Defect criticality analysis on fatigue life of L-PBF 17-4 PH stainless steel via machine learning

Li, Anyi; Baig, Shaharyar; Liu, Jia; Shao, Shuai

doi:10.1016/j.ijfatigue.2022.107018

Cited by 23 publications

(13 citation statements)

References 47 publications

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“…Therefore, the effective processing of the fatigue performance database is the first task to realize accurate prediction. As shown in Figure 4 and Table 1, there are several approaches to establish a reliable fatigue performance database, which can be divided into three main categories: independent (experiment and simulation), [1,4,5,9,29, literature based, [13,[75][76][77][78][79][80][81][82][83][84][85] and data augmentation based. [86][87][88][89][90][91][92][93][94] Even an efficient data-driven method cannot construct an effective model via an insufficient database; thus, the establishment of a high-quality database is quite important.…”

Section: Establishment Methods For Fatigue Performance Databasementioning

confidence: 99%

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

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Section: Establishment Methods For Fatigue Performance Databasementioning

confidence: 99%

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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“…The correlation between geometrical features of critical defects and fatigue performance has the potential to establish an algorithmic foundation for the nondestructive fatigue evaluation of additive manufacturing products. In a previous study [38], an integrated data-driven analytical framework was proposed for defect criticality in laser beam powder bed fusion (L-PBF) based on SEM scans of fatigue fractured surfaces. The results demonstrated strong relationships between defect features and fatigue life, achieving a low mean absolute percentage error of 0.101 using kernel support vector regression (SVR).…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning Methods for Fractographic Detection of Fatigue Crack Initiation in Additive Manufacturing

Anidjar,

Lang,

Mega

2024

IEEE Access

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In recent times, there has been a growing surge of interest in additive manufacturing (AM) due to its ability to bring about cost savings and weight reduction in fabricated components. Nevertheless, AM materials are susceptible to imperfections that can severely undermine their ability to withstand fatigue. Consequently, before incorporating these materials into vital structural components, it is imperative to possess a thorough comprehension of their fatigue properties. This necessitates the execution of fatigue tests and manual examination of fracture surfaces. Within this research, we put forward a novel approach involving the development of a machine-learning model tailored to identify the starting point of fatigue cracks within specimens of Titanium Ti-6Al-4V, which were produced using selective laser melting (SLM). Moreover, this model also quantifies the distance of these initiation sites from the material's surface. The proposed method encompasses the segmentation of image sections that are devoid of initiation sites, succeeded by the detection of these sites within the remaining segments. Subsequently, established computer vision techniques are harnessed to compute the distance from the surface. The outcomes of this study underscore a remarkable potential for automating the process of fractographic analysis through the integration of machine learning and computer vision models. These strides in technology hold the promise of streamlining and expediting the fractographic analysis procedure, ushering in a new era of efficiency.

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“…Single source [21][22][23][24]27,[30][31][32][34][35][36] Multi-source [16][17][18][19][20][28][29][30]33 the model, allowing the user to easily consider and implement confidence level bands in the stress life plots. 24 Nonetheless, in, 28 the mean and standard deviation of N f are estimated by using a PINN layout instead of a GPR, with a properly designed custom loss function that uses probability density function and cumulative distribution function with location parameter μ and scale parameter σ.…”

Section: Data Source Articlementioning

confidence: 99%

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

Centola,

Tridello,

Ciampaglia

et al. 2024

Fatigue Fract Eng Mat Struct

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The present review paper addresses the increasing interest in the application of machine learning (ML) algorithms in the assessment of the fatigue response of additively manufactured (AM) metal alloys. This review aims to systematically collect, categorize, and analyze relevant research papers in this domain. The most commonly used ML algorithms are presented, discussing their specific relevance to the fatigue modeling of AM metal alloys. A detailed analysis of the most relevant input features used in the literature to predict the main parameters related to the fatigue response is provided. Each work has been analyzed to highlight its strengths and peculiarities, thereby offering insights into novel methodologies and approaches for addressing critical challenges within this field. Particular attention is dedicated to the role of defects and the related size‐effect, as they strongly influence the fatigue response. In conclusion, this review not only synthesizes existing knowledge but also offers forward‐looking recommendations for future research directions, providing a valuable resource for researchers in the domain of ML‐assisted fatigue assessment for AM metal alloys.

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Defect criticality analysis on fatigue life of L-PBF 17-4 PH stainless steel via machine learning

Cited by 23 publications

References 47 publications

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Transfer Learning Methods for Fractographic Detection of Fatigue Crack Initiation in Additive Manufacturing

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

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