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<p>With growing demand for automotive lightweighting, tailor rolled blank (TRB) have attracted attention due to controllable thickness gradients. However, inherent thickness non-uniformity causes strain localization and higher fracture risk. This study identifies TRB forming damage mechanisms and establishes gradient design criteria to inhibit cracking. Traditional forming prediction models poorly capture thickness-gradient-induced stress triaxiality histories and damage kinetics. To address this, a machine learning-parameterized Gurson–Tvergaard–Needleman (GTN) damage model is developed, achieving &lt; 4.62% fracture prediction error, validated against ISO 12004-2 criteria. Coupled verification via finite element simulation and cupping test shows that yield strength differences across regions with thickness gradients in TRB cause “prior yielding” and “inertia lag” in plastic deformation, which is the main reason for reduced formability. Based on these findings, this study systematically compares transverse and longitudinal thickness gradient design strategies. The results show that longitudinal gradient configurations effectively reduce local stress concentration by 20.68%, enhance the forming uniformity and stability of TRB beam components, and achieve a 12.7% reduction in structural weight. This research provides both theoretical insight and practical guidance for the design and forming of lightweight structures with gradient thicknesses.</p>

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Plastic deformation behavior plastic deformation behavior and ductile damage mechanism of tailor rolled blanks in automotive cross beams: a hybrid experimental and GTN-based simulation study

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Xiujuan Fu,
  • Yifei Wang,
  • Qiaoyun Zhou,
  • Yulan Liu,
  • Qingxin Li

摘要

With growing demand for automotive lightweighting, tailor rolled blank (TRB) have attracted attention due to controllable thickness gradients. However, inherent thickness non-uniformity causes strain localization and higher fracture risk. This study identifies TRB forming damage mechanisms and establishes gradient design criteria to inhibit cracking. Traditional forming prediction models poorly capture thickness-gradient-induced stress triaxiality histories and damage kinetics. To address this, a machine learning-parameterized Gurson–Tvergaard–Needleman (GTN) damage model is developed, achieving < 4.62% fracture prediction error, validated against ISO 12004-2 criteria. Coupled verification via finite element simulation and cupping test shows that yield strength differences across regions with thickness gradients in TRB cause “prior yielding” and “inertia lag” in plastic deformation, which is the main reason for reduced formability. Based on these findings, this study systematically compares transverse and longitudinal thickness gradient design strategies. The results show that longitudinal gradient configurations effectively reduce local stress concentration by 20.68%, enhance the forming uniformity and stability of TRB beam components, and achieve a 12.7% reduction in structural weight. This research provides both theoretical insight and practical guidance for the design and forming of lightweight structures with gradient thicknesses.