eyeDNA : Tool Condition Monitoring for a desktop CNC milling machine

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Ajilo, Deborah (Deborah M.)

Advisor dc:contributor.advisor

• Sanjay E. Sarma.

• Mechanical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/115670

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/115670