Data-driven approach to design fatigue-resistant notched structures

Abstract

Fatigue is the most common failure mode for engineering materials in various industrial applications. Generally, designing new products and components based on typical deterministic fatigue design approaches is slow and expensive. Although numerous investigations have been carried on this topic over decades, there still lacks a robust and efficient method to design a super-fatigue-resistant and testless structure. In this investigation, a novel data-driven approach based on the deep learning algorithm is applied on designing a fatigue-resistant notched structure under different loading via numerical simulations and deep learning. The notches with various shapes are designed by employing the same material. Then finite element (FE) simulations are performed to obtain the mechanical behaviors of notched structures under different loading conditions. The deep learning algorithm is applied to predict mechanical behaviors of unknown notched structures based on input training set of simple notched geometries and relative lower computational cost. It is demonstrated that deep learning on the fatigue-resistant structure is a promising approach of fatigue design. This work offers an alternative design strategy of fatigue-resistant structure and cost-effective solution to accelerate the design of engineering components under different loading conditions.