Reliability-based NC milling parameters optimization using ensemble metamodel

2018 ◽  
Vol 97 (9-12) ◽  
pp. 3359-3369 ◽  
Author(s):  
Xiaoke Li ◽  
Jinguang Du ◽  
Zhenzhong Chen ◽  
Wuyi Ming ◽  
Yang Cao ◽  
...  
Keyword(s):  
Parameters Optimization ◽  
Milling Parameters ◽  
Nc Milling

Research on Parameters Optimization of Virtual NC Milling Based on Parallelism Selection Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3498-3503
Author(s):  
Xiu Lin Sui ◽  
Zheng Wei Kong ◽  
Jiang Hua Ge ◽  
Jia Tai Zhang ◽  
Li Juan Jin
Keyword(s):  
Genetic Algorithm ◽  
Physical Simulation ◽  
Parameters Optimization ◽  
Technology Support ◽  
Non Linear Programming ◽  
Milling Parameters ◽  
System A ◽  
Nc Milling ◽  
Optimized Model ◽  
Set Up

To resolve key technology of parameter optimization of virtual NC milling physical simulation system, a multi-objectives optimized model was set up by non-linear programming theory and resolved by improved parallelism selection genetic algorithm. It was optimized when milling characters are sufficiently considered and machine quality was guaranteed. Sub-population size is decided by weighting each optimized object and calculating adaptation to assure importance of optimized objects and fasten convergence velocity. A kind of software that can optimize milling parameters of virtual NC was developed, and optimized strategy and validity of algorithm are verified which provides technology support for realizing virtual NC milling physical system.

Numerical Analysis of Surface Residual Stress of NC Milling 7075-T7451 Aluminum Alloy

2009 ◽  
Vol 407-408 ◽  
pp. 718-722
Author(s):  
Hong Feng Wang ◽  
Dun Wen Zuo ◽  
Li Tao Wang ◽  
Hong Miao ◽  
Hong Jun Wang
Keyword(s):  
Residual Stress ◽  
Aluminum Alloy ◽  
Numerical Analysis ◽  
Mathematic Model ◽  
Regression Testing ◽  
Surface Residual Stress ◽  
Orthogonal Regression ◽  
Milling Parameters ◽  
Finished Surface ◽  
Nc Milling

The mathematic model was established between finished surface residual stress and milling parameters by orthogonal regression testing. The rationality of the model was certified by FEM and test. The simulation hypothesis and process were verified by the model. The test showed that the model and FEM were feasible.

Design Milling Parameters Optimization System Based on Thin-Walled Parts

2011 ◽  
Vol 189-193 ◽  
pp. 2482-2485
Author(s):  
Xin Hua Mao ◽  
Zhi Gang Hu ◽  
Ting Ting Huang
Keyword(s):  
Structural Features ◽  
Parameters Optimization ◽  
Processing Conditions ◽  
Milling Force ◽  
Optimization System ◽  
Milling Parameters ◽  
Low Stiffness ◽  
Thin Walled

Because of its low stiffness and intensity structural features, thin-walled parts affected by milling force, easily produce deformation and vibration among processing. In this paper, by optimizing milling parameters, it can be realized to control the size of the dynamic milling force and the milling state. Then it reaches the purpose to decrease workpiece deformation, and makes processing conditions maintain a stable. It not only reduces deformation caused by the vibration, but also makes thin-walled parts errors meet the tolerance requirements.

Construction of the Optimal Milling Parameters Database for Radar Key Parts

2010 ◽  
Vol 44-47 ◽  
pp. 340-344 ◽  
Author(s):  
Feng Xu ◽  
Jian Jun Zhu ◽  
Dai Qiang Peng ◽  
Xiao Jun Zhang ◽  
Dun Wen Zuo
Keyword(s):  
Data Model ◽  
Material Removal ◽  
Removal Rate ◽  
General Structure ◽  
Basic Function ◽  
Parameters Optimization ◽  
Optimal Method ◽  
Milling Parameters ◽  
Global Optimal ◽  
Entity Relationship

In this paper, the study is carried out on the milling parameters optimization and cutting database development of radar key parts in electronics industry. The method is proposed in detail on parameters optimization. The material removal rate is chosen as optimization objective. The cutting constraints include machining tool power, tool life, surface quality, deformation and strength of milling cutter and the chatter stability. The genetic algorithm is selected as global optimal method. At last, it presents the solutions of cutting database based on Web, which include general structure, basic function and entity relationship data model.

Design of Machine Tool Setting and Parameter Optimization

2013 ◽  
Vol 706-708 ◽  
pp. 1132-1135
Author(s):  
Xiu Fen Xu
Keyword(s):  
Machine Tool ◽  
Optimization Design ◽  
Production Efficiency ◽  
Cutting Parameters ◽  
Economic Benefits ◽  
Parameters Optimization ◽  
Machining Process ◽  
Feed Speed ◽  
Processing Efficiency ◽  
Nc Milling

To solve the existing problems in the NC machining process, the optimization design of cutting parameters in NC milling machine with genetic algorithm. With the maximum production efficiency as the optimization objective, the spindle speed, feed speed, milling width, depth and other parameters as optimal variables, establishes the optimization mathematical model of machine tool. The optimization results show that: parameters optimization can significantly improve the processing efficiency, and bring economic benefits for enterprises

Research on Finite Element Simulation and Parameters Optimization of Milling 7050-T7451 Aluminum Alloy Thin-walled Parts

2020 ◽  
Vol 14 ◽  
Author(s):  
Song Yang ◽  
Tie Yin ◽  
Feiyue Wang
Keyword(s):  
Aluminum Alloy ◽  
Complex Structure ◽  
Parameters Optimization ◽  
Thin Wall ◽  
Machining Parameters ◽  
Milling Parameters ◽  
Machining Deformation ◽  
Milling Temperature ◽  
Thin Walled ◽  
Milling Forces

Background: Thin-walled parts of aluminum alloy are easy to occur machining deformation duo to the characteristics of thin wall, low rigidity, and complex structure. Objective: To reduce and control the machining deformation, it is necessary to select reasonable machining parameters. Method: The influence of milling parameters on the milling forces, milling temperature, and machining deformation was analyzed through the established model based on ABAQUS. Then, the corresponding empirical formula was obtained by MATLAB, and parameters optimization was carried out as well. Besides, a lot of patents on machining thin-walled parts were studied. Results: The results shown that the prediction error of milling forces is about 15%, and 20% of milling temperature. In this case, the optimized milling parameters are as follows: ap=1 mm, ae=0.1 mm, n=12 000 r/min, and f=400 mm/min. It is of great significance to reduce the machining deformation and improve the machining quality of thin-walled parts.

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