Prediction of mechanical properties is an essential part of material design. State-of-the-art simulation-based prediction requires data on microstructure and inter-component interactions of material. However, due to high costs and time limitations, such parameters, which are especially required for the simulation of advanced properties, are not always available. This paper proposes a data-driven approach to predicting the labor-consuming fracture toughness based on a series of standard, easy-to-measure mechanical characteristics. Three supervised machine-learning (ML) models (artificial neural networks, a random forest algorithm, and gradient boosting) were designed and tested for the prediction of mechanical properties of pultruded composites. A considerable dataset of mechanical properties was acquired as results of standard tensile, compression, flexure, in-plane shear, and Charpy tests and utilized as the input to predict the fracture toughness. Furthermore, this study investigated the correlations between the obtained mechanical characteristics. Analysis of ML performance showed that fracture toughness had the highest correlations with longitudinal bending and transverse tension and a strong correlation with the longitudinal compression modulus and tensile strength. The gradient boosting decision tree-based algorithms demonstrated the best prediction performance for fracture toughness, with an MSE less than 10% of the average value, providing a prediction within the range of experimental error. The ML algorithms showed potential in terms of determining which macro-level parameters can be used to predict micro-level material characteristics and how. The results provide inspiration for future pultruded composite material design and can enhance the numerical simulations of material.

Developing bone scaffolds can greatly improve the patient’s quality of life by accelerating the rehabilitation process. In this paper, we studied the process of composite polycaprolactone supercritical foaming for tissue engineering. The influence of graphene oxide and reduced graphene oxide on the foaming parameters was studied. The structural and mechanical properties were studied. The scaffolds demonstrated mechanical flexibility and endurance. The co-culturing and live/dead tests demonstrated that the obtained scaffolds are biocompatible. Different composite scaffolds induced various surface cell behaviors. The experimental data demonstrate that composite foams are promising candidates for in vivo medical trials.

The present paper is focused on an experimental study of the damage-to-failure mechanism of additively manufactured 316L stainless steel specimens subjected to very high cycle fatigue (VHCF) loading. Ultrasonic axial tension-compression tests were carried out on specimens for up to 10⁹ cycles, and fracture surface analysis was performed. A fine granular area (FGA) surrounding internal defects was observed and formed a “fish-eye” fracture type. Nonmetallic inclusions and the lack of fusion within the fracture surfaces that were observed with SEM were assumed to be sources of damage initiation and growth of the FGAs. The characteristic diameter of the FGAs was ≈500 μm on the fracture surface and were induced by nonmetallic inclusions; this characteristic diameter was the same as that for the fracture surface induced by a lack of fusion. Fracture surfaces corresponding to the high cycle fatigue (HCF) regime were discussed as well to emphasize damage features related to the VHCF regime.