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<p>γ-TiAl alloy is a promising candidate to replace nickel-based superalloys in aircraft engines due to its low density and excellent high-temperature performance. However, optimizing its surface integrity through shot peening (SP) remains challenging due to induced defects. This study investigates the effects of different SP conditions on the surface integrity of γ-TiAl alloy and establishes empirical models linking SP parameters with surface characteristics. Results show that ceramic shots provide superior surface quality. Surface roughness and residual stress initially increase with SP intensity and coverage but decline at higher levels due to surface degradation. Empirical models developed via linear regression predict surface roughness, residual stress, and microhardness with errors within 23%, 33%, and 15%, respectively. The predicted in-depth profiles align closely with experimental results, demonstrating the models’ reliability and practical applicability in process optimization.</p>

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The changing law and empirical model of surface integrity characteristics in shot peening γ-TiAl alloy

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Liang Tan,
  • Peiyu Yuan,
  • Manqi Wang,
  • Tao Fan,
  • Dongbo Jia,
  • Changfeng Yao

摘要

γ-TiAl alloy is a promising candidate to replace nickel-based superalloys in aircraft engines due to its low density and excellent high-temperature performance. However, optimizing its surface integrity through shot peening (SP) remains challenging due to induced defects. This study investigates the effects of different SP conditions on the surface integrity of γ-TiAl alloy and establishes empirical models linking SP parameters with surface characteristics. Results show that ceramic shots provide superior surface quality. Surface roughness and residual stress initially increase with SP intensity and coverage but decline at higher levels due to surface degradation. Empirical models developed via linear regression predict surface roughness, residual stress, and microhardness with errors within 23%, 33%, and 15%, respectively. The predicted in-depth profiles align closely with experimental results, demonstrating the models’ reliability and practical applicability in process optimization.