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<p>Predicting aero-engine blade machining quality under data scarcity remains challenging owing to the strong coupling of process variables. We present GA-KAN, which combines localized B-spline feature representations with gated attention to adaptively capture inter-feature relationships. Residual connections and a hybrid regularization scheme (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(L_1\)</EquationSource> </InlineEquation> and attention-aware regularization) improve optimization stability and generalization. We evaluate GA-KAN on a proprietary blade milling dataset and a public CNC turning dataset. GA-KAN outperforms competitive baselines, with the average RMSE reduced by 3.3% for roughness and residual-stress targets on the blade milling dataset and by 4.7% for the four roughness indicators on the turning dataset. These results demonstrate GA-KAN’s effectiveness and support its practical use in precision blade machining and other data-constrained manufacturing settings.</p>

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Machining quality prediction of aero-engine blades under data-constrained conditions using a gated attention-enhanced Kolmogorov–Arnold network

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Shuoshan Zhang,
  • Zhongde Shan,
  • Changfeng Yao,
  • Qiaoyun Wu,
  • Jun Wang

摘要

Predicting aero-engine blade machining quality under data scarcity remains challenging owing to the strong coupling of process variables. We present GA-KAN, which combines localized B-spline feature representations with gated attention to adaptively capture inter-feature relationships. Residual connections and a hybrid regularization scheme ( \(L_1\) and attention-aware regularization) improve optimization stability and generalization. We evaluate GA-KAN on a proprietary blade milling dataset and a public CNC turning dataset. GA-KAN outperforms competitive baselines, with the average RMSE reduced by 3.3% for roughness and residual-stress targets on the blade milling dataset and by 4.7% for the four roughness indicators on the turning dataset. These results demonstrate GA-KAN’s effectiveness and support its practical use in precision blade machining and other data-constrained manufacturing settings.