Numerical control machine tool spindle dynamic characteristics analysis in high efficiency machining

Computer numerical control machine tool spindle performance critically influences manufacturing precision and operational efficiency in modern production systems. This study presents a data-driven framework integrating multivariate analytics and machine learning to systematically analyze spindle dynamics, process parameters, and quality outcomes using experimental datasets from the NASA Prognostics Center. Correlation analysis reveals spindle vibration as the most significant predictor of surface roughness, while cutting depth strongly correlates with power consumption. Principal component analysis identifies two orthogonal subspaces governing 65.3% of variance, distinguishing machine dynamics from cutting parameters. Unsupervised clustering categorizes operational states into three distinct regimes—baseline, heavy-duty, and unstable—with unique vibration-load interaction patterns. Comparative modeling demonstrates ensemble methods, particularly random forest regression, achieve superior predictive accuracy for surface roughness compared to linear models. Feature importance quantification highlights vibration metrics and cutting parameters as dominant contributors to predictive performance. The framework enables predictive maintenance by establishing cluster-specific vibration thresholds, reducing surface roughness excursions by 34% in validation trials, advancing spindle health monitoring by decoupling parameter interactions, enabling regime-specific performance evaluation, and establishing vibration-based quality prediction models. These insights provide a methodological foundation for intelligent process optimization and predictive maintenance in high-precision manufacturing applications.