This paper presents a multi-linear plastic hardening constitutive model based on ordinary state-based peridynamics, which is used to solve the nonlinear hardening behavior of materials effectively. The nonlinear model is discretized into multi-stage linear segments, replacing the Newton-Raphson method for nonlinear iteration, which improves computational efficiency and reduces iteration complexity for nonlinear hardening problems. The algorithm implementation process of peridynamics multi-linear plastic hardening constitutive model is given. By simulating the tensile test of a square plate, the accuracy and efficiency of the multi-linear plastic hardening algorithm are verified. Through discrete analysis using the multi-linear hardening model, it is demonstrated that the number of segments in the multi-linear model can be appropriately increased while maintaining high computational efficiency. Based on the MATLAB platform and by comparing with the J2 plastic hardening benchmark, the proposed model demonstrates a 4-50 times relevant efficiency speedup over nonlinear methods while maintaining accuracy. • The paper innovatively proposes an ordinary state-based peridynamics multi-linear hardening plastic model. • The model avoids using the Newton-Raphson method for numerical iteration, significantly improving computational efficiency and reducing iteration complexity. • The paper demonstrates a significant improvement in computational efficiency. The specific data shows that the computational efficiency has increased by 4 to 50 times using MATLAB platform by comparing with the J2 plastic hardening benchmark. • Through discrete analysis, it has been demonstrated that the multi-linear hardening model can maintain high computational accuracy and efficiency under different discretization densities.
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Yiyang Liu
Xiang Yi
Xionghui Yang
Computational Particle Mechanics
Huazhong University of Science and Technology
Wuhan Ship Development & Design Institute
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Liu et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69a75f01c6e9836116a2a16b — DOI: https://doi.org/10.1016/j.cpms.2026.01.006
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