Development of scientific foundations for an intelligent cutting tool wear assessment system and adaptive control of cutting parameters

Abstract

This study develops a methodological framework for intelligent cutting tool wear assessment and adaptive adjustment of cutting parameters in CNC machining. The framework integrates machine-vision measurements, tool-state estimation, wear-dependent cutting-load prediction, and supervisory feed correction. The methodology combines analytical modeling with a structured synthesis of research materials. Sequential tool images are geometrically registered and segmented to determine local, mean, and maximum flank-wear widths. Calibration and boundary-localization uncertainties are considered when assessing measurement reliability. A multirate estimation architecture combines intermittent visual observations with more frequently acquired process signals, accounting for the corresponding machining conditions. The estimated wear state is incorporated into the cutting-load model, and its uncertainty is propagated into the predicted cutting force. The analytical relationship shows that the contribution of wear-estimation uncertainty depends on the sensitivity of the load model to wear. Both measurement reliability and model uncertainty therefore influence the confidence associated with load predictions. The framework provides a basis for bounded supervisory feed correction using predicted loading and its uncertainty. The findings establish methodological relationships between measurement, estimation, prediction, and control rather than demonstrating new experimental improvements in machining performance. Independent CNC experiments are required to calibrate the models, evaluate prediction accuracy, and assess practical control benefits under changing machining conditions