Background: This study delves into the significance of face milling tools in machining, emphasizing the need for timely fault diagnosis to enhance the efficiency of manufacturing processes. By examining defect scenarios such as flank wear, breakage and chipping, along with a reference for good tool condition, the research aims to improve diagnostic accuracy and optimize manufacturing performance.

Methodology: Vibration signals generated during milling operations are analyzed to identify tool faults. A feature extraction process incorporating statistical, histogram, and ARMA features is employed to gain a nuanced understanding of tool behavior. Feature selection is performed using the J48 decision tree algorithm which helps identify the most relevant features. Subsequently, 13 tree-based classifiers are applied to classify tool faults effectively.

Results: A comparative analysis of classification outcomes provides practical insights into the most effective features for fault diagnosis in milling tools. The study’s findings show that the combination of ARMA features with Extra trees achieved an impressive accuracy of 96.88% for milling tool fault diagnosis. The outcomes from the study contribute to real-world applications by enhancing diagnostic methodologies, ultimately advancing fault detection and classification in machining processes.