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Fatigue01:21

Fatigue

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Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
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Data-Physics Fusion-Driven Defect Predictions for Titanium Alloy Casing Using Neural Network.

Peng Yu1, Xiaoyuan Ji1, Tao Sun1

  • 1School of Materials Science and Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

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Summary

This study develops accurate prediction models for porosity defects in titanium alloy casings, crucial for aero engine safety. The research identifies key process parameters influencing defect formation, offering a strategy to improve casting quality.

Keywords:
Ti alloyinvestment castingmulti-regressionneural networkshrinkage defects

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Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Aerospace Engineering

Background:

  • Titanium alloy casings are critical components in aero engines, requiring high integrity.
  • Porosity defects in investment-cast titanium alloy casings significantly impact operational safety and stability.
  • Current methods struggle to effectively control process parameter fluctuations affecting defect formation.

Purpose of the Study:

  • To propose a strategy for controlling the influence of process parameters on shrinkage volume and number in titanium alloy casings.
  • To develop and compare prediction models for porosity defects in ZTC4 alloy casings.
  • To identify the sensitivity of casing defects to key process parameters.

Main Methods:

  • Simulated gravity investment casting of ZTC4 titanium alloy casings.
  • Construction of multiple regression prediction models for porosity volume and number.
  • Development of neural network prediction models for porosity volume and number.
  • Analysis of the influence of pouring temperature, pouring time, and mold shell temperature on defects.

Main Results:

  • Both multiple regression and neural network models achieved over 99% accuracy in predicting shrinkage cavity total volume.
  • Neural network models demonstrated higher accuracy than multiple regression models.
  • The neural network model accurately predicted defect volume and number based on pouring temperature, pouring time, and mold shell temperature.
  • Identified pouring temperature as the most sensitive parameter, followed by pouring time and mold temperature.

Conclusions:

  • Neural network models offer a robust approach for predicting and controlling porosity defects in titanium alloy casings.
  • Optimizing the process parameter window effectively mitigates the impact of parameter fluctuations on defect formation.
  • The findings provide valuable insights for improving actual production control processes to reduce casting defects.