Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning

Zhang, Ziyang; Shi, Junchuan; Yu, Tianyu; Santomauro, Aaron; Gordon, Ali P.; Gou, Jihua; Wu, Dazhong

doi:10.1115/1.4047477

Cited by 39 publications

(16 citation statements)

References 29 publications

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“…Dataset used for the training purpose was obtained from 3D microstructural images of the composites. In order to compare the advantages of additive manufacturing over the conventional method, Zhang et al [348] used ensemble based machine learning model to predict the flexural strength of CFRP composites and compared the results with a linear regression and Physics based model. Ensemble based methodology achieved the best accuracy along with establishing an efficient relationship among the number of fiber layers, its orientation and matrix infill patterns.…”

Section: Ensemble Based Methodsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

107

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Ensemble Based Methodsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

107

View full text Add to dashboard Cite

show abstract

“…Bagging and boosting are the 2 most frequently used approaches for constructing ensemble models (Dietterich 2000). Among these 2 approaches, algorithms that involve bagging, such as RF, and algorithms that use boosting, such as GBDT and XGBoost, have demonstrated robust performance in predicting material properties in recent studies (Kern et al 2019; Marani and Nehdi 2020; Song et al 2020; Zhang et al 2020). The detailed demonstration of each algorithm is in Appendix Algorithm Basis.…”

Section: Discussionmentioning

confidence: 99%

Interpretable AI Explores Effective Components of CAD/CAM Resin Composites

Sakai

Tanaka

et al. 2022

J Dent Res

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High flexural strength of computer-aided manufacturing resin composite blocks (CAD/CAM RCBs) are required in clinical scenarios. However, the conventional in vitro approach of modifying materials’ composition by trial and error was not efficient to explore the effective components that contribute to the flexural strength. Machine learning (ML) is a powerful tool to achieve the above goals. Therefore, the aim of this study was to develop ML models to predict the flexural strength of CAD/CAM RCBs and explore the components that affect flexural strength as the first step. The composition of 12 commercially available products and flexural strength were collected from the manufacturers and literature. The initial data consisted of 16 attributes and 12 samples. Considering that the input data for each sample were recognized as a multidimensional vector, a fluctuation range of 0.1 was proposed for each vector and the number of samples was augmented to 120. Regression algorithms—that is, random forest (RF), extra trees, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting—were used to develop 5 ML models to predict flexural strength. An exhaustive search and feature importance analysis were conducted to analyze the effective components that affected flexural strength. The R2 values for each model were 0.947, 0.997, 0.998, 0.983, and 0.927, respectively. The relative errors of all the algorithms were within 15%. Among the high predicted flexural strength group in the exhaustive search, urethane dimethacrylate was contained in all compositions. Filler content and triethylene glycol dimethacrylate were the top 2 features predicted by all models in the feature importance analysis. ZrSiO4 was the third important feature for all models, except the RF model. The ML models established in this study successfully predicted the flexural strength of CAD/CAM RCBs and identified the effective components that affected flexural strength based on the available data set.

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“…On the one hand, a continuous filament could be cut using a Markforged Mark 2 printer. [329,335] On the other hand, such filament cannot be cut when used for pultrusion in a modified Prusa or Tevo for example. [165,203] Either way, the optimization of continuous filament structure is more complex.…”

Section: Genetic Algorithmmentioning

confidence: 99%

The Design of 4D‐Printed Hygromorphs: State‐of‐the‐Art and Future Challenges

Kergariou

Demoly

Perriman

et al. 2022

Adv Funct Materials

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In recent years, 4D printing has allowed the rapid development of new concepts of multifunctional/adaptive structures. The 4D printing technology makes it possible to generate new shapes and/or property-changing capabilities by combining smart materials, multiphysics stimuli, and additive manufacturing. Hygromorphs constitute a specific class of new smart materials where their properties and morphing capabilities are dependent on the surrounding humidity, which drives actuation. Although multiple efforts have been made to fabricate hygromorph demonstrators, a comprehensive design process to produce hygromorphs by multiple 4D printing techniques is not yet available. The broad aim of this review and concept paper is to i) highlight existing scientific and technology gaps in the field of 4D-printed hygromorphs, ii) identify tools existing in other research fields for filling those gaps, and iii) discuss a series of guidelines for tackling future challenges and opportunities to develop 4D-printed composite hygromorph materials and related manufacturing processes. Accordingly, this review describes the materials and additive manufacturing techniques used for hygromorph composite fabrication. Moreover, the relevant parameters that control actuation, the models selection and performance, the design methods and the actuation measurements for customized 4D-printed hygromorph materials, are discussed.

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Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning

Cited by 39 publications

References 29 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Interpretable AI Explores Effective Components of CAD/CAM Resin Composites

The Design of 4D‐Printed Hygromorphs: State‐of‐the‐Art and Future Challenges

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