Milling Parameters Optimization of MgCa0.8 Alloy Based on Orthogonal Experiment

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.

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Research on Cutting Parameters Optimization of High-Speed Milling Based on Orthogonal Experiment

Intelligent Robotics and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-540-88518-4_112 ◽

2008 ◽

pp. 1047-1055

Author(s):

Yubo Liu ◽

Can Zhao ◽

Jinping Hu ◽

Mingjun Feng

Keyword(s):

High Speed ◽

Orthogonal Experiment ◽

Cutting Parameters ◽

Parameters Optimization ◽

High Speed Milling ◽

Cutting Parameters Optimization

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Construction of the Optimal Milling Parameters Database for Radar Key Parts

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.340 ◽

2010 ◽

Vol 44-47 ◽

pp. 340-344 ◽

Cited By ~ 1

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.

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Study of Low Carbon Low Alloy Steel Annealing Process Parameters Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.631-632.649 ◽

2013 ◽

Vol 631-632 ◽

pp. 649-659

Author(s):

Mao Yu Zhao ◽

Qian Wang Chen

Keyword(s):

Yield Strength ◽

Alloy Steel ◽

Process Parameters ◽

Orthogonal Experiment ◽

Annealing Process ◽

Parameters Optimization ◽

Grey Theory ◽

Low Carbon ◽

Low Alloy Steel ◽

Sem Images

A suitable match of annealing process parameters is critical for obtaining the fine microstructure of material. Low carbon low alloy steel (20CrMnTi) was heated for various durations near Ac temperature to achieve fine pearlite and ferrite grains. Annealing temperature and time were used as independent variables, and material property data were acquired by orthogonal experiment under intercritical annealing followed by subcritical annealing process (IASAP). The weights of plasticity (hardness, yield strength, section shrinkage, and elongation) of annealing material were calculated by analytic hierarchy process, and then the process parameters were optimized by using the grey theory system. The results observed by SEM images show that the optimized material microstructure consists of refining and distributing uniformly ferrite-pearlite grains, and smaller lamellar cementites. Morphologies on tension fracture surface of the optimized material indicates that the numbers of dimple fracture show more finer toughness obviously comparing with other annealing materials. Moreover, the yield strength value of the optimized material decreases apparently measured by tensile test. Thus, the new optimized strategy is accurate and feasible.

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Investigation of the Milling Performance about Titanium Alloy Based on Orthogonal Experiment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.2867 ◽

2010 ◽

Vol 44-47 ◽

pp. 2867-2871

Author(s):

Ke Yan Tang ◽

Li Hua Zhou ◽

Li Song

Keyword(s):

Titanium Alloy ◽

Orthogonal Experiment ◽

Milling Process ◽

Machining Accuracy ◽

Milling Force ◽

Milling Parameters ◽

Milling Performance ◽

Orthogonal Experiments ◽

Ta15 Titanium Alloy ◽

Single Factor Experiment

The milling process has been widely used for manufacturing aeronautical materials. It is very important to choose the reasonable milling parameters for improving the machining accuracy and surface quality. In this paper, the orthogonal experiments are made to construct the relationships between the milling force and milling parameters to TA15 titanium alloy. And the milling parameters mainly include milling speed, milling depth, milling width and feed engagement. And the empirical equations of milling force are gotten by regression analysis, and the equations are tested to be correct by the single factor experiment. The result is quite important for further research on the milling performance and can serve as a reference to production of titanium alloys.

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

Recent Patents on Engineering ◽

10.2174/1872212114999200902153633 ◽

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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