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

Abstract

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.

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

“…In doing so, it is hoped to encourage materials scientists who do not focus on simulations to use ML as a potential tool for their data analyses in the future. Another alternative approach may be the use of Artificial Neural Networks (ANN) for supervised learning in materials research [75,76]. ANN are a core component of deep learning and are useful for highly complex ML tasks, such as classification of billions of images, for speech recognition, or robotics.…”

Design allowables of composite laminates: A review

“…82 Schmidt et al 83 provide an extensive review on the application of ML methods to material science up to the year 2019 and the reviewed methods are listed in Table 5 with the related field of application, which are mainly related at the atomic scale. Detailed classification of ANN methods for prediction and classification of material properties can be found in Sharma et al 84…”

“…82 Schmidt et al 83 provide an extensive review on the application of ML methods to material science up to the year 2019 and the reviewed methods are listed in Table 5 with the related field of application, which are mainly related at the atomic scale. Detailed classification of ANN methods for prediction and classification of material properties can be found in Sharma et al 84 Machine Learning is widely used for many other applications in composite material domain and several works focused on material optimization and design are presented in literature. [85][86][87][88][89][90] However, a thorough search of the relevant literature yielded only one article related to the usage of ML algorithms for the prediction of material allowables 91 that is the focus of the current review.…”

Aging effects on free vibration and damping characteristics of polymer‐based biocomposites: A review

“…Biocomposites are gaining popularity in composite materials, and their use is developing due to their fully/partially biodegradable qualities. [ 49–62 ]…”

“…Biocomposites are gaining popularity in composite materials, and their use is developing due to their fully/partially biodegradable qualities. [49][50][51][52][53][54][55][56][57][58][59][60][61][62] Biobased polymers, plastics, and biocomposites can provide more environmentally friendly materials with lower carbon emissions. It is a suitable alternative to plastics to be replaced by biocomposites with wood or natural fibers.…”