Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading

IF 5.7 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue Pub Date : 2024-09-12 DOI:10.1016/j.ijfatigue.2024.108608

Michal Bartošák , Jiří Halamka , Libor Beránek , Martina Koukolíková , Michal Slaný , Marek Pagáč , Jan Džugan

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引用次数: 0

Abstract

Axial-torsional Low-Cycle Fatigue (LCF) tests were conducted under strain control on Additively Manufactured (AM) 316L stainless steel using laser powder bed fusion. The tests covered various strain amplitudes under tension-compression, proportional, and pure shear loading paths. The AM 316L stainless steel exhibited cyclic softening and transgranular cracking under all the investigated loading conditions. The presence of deposition defects, predominantly the lack of fusion type, was identified as the main factor influencing the crack initiation and propagation, as well as the scatter in the fatigue lifetime. Therefore, to account for the damaging effects of these deposition related defects on fatigue lifetime, a novel physics-informed neural network was proposed. Subsequently, this neural network was combined with the critical plane approach, based on the tensile mode of failure, in order to predict the lifetime of AM 316L stainless steel. The predicted data exhibited a good correlation with the experimental results.

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利用物理信息神经网络预测多轴低循环疲劳载荷下激光粉末床熔融加工 316L 不锈钢的使用寿命

利用激光粉末床熔融技术，在应变控制下对快速成型（AM）316L 不锈钢进行了轴向扭转低循环疲劳（LCF）试验。试验涵盖了拉伸-压缩、比例和纯剪切加载路径下的各种应变幅度。在所有研究的加载条件下，AM 316L 不锈钢都出现了循环软化和跨晶格裂纹。沉积缺陷（主要是缺乏熔合类型）的存在被认为是影响裂纹萌发和扩展以及疲劳寿命分散的主要因素。因此，为了解释这些与沉积相关的缺陷对疲劳寿命的破坏性影响，提出了一种新的物理信息神经网络。随后，该神经网络与基于拉伸失效模式的临界面方法相结合，以预测 AM 316L 不锈钢的寿命。预测数据与实验结果呈现出良好的相关性。

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来源期刊

International Journal of Fatigue 工程技术-材料科学：综合

CiteScore

10.70

自引率

21.70%

发文量

619

审稿时长

58 days

期刊介绍： Typical subjects discussed in International Journal of Fatigue address: Novel fatigue testing and characterization methods (new kinds of fatigue tests, critical evaluation of existing methods, in situ measurement of fatigue degradation, non-contact field measurements) Multiaxial fatigue and complex loading effects of materials and structures, exploring state-of-the-art concepts in degradation under cyclic loading Fatigue in the very high cycle regime, including failure mode transitions from surface to subsurface, effects of surface treatment, processing, and loading conditions Modeling (including degradation processes and related driving forces, multiscale/multi-resolution methods, computational hierarchical and concurrent methods for coupled component and material responses, novel methods for notch root analysis, fracture mechanics, damage mechanics, crack growth kinetics, life prediction and durability, and prediction of stochastic fatigue behavior reflecting microstructure and service conditions) Models for early stages of fatigue crack formation and growth that explicitly consider microstructure and relevant materials science aspects Understanding the influence or manufacturing and processing route on fatigue degradation, and embedding this understanding in more predictive schemes for mitigation and design against fatigue Prognosis and damage state awareness (including sensors, monitoring, methodology, interactive control, accelerated methods, data interpretation) Applications of technologies associated with fatigue and their implications for structural integrity and reliability. This includes issues related to design, operation and maintenance, i.e., life cycle engineering Smart materials and structures that can sense and mitigate fatigue degradation Fatigue of devices and structures at small scales, including effects of process route and surfaces/interfaces.