scholarly journals Surface roughness prediction model and experimental results based on multi-wavelength fiber optic sensors

2016 ◽  
Vol 24 (22) ◽  
pp. 25119 ◽  
Author(s):  
Nan-nan Zhu ◽  
Jun Zhang
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Fiber Optic ◽  
Fiber Optic Sensors ◽  
Experimental Results ◽  
Surface Roughness Prediction ◽  
Multi Wavelength ◽  
Roughness Prediction

LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor

2017 ◽  
Vol 15 (9) ◽  
pp. 091201
Author(s):  
Li Fu Li Fu ◽  
Jun Luo Jun Luo ◽  
Weimin Chen Weimin Chen ◽  
Dong Zhou Dong Zhou ◽  
Zhongling Zhang Zhongling Zhang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Fiber Optic ◽  
Fiber Optic Sensor ◽  
Surface Roughness Prediction ◽  
Optic Sensor ◽  
Roughness Prediction

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.

Surface roughness prediction model of SiCp/Al composite in grinding

2019 ◽  
Vol 155 ◽  
pp. 98-109 ◽  
Author(s):  
Chuanmin Zhu ◽  
Peng Gu ◽  
Yinyue Wu ◽  
Dinghao Liu ◽  
Xikun Wang
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Surface Roughness Prediction ◽  
Al Composite ◽  
Roughness Prediction

Selection of Optimal Tool Geometry and Cutting Conditions Using a Surface Roughness Prediction Model for Hard Turning

2008 ◽  
Author(s):  
Dilbag Singh ◽  
P. Venkateswara Rao
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Hard Turning ◽  
Second Order ◽  
Cutting Conditions ◽  
Tool Geometry ◽  
Surface Roughness Prediction ◽  
Machining Conditions ◽  
Roughness Prediction ◽  
Selection Of

Due to technical and economical factors, hard turning is competing successfully with the grinding process in the industries. However, due to the large number of variables and their interactions affecting the hard turning process, the process control becomes complex. So, the selection of optimal machining conditions for good surface quality, in hard turning, is of great concern in the manufacturing industries these days. In the present work, experimental investigation has been conducted to study the effect of the tool geometry (effective rake angle and nose radius) and cutting conditions (cutting speed and feed) on the surface roughness during the hard turning of the bearing steel with mixed ceramic inserts. Central composite design was employed for experimentation. The first and the second order mathematical models were developed in terms of machining parameters by using the Response Surface Methodology (RSM) on the basis of the experimental results. Results show that all the factors and their interactions were significantly influencing the surface roughness. Analysis of Variance (ANOVA) indicated that the second order surface roughness model was significant. Further, the surface roughness prediction model has been optimized by using genetic algorithms (GA). The genetic algorithm program gives minimum values of surface roughness and their respective optimal machining conditions (cutting conditions and tool geometry).

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