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<p>In press forming, high panel quality is required despite variations in material properties and lubrication conditions. However, deformation behavior during forming cannot be directly observed because of a closed die set. Consequently, variations in panel quality are typically identified only through post-forming inspection. To address this limitation, a framework that integrates nonsequential data assimilation with a computer-aided engineering surrogate model is proposed. The framework estimates initial states related to forming behavior from observation data collected during forming and predicts panel quality after forming. The proposed framework is applied to the square-cup deep drawing of a mild steel sheet. Material draw-in measured at intermediate forming stages is used as observation data to estimate initial-state parameters. Subsequently, material draw-in, wrinkle height, and thickness distribution of the formed panel are predicted based on the estimated states. The framework is validated through numerical simulations and press forming experiments.</p>

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In-Process Prediction of Formed Panel Quality and Internal State Variations in Press Forming via Data Assimilation with CAE Surrogate Models

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Haruki Sato,
  • Eiichi Ota,
  • Yasumoto Sato

摘要

In press forming, high panel quality is required despite variations in material properties and lubrication conditions. However, deformation behavior during forming cannot be directly observed because of a closed die set. Consequently, variations in panel quality are typically identified only through post-forming inspection. To address this limitation, a framework that integrates nonsequential data assimilation with a computer-aided engineering surrogate model is proposed. The framework estimates initial states related to forming behavior from observation data collected during forming and predicts panel quality after forming. The proposed framework is applied to the square-cup deep drawing of a mild steel sheet. Material draw-in measured at intermediate forming stages is used as observation data to estimate initial-state parameters. Subsequently, material draw-in, wrinkle height, and thickness distribution of the formed panel are predicted based on the estimated states. The framework is validated through numerical simulations and press forming experiments.