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Using the Machine Vision Method to Develop an On-machine Insert Condition Monitoring System for Computer Numerical

Wei-Heng Sun¹, Syh-Shiuh Yeh²

¹Institution of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan. sun245689@gmail.com.

Materials (Basel, Switzerland)

|October 17, 2018

Summary

This study presents a machine vision system for on-machine monitoring of cutting tool conditions in CNC machines. The system accurately identifies tool fracture, built-up edge, chipping, and flank wear, ensuring stable operation.

Keywords:

computer numerical control machine vision on-machine monitoring tool insert condition turning machine tools

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Area of Science:

Manufacturing Engineering
Machine Vision
Materials Science

Background:

Effective tool condition monitoring is crucial for optimizing cutting processes in computer numerical control (CNC) machines.
Existing methods often struggle with environmental variations and real-time feedback during machining.

Purpose of the Study:

To develop an on-machine turning tool insert condition monitoring system using machine vision.
To accurately identify four critical insert conditions: fracture, built-up edge (BUE), chipping, and flank wear.

Main Methods:

A machine vision system with controlled lighting was designed for capturing insert images.
Image processing techniques including outer profile construction and wear region analysis were implemented.
Grayscale value histograms were used to quantify wear and classify insert conditions.

Main Results:

The system successfully identified insert fracture, BUE, chipping, and wear in on-machine tests.
The standard deviation for chipping and wear amount was found to be low (0.67% and 0.62% of the average value, respectively).

Conclusions:

The proposed machine vision system provides a stable and accurate method for on-machine tool condition monitoring.
This system enhances the reliability and efficiency of CNC machining processes by enabling real-time tool status assessment.