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Tool Condition Monitoring (TCM) plays a significant role in maintaining machining quality, reducing equipment idle time, and improving TCM often affords near-real-time detection of wear-out phenomena. A new method for vibration-based fault diagnosis of round insert face milling tools based on analysis in the time-domain and machine learning, the vibration signals were obtained using a spindle-integrated piezoelectric accelerometer during milling with controlled conditions. Healthy, flank wear, edge chipping, built-up edge and mixed fault conditions were studied from the statistical features extracted. Feature selection was implemented using Recursive Feature Elimination and Mutual Information ranking. Distributed computing was used to pilot SVM and XGBoost classifiers. The output indicated that the test accuracy of XGBoost was better (92.4% accuracy) than SVM (89.2%), while both had lower errors and shorter estimates as well. The presented approach is a cost-effective and real-time applicable method for intelligent monitoring of the condition of the monitoring tool in CNC machining. This study contributes to the development of data-driven predictive maintenance systems for smart manufacturing.
Spur gears are particularly vulnerable to vibration-induced resonance, noise production, dynamic stress concentration, and early fatigue failure while running at high speeds and under cyclic loading circumstances. The dynamic performance of conventional steel gears in sophisticated transmission systems is limited by their weak intrinsic damping capability, despite their high strength and wear resistance. This work uses advanced finite element modal and harmonic response analysis to examine the dynamic behavior and vibration attenuation properties of hybrid carbon-fiber-reinforced metal matrix composite (MMC) spur gears. Six material configurations were compared, including carbon-fiber/epoxy composite, stainless-steel-reinforced hybrids (CF/Epoxy/SS316 and CF/Epoxy/SS304), aluminum-reinforced hybrids (CF/Epoxy/Al6082 and CF/Epoxy/Al1050), and SCM420H steel. Equivalent orthotropic elastic formulations obtained using rule-of-mixtures homogenization were used to represent the composite materials. Mesh-independent models with realistic elastic support and frictional contact boundary conditions were used in ANSYS Workbench 2023 R1 for finite element simulations. The Block Lanczos solver was used for modal analysis in order to obtain natural frequencies and mode shapes. Harmonic response analysis was then used to assess resonance characteristics. Rayleigh damping implementation and characterization based on Dynamic Mechanical Analysis (DMA) were used to incorporate damping features. The findings show that, in comparison to traditional steel gears, all hybrid composites have noticeably higher natural frequencies and better damping characteristics. Because of its greater specific stiffness, Composite A (80% carbon fiber + 20% epoxy resin) showed the largest natural frequency range of 47.1–56.3 kHz. The optimum balance between lightweight properties, rigidity, and vibration attenuation was demonstrated by hybrid composites reinforced with aluminum. In comparison to steel gears, Composite C2 (CF/Epoxy/Al1050) achieved the highest damping ratio (ζ = 0.08) and lowered resonant amplitudes by around 40%. Additionally, a nearly 30% decrease in root fillet stress concentration was found by dynamic stress analysis, suggesting enhanced fatigue resistance and crack suppression capacity. Aluminum-based hybrids perform better because of their reduced density, better stiffness-to-weight ratio, less rotational inertia, and increased interfacial energy dissipation. The promise of hybrid carbon-fiber metal matrix composites for lightweight, vibration-resistant, and resonance-safe spur gear applications in cutting-edge automotive and industrial transmission systems is demonstrated by the developed orthotropic finite element framework.