Micro-end-milling—I. Wear and breakage

Tansel, İbrahim N.; Rodrı́guez, Oscar; Trujillo, M; Paz, Eduardo; Li, W.

doi:10.1016/s0890-6955(98)00015-7

Cited by 118 publications

(66 citation statements)

References 17 publications

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“…However, the wavelet transform is increasingly being used for process monitoring. Wavelet transforms have two advantages over segmental averaging [5]: first, they represent the system more accurately if the waveforms are optimised by considering signal characteristics. Secondly, wavelet parameters can be used for many other purposes such as identification of tool breakage, run out and flute deviation.…”

Section: Related Workmentioning

confidence: 99%

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Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Choi

Narayanaswami

Chandra

2004

The International Journal of Advanced Manufacturing Technology

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Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.

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Section: Related Workmentioning

confidence: 99%

“…Detailed wavelet coefficients have been used for breakage detection, particularly in drilling [4,12,13,14,15] and wavelet approximation coefficients have been used in monitoring tool wear in end milling [5,6,7,10]. In previous work in the literature, tool wear has been estimated by comparison with pre-worn tools.…”

Section: Related Workmentioning

confidence: 99%

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Choi

Narayanaswami

Chandra

2004

The International Journal of Advanced Manufacturing Technology

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“…Tansel et al [28][29][30] estimated the wear of micro-end mill by the cutting force and neural network. The AE signal for micro-tool breakage was also investigated and monitored by the neural network classifier.…”

Section: Introductionmentioning

confidence: 99%

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Hsieh

Chiou

2011

Int J Adv Manuf Technol

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This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor

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“…However, micro hard milling is not widely applied in industries yet due to its low process reliability. The main problem encountered is poor tool performance in terms of low stiffness and edge strength causing premature breakage, severe wear, and short tool life [3][4][5][6][7][8][9]. As a result, the quality of the machined workpiece cannot match the required specifications in applications [2,[9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design of micro square endmills for hard milling applications

Oosterling

Hoogstrate

et al. 2011

Int J Adv Manuf Technol

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In experiments of machining hardened tool steels (such as AISI H11, H13, and D2, up to 56 HRC) by commercial Ø 0.5 mm square endmills, it is observed that the tested micro endmills showed severe wear at an early stage of the process due to chipping off around cutting edge corners, resulting in unsatisfactory tool life and product appearance (burr formation). Detailed examination of current tool geometry shows that it is mainly inherited from that of macro endmills, making the cutting edge corners the weakest part on the tool. As the micromilling process is characterized by small values of machining parameters, the cutting edge corners of the micro endmill are the most loaded part of the cutting edges. New design rules are studied for improving the stiffness and strength of micro endmills used in micro hard milling applications. Analytical modelling and finite element method analysis are used to aid the design of tool geometry. By using a larger neck angle, optimizing tool core geometry, and choosing a negative rake angle, tool stiffness and cutting edge strength are improved. The new endmill designs, both two-flute and four-flute, are tested in experiments on hardened tool steels and showed considerable lower tool wear and increased tool life. Furthermore, the geometrical accuracy and appearance of the workpiece (burr formation) has been improved drastically.

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Micro-end-milling—I. Wear and breakage

Cited by 118 publications

References 17 publications

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Design of micro square endmills for hard milling applications

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