This review paper comprehensively examines the dynamic landscape of 3D printing and
Machine Learning utilizing biodegradable polymers and their composites, presenting a …

This comprehensive review discusses the recent progress in synthesis, properties,
applications, 3D printing and machine learning of graphene quantum dots (GQDs) in …

Advancement of novel electromagnetic inference (EMI) materials is essential in various
industries. The purpose of this study is to present a state-of-the-art review on the methods …

This study intends to predict the tensile strength of polymer concrete (PC) composites using
one of the deep learning-based methods called deep neural network (DNN). A database …

Using artificial intelligence‐based methods in predicting material properties, in addition to
high accuracy, saves time and money. This paper models and predicts the fracture …

The current study is an attempt to investigate the usefulness of a deep learning-based
method, backpropagation deep neural network (DNN) for the prediction of the mechanical …

One of the basic points of Industry 5.0 is to make the industry sustainable. There is a need to
develop circular processes that reuse, repurpose, and recycle natural resources, and thus …

The correct prediction of a composite parts' final performance is of paramount importance
during the initial design phase of the manufacturing process. To this end the correct …

Plastic is widely used all over the world and its production has been increasing continuously
in recent years. But plastic presents significant problems about its end-of-life given its …

In this paper, the geometry factors of mode I and mode II (YI and YII) and the normalized T‐
stress (T*) of the centrally cracked Brazilian disk specimen are predicted using a deep …