Surface Roughness Prediction Model Based on AE in Grinding

2014 ◽  
Vol 701-702 ◽  
pp. 150-153
Author(s):  
Ning Ding ◽  
Wen Ze Yu
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Acoustic Emission ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Surface Roughness Prediction ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Roughness Prediction ◽  
Ae Signals

Based on the theory of roughness during grinding and the theory of fuzzy-neural network, a new intelligent prediction model is developed in this paper. The inputs for the model are the grinding parameters and the AE signals. Beijing Shenghua SAEU2S system was used to collect and analyze the signals of acoustic emission. The experiment was conducted, and the results verify the feasibility of the proposed model.

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.

An Intelligent Prediction of Surface Roughness on Precision Grinding

2017 ◽  
Vol 261 ◽  
pp. 221-225
Author(s):  
Ning Ding ◽  
Chang Long Zhao ◽  
Xi Chun Luo ◽  
Qing Hua Li ◽  
Yao Chen Shi
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Precision Grinding ◽  
Wheel Wear ◽  
Surface Roughness Prediction ◽  
Fuzzy Neural ◽  
Roughness Prediction ◽  
Wear Condition

Precision grinding is generally used as the final finishing process, and it determines the surface quality of the machined component. It’s very difficult to achieve on-line measurement of the surface roughness. The purpose of this research was to study the surface roughness prediction and avoid the defect happening in the grinding process. A surface roughness prediction model was proposed in this paper, which presented the relationship between surface roughness and the wear condition of grinding wheel and grinding parameters. An AE sensor was used to collect the grinding signals during the grinding process to obtain the grinding wheel wear condition. Besides, a fuzzy neural network was used to obtain the prediction surface roughness. Grinding trials were performed on a high precision CNC cylindrical grinder (MGK1420) to evaluate the surface roughness prediction model. Experiment verified that the developed prediction system was feasible and had high prediction accuracy.

Intelligent Dynamic Roughness Identification Model in Grinding

2011 ◽  
Vol 48-49 ◽  
pp. 621-624
Author(s):  
Ning Ding ◽  
Ding Tong Zhang ◽  
Ye He
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Fuzzy Neural Network ◽  
Vibration Signal ◽  
Dynamic Identification ◽  
Grinding Parameters ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
External Cylindrical Grinding ◽  
Identification Model

In this paper, first, the relation between roughness and grinding parameters is built. Then, an intelligent dynamic identification model of surface roughness is developed, which bases on the theory of roughness during grinding and the theory of fuzzy-neural network. The inputs for the model are the grinding parameters. Besides, an accelerometer is used to gather the dynamic vibration signal in real time. The model was used in the external cylindrical grinding experiment, and the result verified that the proposed model was feasible.

scholarly journals Analysis of College Student Registration Management and Change Prediction Based on Mutated Fuzzy Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Wang ◽  
Lie Jiao ◽  
Chunzhi Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Correct Prediction ◽  
Proposed Model ◽  
School Teaching ◽  
Fuzzy Neural ◽  
Optimization Function ◽  
Model Training

Nowadays, a large number of students' academic registrations change every year in universities, but most of these cases are recorded and mathematically and statistically analysed through forms or systems, which are cumbersome and difficult to find some potential information in them. Therefore, timely and effective prediction of student registration changes and early warning of student registration changes by technical means is an important part of university registration management. At present, relevant research is mostly based on mathematical statistical analysis methods such as students' current credit evaluation or course score averages and seldom uses data mining and other technical methods for in-depth research. In this paper, we propose a mutated fuzzy neural network (MFNN) based prediction model for student registration changes in colleges and universities, which can provide supplementary reference decisions for school registration management for school teaching managers. In this paper, we first construct the corresponding prediction model of academic registration variation, define the relevant parameters, and model the optimization problem and propose the objective optimization function. Second, the proposed model is optimized by adding principal component analysis (PCA) to the original model to improve the efficiency of model training and the correct prediction rate. It is verified that the proposed model can effectively predict individual students' academic registration changes with a prediction accuracy of nearly 92.91%.

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

Surface Roughness Prediction Based on Acoustic Emission Signals in High-Precision Diamond Turning of Rapidly Solidified Optical Aluminum Grade (RSA443)

2020 ◽  
Vol 841 ◽  
pp. 363-368
Author(s):  
Zvikomborero Hweju ◽  
Khaled Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Prediction Accuracy ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Surface Roughness Prediction ◽  
Peak Rate ◽  
Signal Parameters ◽  
Roughness Prediction ◽  
Ae Signal

Acoustic emission signal-based prediction of surface roughness has been utilized widely, yet little work has been done in this regard on RSA443. This paper seeks to study the correlation between acoustic emission (AE) signal parameters and surface roughness. Estimation of surface roughness using AE signal parameters and subsequent examination of the influence of AE signal parameters (root mean square, peak rate and prominent frequency) on the accuracy of the RSM model in surface roughness prediction are carried out. The experiment is designed using the Taguchi L9 orthogonal array to minimize the number of experiments. Emitted acoustic signals are captured using a Piezotron sensor. Three RSM models are formulated and compared in this study: a model that uses only critical machining parameters (cutting speed, depth of cut and feed rate), a model that uses only AE signal parameters (root mean square, peak rate and prominent frequency) and a model that uses both critical machining parameters and AE signal parameters. An assessment based on the models’ mean absolute percentage error (MAPE) is made to see if AE signal parameters have any contribution towards surface roughness prediction accuracy. The order of parameter significance in the most accurate model is investigated in this paper. The mean absolute percentage error results for the models indicate that the model in which AE signal parameters are utilized in conjunction with critical machining parameters has the highest prediction accuracy of 97.32%. The model that utilizes only critical machining parameters has a prediction accuracy of 96.35% while the one that utilizes only AE signal parameters has a prediction accuracy of 84.43%. It is observed that the order of parameter significance from the most to the least significant is as follows: feed rate, cutting speed, peak rate, AErms, depth of cut and prominent frequency.

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