In-process Implementation of Machine Vision Technology for Tool Wear Measurement.

Abstract

In the manufacturing sector, following the high level of automation technology. However, a few key challenges stop the whole machining process from being fully automated. One of these problems is the computerized tool wear monitoring. Automatic tool condition monitoring is becoming increasingly important in the metal cutting industry due to the wear on the tool impacts the efficiency of the manufactured component. Using a worn-out cutting tool contributes to low-quality parts that do not meet with tolerances and have a low finish. However, replacing the cutting tool unnecessarily leads to higher manufacturing costs. Traditionally, tool wear is measured directly using optical microscope which is an offline and time consuming technique. Therefore a great deal of many researcher efforts has been made to incorporate "intelligent" systems that monitor the machining cutting tool conditions by directly or indirectly using thermal, force, acoustic, acceleration, and vision sensor signals. In indirect tool wear measurement techniques fully dependent on the machining process parameters. After carryout literature review, it has been observed that optimization of critical parameters affecting tool wear measurement using machine vision techniques has lot of scope. The work discussed in this study focuses on developing a direct automated tool condition monitoring system by calculating tool wear parameters, especially flank wear, using the machine vision system in the CNC machine tool. This tool wear measuring system reduces the need for manual inspection and reduces the time needed to measure wear. In this method, the cutting tool inserts images were collected by using the industrial camera with special orthographic projection lens and proper ring light illumination system during the machining of hardened steel using coated carbide tools , and the wear boundary is extracted by using designed customizing the GUI using the python programming tool of digital image processing tools such as image enhancement, image segmentation, image morphology operation, and edge detection, and afterwards, the cutting tool inserts wear value is extracted and recorded by using Hough line transformation function and novel technique of pixels scanning. Based on the recorded value of flank wear parameters to assess the tool's health and whether or not the tool should be used for further machining of engineering components.