Open Access

REVIEW

Fatigue Resistance in Engineering Components: A Comprehensive Review on the Role of Geometry and Its Optimization

Ibrahim T. Teke^1,2, Ahmet H. Ertas^2,*

1 Department of Mechanical Engineering, Faculty of Engineering, Haliç University, Istanbul, 34060, Turkiye
2 Department of Mechanical Engineering, Faculty of Engineering & Natural Sciences, Bursa Technical University, Bursa, 16330, Turkiye

* Corresponding Author: Ahmet H. Ertas. Email:

(This article belongs to the Special Issue: AI and Optimization in Material and Structural Engineering: Emerging Trends and Applications)

Computer Modeling in Engineering & Sciences 2025, 144(1), 201-237. https://doi.org/10.32604/cmes.2025.066644

Received 14 April 2025; Accepted 25 June 2025; Issue published 31 July 2025

Abstract

Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading. While earlier studies mainly examined material properties and how stress affects lifespan, this review offers the first comprehensive, multiscale comparison of strategies that optimize geometry to improve fatigue performance. This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets, notches, and overall structural layouts. We analyze and combine various methods, including topology and shape optimization, the ability of additive manufacturing to fine-tune internal geometries, and reliability-based design approaches. A key new contribution is our proposal of a standard way to evaluate geometry-focused fatigue design, allowing for consistent comparison and encouraging validation across different fields. Furthermore, we highlight important areas for future research, such as incorporating manufacturing flaws, using multiscale models, and integrating machine learning techniques. This work is the first to provide a broad geometric viewpoint in fatigue engineering, laying the groundwork for future design methods that are driven by data and centered on reliability.

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