Combined Pulse Characterization and Discrimination for Micro-EDM Milling Tool Wear Study

In micro machining, turn-milling tool wear is a key factor for part surface quality. This paper carries on experiments on end mills wear in micro turn-milling machining, aiming to research the wear form and provide some reference data for developing wear standard of small diameter end mills. To measure wear condition of end mills, machine vision technique is utilized. This paper designs and sets up an online end mill wear measurement system for a micro turn-milling process center. With a series of experiments on small diameter end mills, wear conditions of different cutting positions are researched. Based on analysis of experiment data, wear characteristics and wear rule for micro turn-milling process are summarized in this paper.

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A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-015-8148-1 ◽

2015 ◽

Vol 86 (1-4) ◽

pp. 769-780 ◽

Cited By ~ 15

Author(s):

P. J. García-Nieto ◽

E. García-Gonzalo ◽

J. A. Vilán Vilán ◽

A. Segade Robleda

Keyword(s):

Experimental Data ◽

Support Vector Machine ◽

Tool Wear ◽

Predictive Model ◽

Support Vector ◽

Model Based ◽

Milling Tool

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Precision Cutting of Structured Ceramic Molds with Micro PCD Milling Tool

International Journal of Automation Technology ◽

10.20965/ijat.2011.p0277 ◽

2011 ◽

Vol 5 (3) ◽

pp. 277-282 ◽

Cited By ~ 6

Author(s):

Hirofumi Suzuki ◽

◽

Tatsuya Furuki ◽

Mutsumi Okada ◽

Katsuji Fujii ◽

...

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

Tungsten Carbide ◽

Polycrystalline Diamond ◽

Micro Milling ◽

Grinding Wheels ◽

Form Accuracy ◽

Structured Surfaces ◽

Milling Tool ◽

Milling Tools

Micro milling tools made of PolyCrystalline Diamond (PCD) have been developed to machine ceramic micro dies and molds. Cutting edges are ground with diamond wheels. PCD milling tool wear is evaluated by cutting binder-less tungsten carbide spherical molds and machining structured surfaces for trial. Results of experiments clarified that PCD milling tool life is over 10 times that of resinoid diamond grinding wheels, and that form accuracy was 0.1 µm-0.3 µm P-V and surface roughness was 10 nm Rz.

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The monitoring of micro milling tool wear conditions by wear area estimation

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2017.02.004 ◽

2017 ◽

Vol 93 ◽

pp. 80-91 ◽

Cited By ~ 37

Author(s):

Kunpeng Zhu ◽

Xiaolong Yu

Keyword(s):

Tool Wear ◽

Micro Milling ◽

Wear Area ◽

Area Estimation ◽

Milling Tool

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Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.541-542.1419 ◽

2014 ◽

Vol 541-542 ◽

pp. 1419-1423 ◽

Cited By ~ 3

Author(s):

Min Zhang ◽

Hong Qi Liu ◽

Bin Li

Keyword(s):

Wavelet Transform ◽

Tool Wear ◽

Condition Monitoring ◽

Shannon Entropy ◽

Spindle Motor ◽

Face Milling ◽

Motor Current ◽

Milling Tool ◽

Spindle Motor Current ◽

Wear Condition

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

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