INTELLIGENT RECOGNITION OF CNC PRODUCTION STATES FOR IMPROVED MANUFACTURING EFFICIENCY
DOI:
https://doi.org/10.67772/xhavkr56Keywords:
CNC machining, production-state recognition, machine learning, manufacturing efficiency, process monitoringAbstract
Intelligent identification of CNC production states can be used to assist real-time monitoring, and it can distinguish production and preparation, auxiliary operations, etc., and identify whether the production is in the normal state. In this study, a machine-learning-based framework to identify nine different milling states of CNC milling and determine their significance for manufacturing efficiency was created. The 25,286 observations of time-series data in 18 experiments were analysed with 45 motion, electrical, spindle and feed-rate data predictors. The classifiers under study were Gaussian Naives Bayes, Decision Tree, Random Forest and Extra Trees classifiers and evaluated using 80:20 split of data in the stratified fashion and experiment-grouped validation as a robustness check. Extra Trees had the highest on holdout (74.36%), balanced (73.73%) and macro-F1 (74.11%) scores. The best class-specific F1 score was achieved by Layer 3 Down and the best recall was achieved by Repositioning. A mean productive-time percentage was calculated for active cutting and these values were higher than for interrupted experiments (69.29%). Nevertheless, the performance of grouped validation was not as high, suggesting that the results were not easily transferable to new experiments. The framework illustrates the importance of multivariate CNC signals for state-aware monitoring, and emphasizes the need for more industrial data and more temporal structured models.
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