Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Abstract

Additive manufacturing (AM) has emerged as a very promising technology for producing complex metallic components with enhanced design flexibility. However, the mechanical properties and fatigue behavior of AM metals differ significantly from conventionally manufactured materials, thereby presenting challenges in accurately predicting their fatigue life. This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of AM metals, with a particular emphasis on machine learning (ML) modeling techniques. This review recalls recent developments and achievements in fatigue characteristics of AM metals, ML‐based approaches for fatigue life prediction of AM metals, and non‐ML‐based methodologies for the same purpose. In particular, some commonly used regression and classification techniques for fatigue evaluation of AM metals are summarized and elaborated. The study intends to furnish researchers, engineers, and practitioners in the field of AM with a guidance for the accurate and efficient prediction of fatigue life in AM metal components.

Review on machine learning techniques for the assessment of the fatigue response of additively manufactured metal parts

“…Several NN types are categorized and their applications to fatigue are classified into five groups: fatigue damage diagnosis, fatigue life prediction, fatigue load, fatigue crack, and fatigue strength. In, 26 a review of recent developments and future trends in fatigue life prediction of AM metals is provided, focusing on both ML-based and non-ML-based models, organized according to the most commonly adopted classification and regression techniques.…”

Novel models for fatigue life prediction under wideband random loads based on machine learning

“…At present, machine learning models have been applied extensively in the field of fatigue life prediction. 16,17 Kang et al 18 developed an ANN model to fit the complex relationship between the 0th-order spectral moment and shape parameters of a power spectrum and the rainflow amplitude probability function. Durodola et al 15 estimated wideband random fatigue life using an ANN model with the spectral moments of a power spectrum and material parameters as inputs and the fatigue damage as an output.…”