Research on inverse identification of johnson-cook constitutive parameters for turning 304 stainless steel based on coupling simulation

Zou, Zichuan; He, Lin; Zhou, Tao; Wen-Qian, Zhang; Tian, Pengfei; Zhou, Xiaorong

doi:10.1016/j.jmrt.2023.01.090

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Renowned for its robust global search capability, GA has been applied in parameter identification. Zhou et al utilized the genetic algorithm to identify and improve J-C constitutive model, achieving higher precision in predicting cutting forces 13,19 . Such findings indicated that genetic algorithm could effectively search for optimal solution in multifactorial cutting experiments to find the best approaches.…”

Section: Genetic Algorithm Identification Of J-c Model Constitutive P...mentioning

confidence: 99%

“…6, this study collated data from extensive literature reviews, presenting constitutive parameters values, as detailed in Tab.4. Research revealed that among the five constitutive parameters A, B, n, C, and m, A and B have a significant impact on simulated cutting force, while the impact of C is minor 13 . However, after simulation tests, within the range of 0.004 ≤ C ≤ 0.14, larger value of parameter C resulted in unstable simulation result and irregular chip morphology.…”

Section: Genetic Algorithm Identification Of J-c Model Constitutive P...mentioning

confidence: 99%

“…Typically, the applicable strain rate range in Split Hopkinson Pressure Bar (SHPB) experiments lies between 10 3 s -1 and 10 4 s -1 , while in quasi-static tests, it spans from 10 -4 s -1 to 10 -1 s -1 . However, during machining processes, the strain rate of the material can reach up to 10 5 s -1 13 . This indicates that the constitutive parameters derived from SHPB or quasi-static tests do not accurately represent the stress-strain relationship of materials under cutting conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al 19 realized the least squares identification of J-C constitutive parameters and improved the prediction accuracy of cutting force by modeling and simulation for H13 steel and Ti-6Al-4V titanium alloy. Zou et al 13 implemented the reverse identification of J-C parameters of 304 stainless steel by adopting the minimum error between experimental value and simulated value of cutting force as the objective function of genetic algorithm. They also established the three-dimensional (3-D) model as the initial model for finite element iteration, compared the experimental cutting force, chip morphology, and residual stress.…”

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm-based parameter identification of Johnson- Cook constitutive model for 304 stainless steel

Jiang,

Ding,

Wang

et al. 2024

Preprint

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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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Section: Genetic Algorithm Identification Of J-c Model Constitutive P...mentioning

confidence: 99%

Section: Genetic Algorithm Identification Of J-c Model Constitutive P...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations