The Incorporation of AI into Preventive Maintenance of Machine Milling Bits.

Presenter: Devin Current

Faculty Sponsor: Soumitra Basu

School: Fitchburg State University

Research Area: Mechanical Engineering

Session: Poster Session 2, 11:30 AM - 12:15 PM, 163, C21

ABSTRACT


Throughout the years engineering has leaned more into automated systems to reduce the amount of error through controllable variables such as automated milling machines to meet near perfect precision. This project goes to expand on that idea, but looking more at our tools themselves for example milling bits. Undetected defects from micro fractures, unseen flute wear and the materials degrading down from extended use which all could lead to catastrophic tool failure in the worst case leading to unplanned downtimes, missed tolerances, and potentially bodily harm. This project proposes the implementation of the ever growing AI systems and developing it to identify early stages of tool failure. Integrating high grade imaging with a real time auto detection systems framework the aim is to identify critical structures and cutting regions to determine if the bit is suitable for the task at hand or if it should be sent to repairs. A custom designed rotational holder shall offer a full consistent image of the tool bits structure and flutes for the comprehensive inspection. Using the visuals provided from the high definition camera, it will be fed into a detection system which will help determine if the wear pattern predicted will interfere with tolerances with different bit materials such as carbide and HSS. The incorporation of intelligent tool monitoring will switch from a reactive preventive maintenance to a more proactive data driven maintenance standpoint which would continue to evolve and grow at the already fast paced evolving field of Engineering.


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