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Research on the Optimization of Milling Parameters and Simulation of Thin-walled Parts Based on the Machining Errors Control

Author: LiMu
Tutor: ChenWeiFang
School: Nanjing University of Aeronautics and Astronautics
Course: Mechanical and Electronic Engineering
Keywords: Finite Element Artificial Neural Networks Machining deformation prediction Genetic Algorithms Cutting Parameter Optimization
CLC: TG54
Type: Master's thesis
Year: 2010
Downloads: 216
Quote: 4
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Abstract


The cutting of the thin-walled parts prone to distortion , it is difficult to guarantee the machining accuracy . Around the low - stiffness parts to study the optimization of cutting parameters , has great significance for improving processing accuracy , reduce production costs and improve equipment utilization . The use the modern cutting theory , finite element simulation technology , mathematical modeling and optimization of analytical methods to seek the optimal combination of cutting parameters is an important direction of modern machining process . Metal cutting theory and computer simulation technology to study the process of milling of aluminum alloy , designed deformation prediction model based on artificial neural network processing , and cutting multi-objective optimization model based on genetic algorithms to solve the last CNC milling parameters optimization system development is completed research work are as follows : 1. based hot - machine coupled elastoplastic mechanics theory , the application of ABAQUS finite element simulation software to create a three-dimensional finite element model of the milling process in thin-walled parts analyzed milling process in thin-walled parts machining distortion, and the correctness of the model is verified by experiments . In order to establish the relationship between the cutting parameters and machining deformation , on the basis of the finite element prediction processing deformation to study the artificial neural network method , finite element simulation data input of BP neural network was trained to determine more than cutting parameters under BP network processing deformation prediction model . Established in order to obtain different target milling machining parameter optimization of cutting parameters model optimized cutting parameters , the application of genetic algorithms to find the optimal parameters can be obtained through the comparison of the simulation and empirical parameters of the finite element method , more good processing accuracy, lower cost and higher processing efficiency. On the basis of the above study , as the development platform of VC 6.0 with SQL Server 2000 database , and the development of thin-walled parts CNC milling parameters to optimize the system , the basic data management system , the machining deformation prediction as well as cutting parameters optimization , etc. function.

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Metal cutting and machine tools > Milling and milling machine
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