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The Research On The Technique Of Tool Wear Monitoring In Plunge Milling

Posted on:2008-11-16Degree:MasterType:Thesis
Country:ChinaCandidate:H L LiuFull Text:PDF
GTID:2121360245992584Subject:Mechanical Manufacturing and Automation
Abstract/Summary:PDF Full Text Request
Plunge milling is good at machining curved surface, slot-cut and machining with longer cutter of suspending length, the machining efficiency is better than that of other milling methods. So plunge milling is approved and applied in the actual manufacture gradually. In this paper, the technique of tool wear monitoring and the wear mechanism of plunge milling cutter are studied, which are consulted in cutter's designing and using. The concrete works are as follows:The hardware platform of testing system and the software platform including data acquisition system and data analysis system are built. The functions of the software platform include real-time data acquisition, consecutive data store and data analysis. The tool wear monitoring system on line which includes tool wear monitoring on line and alarm changing cutter functions is built.The technique of tool wear monitoring in plunge milling is studied. According to the characteristics of cutting force signals and vibration signals while tool wear values are different, the mean of cutting force signals and the root mean square (RMS) of vibration signals are selected as characteristic quantities. The model between tool wear and the characteristic quantities is built using BP artificial neural network.Tool wear is a big problem for cutting Titanium alloy (Ti-6Al-4V). The wear mechanism of plunge milling cutter are studied. Scanning electron microscope and energy spectroscopy are used to analyze the forms of tool wear. It's found that the main forms of tool wear in cutting Titanium alloy are felted wear and diffuse wear, and wet machining can increase the life of cutting tool obviously than dry machining by a lot of experiments.
Keywords/Search Tags:plunge milling, tool wear monitoring, BP artificial neural network, wear mechanism
PDF Full Text Request
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