Wan, J., Yar, K. P., Du, C., & Low, M. Y. H. (2023). Sensor Data Analytics for Tool Condition Anomaly Detection with Machine Learning Techniques. In (Editor), Proceedings of the 2023 5th International Electronics Communication Conference. https://doi.org/10.1145/3616480.3616486
Abstract:
Tool condition anomaly detection is a critical aspect of machining
processes, ensuring product quality, cost-effectiveness, and operational
safety. This paper presents a study of tool condition anomaly
detection in computer numerical control (CNC) machining using
multiple sensor signals and machine learning models. The study employed
four sensors, namely spindle vibration, tool vibration, tool
force, and acoustic emission, to collect data for ten different signals.
Eight process parameters, involving different cutting speeds, feed
rates, and depths of cut, were tested one at a time until the tool wear
size reached 0.15mm. The tool wear size was periodically measured
using a digital microscope. The sensor data was collected during
each machine run at a sampling rate of 25600Hz. The experiments
were conducted in sets of eight, with each set repeated four times.
Tool wear exceeding 0.1mm was considered a worn condition, while
anything below was considered healthy. The data was processed
by computing the Welch Power Spectral Density (PSD) for each
signal and summing the PSD within pre-defined frequency ranges.
Feature selection was performed using Recursive Feature Elimination
(RFE) combined with Random Forest Classifier (RFC). The
selected features were then used to train and test Support Vector
Machine (SVM), k-Nearest Neighbors (kNN), Multilayer Perceptron
(MLP), Naive Bayes (NB), and Decision Tree (DT) models. The
prediction performance was evaluated using classification reports
and confusion matrices. The study explored the impact of changing
the bin size of the sum PSD and repeating the feature selection
and model training and testing. The results demonstrated that with
force x-axis, a bin size of 50Hz for summing PSD yielded the best
performance, with the MLP model achieving high precision, recall,
and F1-score values.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.