Poster Session 2, 11:30 AM - 12:15 PM: Room 163 [C23]

AI in Production Monitoring of Machining Processes

Presenter: Jamesohn LaValley

Faculty Sponsor: Soumitra Basu

School: Fitchburg State University

Research Area: Mechanical Engineering

ABSTRACT

This project presents the concept and design for an AI system that monitors manufacturing production lines, with a specific focus on tool performance. It will also be able to predict replacements of cutting tools before a fall in machining efficiency occurs. The proposed system would utilize censored data from the machines to continuously monitor tool behavior, output quality, and production rates. To start with this the AI program would first observe the different tools or machines in a production line and store data on how long their life spans are and note where the threshold is for being inefficient and affecting productivity per tool. Once the AI has enough data collected in its database, it can then use all that information and start to predict when tools will start to dip in efficiency so that the necessary adjustments can be made to prevent that and develop a preventive maintenance strategy. Overall, this AI system will be able to estimate when specific tools are likely to require servicing or replacement. This enables proactive scheduling, reduces unplanned downtime, and improves overall equipment effectiveness.