Smart Machining of Titanium Alloy Using ANN Encompassed Prediction Model and GA Optimization

2021 ◽  
pp. 9-20
Author(s):  
V. Kaviarasan ◽  
Sangeetha Elango ◽  
Ezra Morris Abraham Gnanamuthu ◽  
R. Durairaj
Keyword(s):  
Titanium Alloy ◽  
Prediction Model ◽  
Smart Machining

Analytical Prediction and Experimental Investigation of Burnishing Force in Rotary Ultrasonic Roller Burnishing Titanium Alloy Ti–6Al–4V

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Jian Zhao ◽  
Zhanqiang Liu ◽  
Bing Wang ◽  
Yukui Cai ◽  
Qinghua Song
Keyword(s):  
Titanium Alloy ◽  
Prediction Model ◽  
Milling Process ◽  
Analytical Prediction ◽  
Ultrasonic Power ◽  
Machined Surface ◽  
Softening Effect ◽  
Force Prediction ◽  
Contact Width ◽  
Acoustic Softening

Abstract Ultrasonic burnishing is usually applied to make machined surface modification. The acoustic softening effect caused by ultrasonic vibration is beneficial to the machining of difficult-to-cut materials. In the present work, a burnishing force prediction model was proposed for rotary ultrasonic burnishing of titanium alloy Ti–6Al–4V, whose surface had been machined with the face milling process. Firstly, the contact between the burnishing roller and one single milling mark was analyzed with plane strain assumption based on the Boussinesq–Flamant contact problem. Then, the effect of ultrasonic softening on the yield stress of Ti–6Al–4V was investigated. The critical contact width and contact load that the burnishing roller crushed on one single milling mark were examined to confirm the feasibility of the proposed ultrasonic burnishing force prediction model. The experimental verifications were carried out at various ultrasonic powers. The burnishing forces from experiment measurements were consistent with the calculated results from the proposed model. The mean deviations between theoretical and experimental results of the ultrasonic burnishing force were 10.4%, 12.2%, and 15.2%, corresponding to the ultrasonic power at the level of 41 W, 158 W, and 354 W, respectively.

Development of the Dynamic Globularization Prediction Model for Ti-17 Titanium Alloy Using Finite Element Method

2015 ◽  
Vol 24 (4) ◽  
pp. 1771-1780 ◽  
Author(s):  
Zhiqiang Jia ◽  
Weidong Zeng ◽  
Jianwei Xu ◽  
Jianhua Zhou ◽  
Xiaoying Wang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Titanium Alloy ◽  
Prediction Model ◽  
Element Method

The fracture toughness and its prediction model for Ti–5Al–5Mo–5V–1Cr–1Fe titanium alloy with basket-weave microstructure

2015 ◽  
Vol 632 ◽  
pp. 748-755 ◽  
Author(s):  
Xiaohui Shi ◽  
Weidong Zeng ◽  
Chunling Shi ◽  
Haojun Wang ◽  
Zhiqiang Jia
Keyword(s):  
Fracture Toughness ◽  
Titanium Alloy ◽  
Prediction Model

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.

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