评估板材金属成型工艺中的鲍-维尔兹比基（BW）损伤模型

IF 2.2 3区工程技术 Q2 MECHANICS

Archive of Applied Mechanics Pub Date : 2024-11-25 DOI:10.1007/s00419-024-02717-z

A. Hosseinpour, F. Haji Aboutalebi, M. S. Sadeghi Nezhad

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引用次数: 0

摘要

钣金成型过程中的损伤增长现象是制造业最重要的问题之一。预测裂纹的产生和位置可以帮助工程师推迟故障的发生，并生产出安全的零件。在这项研究中，首先对 Bao-Wierzbicki 损伤准则采用了六种不同的校准方法。然后，为了评估校准方法的准确性，对一些具有正应力三轴性的传统板材成形过程进行了数值模拟。最后，将数值预测结果与经验测试结果进行比较。比较结果表明，双曲二次双曲 3 (HQH3) 方法是最好的校准方法，因为它精度高，校准所需的实验测试次数少。

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Evaluation of Bao-Wierzbicki (BW) damage model in sheet metal forming processes

Damage growth phenomenon in sheet metal forming processes is one of the most important issues in the manufacturing industries. Predicting the onset and location of crack can help engineers to postpone the failure as well as to produce safe parts. In this research, first, six different calibration methods are employed for the Bao-Wierzbicki damage criterion. Then, to evaluate the accuracy of the calibration approaches, a few conventional sheet metal forming processes with the positive stress triaxiality are numerically simulated. Finally, the numerical predicted results are compared with the empirical tests. The comparisons reveal that the hyperbolic quadratic hyperbolic 3 (HQH3) method is the best calibration approach, due to its accuracy and the fewer number of required experimental tests for the calibration.

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来源期刊

Archive of Applied Mechanics 物理-力学

CiteScore

4.40

自引率

10.70%

发文量

234

审稿时长

4-8 weeks

期刊介绍： Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.