Fatigue life prediction for vibration isolation rubber based on parameter‐optimized support vector machine model

Abstract

Given the small sample size, nonlinearity, and large dispersion of the measured data of fatigue performance for vibration isolation rubbers, the fatigue life prediction model for vibration isolation rubber materials was established using a support vector machine (SVM). A modified gravity search algorithm (MGSA) is proposed to optimize the parameters of the SVM. Using environmental temperature, the Rockwell hardness of the rubber compound, and the engineering strain peak as the input variables, the model was trained based on the experimental fatigue data of vibration isolation rubber materials. For comparison, the standard genetic algorithm, the standard particle swarm algorithm, and the standard simulated annealing algorithm are also implemented. Moreover, a back propagation neural network regression model is applied to the life prediction, with the conclusion that the prediction accuracy and the efficiency of MGSA are better than those of extant methods. This work can provide reference for further fatigue life prediction and structural improvement of rubber parts.

Soft computing methods for fatigue life estimation: A review of the current state and future trends

“…Article/conference paper Publications using genetic algorithms Article Ma et al, 34 Zhang and Lin, 38 Vadood et al, 43 Liu et al, 46 Lotfi and Beiss, 49 Majidian and Saidi, 56 Gao et al, 76 Canyurt, 89 Karakas et al, 90 Chen et al, 91 Kamal et al, 92 Zhang et al, 93 Agius et al 94 and Deveci and Artem 95 Conference paper Susmikanti, 17 Rohman et al, 18 Cai et al, 30 Zhaohua, 35 Tian 79 and Xu and Cui 96 prediction method for different purposes, such as the creation of constant life diagrams. Mohanty et al 101 estimated the fatigue crack growth rate of aluminium alloys (2024-T3) using GP and ANN, whereas GP provided a higher precision than ANN.…”

“…Benchmarking Marquardt and Zenner, 27 Mathur et al, 29 Liao et al, 31 Artymiak et al 51 and Kim et al 133 Case study Yang et al, 1 Al Assaf and El Kadi, 12 Bezazi et al, 13 Salmalian et al, 14 Figueira Pujol and Andrade Pinto, 15 Salmalian et al, 16 Rohman et al, 18 Kong et al, 19 Han, 20 Aymerich and Serra, 21 Venkatesh and Rack, 22 Pleune and Chopra, 23 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Cai et al, 30 Al-Assadi et al, 32 Kumar et al, 33 Ma et al, 34 Zhaohua, 35 Xu et al, 36 Barsoum et al, 37 Zhang and Lin, 38 Mohanty et al, 40 Uygur et al, 41 Xiang et al, 42 Vadood et al, 43 Mishra et al, 44 Mohanty, 45 Liu et al, 46 Martinez and Ponce, 47 Barbosa et al, 48 Lotfi and Beiss, 49 Razzaq et al, 50 Srinivasan et al, 52 Al-Assaf and El Kadi, 53 Park and Kang, 54 Vassilopoulos et al, 55 Majidian and Saidi,…”

“…Fawaz-Ellyin Ertas, 111 Ertas and Sonmez, 117 Ertas and Sonmez 118 and Deveci and Artem 130 Statistical models Figueira Pujol and Andrade Pinto, 15 Pleune and Chopra, 23 Vadood et al, 43 Liu et al, 46 Xiao et al, 59 Abdalla and Hawileh, 65 Moghaddam et al, 69 Zhang et al, 93 Vassilopoulos et al 100 and Azarhoosh et al 102…”

“…Al Assaf and El Kadi, 12 Bezazi et al, 13 Rohman et al, 18 Kong et al, 19 Han, 20 Sohn and Bae, 24 Vassilopoulos et al, 28 Cai et al, 30 Kumar et al, 33 Zhaohua, 35 Vadood et al, 43 Liu et al, 46 Barbosa et al, 48 Lotfi and Beiss, 49 Razzaq et al, 50 Vassilopoulos et al, 55 Mathew et al, 57 Mohanty et al, 58 Mohanty et al, 60 Karakas, 63 Karakas and Tomasella, 66 Jin et al, 67 Abdalla and Hawileh, 75 Gao et al, 76 El Kadi and Al-Assaf, 77 Tian, 79 Song et al, 81 Ahmad et al, 82 Zhang et al 93…”

“…Rohman et al, 18 Kong et al, 19 Pleune and Chopra, 23 Vassilopoulos et al, 28 Mohanty et al, 40 Mishra et al, 44 Mohanty, 45 Liu et al, 46 Lotfi and Beiss, 49 Mohanty et al, 58 Xiao et al, 59 Moghaddam et al, 69 Durodola et al, 70 Yan et al, 72 Ahmad et al, 82 Zhang et al, 93 Deveci and Artem, 95 Vassilopoulos et al, 100 Azarhoosh et al, 102 Sai et al 131…”