Predictive Models for Mechanical Properties of Polymeric Composites Reinforced with Particles and Fibers: A Comprehensive Review and Comparative Analysis
摘要整理
The proliferation of predictive models for the mechanical properties of polymeric composites has created a paradox of choice, where an abundance of options impedes rational selection and slows material innovation.This review addresses this critical challenge by transcending a simple inventory of techniques to deliver a rigorous, data-driven comparative analysis spanning analytical, numerical and machine learning (ML) paradigms.We systematically quantify the fundamental trade-offs between model accuracy, computational cost, data requirements and physical interpretability.Our analysis reveals a distinct performance hierarchy: while traditional analytical models provide rapid but coarse estimates (10-30% error), advanced ML and hybrid models achieve the highest fidelity (average discrepancy < 6%), albeit often at the cost of interpretability.The culmination of this work is a novel decision-making flowchart that synthesizes these findings into a practical, actionable guide.This framework transforms the ad-hoc process of model selection into a structured, evidence-based methodology, empowering researchers and engineers to strategically balance performance requirements with practical constraints.