Abstract
Efficient analytical model directly enhances the reliability evaluation of flexible mechanism under operation. In this paper, genetic algorithm-based extremum neural network (GA-ENN) is developed as reliability model by introducing the thoughts of extremum and genetic algorithm (GA) into artificial neural network to address the key problems comprising transient response and modeling precision in the dynamic reliability analysis of flexible mechanism in a time domain. The thought of extremum is adopted to simplify transient response process as one extremum value to the difficulty of dynamic reliability analysis induced by transient process response, and the GA is applied to find the optimal model parameters of reliability model. The dynamic reliability analysis of two-link flexible robot manipulator (TFRM) (a typical flexible mechanism) was implemented based on the GA-ENN method, regarding the input random variables of material density, elastic modulus, section sizes of components, and the output response of components’ deformations. From the analysis, the comprehensive reliability of the TFRM is 0.951 when the allowable deformation is 1.8 × 10−2 m. Besides, the maximum deformations of the two components follow the normal distributions with the means of 1.45 × 10−2 m and 1.69 × 10−2 m and the standard variances of 6.77 × 10−4 m and 4.08 × 10−4 m, respectively. Through the comparison of methods, it is illustrated that the developed GA-ENN improves the simulation efficiency and modeling accuracy by overcoming the problems of transient response and model parameter optimization in the dynamic reliability analysis of TFRM.
Highlights
As one of the important parts in mechanical system in robotics, satellite, aircraft, aeroengine, and so forth, flexible mechanism severely influences the safety and usability of mechanical system
The investigation of flexible mechanism attracts a lot of attention with the emphasis on the control strategy [1,2,3] and the modeling and solution of dynamic equation [4, 5]. e reliability evaluation of flexible mechanism has become one interesting topic in ensuring the secure operation of mechanical system [6]
Fei et al developed decomposed-coordinated surrogate modeling strategy for compound function approximation and a turbine-blisk reliability evaluation [7], and the method was applied aeroengine blade-tip clearance and its components [8,9,10]; Li et al employed support vector machine in structural reliability analysis [11]; Kaymaz applied Kriging method to complete structural reliability problems [12]; Xiong et al presented a double weighted stochastic RSM for reliability analysis [13]; Gavin et al gave the RSM-based high-order limit state functions for reliability analysis [14]; Ren et al established neural network response surface model for reliability analysis based on artificial neural network (ANN) with high accuracy and nonlinear mapping capability [15]; Mathematical Problems in Engineering
Summary
As one of the important parts in mechanical system in robotics, satellite, aircraft, aeroengine, and so forth, flexible mechanism severely influences the safety and usability of mechanical system. Due to the transient response and complicated analysis, the simulation efficiency and model precision of flexible mechanism reliability analysis are unacceptable if the response surface methods effectively used in structural reliability analysis are directly employed. Is is because of the following: (1) as BP-ANN model is established, training algorithms have local optimization rather global optimization and difficult convergence; (2) the weights and thresholds (model parameters) in BP-ANN model are so imprecise that the BP-ANN model has low approximation accuracy; (3) modeling speed is too low to implement the reliability analysis of flexible mechanism due to noneffective transient processing. Is proposed method is adopted to improve the modeling precision and simulation efficiency in the reliability analysis of flexible mechanism by simplifying the response process as a response extremum value and employing GA to find the optimal parameters of ANN model, respectively.
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