Data‐Driven Fault Diagnosis of Drilling Tools: Methods and Applications - Chandan M. N; Himadri Majumder; Sharad Mulik; Nikhil Rangaswamy; Mukesh Kumar; Sowmyashree H. Srinivasaiah (Engineering Reports, 2025)
ABSTRACT Effective monitoring of drilling tool condition is crucial in mechanical metal cutting to prevent tool failure, maintain machining accuracy, and ensure superior surface finish quality. Tool breakage or wear can cause catastrophic machine downtime, reduce dimensional accuracy, and deteriorate the surface finish of machined parts, thereby impacting productivity and operational costs. To address these challenges, this paper presents a data‐driven fault diagnosis approach that leverages vibration signal analysis for real‐time condition monitoring of drilling tools. In this study, vibration signals were collected using a piezoelectric accelerometer mounted on a CNC drilling machine during operations involving both new and worn tools. Various stages of tool wear were examined to capture comprehensive vibration data reflective of different fault conditions. Statistical features were extracted from these vibration signals, including measures such as mean, variance, kurtosis, and skewness, to characterize the tool's health status effectively. For fault diagnosis, a best‐first tree classifier was employed due to its robustness and interpretability in handling features and obtained accuracy of 96.23% that validates the potential of the proposed data‐driven approach. The proposed method offers several advantages, including non‐invasiveness, real‐time applicability, and scalability across different manufacturing setups. By integrating vibration‐based condition monitoring with machine learning techniques, the approach facilitates early fault detection, enabling predictive maintenance strategies that can significantly reduce unplanned downtime, extend tool life, and improve overall manufacturing productivity. In conclusion, the paper demonstrates that a data‐driven, vibration‐based fault diagnosis system combined with an effective classification algorithm can serve as a practical solution for continuous monitoring of drilling tool conditions, thereby supporting enhanced operational efficiency in metal cutting industries.