Digital Image Processing for Counting Chips in Micro-End-Milling

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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Digital Image Processing for Counting Chips in Micro-End-Milling

Cited by 2 publications

References 7 publications

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

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

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

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