STUDY AND PREDICTION OF TOOL WEAR USING MACHINE LEARNING

Industry 4.0 demands high degree of automation and accuracy, high efficiency, high productivity, less rejection, less manpower, and shorter lead time in production. NC, CNC and automated machine shops are playing vital role for higher productivity. Similarly, Quality inspection of the product should have higher productivity and shorter lead time. In Machining processes, tool wear significantly affects product quality, production efficiency, and cost. The ability to predict tool wear and remaining tool life is important for optimizing machining operations. This project focuses on study and predictive model using machine learning techniques to estimate tool wear and remaining tool life. These models will utilize various input parameters such as cutting speed, feed rate, material properties, tool geometry and many more to forecast tool wear progression. By analysing historical data on tool wear and performance, the machine learning model will learn tool behavior and various significant parameters, enabling accurate predictions. The outcome of this project aims to enhance machining efficiency by reducing unplanned downtime and optimizing tool replacement schedules, ultimately leading to cost savings and improved productivity.