Study on In-Situ Tool Wear Detection during Micro End Milling Based on Machine Vision

Area of Science:

• Materials Science
• Mechanical Engineering
• Manufacturing Technology

Background:

• Traditional in situ tool wear monitoring in micro end milling faces challenges in accurately locating wear zones and measuring wear levels.
• Existing methods often rely on indirect signals, limiting precise wear assessment.

Purpose of the Study:

• To design and establish an in situ monitoring system using machine vision for micro end milling of Ti6Al4V.
• To develop and validate image processing algorithms for quantifying tool wear indicators.
• To analyze the relationship between tool wear, cutting time, and influencing factors.

Main Methods:

• Development of a machine vision-based in situ monitoring system.
• Analysis of tool wear zones and identification of evaluation indicators.
• Proposal and verification of image processing algorithms for wear measurement.
• Experimental validation through micro end milling of titanium alloy Ti6Al4V.

Main Results:

• The machine vision system successfully monitored tool wear in real-time during micro end milling.
• Image processing algorithms accurately measured key wear indicators.
• The study established a correlation between wear level, cutting duration, and specific wear indicators.
• The primary wear types and their evolution were identified.

Conclusions:

• The developed machine vision system provides an effective solution for in situ tool wear monitoring in micro end milling.
• The proposed image processing algorithms offer reliable quantification of wear indicators.
• Understanding wear behavior through this system aids in optimizing machining processes for titanium alloys.