Effect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learning

Abstract

The posterior statistical distributions of fatigue strength are determined using Bayesian inferential statistics and the Metropolis Monte Carlo method.This study explores how structural heterogeneity affects ultrahigh cycle fatigue strength in additive manufacturing. Monte Carlo methods and procedures may assist estimate fatigue strength posteriors and scatter. The acceptable probability in Metropolis Monte Carlo relies on the Markov chain's random microstructure state. In addition to commonly studied variables, the proportion of chemical composition was demonstrated to substantially impact fatigue strength if fatigue lifetime in crack propagation did not prevail due to high threshold internal notches. The study utilizes an algorithm typically used for quantum mechanics to solve the complicated multifactorial fatigue problem. The inputs and outputs are modified by fitting the microstructural heterogeneities into the Metropolis Monte Carlo algorithm. The main advantage here is applying a general-purpose nonphenomenological model that can be applied to multiple influencing factors without high numerical penalty.

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

“…The fatigue strength refers to the maximum stress of the material under infinite alternating loads without damage. Previous studies [162][163][164] indicated that there was a certain relationship between the fatigue strength and the yield strength, but the quantitative relationship was not clear enough, resulting in large error of the predicted results. The fatigue strength prediction models based on data-driven methods can accurately predict the fatigue strength by learning the relationship between component features and fatigue strength.…”

Integrity Assessment of Electron‐Beam‐Welded Joints of Additively Manufactured AlSi10Mg Components

“…It was previously reported that microstructural heterogeneity can affect the cyclic properties of several metallic materials. [37][38][39] Microstructural heterogeneity can act as microstructural notch resulting in plastic deformation localization and eventually crack initiation. [37,40,41] In the present study, a heterogeneous microstructure across the weld of AM-cast is present.…”

Very high‐cycle fatigue life prediction of high‐strength steel based on machine learning

“…12 A reinforcement ML method was developed to evaluate the effect of microstructural heterogeneity on the VHCF fatigue strength. 13 The comparison between the traditional stress concentration factor methods and ML prediction methods indicated that ML exhibits superior predictive performance. 14 The previous findings indicate that the application of ML method in the VHCF life prediction of high-strength steel is feasible.…”

“…Multiple independent extreme learning machines are integrated into the fatigue life prediction model with distinct neural network configurations to simulate the complex correlations among mean stress levels, material properties, and fatigue lives 12 . A reinforcement ML method was developed to evaluate the effect of microstructural heterogeneity on the VHCF fatigue strength 13 . The comparison between the traditional stress concentration factor methods and ML prediction methods indicated that ML exhibits superior predictive performance 14 .…”