Prognostics and Health Management in Milling Operations: An Integrated Analysis of Tool Wear Trajectories and Reliability
Dit Suthiwong
Transaction on Machine Design, Vol. 14, No. 2, 2026.
DOI: https://doi.org/10.6025/tmd/2026/14/2/67-84
Link: https://www.dline.info/tmd/fulltext/v14n2/tmdv14n2_2.pdf
Abstract:
Accurately predicting cutting tool life is essential for minimizing unplanned downtime and optimizing
predictive maintenance in modern milling operations. While existing tool condition monitoring studies focus
primarily on wear detection, they often lack integrated degradation trajectory modeling and reliability
assessment. This study proposes a comprehensive Prognostics and Health Management (PHM) framework
combining vibration based condition monitoring with stochastic state space modeling to evaluate tool wear
progression.
Utilizing the "Roughness in Milling Process" dataset, which pairs tri-axial vibration signals with surface
roughness measurements across eight workpieces, the research constructs a normalised degradation index
to track tool deterioration. The methodology incorporates wear state classification, Hidden Markov Models
(HMM), and Hidden Semi-Markov Models (HSMM) with Weibull duration distributions to capture realistic
state transitions. Furthermore, an absorbing Markov chain framework is applied to reliability analysis,
alongside Monte Carlo simulations for probabilistic estimation of Remaining Useful Life (RUL).
Results demonstrate a strong monotonic correlation between increasing vibration magnitudes and worsening
surface roughness. The HSMM effectively modeled prolonged intermediate degradation stages, while
probabilistic RUL forecasts provided actionable insights for maintenance scheduling. Although constrained
by the dataset's limited size, this integrated approach successfully bridges raw sensor data with actionable
reliability metrics. Ultimately, the proposed framework establishes a robust, data-driven foundation for
next-generation predictive maintenance strategies aligned with Industry 4.0 intelligent manufacturing
paradigms.