Improved differential evolutionary algorithm for nonlinear identification of a novel vibration‐assisted swing cutting system

Abstract

Vibration-assisted swing cutting (VASC) is a new precision machining technology. VASC not only inherits the characteristics of EVC intermittent cutting but also alleviates the problem of EVC residual height. However, system identification is key if you want to achieve precise control. In order to solve this problem, a new improved differential evolutionary (IDE) algorithm is proposed to identify and optimize the Hammerstein-Wiener model parameter in VASC system. IDE algorithm is applied to transform the identification problem of the model into the optimization problem in the parameter space, and the optimal solution of the parameter of the model in the parameter space is obtained. Meanwhile, the IDE algorithm and the conventional five differential evolutionary algorithms perform performance comparison tests on six different test functions. The test results show that the IDE algorithm is strengthening the global search capability, accelerate the convergence rate to the global optimal solution, and indicate that the IDE algorithm can be effectively applied to the parameter optimization of Hammerstein-winner model. Based on the input and output data collected from the experiment, the accuracy of the identification model can be up to 98%, which prove the superiority of the proposed IDE algorithm for system identification. KEYWORDSHammerstein-Wiener model, improved differential evolutionary algorithm, system identification, test functions, vibration-assisted swing cutting 1066

An analytical cutting force model of quasi-intermittent vibration assisted swing cutting based on predictive machining theory

“…Based on elliptic vibration cutting (EVC), the Quasiintermittent vibration assisted swing cutting (QVASC) method is proposed to improve the difficult-to-cut materials' machinability, which can inherit the advantage of EVC and eliminate its residual height. [1][2][3] The application of vibration along the cutting direction and the radial direction in EVC 4,5 leads to a smaller cutting force, longer tool life, and higher surface quality. 6 However, the traditional EVC technology is not suitable for processing overly complex and curved surfaces.…”

“…Subsequently, based on QVASC design and modeling, he conducted cutting experiments to confirm that the QVASC technique can suppress residual errors between adjacent objects very simply. 2 Research on other aspects are slightly less, an improved differential evolution algorithm (IDE) was proposed by Wang et al 3 to identify and optimize Hammerstein-Wiener model parameters in the QVASC system. He used the IDE algorithm to convert the identification problem of the model into an optimization problem in the parameter space, and he obtained the optimal solution of the model parameters in the parameter space.…”

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

“…22 The evolutionary algorithms are population-based methods and they try to simulate natural evolution processes to achieve a goal which is generally expressed as a fitness function to optimize. 23 Practically, the evolutionary algorithms are iterative processes in which the candidate solutions evolve vs the generation, choosing an initial population randomly. 24,25 The differential evolution (DE) algorithm is one of the evolutionary algorithms with the simplicity of implementation and good performance in finding the approximation solutions of high complexity problems.…”

“…The evolutionary algorithms are population‐based methods and they try to simulate natural evolution processes to achieve a goal which is generally expressed as a fitness function to optimize 23 . Practically, the evolutionary algorithms are iterative processes in which the candidate solutions evolve vs the generation, choosing an initial population randomly 24,25 .…”