FlankPix: An Image Segmentation Algorithm for Flank Wear Analysis in Monel K500 Turning
Vedagiri Dilli Ganesh, Rammohan Bommi
Abstract
In the study involving turning Monel K500 with a Cubic Boron Nitride Insert, experiments assessed flank wear using the Edge Detection Technique, which is crucial for maintaining machined surface quality and the workpiece's fatigue resistance.Monel K500, widely used in aerospace for high-temperature applications, demands preserved surface integrity and minimized tool wear.This research aimed to establish optimal machining parameters for this specific alloy-insert combination while developing a novel method for precise flank wear measurement.FlankPix, an innovative tool wear monitoring approach in machining, employs pixel distance measurement and image edge detection to identify tool edges accurately while disregarding background noise for precise measurement.This method ensures a smooth tool contour, effectively evaluating the tool's condition.With an average prediction error of 1.29%, FlankPix significantly enhances accuracy in assessing tool geometry changes, improving product quality, and reducing errors in tool condition estimation during manufacturing.