Analysis and Optimization on Prediction Model of Surface Roughness for Mandrels in Meso-scale

2011 ◽  
Vol 47 (03) ◽  
pp. 174 ◽  
Author(s):  
Yanhua HUANG
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Meso Scale

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.

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.

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.

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

EVALUATION OF A REGRESSION PREDICTION MODEL FOR SURFACE ROUGHNESS OF WOOD–POLYETHYLENE COMPOSITE (WPC)

2017 ◽  
Vol 24 (Supp02) ◽  
pp. 1850033
Author(s):  
WENYONG SHI ◽  
YAN MA ◽  
CHUNMEI YANG ◽  
BIN JIANG ◽  
ZHE LI
Keyword(s):  
Surface Roughness ◽  
Surface Morphology ◽  
Prediction Model ◽  
Surface Quality ◽  
Cutting Parameters ◽  
Wood Powder ◽  
Polyethylene Composite ◽  
Milled Surface ◽  
Powder Content

Milling processing is an important way to obtain wood–polyethylene composite (WPC) end products. In order to improve the processing efficiency and surface quality of WPC and meet the practical application requirements, this paper focussed on morphology and roughness of the WPC-milled surface and studied surface quality changes under different cutting parameters and milling methods through multi-parameters milling experiments. The milling surface morphology and roughness of WPC were analyzed and measured during cut-in, cutting and cut-out sections. It also revealed the affect rule of different cutting parameters and milling methods on milled surface morphology and roughness. The results show that the milling surface roughness of WPC products with wood powder content of 70% is significantly larger than the one whose wood powder content is 60%, and defects such as holes are also relatively more. Finally, a surface roughness prediction model was established based on the mathematical regression method and its multi-factor simulation was carried out. A comparative analysis of predictive and experimental values was performed to verify the reliability of the model. It could also provide theoretical guidance and technical guarantee for high processing quality of WPC milling and cutting.

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