An integrated decision framework for phase composition optimization in polymer bio-composites

Bio-composites have emerged as promising alternatives to conventional materials owing to their lightweight nature, mechanical strength, cost-effectiveness, recyclability, and environmental compatibility. However, achieving an optimal reinforcement composition remains critical for ensuring sustainable and economically viable polymer bio-composites. This study introduces an integrated Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision-Making (MCDM) framework for phase composition optimization in particle-reinforced polymer bio-composites. Mechanical, thermal, and physicochemical properties were systematically evaluated using secondary data derived from previously published experimental results to identify the most effective reinforcement ratio. An inter‑rater reliability test is also conducted to check the internal data consistency (analysis yielded a Cronbach’s alpha value of 0.9567, which is well above the acceptable threshold). Result reveals that 5 wt% reinforcement is the optimal composition, offering balanced improvements in strength, stability, and eco-efficiency. In this research dimensional sensitivity analysis is carried out and shows the robustness of the outcome. By overcoming the limitations of conventional trial-and-error approaches, the proposed framework provides a structured, data-driven methodology that bridges mechanical performance with environmental responsibility and cost-effectiveness. Practical implications include enhanced precision in material selection and the development of eco-friendly composites suitable for automotive, construction, packaging, and aerospace applications. The originality of this work lies in the novel integration of experimental property datasets with an AHP-based optimization strategy, applied for the first time to rubber seed shell waste as a sustainable reinforcing material. This combined analytical–experimental approach establishes a scalable model for future bio-composite development in addition with other MCDM and AI driven framework.