Experimental validation of chip production rate as a tool wear identification in micro-endmilling

Lee, Jue-Hyun; Sodemann, Angela A.; Bajaj, Anuj K.

doi:10.1007/s00170-019-03602-8

Cited by 1 publication

(4 citation statements)

References 15 publications

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“…In the previous simulation and experiment, because the cutting time for each experiment was relatively short, the MCT was fixed by setting the cutting edge radius as constant. The change in the number of chips was observed when the UCT was made smaller than the MCT by decreasing the federate [32].…”

Section: Overview Of Previous Researchmentioning

confidence: 99%

“…A schematic diagram of the experimental setup is shown in Figure 2. Six sets of cutting tests were performed with a 2-flute 200 μm endmill and brass workpiece [32]. The spindle speed and the depth of cut were set to 80,000 rpm and 40 μm, respectively.…”

Section: Previous Experimentsmentioning

confidence: 99%

“…The purpose of the calibration for this sensor is to detect the drop in the number of chips more accurately. Previous studies have shown that a large drop in the number of chips can be seen in the number of chips above a certain size [32,33]. The calibration of this sensor is to find the appropriate threshold of size filtering to filter out chips smaller than a certain size and to count only the number of large chips.…”

Section: Calibrationmentioning

confidence: 99%

“…In order to overcome these drawbacks, a cutting edge radius estimator based on chip production rate has been developed. This estimator can operate as an online system by measuring the chip production rate during the cutting process and yet it is based on the MCT effect which explains the physical relationship between the cutting edge radius and the measured data [32]. However, the reliability of this estimator has not been evaluated.…”

Section: Introductionmentioning

confidence: 99%

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Reliability of Cutting Edge Radius Estimator Based on Chip Production Rate for Micro End Milling

Lee

Sodemann

2019

JMMP

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In this paper, the reliability of a new online cutting edge radius estimator for micro end milling is evaluated. This estimator predicts the cutting edge radius by detecting the drop in the chip production rate as the cutting edge of a micro end mill slips over the workpiece when the minimum chip thickness (MCT) becomes larger than the uncut chip thickness (UCT), thus transitioning from the shearing to the ploughing dominant regime. This study proposes a method of calibrating the cutting edge radius estimator by determining two parameters from training data: a ‘size filtering threshold’ that specifies the smallest-size chip that should be counted, and a ‘drop detection threshold’ that distinguishes the drop in the number of chips at the actual critical feedrate from the number drops at the other feedrates. This study then evaluates the accuracy of the calibrated estimator from testing data for determining the ‘critical feedrate’—the feedrate at which the MCT and UCT will be equal. It is found that the estimator is successful in determining the critical feedrate to within 1 mm/s in 84% of trials.

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Section: Overview Of Previous Researchmentioning

confidence: 99%

Section: Previous Experimentsmentioning

confidence: 99%

Section: Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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