Tool Wear Prediction when Maching Ti6Al4V Using Different Tool Materials

Yi Hang Fan; Min Li Zheng; Ming Ming Cheng; D.Q. Zhang

doi:10.4028/www.scientific.net/AMR.188.325

Paper Titles

Parameters Optimization of Turning Free Cutting Steel Based on the Coupling Method of Response Surfaces
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Optimization of Cutting Parameters for Desirable Surface Roughness in End-Milling Hardened AISI H13 Steel under a Certain Metal Removal Rate
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Deformation Control and Chatter Suppression in 5-Axis Milling of Thin-Walled Blade
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Experimental Research on Dry Cutting 34CrNiMo6
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Tool Wear Prediction when Maching Ti6Al4V Using Different Tool Materials
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Research on the Topography Features of the Densely Bonded Diamond Grinding Wheel Dressed by Elliptical Ultrasonic Vibration
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Parametric Design of Face Milling Cutter Based on UG Template Model
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The Optimization on Case-Based Reasoning to Processing Piece Large Cylinders of Heavy Cutting Process
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Tool with Heavy-Duty Machining Tube Section of the Fatigue Analysis
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 188Tool Wear Prediction when Maching Ti6Al4V Using...

Tool Wear Prediction when Maching Ti6Al4V Using Different Tool Materials

Abstract:

The tool life is different when using different tool materials machine Ti6Al4V under different cutting conditions. Based on the experiments of cutting Ti6Al4V using different tool materials, the tool wear prediction model is established with BP neural network theory. The wear prediction model is verified by further experiments. The results show that there is a valid prediction interval when using BP neural network predicts tool wear. When the cutting speed is in the vicinity of the training sample data, the relative error of the tool wear prediction and experimental results is less than 10%, which can meet the actual production. The tool wear prediction model is helpful for cutting tool material selection, cutting speed optimization and predomination the time of changing tool accurately when cutting titanium alloy.

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Periodical:

Advanced Materials Research (Volume 188)

Pages:

325-329

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.188.325

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Online since:

March 2011

Authors:

Yi Hang Fan, Min Li Zheng, Ming Ming Cheng, D.Q. Zhang

Keywords:

BP Neural Network (BPNN), Tool Material, Tool Wear, Wear Prediction Model

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