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

Song Zhang; Jian Feng Li

doi:10.4028/www.scientific.net/AMR.325.418

Paper Titles

The Influence of SiC Particles on Tool Wear in Machining of Al/SiC Metal Matrix Composites Produced by Powder Extrusion
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The Cutting Performance of Ultra-Smooth Composite Diamond Coated WC-Co Inserts in Dry Turning Al/SiC-MMC
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Milling Experimental Investigation on Titanium Alloy Ti6Al4V under Different Cooling/Lubrication Conditions
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Machined Surface Analysis in High-Speed Dry Milling of Ti-6Al-4V Alloy with Coated Carbide Inserts
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Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy
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Lubricant Applying Effect Mechanism in Inconel 718 Cutting - Effects of Cutting Speed and Depth of Cut -
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Empirical Modeling and Analysis of Surface Roughness in Milling Process of Nickel-Based Super Alloy Nimonic 115 through Response Surface Methodology
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Ultrasonic Coring of Glass Disks Using Double Core Tool
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Drilling of Borosilicate Glass with Feedback Control Based on Cutting Force
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 325Prediction of Surface Roughness Using...

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

Abstract:

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.

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Periodical:

Advanced Materials Research (Volume 325)

Pages:

418-423

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.325.418

Citation:

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Online since:

August 2011

Authors:

Song Zhang, Jian Feng Li

Keywords:

BP Neural Network (BPNN), End Milling, Surface Roughness (SR), Ti-6Al-4V Alloy

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