Application of machine learning algorithms for tool condition monitoring using python

Enormous amounts of real-time data analyzed by using AI's analytical tools will improve decision-making and provide business users with improved visibility-whether it's minimizing asset downtime, enhancing manufacturing quality, automating production, forecasting demand, maximizing inventory levels or improving risk management. In this case study, we incorporate predictive maintenance for the milling process, to prevent the excessive expense and loss of time due to unexpected malfunctions of the cutting tool, with the help of machine learning algorithms and Python we interpret predictive maintenance (pdM) to ensure optimal product output. This study provides a framework for applying machine learning to forecast tool wear, thus evaluating the tool's remaining useful life (RUL) for best output in terms of expense, efficiency and time. We'll analyze the data set, clean, modify and remove various attributes before implementing it with different machine learning models to predict the remaining useful life and later comparing it with the wear performance of the actual tool.