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March 12, 2026Industrial Lubrication and Tribology

Research on the prediction of wear distribution of ball-end mill based on milling GH4169 nickel-based superalloy

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Authors

LYLi YangXi'an University of Science and TechnologyYSYuan SunXi'an University of Science and TechnologyLXLiwang XiaXi'an University of Science and Technology

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Implication

Demonstrates a model predicting tool wear in milling, indicating practical applications in aerospace manufacturing.

Key Points

  • The aim is to establish a model to accurately predict tool wear in GH4169 milling using optimization techniques.
  • Utilized response surface methodology (RSM) to optimize cutting parameters.
  • Developed a Whale Optimization Algorithm-backpropagation (WOA-BP) neural network model.
  • Collected data on machining angle and time to predict localized tool wear.
  • Achieved a root mean square error of 1.69µm and a mean absolute percentage error of 1.14%.
  • Outperformed conventional BP networks in terms of robustness and generalization.
  • Model supports efficient tool wear prediction for milling nickel-based superalloys.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69b257df96eeacc4fcec6ee3https://doi.org/10.1108/ilt-06-2025-0308
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