Fatigue performance of metal additive manufacturing: a comprehensive overview

Virtual and Physical Prototyping, Journal Year: 2024, Volume and Issue: 19(1)

Published: March 21, 2024

Fatigue life assessment of metal additive manufacturing (AM) products has remained challenging due to the uncertainty as–built defects, heterogeneity microstructure, and residual stress. In past few years, many works have been conducted develop models in order predict fatigue AM samples by considering existence inherent defects. This review paper addresses main issues regarding parts effect defects post processing strategies. Mechanisms that are contributing failure categorized discussed detail. Several modelling approaches exist case prediction. The common compatible with properties thoroughly explained discussing previous highlighting their major conclusions. addition, use machine learning is identified as future high performance. challenge today's fracture community was estimation complex geometries presence different types anisotropic state work proposes available tackle this challenge.

Language: Английский

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A novel fatigue life prediction method for the laser deposition repaired TA15 component with annealing heat treatment

Engineering Failure Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 109649 - 109649

Published: April 1, 2025

Language: Английский

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A microstructure sensitive machine learning-based approach for predicting fatigue life of additively manufactured parts

International Journal of Fatigue, Journal Year: 2024, Volume and Issue: unknown, P. 108724 - 108724

Published: Nov. 1, 2024

Language: Английский

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Cost and Carbon Emission Analysis of Aircraft Gate Hinge Arm Parts Manufactured by Laser Powder Bed Fusion Based on Innovative Design

Published: Jan. 1, 2024

Language: Английский

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