scholarly journals Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks

2021 ◽  
Vol 12 (1) ◽  
pp. 393
Author(s):  
Cheng-Hung Chen ◽  
Shiou-Yun Jeng ◽  
Cheng-Jian Lin
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Metal Cutting ◽  
End Milling ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Linear Regression Method ◽  
Mechanical Cutting ◽  
Cnc End Milling

In the metal cutting process of machine tools, the quality of the surface roughness of the product is very important to improve the friction performance, corrosion resistance, and aesthetics of the product. Therefore, low surface roughness is ideal for mechanical cutting. If the surface roughness of the product can be predicted, not only the quality of the product can be improved but also the processing cost can be reduced. In this study a back propagation neural network (BPNN) was proposed to predict the surface roughness of the processed workpiece. ANOVA was used to analyze the influence of milling parameters, such as spindle speed, feed rate, cutting depth, and milling distance. The experimental results show that the root mean square error (RMSE) obtained by using the back propagation neural network is 0.008, which is much smaller than the 0.021 obtained by the traditional linear regression method.

Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy

2011 ◽  
Vol 325 ◽  
pp. 418-423 ◽  
Author(s):  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Back Propagation Neural Network ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Bpnn Model

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

2012 ◽  
Vol 576 ◽  
pp. 91-94 ◽  
Author(s):  
Erry Yulian Triblas Adesta ◽  
Muataz H.F. Al Hazza ◽  
M.Y. Suprianto ◽  
Muhammad Riza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Temperature ◽  
Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Cnc End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

Evaluating Artistic Voice of Singing Objectivly Using BPNN

2012 ◽  
Vol 546-547 ◽  
pp. 1240-1244 ◽  
Author(s):  
Xiao Wu Li ◽  
Hong Bo Ouyang
Keyword(s):  
Neural Network ◽  
Subjective Evaluation ◽  
Back Propagation ◽  
Average Energy ◽  
Back Propagation Neural Network ◽  
Fuzzy Synthetic Evaluation ◽  
Bp Network ◽  
Artistic Voice ◽  
And Training

In this paper, an objective way to evaluate artistic voice of singing is discussed. The model transforms artistic voice evaluation indicators into qualified data as BP network input and takes fuzzy synthetic evaluation results as output. The authors take F1(the first Formant), F3(the third Formant), vocal range, perturbation of F1, perturbation of F3 and average energy as the evaluating parameters and assess the quality of singing voices with BPNN(back propagation neural network). The results are then compared with the subjective evaluation of experienced professionals. Experiments show that BP neural network is effective to evaluate the singing voices, thus to be helpful to scientific guidance of selecting and training the talent of artistic voice.

Prediction for Air-Bending Springback Radius of Sheet Metal Using Back Propagation Neural Network and Micro Genetic Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 1174-1177
Author(s):  
Ze Min Fu ◽  
Guang Ming Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sheet Metal ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Accurate Prediction ◽  
Air Bending ◽  
Micro Genetic Algorithm ◽  
Bpnn Model

Springback radius is a very important factor to influence the quality of sheet metal air-bending forming. Accurate prediction of springback radius is essential for the design of air-bending tools. In this paper, a three-layer back propagation neural network (BPNN), integrated with micro genetic algorithm (MGA), is proposed to solve the problem of springback radius. A micro genetic algorithm is used for minimizing the error between the predictive value and the experimental one. Based on air-bending experiment, the prediction model of springback radius is developed by using the integrated neural network. The results show that more accurate prediction of springback radius can be obtained with the MGA-BPNN model. It can be taken as a valuable tool for air-bending forming of sheet metal.

Application of Neural Network in Free Air Ball Forming for Wire Bonder

1999 ◽  
Author(s):  
Jau-Liang Chen ◽  
Yeh-Chao Lin ◽  
Chun-Hsien Liu ◽  
Wen-Chang Kuo ◽  
Tzung-Ching Lee
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Tail Length ◽  
Back Propagation Neural Network ◽  
Ball Bond ◽  
Error Back Propagation ◽  
Fine Pitch ◽  
Free Air ◽  
Network Method

Abstract The shape and size of free-air-ball formation deeply affect the quality of wire bonding. It not only affects the bondability of first bond (ball bond), but also affects the possibility of processing low loop height bonding for thin form packages and high I/O fine pitch packages. Several parameters, such as tail length, spark gap, supplied voltage, current and time of electrical flame-off unit etc., will affect the free-air-ball formation. This paper represents a study of using error-back-propagation neural network method to analyze the effect of each parameter and to predict the final result of the ball forming. From the experiment, it is shown that neural network can not only be used to precisely predict the size of ball formation, but also saves sampling time.

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