Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been proposed for the tool condition monitoring, based on the data acquired through acquisition techniques. Despite the continuous intensive scientific research for more than a decade, the development of tool condition monitoring is an on-going attempt. The proposed method deals with monitoring the health condition of the carbide inserts using vibration analysis. The statistical information extracted from the vibration signals was analyzed using machine learning approach in order to predict the tool condition.
Benchmark of Automated Machine Learning with State-of-the-Art Image Segmentation Algorithms for Tool Condition Monitoring