Jiang Xinyang, Ding Jinfu, Wang Chengwu, Shiju E, Hong Ling, Yao Weifeng, Wang Huadong, Zhou Chongqiu, Yu Wei
College of Engineering, Zhejiang Normal University, Jinhua, 321004, China.
Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China.
Sci Rep. 2024 Sep 11;14(1):21221. doi: 10.1038/s41598-024-71671-1.
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.
针对实际实验切削力与其通过传统本构模型参数识别方法得出的预测值之间的显著差异，通过遗传算法对304不锈钢的Johnson-Cook（J-C）本构模型进行了反向识别研究。考虑实际切削区以及进给运动对前刀面（后刀面）角度的影响，建立了不等分剪切区模型以实现对剪切区应力的理论计算。通过切削实验发现，主轴转速起初与切削力呈负相关，随后变为正相关；对车削力的经验公式（EXP模型）进行了修正，结果表明EXP模型无法为切削力提供最优预测值。首先通过模拟分析研究了J-C本构参数C对切削形态的影响，并确定了C的合适值，然后通过遗传算法获得了其他四个本构参数的精确值。此外，利用有限元软件通过三维（3-D）模拟获得了JC1模型（由分离式霍普金森压杆试验得到）和JCM模型（使用遗传算法的改进模型）中的切削力模拟值。结果表明，实际实验切削力与其模拟值（由JCM模型得到）之间的最大误差为14.8%，平均误差为6.38%。这些结果优于JC1和EXP模型，表明通过遗传算法识别的JCM模型更可靠。
