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<p>Assessment of tool failure resulting from the impact of tool wear is of paramount importance in the development of smart machining systems. To address this challenge, a dual-phase approach was proposed, leveraging the statistical correlation between experimentally collected cutting tool vibration data and circularity error. This method also involves the development of stochastic tool life probability models to accurately estimate the remaining useful life (RUL). Experiments were performed on Nimonic-90, a nickel superalloy, using a tungsten carbide (WC) drill bit under various cutting speeds and feed rates. Flank wear measurement was used as a criterion for hole drilling, enabling the prediction of the number of holes that could be drilled and associated tool vibration data. The experimental results show that the statistically derived correlation is applicable for real-time tool-life prediction during machining. This technique remains effective under different cutting conditions, emphasizing the importance of real-time monitoring of tool vibrations.</p>

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Tool life prediction through vibration monitoring during drilling of Nimonic 90

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Konchada Sarayu Keerthika,
  • Mogatadikala Asritha,
  • Ramavath Nikhitha Kumari,
  • K R Ashwinkumar Hegde,
  • Gangadharudu Talla

摘要

Assessment of tool failure resulting from the impact of tool wear is of paramount importance in the development of smart machining systems. To address this challenge, a dual-phase approach was proposed, leveraging the statistical correlation between experimentally collected cutting tool vibration data and circularity error. This method also involves the development of stochastic tool life probability models to accurately estimate the remaining useful life (RUL). Experiments were performed on Nimonic-90, a nickel superalloy, using a tungsten carbide (WC) drill bit under various cutting speeds and feed rates. Flank wear measurement was used as a criterion for hole drilling, enabling the prediction of the number of holes that could be drilled and associated tool vibration data. The experimental results show that the statistically derived correlation is applicable for real-time tool-life prediction during machining. This technique remains effective under different cutting conditions, emphasizing the importance of real-time monitoring of tool vibrations.