Creep–Fatigue Experiment and Life Prediction Study of Piston 2A80 Aluminum Alloy

Abstract

In order to improve the reliability and service life of vehicle and diesel engine, the fatigue life prediction of the piston in a heavy diesel engine was studied by finite element analysis of piston, experiment data of aluminum alloy, fatigue life model based on energy dissipation criteria, and machine learning algorithm. First, the finite element method was used to calculate and analyze the temperature field, thermal stress field, and thermal–mechanical coupling stress field of the piston, and determine the area of heavy thermal and mechanical load that will affect the fatigue life of the piston. Second, based on the results of finite element calculation, the creep–fatigue experiment of 2A80 aluminum alloy was carried out, and the cyclic response characteristics of the material under different loading conditions were obtained. Third, the fatigue life prediction models based on energy dissipation criterion and twin support vector regression are proposed. Then, the accuracy of the two models was verified using experiment data. The results show that the model based on the twin support vector regression is more accurate for predicting the material properties of aluminum alloy. Based on the established life prediction model, the fatigue life of pistons under actual service conditions is predicted. The calculation results show that the minimum fatigue life of the piston under plain condition is 2113.60 h, and the fatigue life under 5000 m altitude condition is 1425.70 h.

Structure optimization of gasket based on orthogonal experiment and NSGA-II

“…In this section, the orthogonal experiment design 31 and statistical analysis are adopted to analyze the influence of geometric parameters on thermal mechanical conditions of gasket combined with finite element method.…”

Machine learning‐based genetic feature identification and fatigue life prediction

“…Although prediction accuracies of these models have been continuously improved by introducing new external or internal factors, their applications are still limited in multiaxial fatigue life prediction due to the complicated nonlinear relationships among multiple factors. For this reason, machine learning methods have gradually gained more and more attention and demonstrated a promise potential in accurately predicting fatigue life 10–13 . There are more and more extensive applications of them in the regarding issue in two ways: To use machine learning methods to directly predict fatigue life or properties through fatigue data.…”

“…For this reason, machine learning methods have gradually gained more and more attention and demonstrated a promise potential in accurately predicting fatigue life. [10][11][12][13] There are more and more extensive applications of them in the regarding issue in two ways: 1. To use machine learning methods to directly predict fatigue life or properties through fatigue data.…”

Prediction of 316 stainless steel low-cycle fatigue life based on machine learning

“…Due to the powerful abilities of machine learning such as data processing and data analysis, the method has been widely used in the fields of data mining, automatic speech recognition, computer vision, and fault detection and diagnosis. At present, it also has some applications in life prediction [19][20][21][22] . However, there are few studies on low-cycle fatigue life prediction of 316 stainless steel using a machine learning model.In this paper, the low-cycle fatigue life of 316 stainless steel is predicted by machine learning.…”