AI-Based Predictive Tool-Life Computation in Manufacturing Industry

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 causesincreased 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 leadsto a detailed log for parameters related to toolwear including current, voltage, acceleration in 3D, motor rpm,and tool temperature respectively. Detailed experimentation has been performed to investigate the importance of different parameters. Adirect relationship betweencurrent and tooltemperature was observedleading 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 signifythe effectiveness of Machine Learning towards advance scheduling for maintenance. The proactive maintenance strategyhelps in potential benefits such as avoiding further costs, avoidance of disruptions,and increased efficiency productivity, thereby enhancing tool life cycles.