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Parameter identification of Johnson-Cook constitutive model based on genetic algorithm and simulation analysis for 304 stainless steel.
Jiang, Xinyang; Ding, Jinfu; Wang, Chengwu; Shiju, E; Hong, Ling; Yao, Weifeng; Wang, Huadong; Zhou, Chongqiu; Yu, Wei.
Afiliación
  • Jiang X; College of Engineering, Zhejiang Normal University, Jinhua, 321004, China.
  • Ding J; College of Engineering, Zhejiang Normal University, Jinhua, 321004, China.
  • Wang C; College of Engineering, Zhejiang Normal University, Jinhua, 321004, China. cwuwang@126.com.
  • Shiju E; Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China. cwuwang@126.com.
  • Hong L; College of Engineering, Zhejiang Normal University, Jinhua, 321004, China.
  • Yao W; Zhejiang Shuohe Robot Technology Co., Ltd, Jinhua, 321000, China. hongling@seokho.cn.
  • Wang H; College of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing, 321017, China.
  • Zhou C; College of Engineering, Zhejiang Normal University, Jinhua, 321004, China.
  • Yu W; College of Engineering, Zhejiang Normal University, Jinhua, 321004, China.
Sci Rep ; 14(1): 21221, 2024 Sep 11.
Article en En | MEDLINE | ID: mdl-39261555
ABSTRACT
Addressing the significant discrepancy between actual experimental cutting force and its predicted values derived from traditional constitutive model parameter identification methods, a reverse identification research of the Johnson-Cook (J-C) constitutive model for 304 stainless steel was conducted via genetic algorithm. Considering actual cutting zone and the influence of feed motion on the rake (flank) angle, an unequal division shear zone model was established to implement the theoretical calculation for shear zone stress. Through cutting experiments, the spindle speed was negatively correlated with the cutting force at first, and then became positively correlated; The empirical formula (EXP model) for turning force was corrected, revealing that the EXP model was unable to provide optimal predicted values for cutting force. The influence of the J-C constitutive parameter C on the cutting morphology was firstly investigated through simulation analysis, and determined an appropriate value for C, then obtained the precise values for the other four constitutive parameters by genetic algorithm. Moreover, the simulated values of cutting force in JC1 model (obtained from the Split Hopkinson Pressure Bar test) and JCM model (the improved model using genetic algorithm) were obtained by three-dimensional (3-D) simulation via FEM software. The results indicated that, the maximum error between actual experimental cutting force and its simulated values (by JCM model) was 14.8%, with an average error of 6.38%. These results outperformed the JC1 and EXP models, suggesting that the JCM model identified via genetic algorithm was more reliable.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido