A neural network approach to fatigue life prediction

Pujol, João Carlos Figueira; Pinto, João Mário Andrade

doi:10.1016/j.ijfatigue.2010.09.003

Cited by 81 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, health monitoring and lifetime prediction for engineering structures has traditionally been a largely datadriven area. Recent progress in Bayesian methods and machine learning, in particular artificial neural networks, has motivated a considerable number of publications introducing new datadriven approaches for lifetime prediction (Freitag et al, 2009;Silverio Freire Júnior et al, 2009;Figueira Pujol and Andrade Pinto, 2011;Sikorska et al, 2011;Mosallam et al, 2016).…”

Section: Predictivementioning

confidence: 99%

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

et al. 2019

View full text Add to dashboard Cite

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment-and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant Bock et al. Machine Learning in Materials Mechanics identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

show abstract

Section: Predictivementioning

confidence: 99%

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Figueira Pujol and Andrade Pinto 15 developed a new method based on NN for fatigue life prediction. A feedforward NN was utilized to generate a probability distribution which was later combined with a linear cumulative function to predict the fatigue life.…”

Section: Category Selectionmentioning

confidence: 99%

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Kalaycı

Karagöz

Karakaş

2020

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

Fatigue causes cracking or breakage in a material due to repeated loads; it causes the material to become unusable. Therefore, knowing the fatigue life of materials is crucial for the implementation of designs, economy and human life. Soft computing methodologies, a subset of artificial intelligence emerging to simulate human intelligence, deal with approximate models and seek solutions to complex real-life problems relying on both computational power of machines and the high accuracy of the algorithms. In this study, soft computing methods adapted for estimating/predicting the fatigue life of engineering structures and materials are investigated. For this purpose, 95 articles published between 1995 and 2020 have been examined in detail. With this review, it is aimed to reveal the efficiency of soft computing methods and contribute to their development. Recommendations have been made to draw attention to these methodologies which are expected to be used in many areas in the future.

show abstract

“…On the other hand, recently artificial intelligence-based approaches such as neural networks (NNs) are extensively implemented to analyze and optimize multi-objective and complex problems [41][42][43][44]; as well as their wide applications in fatigue life estimation [45][46][47][48][49][50]. Based on the available data in the literature different types of NNs were utilized for fatigue behavior prediction and analyses [51][52][53][54][55][56][57]. It should be mentioned that there are also different methods for fatigue life assessment such as XFEM [58,59] and phase field method [60,61].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

Maleki

Ünal

Sahebari

et al. 2022

JMSE

View full text Add to dashboard Cite

In this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 µm were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10–15 µm had the highest effects on fatigue life improvement.

show abstract

A neural network approach to fatigue life prediction

Cited by 81 publications

References 25 publications

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

Contact Info

Product

Resources

About