scholarly journals Multi-response optimization of cutting force and surface roughness in carbon fiber reinforced polymer end milling using back propagation neural network and genetic algorithm

2019 ◽  
Author(s):  
Philipus Andreas Lega Laot ◽  
Suhardjono ◽  
Sutikno ◽  
Sampurno
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Reinforced Polymer ◽  
Response Optimization

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.

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.

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