Modeling and detection of the prepared tool edge radius

Area of Science:

• Manufacturing Engineering
• Materials Science
• Optical Metrology

Background:

• High-speed and high-efficiency machining necessitates precise tool edge preparation.
• Tool edge preparation enhances tool life, cutting performance, and surface quality.
• Accurate modeling and detection of micron-level tool edge contours are critical for effective edge preparation.

Purpose of the Study:

• To develop a mathematical model for the prepared tool edge radius.
• To establish a machine vision-based detection method for micron-level tool edge contours.
• To validate the accuracy and feasibility of the proposed model and detection system.

Main Methods:

• Established a mathematical model for milling tool trajectory using Matlab.
• Developed material removal models based on energy principles and statistical methods for single and multiple abrasive particles.
• Constructed a machine vision contour detection system using LabVIEW, incorporating histogram specification, median filtering, multi-threshold segmentation, and Canny edge detection.

Main Results:

• The mathematical model showed a maximum error of 11.18 μm and a minimum error of 0.07 μm compared to SEM measurements.
• The machine vision detection system exhibited a maximum difference of 2.71 μm (9.2%) and a minimum difference of 0.31 μm (1.2%) against SEM.
• The results demonstrate high accuracy and feasibility of both the mathematical model and the detection system.

Conclusions:

• The study elucidates the mechanisms of tool edge preparation by abrasive particles.
• A validated mathematical model for edge radius provides a foundation for understanding edge preparation effects.
• The proposed machine vision method is effective for detecting micron-level tool edges, supporting high-speed machining advancements.