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.

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 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.

Fault Diagnosis Expert System for Special Electronic Equipment Based on the Fuzzy Neural Network

2012 ◽  
Vol 241-244 ◽  
pp. 401-404
Author(s):  
Xue Zhong Yin ◽  
Jie Gui Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Electronic System ◽  
Electronic Equipment ◽  
Diagnostic Model ◽  
Model Based ◽  
Proposed Model ◽  
Fuzzy Neural

In order to improve the efficiency and reliability of fault diagnosis for the special electronic equipment, an intelligent fault diagnostic model based on Fuzzy Neural Network (FNN) is proposed. Firstly, the fault diagnosis model based on the FNN Expert System (ES) is built. Secondly, the fault diagnosis expert system of the special electronic equipment based on this model is introduced. Finally, experiments show that the proposed model is correct and the FD system is effective. Moreover, the given method provides a new way of fault diagnosis for other modern electronic system.

A FUZZY NEURAL NETWORK MODEL FOR FUZZY INFERENCE AND RULE TUNING

1994 ◽  
Vol 02 (03) ◽  
pp. 265-277 ◽  
Author(s):  
KEON-MYUNG LEE ◽  
DONG-HOON KWANG ◽  
HYUNG LEEK WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Target System ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Fine Tune

It is relatively easy to create rough fuzzy rules for a target system. However, it is time-consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the process of fuzzy inference the defuzzification operation takes most of the inferencing time. In this paper, we propose a fuzzy neural network model which makes it possible to tune fuzzy rules by employing neural networks and reduces the burden of defuzzification operation. In addition, to show the applicability of the proposed model we perform an experiment and present its result.

Study of Intelligent Prediction Control of Surface Roughness in Grinding

2007 ◽  
Vol 329 ◽  
pp. 93-98
Author(s):  
Ning Ding ◽  
Long Shan Wang ◽  
Guang Fu Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Prediction Control

A surface roughness intelligent prediction control system during grinding is built. The system is composed of fuzzy neural network prediction subsystem and fuzzy neural network controller. In the fuzzy neural network prediction subsystem, the vibration data are added to the inputs besides the grinding condition, such as feed and speed, so as to improve the dynamic performance of the prediction subsystem. The fuzzy neural network controller is able to adapt grinding parameters in process to improve the surface roughness of machined parts when the roughness is not meeting requirements. Experiment verifies that the developed prediction control system is feasible and has high prediction and control accuracy.

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%.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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