2026-09-19T14:47:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1156702022-01-13T07:54:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sanjay E. Sarma.
Ajilo, Deborah (Deborah M.)
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2018-05-23T16:29:33Z
2018-05-23T16:29:33Z
2018
2018
http://hdl.handle.net/1721.1/115670
1036985534
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-84).
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.
by Deborah Ajilo.
S.M.
84 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
eyeDNA : Tool Condition Monitoring for a desktop CNC milling machine
Eye deoxyribonucleic acid : Tool Condition Monitoring for a desktop CNC milling machine
Tool Condition Monitoring for a desktop CNC milling machine
Thesis
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<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.</Abstract>
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