Data-driven Prognosis of Fatigue-induced Delamination in Composites using Optical and Acoustic NDE methods

Banerjee, Portia; Palanisamy, Rajendra Prasath; Haq, Muhammad Zia Ul; Удпа, Лалита; Deng, Yiming

doi:10.1109/icphm.2019.8819426

Cited by 13 publications

(4 citation statements)

References 37 publications

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“…[ 23,131–134 ] After the establishment of the database and the analysis of the IFs, different data‐driven algorithms were used to predict the fatigue performance. Specifically, several effectively approaches (Bayesian model, [ 135–137 ] ANN, [ 67,129,138–141 ] particle swarm optimization (PSO)‐BPNN, [ 75,142 ] Ant colony optimization‐BPNN, [ 39,55 ] SVM, [ 56,143–145 ] GA‐ANN, [ 146 ] GA‐BPNN, [ 146,147 ] RF, [ 89,137,148,149 ] DNN, [ 112,150–152 ] convolution neural network (CNN), [ 153–155 ] long short‐term memory (LSTM), [ 156–158 ] radial basis function neural network (RBFNN), [ 53,88,159,160 ] etc.) were developed to realize the fatigue performance prediction of welded joints.…”

Section: Progress In Prediction Approachesmentioning

confidence: 99%

“…[23,[131][132][133][134] After the establishment of the database and the analysis of the IFs, different data-driven algorithms were used to predict the fatigue performance. Specifically, several effectively approaches (Bayesian model, [135][136][137] ANN, [67,129,[138][139][140][141] particle swarm optimization (PSO)-BPNN, [75,142] Ant colony optimization-BPNN, [39,55] Figure 12. Prediction framework of cyclic stress-strain property from microstructure via FEM, two-point correlation, and ML.…”

Section: Progress In Prediction Approachesmentioning

confidence: 99%

See 1 more Smart Citation

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: Progress In Prediction Approachesmentioning

confidence: 99%

Section: Progress In Prediction Approachesmentioning

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

View full text Add to dashboard Cite

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“…Evolutionary computational methods such as genetic algorithms [117,121] and swarm optimization algorithms [122] have been used for solving inverse problems. Classical AI methods based on probabilistic approaches have been developed with known physics and pure data-driven based models too [70,[123][124][125][126][127][128][129][130][131][132][133][134][135][136][137][138]. The method of reinforcement learning has not yet been in practical use, but there are very few conceptual works on the inspection during the manufacturing process [139].…”

Section: Ai Paradigms In Compositesmentioning

confidence: 99%

“…Kalman filter and particle filterconventional data-driven methods were also used to identify the delamination region from AE guide waves on fatigues tests. These delamination regions were validated by the optical transmission scans and the future damage areas were accurately predicted with 95% confidence intervals [123].…”

Section: Diagnostics Paradigm-with Sensorsmentioning

confidence: 99%

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Elenchezhian

Vadlamudi

Raihan

et al. 2021

Smart Mater. Struct.

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In the era of the 4th industrial revolution of big data, artificial intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous MS. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and prognostics health management of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for diagnosis, prognosis, and control.

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Highly accelerated life testing (HALT): A review from a statistical perspective

Collins,

Huzurbazar,

Warr

2024

WIREs Computational Stats

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Despite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated‐stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). This review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high‐stress test‐analyze‐fix‐test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control

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Data-driven Prognosis of Fatigue-induced Delamination in Composites using Optical and Acoustic NDE methods

Cited by 13 publications

References 37 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

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Highly accelerated life testing (HALT): A review from a statistical perspective

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