A Comprehensive Review on Finite Element Analysis, Fatigue Behaviour and Structural Optimization of Composite Leaf Springs for Heavy Vehicle Suspension Systems

Abstract

Heavy commercial vehicle fleets are essential for transportation, logistics, mining, and construction industries, where maintenance efficiency and vehicle reliability significantly influence operational performance and profitability. This review paper presents a comprehensive analysis of fleet maintenance strategies, reliability assessment methods, and maintenance cost optimization techniques used in heavy commercial vehicle operations. The study reviews traditional, preventive, condition-based, predictive, and reliability-centered maintenance approaches, along with reliability evaluation tools such as Weibull analysis, FMEA, FTA, Reliability Block Diagrams, and Markov models. Emerging technologies including Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Digital Twins, and Industry 4.0 frameworks are also examined for their role in improving fleet reliability and reducing maintenance expenditure. Furthermore, optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), and NSGA-II are reviewed for maintenance planning and decision-making. The findings indicate that data-driven and intelligent maintenance systems can substantially enhance fleet availability, minimize downtime, and improve cost effectiveness. The review also identifies key research gaps and future opportunities for developing sustainable and autonomous fleet maintenance frameworks.

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