Optimization of Milling Parameter Based on Modified Genetic Algorithm

Jing Ying Zhang; Si Qin Pang; Qi Xun Yu

doi:10.4028/www.scientific.net/KEM.431-432.531

Paper Titles

Study on the Fabrication and Cutting Performance of HFCVD Diamond Coated Silicon Nitride Inserts
p.515

Simulation of the Temperature Distribution on Interior Hole Surfaces of Drawing Dies in HFCVD Reaction
p.519

Microstructure and Mechanical Properties of Multi-Scale Titanium Diboride Matrix Nanocomposite Ceramic Tool Materials
p.523

Simulation of the Effect of Initial Grain Size of Matrix on Microstructure Evolution in Nano-Composite Ceramic Tool Material
p.527

Optimization of Milling Parameter Based on Modified Genetic Algorithm
p.531

Fabrication and Characterization of NiCr/NiSi Functional Thin Films on Temperature Measurement of Cutter Sensor
p.535

Milling Force Modeling for Ti-5Al-5Mo-5V-1Cr-1Fe Alloy
p.539

Cutting Parameters Optimization by Fuzzy Synthetic Evaluation and BP Neural Network in Milling Aluminum Alloy
p.543

An Investigation of the Wear Mechanism for Carbide Tools in Face Milling the Ti-5Al-4.75Mo-4.75V-1Cr-1Fe Alloy
p.547

HomeKey Engineering MaterialsKey Engineering Materials Vols. 431-432Optimization of Milling Parameter Based on...

Optimization of Milling Parameter Based on Modified Genetic Algorithm

Abstract:

This article establishes the multi-target optimization model of numerically-controlled milling parameter and illustrates the optimization design and system realization through modified Genetic Algorithm (GA). This system adopts double-breakpoint crossing and self-adaptive mutation and inverse operator through transgenic technology. It improves its local searching ability and calculation speed.