Scholarlink

<p>Stamping of large thin-walled metal covers involves complex multi-objective conflicts among cracking, wrinkling, and springback, where purely data-driven models often suffer from poor generalization under limited sample conditions. To address this, a Physics-Guided Extra Trees (PG-ET) collaborative optimization framework is proposed. The framework reformulates the optimization problem by introducing defect-oriented physical descriptors derived from plastic deformation and frictional instability mechanisms. First, a physics-guided feature pool containing high-order coupling descriptors is constructed, followed by target-specific feature decoupling using permutation importance analysis. Subsequently, Yeo-Johnson target transformation and the fully randomized partition mechanism of Extra Trees are combined to improve robustness against feature collinearity. The developed PG-ET model achieves high prediction accuracy: the absolute error for thinning is as low as 0.0024, the coefficient of determination (<i>R</i><sup><i>2</i></sup>) for thickening reaches 0.9662, and the <i>R</i><sup><i>2</i></sup> for springback prediction reaches 0.8637. Finally, the PG-ET surrogate is integrated with Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) decision-making to identify the optimal process parameters. Industrial validation on a 55-inch television (TV) backplane demonstrates that, while preventing cracking, the proposed framework reduces macro-warpage by 48.5% and maximum thickening by 26.6%. The results indicate that embedding physically interpretable deformation descriptors into surrogate modeling effectively enhances optimization reliability for complex thin-walled forming.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-objective optimization of stamping process based on physics-guided extra trees for large thin-walled parts

  • 期刊论文

  • 发表日期：

    2026年7月9日

查看全文
  • Xizhang Wen,
  • Tong Zhu,
  • Jiachang Wang,
  • Xiangwei Xu,
  • Yibo Wang,
  • Song Zhang

摘要

Stamping of large thin-walled metal covers involves complex multi-objective conflicts among cracking, wrinkling, and springback, where purely data-driven models often suffer from poor generalization under limited sample conditions. To address this, a Physics-Guided Extra Trees (PG-ET) collaborative optimization framework is proposed. The framework reformulates the optimization problem by introducing defect-oriented physical descriptors derived from plastic deformation and frictional instability mechanisms. First, a physics-guided feature pool containing high-order coupling descriptors is constructed, followed by target-specific feature decoupling using permutation importance analysis. Subsequently, Yeo-Johnson target transformation and the fully randomized partition mechanism of Extra Trees are combined to improve robustness against feature collinearity. The developed PG-ET model achieves high prediction accuracy: the absolute error for thinning is as low as 0.0024, the coefficient of determination (R2) for thickening reaches 0.9662, and the R2 for springback prediction reaches 0.8637. Finally, the PG-ET surrogate is integrated with Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) decision-making to identify the optimal process parameters. Industrial validation on a 55-inch television (TV) backplane demonstrates that, while preventing cracking, the proposed framework reduces macro-warpage by 48.5% and maximum thickening by 26.6%. The results indicate that embedding physically interpretable deformation descriptors into surrogate modeling effectively enhances optimization reliability for complex thin-walled forming.