AI-Based Predictive Tool-Life Computation in Manufacturing Industry
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 1
Abstract
For maximum productivity and optimal utilization of tools, predictive maintenance serves as a standard operation procedure in the manufacturing industry. However, unnecessary or delayed maintenance both causes increased downtime and loss of revenue which should be optimized. Accordingly, this paper presents a method for predicting the maintenance requirement to ensure the optimal utilization of the tools. The experimental data for this research has been collected from a CNC lathe machine in a manufacturing plant for multiple days. The CNC machine equipped with three sensors leads to a detailed log for parameters related to tool wear including current, voltage, acceleration in 3D, motor rpm, and tool temperature respectively. Detailed experimentation has been performed to investigate the importance of different parameters. A direct relationship between current and tool temperature was observed leading to an immediate halt of machine operations. In the subsequent step, maintenance prediction was performed using Logistic regression and Random Forest technique respectively to validate the machine behavior. The retrospective data validated the performance with precise accuracy equal to 98% and 95% for both of methods respectively. The promising results predicting the maintenance schedule of the Lathe machine signify the effectiveness of Machine Learning towards advance scheduling for maintenance. The proactive maintenance strategy helps in potential benefits such as avoiding further costs, avoidance of disruptions, and increased efficiency productivity, thereby enhancing tool life cycles.
Authors and Affiliations
Muhib Aleem, M Moazzam Jawaid, Shahnawaz Talpur, Aisha Zahid Junejo, Sheheryar Ahmed Khan, Muhammad Ahmed
Keywords
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