Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach

Horňas, Jan; Běhal, Jiří; Homola, Petr; Senck, Sascha; Holzleitner, Martin; Godja, Norica; Pásztor, Zsolt; Hegedüs, Bálint; Doubrava, Radek; Růžek, Roman; Petrusová, Lucie

doi:10.1016/j.ijfatigue.2022.107483

Cited by 41 publications

(21 citation statements)

References 46 publications

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“…In contrast to destructive specimen preparation and microscopic evaluation, XCT provides three-dimensional volume data of internal structures including the spatial distribution of pores. Even though porosity in the analyzed samples is low (0.02 %), large surface-near pore clusters can act as initiation points for fatigue cracks [8]. XCT scans of the WAAM Mg samples clearly show a multitude of internal defects that could not be quantified and visualized in this resolution using another NDT technique like thermographic or ultrasound testing.…”

Section: Discussionmentioning

confidence: 92%

Wire Arc Additive Manufacturing (WAAM) and microstructural analysis of Magnesium parts

Senck¹,

Holzleitner²,

Glinz³

et al. 2023

eJNDT

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Section: Discussionmentioning

confidence: 92%

Wire Arc Additive Manufacturing (WAAM) and microstructural analysis of Magnesium parts

Senck¹,

Holzleitner²,

Glinz³

et al. 2023

eJNDT

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“…It possesses characteristics of flexibility and robustness, rendering it highly applicable in tasks such as data exploration, feature selection, and correlation analysis. Several studies have already used Spearman's rank correlation for feature selection 44–46 . However, in certain scenarios, due to nonlinear interactions, variables with low correlation can still make significant contributions to predictions.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies have already used Spearman's rank correlation for feature selection. [44][45][46] However, in certain scenarios, due to nonlinear interactions, variables with low correlation can still make significant contributions to predictions. Additionally, the presence of a significant correlation between two variables does not necessarily imply a causal relationship between them.…”

Section: Spearman Correlation Analysismentioning

confidence: 99%

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

Duan,

Zhang,

Wang

et al. 2024

Energy Science & Engineering

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Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

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“…Many scholars have developed fatigue life prediction models of AM metals by using ANN algorithm. For example, Horňas et al 151 proposed a framework based on ANN algorithm and Spearman rank correlation analysis to examine the influence of material defects and stress amplitude on the fatigue life of SLM Ti‐6Al‐4V. Based on the neuro‐fuzzy algorithm, Zhang et al 152 constructed a fatigue life dataset of SLM 316L stainless steel specimens including different processing conditions (layer thickness, scanning speed, and laser power), post‐treatment (annealing and HIP), and cyclic stress.…”

Section: Ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

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Additive manufacturing (AM) has emerged as a very promising technology for producing complex metallic components with enhanced design flexibility. However, the mechanical properties and fatigue behavior of AM metals differ significantly from conventionally manufactured materials, thereby presenting challenges in accurately predicting their fatigue life. This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of AM metals, with a particular emphasis on machine learning (ML) modeling techniques. This review recalls recent developments and achievements in fatigue characteristics of AM metals, ML‐based approaches for fatigue life prediction of AM metals, and non‐ML‐based methodologies for the same purpose. In particular, some commonly used regression and classification techniques for fatigue evaluation of AM metals are summarized and elaborated. The study intends to furnish researchers, engineers, and practitioners in the field of AM with a guidance for the accurate and efficient prediction of fatigue life in AM metal components.

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Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach

Cited by 41 publications

References 46 publications

Wire Arc Additive Manufacturing (WAAM) and microstructural analysis of Magnesium parts

Wire Arc Additive Manufacturing (WAAM) and microstructural analysis of Magnesium parts

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

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