Data-Driven Damage Initiation Criteria for Carbon Fiber Reinforced Polymer Composites

Post, Alex; Lin, Shiyao; Ustun, Ilyas; Waas, Anthony M.

doi:10.2514/6.2022-0104

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…[22] The authors' previous work discusses the application of binary search (BS), Linear Regression, and Neural Networks to improve the computational efficiency of predicting damage initiation. [23] This previous work, however, considered only two possible models, analyzed only a numeric prediction, and used an inherently limited training data set. This paper will show that accuracy can be significantly increased by improving the training data set to better represent the physical reality, analyzing a combination of numeric and discrete predictions, and better formulating the problem statement to reflect the true objective of the damage initiation criteria.…”

Section: Introductionmentioning

confidence: 99%

Determining damage initiation of carbon fiber reinforced polymer composites using machine learning

et al. 2022

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Computational progressive failure analysis (PFA) is vital for the analysis of carbon fiber reinforced polymer (CFRP) composites. The damage initiation criterion is one of the essential components of a PFA code to determine the transition of a material's state from pristine or microscopically damaged to macroscopically damaged. In this paper, data-driven models are developed to determine the matrix damage initiation with the objective to save computation time. For 2D plane stress states, the computational cost for determining damage initiation can be dramatically reduced by implementing a binary search (BS) algorithm and predictive machine learning models. Machine learning models are evaluated against a regression problem of predicting damage crack angle as well as against a classification problem for predicting damage initiation outright. It is found that regression models perform much better for plane stress and 3D stress states when generating failure envelopes. In both cases, a neural network is able to produce a failure envelope that is over 99% accurate while reducing computational cost by over 90%.

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Section: Introductionmentioning

confidence: 99%

Determining damage initiation of carbon fiber reinforced polymer composites using machine learning

et al. 2022

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Embedding deep neural network in enhanced Schapery theory for progressive failure analysis of fiber reinforced laminates

Lin,

Post,

Waas

2023

Journal of Computational Design and Engineering

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Computational progressive failure analysis (PFA) of carbon fiber reinforced polymer composites (CFRP) is of vital importance in the verification and validation process of the structural integrity and damage tolerance of modern lightweight aeronautical structures. Enhanced Schapery Theory (EST) has been developed and applied to predict the damage pattern and load-bearing capacity of various composite structures. In this paper, EST is enhanced by a deep neural network (DNN) model, which enables fast and accurate predictions of matrix cracking angles under arbitrary stress states of any composite laminate. The DNN model is trained by TensorFlow based on data generated by a damage initiation criterion, which originates from the Mohr-Coulomb failure theory. The EST-DNN model is applied to open-hole tension/compression (OHT/OHC) problems. The results from the EST-DNN model are obtained with no loss in accuracy. The results presented combine the efficient and accurate predicting capabilities brought by machine learning tools and the robustness and user-friendliness of the EST finite element model.

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Data-Driven Damage Initiation Criteria for Carbon Fiber Reinforced Polymer Composites

Cited by 2 publications

References 8 publications

Determining damage initiation of carbon fiber reinforced polymer composites using machine learning

Determining damage initiation of carbon fiber reinforced polymer composites using machine learning

Embedding deep neural network in enhanced Schapery theory for progressive failure analysis of fiber reinforced laminates

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