The Monitoring of CNC Machine Processing Condition Based on the BP Neural Network

2012 ◽  
Vol 542-543 ◽  
pp. 95-98 ◽  
Author(s):  
Zhong Qi Sheng ◽  
Jian Yong Wang ◽  
Zhi Peng Liu ◽  
Liang Dong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Processing Condition ◽  
Mapping Function ◽  
Machining Process ◽  
Cnc Machine ◽  
Bp Artificial Neural Network ◽  
Labview Software ◽  
Signal Features ◽  
Artificial Neural

As an example for the test of built monitoring system, the experimentation was carried out to monitor the condition of tool wear, which related with the machining process closely. In pattern recognition, using the nonlinear mapping function of neural network, a three-layer BP artificial neural network was selected to carry out the mapping between the processing status and related signal features. In this way, the monitoring of machining processing was implemented. The Matlab Script node of LabVIEW software was used to call the neural network package of Matlab, which reduced the program development cycle and increased the reliability of the program. According to the cutting experimentation of the finger milling cutter, it is verified that BP artificial neural network can effectively recognize the tool condition and accomplish the wear prediction.

Prediction of 3‐month treatment outcome of IgG4‐DS based on BP artificial neural network

Oral Diseases ◽  
2020 ◽  
Author(s):  
Yanxiong Shao ◽  
Zhijun Wang ◽  
Ningning Cao ◽  
Huan Shi ◽  
Lisong Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Treatment Outcome ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

scholarly journals Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 540-544
Author(s):  
Jayaraj JEEVAMALAR ◽  
Sundaresan RAMABALAN ◽  
Chinnamuthu SENTHILKUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Inconel 718 ◽  
Machining Process ◽  
Artificial Neural ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Matlab Programming ◽  
The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

scholarly journals Application of Wavelet Neural Network in Building Settlement Prediction

2020 ◽  
Vol 198 ◽  
pp. 03014
Author(s):  
Ruijie Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Building Design ◽  
Wavelet Neural Network ◽  
Deformation Monitoring ◽  
Polynomial Method ◽  
Observation Data ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.

Sign in / Sign up

Export Citation Format

Share Document