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A Vibration-Informed Extension of Taylor’s Tool Life Law: Coupling Nonlinear Beam Dynamics with Experimental Turning Data

Publicada
Servidor
Preprints.org
DOI
10.20944/preprints202601.0197.v1

Classical tool life models, including Taylor’s law, have been extensively used in machining practice due to their simplicity and empirical robustness. However, these formulations neglect the explicit influence of machining vibrations, which become critical in turning operations involving slender workpieces and dynamic cutting conditions. This limitation often leads to significant discrepancies between predicted and experimentally observed tool life. In this study, a vibration-informed extension of Taylor’s tool life law is proposed by explicitly incorporating the maximum transverse displacement of the workpiece as a governing dynamic parameter. The vibration amplitude is obtained from a nonlinear beam model accounting for large deflections and realistic boundary conditions, representative of turning configurations. The dynamic response is evaluated through a semi-analytical formulation and validated experimentally using measured displacement signals during cutting operations. Tool life experiments conducted under varying cutting speeds and vibration levels demonstrate that the proposed model significantly improves prediction accuracy compared to the classical Taylor formulation. The results reveal a strong correla-tion between increased transverse displacement amplitudes and accelerated tool wear, highlighting the critical role of vibration-induced dynamic effects on wear mechanisms. The proposed approach provides a physically grounded framework for coupling machining dynamics and tool wear, offering enhanced predictive capability for tool life estimation in vibration-sensitive turning processes. The findings of this work contribute to a deeper understanding of the interaction between structural dynamics and wear evolution in machining and offer practical insights for process optimization and chatter-aware tool life management.

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