽

Surface Roughness ◽

Simulated Annealing ◽

Prediction Model ◽

Simulated Annealing Algorithm ◽

Cutting Conditions ◽

Precision Turning ◽

Genetic Simulated Annealing Algorithm ◽

Ultra Precision ◽

Annealing Algorithm

In ultra-precision turning process, the predictive modeling of surface roughness and the optimization of cutting conditions are the key factors to improve the quality of products and raise the efficiency of equipments. In this paper, the application of genetic algorithm in identifying nonlinear surface roughness prediction model is discussed, and presents mixed genetic-simulated annealing algorithm approach to optimization of cutting conditions in ultra-precision turning. The experiment was carried out with diamond cutting tools, for machining single crystal aluminum optics covering a wide range of machining conditions. The results of fitting of prediction model and optimal cutting conditions using genetic algorithm (GA) are compared with least square method and traditional optimal method.

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Study on Cutting Parameters Optimization of Ball-End Cutters Milling for 2A70 Aluminum Alloy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.144 ◽

2014 ◽

Vol 602-605 ◽

pp. 144-147

Author(s):

Jun Min Xiao ◽

Jin Xie

Keyword(s):

Surface Roughness ◽

Aluminum Alloy ◽

Prediction Model ◽

Cutting Parameters ◽

Parameters Optimization ◽

Least Square ◽

Production Costs ◽

Machining Efficiency ◽

Machined Surface ◽

Cutting Parameters Optimization

Based on experiments of ball-end cutters milling for 2A70 aluminum alloy, the prediction model of surface roughness for 2A70 aluminum alloy is established by using of regression analysis method of least square. Aiming at the actual milling problem in the enterprise the cutting parameters are optimized by using of optimization tool-box of MATLAB software, in the process of solving optimized parameters the machining efficiency is set as the objective function and surface roughness prediction model is set as the constraint condition. The optimized cutting parameters can greatly improve the machining efficiency in the premise of ensuring the quality of machined surface, and it provides the important theory evidence and case reference for NC machining enterprises to reduce production costs.

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Parameter Identification of Surface Roughness in Ultra-Precision Turning Processing Based on GA-RBF Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.940.267 ◽

2014 ◽

Vol 940 ◽

pp. 267-270

Author(s):

Jie Lai Chen

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Surface Roughness ◽

Prediction Model ◽

Parameter Identification ◽

Curve Fitting ◽

Basis Function ◽

Matlab Toolbox ◽

Precision Turning ◽

Ultra Precision

The modeling of surface roughness is the key factor to improve the quality of products and raise the efficiency of process equipment in ultra-precision turning process. In order to analyze the influence of each cutting parameters on surface roughness, the prediction model of surface roughness was constructed, and parameter identification of the prediction model was required. To meet the requirement, the curve fitting tool of MATLAB toolbox and radial basis function neural network optimized by genetic algorithm were respectively used as a case study to validate the feasibility and reliability of the method proposed in this paper. The parameter identification results were given, and the comparison of the parameter identification results shows that the parameter identification based on radial basis function neural network optimized by genetic algorithm has higher prediction accuracy than curve fitting tool of MATLAB toolbox.

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