A critical review of mechanical performance, non-destructive damage monitoring, and AI-driven prediction for sustainable hybrid polymer composites

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Authors: S. Dharani Kumar, Gaurav Arora, Sanjay Mavinkere Rangappa, Suchart Siengchin
Cited by: 3
References: 238

Abstract

In recent years, researchers have been concerned with hybrid laminated polymer composites reinforced with natural fibers (NF) and synthetic fibers (SF). NF/SF hybrid laminated composites (HLCs) offer several benefits, including tailored mechanical properties, low weight, eco-friendliness, low moisture absorption, and ultraviolet (UV) radiation resistance. This review focused on fibers selection, stacking sequences, and ply orientation, which influence the mechanical properties, such as tensile, flexural, impact, fatigue, low-velocity impact (LVI), failure modes, and fracture behaviours of NF/SF HLCs. This review also covers various non-destructive testing (NDT) methods used to evaluate the mechanical properties, failures, and damage in NF/SF HLCs. This review also reveals the role of artificial intelligence (AI) in optimizing the mechanical properties and damage prediction of NF/SF HLCs. The environmental effects, durability behaviour, and life-cycle analysis (LCA) of NF/SF composites are analysed along with their applications. Furthermore, a comprehensive patent landscape analysis and future perspectives of HLCs are presented.

Fields of Study & Subjects

Mechanical Behavior of Composites Smart Materials for Construction Machine Learning in Materials Science

Publication details

Journal / source
Defence Technology
Publisher
Elsevier BV
Publication date
1 May 2026
Language
EN