2 publications

  • Published Subscription Original Research

    Machine Learning-Driven Force Analysis for Tool Wear Prediction Systems

    Abstract: A system designed to forecast tool wear by utilizing a force sensor to monitor the wear of the tool's flank and applying a Convolutional Neural Network (CNN) for forecasting purposes. The methodology is demonstrated through experiments in milling, utilizing dry machining with a ball endmill on a stainless-steel component. The flank wear of the tool is directly assessed using a digital microscope throughout the operation. The forecasts produced by the …

    Published in Journal of Mechatronics and Automation · Vol. 11, Issue 3, 2024 · pp. 16–25 Read article

  • Published Subscription Original Research

    Enhancing Machinability in the Heat Treatment of Inconel 718: A Comprehensive Review

    Abstract: The increasing need for materials with high strength and heat resistance, particularly in aerospace applications, creates machining issues. These materials are generally difficult to machine because to their strong wear resistance, abrasion resistance, and low heat conductivity. This produces strong cutting forces and temperatures, resulting in a limited tool life. variances in the microstructures of various materials can induce variations in machinability due to variances in chemical composition, casting, forging …

    Published in International Journal of Energy and Thermal Applications · Vol. 2, Issue 1, 2024 · pp. 9–20 Read article

Support