A Review of Physics-based Models in Prognostics and Health Management of Laminated Composite Structures

Khan, Asif; Azad, Muhammad Muzammil; Sohail, Muhammad Imran; Kim, Heung-Soo

doi:10.1007/s40684-023-00509-4

Cited by 13 publications

(5 citation statements)

References 122 publications

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“…Augmented by new experimentation techniques, physics-based models provide valuable insight into complex systems' internal states and conditions, which generally require extensive parameter estimation of the components. 108 Additionally, it can be quite challenging for specific applications to perform offline testing of the system to acquire measurements, particularly when the model parametrization and validation further become challenging. 109 With enhanced data perception ability and decreased data storage cost, the complex system gradually accumulates large-scale data, making the databased approaches a better systematic and universal modeling scheme for PHM.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection

Liu

2023

J. Electrochem. Soc.

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Energy systems are integral to most complex systems, and their reliability is often essential to the reliability of those systems. Therefore, monitoring the systems' health is critical to warn of any potential failures and anomalies. Meanwhile, it is pressing to solve the dilemma of balancing experimental complexity and modeling computational cost. The above challenge eventually necessitates predictive or prognostic capability for prognostics and health monitoring (PHM) of complex systems under complexly aggressive working environments. In this perspective, we review and summarize PHM predictive modeling and prognostications of the remaining useful life, anomalies, and failure detection in Lithium-ion Batteries.

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Section: Discussionmentioning

confidence: 99%

Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection

Liu

2023

J. Electrochem. Soc.

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“…The application of ML in composite materials extends across various stages in engineering analysis and design, for instance, encompassing design, 8 inspection, and prediction of failures. 9–11 For example, in the works by Gu and Chen, 12–14 the optimization of an interwoven material is studied, starting from the discretization of the system into equal regions, and addressing them as pixels of an image, each with three possible values or states for the modulus of elasticity. They then use convolutional neural networks to create metamodels capable of predicting mechanical properties such as strength and stiffness.…”

Section: Introductionmentioning

confidence: 99%

Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

Ledesma-Orozco,

Galvis-Chacón,

Rodríguez-Sánchez

2024

Journal of Composite Materials

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In laminated composite materials design, optimization mainly targets the stacking sequence configuration, which is defined by the lamina thickness and fiber orientations within each layer. Recent studies emphasize the increasing role of Machine Learning in promoting innovative composite designs by facilitating the accurate modeling of essential properties such as strength and stiffness. This study introduces two metamodels that utilize feed-forward artificial neural networks, taking laminate thickness and fiber steering angles as input parameters. The output variables, including strain energy density and the Tsai-Wu failure index, enable the prediction of stacking sequence configurations for laminated materials, a capability confirmed in a case study. The results showcase neural network models with the ability to predict these variables, achieving coefficients of determination above 0.90 for testing data. Consequently, this modeling approach has the potential to be a tool for designers, aiding in decision-making processes for the subsequent optimization of stiffness and strength in structural components made of laminated composite materials.

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“…However, rationally modeling and accurately forecasting the growth of cracks poses great challenges, due to the complexity and uncertainty of propagation mechanisms [3,4]. The first challenge lies in the uncertainty of crack measurement.…”

Section: Introductionmentioning

confidence: 99%

“…These models can be broadly categorized into physics-based models and data-driven methods [3]. The physics-based models are primarily exemplified by the Paris model [7], which relies on classical linear elastic mechanics principles, and considers material properties and stress concentration factors [4]. The Paris model, an authoritative empirical physical model, along with its variations [8][9][10][11][12], have been widely studied and adopted for modeling the rate of crack growth.…”

Section: Introductionmentioning

confidence: 99%

A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics

Yang,

Wang,

Chen

et al. 2024

Processes

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Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process exhibits significant randomness, which causes difficulties in fatigue life prediction. To improve prognostic accuracy, a physics-based Tweedie exponential dispersion process (TEDP) model is proposed via integrating Paris Law and the stochastic process. This TEDP model can capture both the crack growth mechanism and uncertainty. Compared with other existing models, the TEDP taking Wiener process, Gamma process, and inverse process as special cases is more general and flexible in modeling complex degradation paths. The probability density function of the model is derived based on saddle-joint approximation. The unknown parameters are calculated via maximum likelihood estimation. Then, the analytic expressions of the distributions of lifetime and product reliability are presented. Significant findings include that the proposed TEDP model substantially enhances predictive accuracy in lifetime estimations of mechanical systems under varying operational conditions, as demonstrated in a practical case study on fatigue crack data. This model not only provides highly accurate lifetime predictions, but also offers deep insights into the reliability assessments of mechanically stressed components.

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A Review of Physics-based Models in Prognostics and Health Management of Laminated Composite Structures

Cited by 13 publications

References 122 publications

Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection

Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection

Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics

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