Experimental Research on the Predictive Model for Surface Roughness of Titanium Alloy in Abrasive Belt Grinding

2013 ◽  
Vol 716 ◽  
pp. 443-448 ◽  
Author(s):  
Rong Kai Cheng ◽  
Yun Huang ◽  
Yao Huang
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Prediction Model ◽  
Significant Influence ◽  
Influence Coefficient ◽  
Linear Regression Method ◽  
Belt Grinding ◽  
Abrasive Belt ◽  
Tc4 Titanium Alloy ◽  
Roughness Prediction

Titanium alloys have been applied to aerospacemedical and other fields. The surface roughness of titanium alloy about these areas is very high. Based on the results of orthogonal test, belt grinding surface roughness prediction model of TC4 Titanium alloy is established using linear regression method. The significant tests of regression equation are conducted and proved that the prediction model has a significant. The results indicate that the model has reliability on the prediction of surface roughness, abrasive belt grinding pressure has certain influence on the surface roughness, and grain size of belt and the belt linear speed have high significant influence on surface roughness and the influence coefficient are-0.9378 and-0.2317. While the contact wheel hardness and workpiece axial feeding speed have no significant influence on surface roughness.

Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy

2021 ◽  
Author(s):  
XueTao Wei ◽  
caixue yue ◽  
DeSheng Hu ◽  
XianLi Liu ◽  
YunPeng Ding ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Finite Element ◽  
Prediction Model ◽  
Simulation Model ◽  
Finite Element Simulation ◽  
Simulation Software ◽  
Surface Roughness Prediction ◽  
Contour Shape ◽  
Element Simulation ◽  
Roughness Prediction

Abstract The processed surface contour shape is extracted with the finite element simulation software, and the difference value of contour shape change is used as the parameters of balancing surface roughness to construct the infinitesimal element cutting finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the central composite test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the finite element simulation model and surface roughness prediction model are verified and analyzed through experiment. The research results show that, compared with the experiment results, the maximum error of finite element simulation model and surface roughness prediction model is 30.9% and12.3%, respectively. So, the model in this paper is accurate and will provide the theoretical basis for optimization study of auxiliary milling process of supersonic vibration.

Experiment and Surface Roughness Prediction Model for Ti-6Al-4V in Abrasive Belt Grinding

2016 ◽  
Vol 1136 ◽  
pp. 42-47 ◽  
Author(s):  
Ya Xiong Chen ◽  
Yun Huang ◽  
Gui Jian Xiao ◽  
Gui Lin Chen ◽  
Zhi Wu Liu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Great Influence ◽  
Grinding Force ◽  
Work Piece ◽  
Belt Grinding ◽  
Abrasive Belt ◽  
Belt Speed ◽  
Feeding Speed

In abrasive belt grinding, abrasive belt granularity, abrasive belt speed,feeding speed and grinding force have a great influence on the surface roughness. In order to predicate the surface roughness of Ti-6Al-4V,a response surface methodology are used to build the model to predict surface roughness,and the influence of various parameters on surface roughness was analysed. The research shows that with the abrasive belt granularity and abrasive belt speed increasing,the work piece surface roughness decreases;with the grinding force and feeding speed increasing,the work piece surface roughness increases. Through the test,the response surface methodology with high prediction accuracy,provides a theoretical basis for the reasonable selection of abrasive belt grinding parameters.

Research on Surface Roughness Prediction Model for High-Speed Milling Inclined Plane of Hardened Steel

2010 ◽  
Vol 97-101 ◽  
pp. 2044-2048 ◽  
Author(s):  
Yuan Ling Chen ◽  
Bao Lei Zhang ◽  
Wei Ren Long ◽  
Hua Xu
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
High Speed ◽  
Hardened Steel ◽  
Prediction Errors ◽  
Inclined Plane ◽  
Workpiece Surface ◽  
Factors Influencing ◽  
Generalized Regression Neural Networks ◽  
Roughness Prediction

As the factors influencing the workpiece surface roughness is complexity and uncertainty, according to orthogonal experimental results, the paper established Empirical regression prediction model and generalized regression neural networks (GRNN) for prediction of surface roughness when machining inclined plane of hardened steel in high speed , moreover, compared their prediction errors. The results show that GRNN model has better prediction accuracy than empirical regression prediction model and can be better used to control the surface roughness dynamically.

Study on the improvement of the surface integrity and efficiency of electrical-discharge-machined TC4 titanium alloy via abrasive flow machining

2020 ◽  
pp. 095440542097109
Author(s):  
Ze Yu ◽  
Dunwen Zuo ◽  
Yuli Sun ◽  
Guohua Li ◽  
Xuemei Chen ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Surface Quality ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Small Hole ◽  
Machining Process ◽  
Machining Efficiency ◽  
Abrasive Flow Machining ◽  
Tc4 Titanium Alloy

To simultaneously optimize the surface quality and machining efficiency of the electrical discharge machining (EDM) processes used to produce titanium alloy quadrilateral group small hole parts, a combined “EDM + AFM” machining technology is proposed in this paper as an efficient and high-quality machining approach. In the proposed method, TC4 titanium alloy is first machined using the EDM process with graphite electrodes and the abrasive flow machining (AFM) process is then used to finish the machined surface. The effects of various electrical parameters on EDM-derived surface quality and improvements in EDM-derived quality under the application of AFM were assessed and, using the final surface roughness as a constraint condition, the effects of various combinations of EDM and “EDM + AFM” on efficiency were studied. The results revealed that the thickness and surface roughness of the superficial recast layer of the TC4 titanium alloy increase with both current and pulse width; in particular, increasing these parameters can increase the surface roughness by two to three grades. Following AFM, the alloy has a more uniform hardness distribution and the surface stress state changes from tensile to compressive stress, indicating that the combined “EDM + AFM” machining scheme can significantly enhance the surface quality of EDM-produced titanium alloy quadrilateral small group holes. The combined scheme achieves a balancing point beyond which increasing the roughness or the number of machining holes enhances either the machining efficiency or the machining surface quality. In the case of typical titanium alloy quadrilateral group small hole parts, the combined machining process can improve the finishing efficiency and total machining efficiency by 71.2% and 25.36%, respectively.

Surface Roughness Prediction Model of Cylinder Grinding

2012 ◽  
Vol 157-158 ◽  
pp. 123-126 ◽  
Author(s):  
Ning Ding ◽  
Yi Chen Wang ◽  
Ding Tong Zhang ◽  
Yu Xiang Shi ◽  
Jian Shi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Vibration Signal ◽  
Feed Speed ◽  
Vibration Data ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Roughness Prediction

Based on the theory of roughness during cylinder grinding and the theory of fuzzy-neural network, a surface roughness intelligent prediction model is developed in this paper. The feed, speed, and the vibration data are the inputs for the model. An accelerometer is used to gather the vibration signal in real time. The model is used in the grinding experiment, and verifies the feasibility of the proposed model.

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