Authors :
Dr. S. P. Jolhe; Sanyojika Gawande; Dhanashri Kove; Sana Saiyyad; Devika Rajgadkar
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/6sv68jrv
Scribd :
https://tinyurl.com/5n6en2nr
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR2211
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This paper introduces a novel Health
Monitoring System (HMS) tailored for CNC machines,
addressing the critical need for maintaining their optimal
performance and preventing unexpected breakdowns in
modern manufacturing settings. The system integrates
various sensors and data acquisition methods to
continuously monitor key parameters like temperature,
vibration, and tool wear. By employing advanced data
analytics and machine learning algorithms, the HMS can
swiftly identify anomalies in real-time, facilitating
proactive maintenance and minimizing operational
downtime. Additionally, the system features a user-
friendly interface for visualizing machine health status
and creating predictive maintenance schedules.
Experimental validation conducted on a CNC machining
center validates the efficacy and reliability of the
developed HMS in enhancing machine efficiency,
prolonging equipment lifespan, and curbing maintenance
expenses. In summary, this Health Monitoring System
offers a robust solution for ensuring the seamless
operation and longevity of CNC machines in modern
manufacturing environments.
This paper introduces a novel Health
Monitoring System (HMS) tailored for CNC machines,
addressing the critical need for maintaining their optimal
performance and preventing unexpected breakdowns in
modern manufacturing settings. The system integrates
various sensors and data acquisition methods to
continuously monitor key parameters like temperature,
vibration, and tool wear. By employing advanced data
analytics and machine learning algorithms, the HMS can
swiftly identify anomalies in real-time, facilitating
proactive maintenance and minimizing operational
downtime. Additionally, the system features a user-
friendly interface for visualizing machine health status
and creating predictive maintenance schedules.
Experimental validation conducted on a CNC machining
center validates the efficacy and reliability of the
developed HMS in enhancing machine efficiency,
prolonging equipment lifespan, and curbing maintenance
expenses. In summary, this Health Monitoring System
offers a robust solution for ensuring the seamless
operation and longevity of CNC machines in modern
manufacturing environments.
