The propagation of small cracks contributes to the majority of the fatigue lifetime for structural
components. Despite significant interest, criteria for the growth of small cracks, in terms of …

This paper describes the use of data analytics tools for predicting the fatigue strength of
steels. Several physics-based as well as data-driven approaches have been used to arrive …

In this research a machine learning model for predicting the rotating bending fatigue
strength and the high-throughput design of fatigue resistant steels is proposed. In this …

Abstract Machine learning (ML) is widely used in the field of material informatics. However,
limitations on the size of available datasets are a key bottleneck in the use of machine …

The stress-life (SN) curve is a fundamental aspect in fatigue analysis. However, fatigue
testing using SN curve is very costly and time-consuming. To solve this, a novel method to …

Fatigue strength is one of the most important mechanical properties of steel. Here we
describe the development and deployment of data-driven ensemble predictive models for …

Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …

Computationally efficient estimation of the fatigue response of polycrystalline materials is
critical for the development of next generation materials in application domains such as …

The focus of goal-oriented materials design is to find the necessary chemistry/processing
conditions to achieve the desired properties. In this setting, a material's microstructure is …

Materials design calls for the (inverse) exploitation of Process-Structure-Property (PSP)
relationships to produce materials with targeted properties. Unfortunately, most materials …