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A Practical, Direct Approach for Fusion of Tool Size Measurement and Flank Wear Prediction of End-Mills in Machining

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A Practical, Direct Approach for Fusion of Tool Size Measurement and Flank Wear Prediction of End-Mills in Machining

Feng, Haibo (2022) A Practical, Direct Approach for Fusion of Tool Size Measurement and Flank Wear Prediction of End-Mills in Machining. Masters thesis, Concordia University.

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Abstract

On-machine measurement (OMM) of cutting tools is to automatically and directly measure them for length and diameter in machining breaks and compensated tool paths in the following machining. Tool condition monitoring (TCM) is to automatically predict tool wear and detect tool failure in machining, which is often evaluated with the width of flank wear land. OMM of cutting tools is widely applied with tool setters in the industry, however, it cannot measure flank wear to monitor tool conditions and predict the tool life. To address the problem in this research, an approach for fusion of tool size measurement and flank wear prediction of end-mills is proposed. First, the model of a fillet end-mill is built. Design principles are studied based on practical machining, which include the smoothness of the end-mill’s flank face, and the relief angle of the fillet cutting edge. Second, the model of an indexable face mill is also built accurately. Third, the geometric relationship between tool radius and flank wear land width is established for the indexable face mill. Then, an experimental method is adopted to optimize measurement locations. At last, experiments are conducted that the end-mill is measured at the locations, and the tool radius is used to calculate the flank wear width. The results show the proposed approach is effective for tool condition monitoring. This research successfully develops the OMM-TCM fusion technology and benefits the manufacturing industry.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:Feng, Haibo
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical Engineering
Date:1 August 2022
Thesis Supervisor(s):Chen, Zezhong
ID Code:990884
Deposited By: Haibo Feng
Deposited On:27 Oct 2022 14:30
Last Modified:27 Oct 2022 14:30
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