eyeDNA : Tool Condition Monitoring for a desktop CNC milling machine
Name
1036985534-MIT.pdf
Description
Full printable version
Size
10.51 MB
Format
Adobe PDF
Checksum (MD5)
608c38e6a84ddd64af5508b1ba80243d
Author(s)
Ajilo, Deborah (Deborah M.)
Advisor(s)
Sanjay E. Sarma.
Alternative Title
Eye deoxyribonucleic acid : Tool Condition Monitoring for a desktop CNC milling machine
Tool Condition Monitoring for a desktop CNC milling machine
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Tool wear is a major obstacle to realizing full automation in metal cutting operations. In this thesis, we designed and implemented a low cost Tool Condition Monitoring (TCM) system using off-the-shelf sensors and data acquisition methods . Peripheral end milling tests were done on a low carbon steel workpiece and the spindle vibration, cutting zone temperature and spindle motor current were recorded. Features from these data sources were used to train decision tree models in MATLAB with the aim of classifying the stages of tool wear. Results showed that the feature sets fusing information from all data sources performed the best, classifying the tool wear stage with up to 93% average accuracy.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-84).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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