Adaptive ranking algorithm for material selection in Mechanical Engineering Design

The selection of engineering materials remains a critical challenge in mechanical design, where multiple, often conflicting, criteria must be evaluated under uncertainty. This study presents a novel decision-support model that integrates adaptive fuzzy variable creation, balanced weighting strategies, and defuzzification techniques to improve fidelity of material ranking and usability. Unlike conventional multi-criteria decision-making (MCDM) methods that either rely solely on crisp datasets or impose high computational costs through complex fuzzy logic, the proposed model offers a flexible and computationally efficient hybrid framework. It combines subjective expert judgments with objective entropy-based weights, enabling directional control of attributes such as maximising strength and minimising cost. The model was benchmarked against an established published method, and results demonstrate high comparability in ranking outcomes, validating its robustness and credibility. Beyond achieving performance parity, the proposed framework offers clearer interpretability, reduced computational complexity, and adaptability across various design contexts. These findings highlight its potential as a scalable and reliable tool for systematic material selection, bridging theoretical advances in MCDM with practical engineering applications.