4.7 Article

A new hybrid identification method for determining the material parameters of thin-walled tube under compressive stress state

期刊

MATERIALS & DESIGN
卷 44, 期 -, 页码 49-58

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2012.07.036

关键词

Inverse identification; Material parameters; Thin-walled tube; Compressive stress state; Computational efficiency

资金

  1. National Natural Science Foundation of China [50905144]
  2. 111 Project [B08040]
  3. Fund of the State Key Laboratory of Solidification Processing in NWPU

向作者/读者索取更多资源

Accurate and fast determination of material parameters of thin-walled tube under compressive stress state is essential for analyzing the compressive-type tube forming process. For the thin-walled tube with hollow structure, it is difficult to determine the material parameters directly from the experiment since buckling occurs easily when the tube suffers axial compressive loading. To accurately and rapidly identify the material parameters of thin-walled tube under compressive stress state, a hybrid inverse identification method is proposed based on tube lateral compression test with combining finite element simulation, regression analysis and genetic algorithm. By employing the proposed method, the Swift law hardening parameters of thin-walled tubes with different materials and specifications under compressive stress state are identified. Furthermore, the efficiency and accuracy of the proposed method are discussed in comparison with the previous researches. The results show that: (1) for 6061-T4 and 1Cr18Ni9Ti tubes, the maximum relative predicting errors of forces in tube lateral compression using the identified material parameters are less than 9%; (2) for aluminum tube empty set 100 x 2 (diameter x thickness, mm), the maximum discrepancies between the simulated and experimental circumferential strains are less than 0.0274 for 30-70% reductions, and the simulated tube profiles deviate from the experiment less than 10% at reductions of 0-78%; and (3) the proposed method almost saves 80% computational time compared with the previous stepwise optimization method. (C) 2012 Elsevier Ltd. All rights reserved.

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