Predictive Tool Life Model in Ti6Al4V High Speed Milling and Cutting Parameter Optimization

Xiao Qin Wang; Xing Ai; Jun Zhao

doi:10.4028/www.scientific.net/KEM.431-432.257

Paper Titles

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Study of PCBN Cutting Tools Wear in High Speed Cutting Hardened Steel GCr15
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AHP Based SWOT Analysis for Green Manufacturing Strategy Selection
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Tool Wear State Diagnosis Based on Wavelet Analysis-BP Neural Network
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Predictive Tool Life Model in Ti6Al4V High Speed Milling and Cutting Parameter Optimization
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A Study on the Abrasive Waterjet Milling Mechanisms of Ceramic Materials
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Experimental Investigation on Brittle-Ductile Transition in Electroplated Diamond Wire Saw Machining Single Crystal Silicon
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Grain-Workpiece Interaction Study of Grinding Process through Single Grain Cutting Simulation
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Experimental Investigation and CFD Simulation of Removal Characteristics in Nanoparticle Colloid Jet Machining
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HomeKey Engineering MaterialsKey Engineering Materials Vols. 431-432Predictive Tool Life Model in Ti6Al4V High Speed...

Predictive Tool Life Model in Ti6Al4V High Speed Milling and Cutting Parameter Optimization

Abstract:

Ti6Al4V is a difficult to machine alloy with low cutting efficiency and server tool wear. A series of orthogonal milling tests with coated and uncoated carbide insert was carried out. The tool life predictive models based on orthogonal experiment were developed. Finally, the cutting parameters for both tools in Ti6Al4V dry milling were optimized based on tool life-efficiency contour analysis.