Machine Learning-Based Prediction of Mechanical Properties of Advanced Engineering Materials: A Review of Data, Models, and Open Challenges

Kancharla Bullibabu, Md. Abid Ali, Bhukya Venkanna, P. Chittibabu — Sat, 17 Oct 2026 00:00:00 +0000

Mechanical testing remains the reference method for characterising strength, hardness, stiffness and fatigue resistance, yet it is slow, destructive and impractical for the large compositional and processing spaces now open to advanced engineering materials. Machine learning (ML) models trained on measured or computed data have therefore been proposed as fast surrogates. The relevant literature is spread across material families, and individual studies differ in how inputs are represented, which model is used, and how generalisation error is estimated, so results are hard to compare directly. This article reviews ML-based prediction of mechanical properties for crystalline compounds, steels, high-entropy alloys, concrete and architected composites. The review is narrative and thematic but follows a documented search strategy: representative peer-reviewed studies, databases and benchmarks are organised by data representation, learning algorithm, validation practice and interpretability, and benchmark results obtained under a common protocol are compared quantitatively. The cited work indicates that tree ensembles and neural networks are applied to composition and processing data, that crystal-graph networks become competitive when roughly 10⁴ or more labelled samples are available, and that knowledge-based descriptors can improve on composition alone in alloy design. Recurring open issues include small and heterogeneous experimental datasets, non-uniform validation protocols, data leakage and overfitting, uncertainty quantification, and the limited use of physical knowledge. The article closes with a decision flow for model selection and a reporting-oriented workflow intended to make future studies easier to reproduce and compare. No new experimental results are presented.

Effectiveness of Ethanol Pulping of Steam-Exploded Reed

Phyong-Gwang Ham, Tan-Gyol Yun, Kun Jong, Kwang-Il Ri, Tang-Hyon Yun — Tue, 03 Nov 2026 00:00:00 +0000

The depletion of fossil fuels necessitates the development of effective utilization technologies for lignocellulosic biomass to secure the future of mankind. The traditional pulp industry must be updated in an efficient manner because of its high consumption of reagents, energy, and raw materials. Steam Explosion (SE) is one of the most efficient and environmentally friendly processes for the pretreatment of lignocellulosic biomass. It combines prehydrolysis and mechanical pretreatment to make the biomass amenable to various further treatments such as pulping and enzymatic treatment. On the other hand, organosolv pulping, which is a delignification process of lignocellulosic biomass using organic solvents like ethanol and acetic acid, is being widely studied all over the world to reduce the pollution of the pulp industry and use all the components of biomass effectively. The aim of this work is to evaluate the effectiveness of the combination of steam explosion and ethanol pulping (EP) for the production of chemical pulp. The study determined the pulping yields, kappa number, and the viscosity of the resulting pulp in the ethanol pulping process of steam-exploded and prehydrolyzed reed chips. The results show that the ethanol pulping of steam-exploded reed is more effective than the conventional prehydrolysis-kraft pulping (PH-KP).

Journal of Advancements in Material Engineering

Machine Learning-Based Prediction of Mechanical Properties of Advanced Engineering Materials: A Review of Data, Models, and Open Challenges

Effectiveness of Ethanol Pulping of Steam-Exploded Reed