GEAR MATERIAL SELECTION THROUGH ENTROPY-BASED TOPSIS AND ARAS: A COMPARATIVE STUDY

Abstract

The selection of appropriate gear materials is a complex engineering decision due to the presence of multiple, often conflicting, performance criteria. Factors such as strength, hardness, wear resistance; weight, cost, and machinability significantly influence the operational efficiency and service life of gears. Traditional selection approaches are often subjective and lack a systematic evaluation framework. To address this challenge, the present study employs a comparative multi-criteria decision-making (MCDM) approach for gear material selection by integrating the Entropy Weight Method (EWM) with two widely used ranking techniques: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Additive Ratio Assessment (ARAS). In this methodology, the Entropy technique is applied to objectively calculate criteria weights based on their inherent variability, minimizing the bias of expert judgment. Subsequently, both TOPSIS and ARAS methods are utilized to rank a set of alternative gear materials considering multiple performance indicators. The study evaluates common engineering materials including alloy steels, cast iron, bronze, and composites against criteria such as tensile strength, hardness, density, wear resistance, thermal stability, and cost-effectiveness. The results from TOPSIS and ARAS are compared to analyze ranking consistency, stability, and sensitivity to weight variations. While both methods demonstrate close agreement in identifying the most suitable alternatives, slight variations in ranking emphasize the importance of method selection in MCDM applications. The findings confirm that alloy steels with superior mechanical properties consistently achieve top ranking, while lightweight and cost-efficient alternatives like composites emerge as competitive substitutes under specific design constraints. This comparative investigation provides a structured and objective framework for gear material selection, offering practical insights for designers and manufacturing engineers. Moreover, the integration of entropy-based weighting with MCDM techniques enhances decision reliability, reduces subjectivity, and can be extended to broader material selection problems in mechanical and manufacturing engineering domains.