## 1 Introduction  
Load-bearing engineering components are frequently exposed to random loading and the risk of occurrence of ratcheting is increased significantly [1; 2; 3; 4; 5]. Offshore structures, pressure vessels and piping system, structures operating in earthquake zones, airplane landing gears and nuclear reactors are typical examples of such components and structures [6; 7; 8]. Structures subjected to stress cycles beyond the elastic limit oblige a trustworthy design, especially in the presence of a significant mean stress from loads such as dead weight or internal pressure [1; 6; 9; 10]. In researches on ratcheting behaviour, bilinear kinematic hardening (BKH), multilinear kinematic hardening (MKIN) [11] and the nonlinear kinematic hardening (NLK) model developed by [PERSON] [12] are commonly used, and are currently incorporated in commercial finite element programs such as ANSYS and ABAQUS [13]. Among the available models in commercial software, the Chaboche model is the most powerful [14]. However, this model overpredicts ratcheting strain under either uniaxial or multiaxial loading when compared with experimental results [13; 15; 16; 17]. A solution to the above problem is found by identifying new material parameters [18] or using a more multifaceted material model [19; 20]. However, implementation of more robust complex models can be time-consuming and complicated [15; 21]. [PERSON] reiterated the parameter identification for the material under consideration as an essential prerequisite for using an NLK model [22]. Accordingly, the dedicated method to figure out the best set of ratcheting parameters can extremely affect the result of ratcheting prediction. [PERSON] et al. proposed a methodical step-by-step concept for specifying the Chaboche model's parameters. In their concept, the parameters were specified via numerical simulation of a hysteresis lower or upper curve [11; 23; 24]. The manual method to determine parameter values might be lengthy and time consuming, requiring numerous trial-and-error iterations for determining a set of parameters values. [PERSON] et al. evaluated the use of heuristic search techniques, such as the genetic algorithm, in automating the parameter value determination of developed models, and thus overcoming the limitations involved in manual calibration [25; 26]. Two approaches to determine Chaboche model parameters are used by [PERSON] et al. [22], namely the manual method and the numerical method, which make use of ABAQUS. They presented practicable applications of the Chaboche model in elastic-plastic ratcheting analysis. Cyclic hardening can have a strong influence on the rate of ratcheting in unsymmetrical stress cycling [25; 26]. In an effort to reduce the hardening-ratcheting coupling, they stabilized the hardening of the test specimens by axial symmetric strain cycling in the range of stress-strain similar to the following ratcheting tests. The positive or negative segment of the last saturated cycle of hysteresis curve was adopted in calculating the ratcheting parameters. However, [PERSON] et al. adopted the monotonic stress-strain curve, instead of a hysteresis curve, obtained from a specimen subjected to unidirectional tension loading, to calibrate the ratcheting parameters [22]. The authors suggested that adopting the monotonic curve data for determining is predictable to present stuffy assessment of ratcheting that could be assumed suitable for design targets. They argued that for the case of stainless steel, the stress flow increases with additional cycles, more than that of the initial monotonic curve (initial half-cycle). The ratcheting parameters are significantly sensitive to the elastic limit stress at which yielding instigates [22; 27]. Precise application of elastic theory that causes a slight amount of nonlinear recovery was addressed [28].