This research works with the optimal design of marble dust-filled polymer composites using a multi-criteria decision-making (MCDM) technique. Polylactic acid (PLA) and recycled polyethylene terephthalate (rPET)-based composites containing 0, 5, 10, and 20 wt% of marble dust were developed and evaluated for various physicomechanical and wear properties. The results showed that the incorporation of marble dust improved the modulus and hardness of both PLA and rPET. Moreover, a marginal improvement in flexural strength was noted while the tensile and impact strength of the matrices were deteriorating due to marble dust addition. The outcomes of wear analysis demonstrated an improvement in wear resistance up until 10 wt% filler reinforcement, after which the incidence of dust particles peeling off from the matrix was observed, thereby reducing its efficiency. The best tensile modulus of 3.23 GPa, flexural modulus of 4.39 GPa, and hardness of 83.95 Shore D were obtained for 20 wt% marble dust-filled PLA composites. The lowest density of 1.24 g/cc and the highest tensile strength of 57.94 MPa were recorded for neat PLA, while the highest impact strength of 30.94 kJ/m² was recorded for neat rPET. The lowest wear of 0.01 g was obtained for the rPET containing 5 wt% marble dust content. The experimental results revealed that for the examined criteria, the order of composite preference is not the same. Therefore, the optimal composite was identified by adopting a preference selection index-based MCDM technique. The findings demonstrated that the 10 wt% marble dust-filled PLA composite appears to be the best solution with favorable physical, mechanical, and wear properties.

The present research work develops an evaluation method based on the hybrid entropy-simple additive weighting approach to select the best biocomposite material based on several potentially conflicting criteria. Poly(lactic acid) (PLA) biocomposites with varying proportions of wood waste (0, 2.5, 5, 7.5, and 10 by weight) was developed and evaluated for physical, mechanical, and sliding wear properties. The biocomposite containing 10 wt.% wood waste exhibited the lowest density (1.183 g/cm³) and highest modulus properties (tensile modulus = 2.97 GPa; compressive modulus = 3.46 GPa; and flexural modulus = 4.03 GPa). The bare PLA exhibited the highest strength properties (tensile strength = 57.96 MPa; compressive strength = 105.67 MPa; impact strength = 15.25 kJ/m²), whereas flexural strength (100.43 MPa) was the highest for 5 wt.% wood waste filled biocomposite. The wear of PLA decreased with 2.5 wt.% wood waste incorporated and increased with further addition of wood waste. The experimental results revealed a high compositional dependence with no discernible trend. As a result, prioritizing biocomposites' performance to choose the best from various biocomposite alternatives becomes tough. Therefore, a multi-criteria decision-making process based on a hybrid entropy-simple additive weighting approach was applied to find the optimal biocomposite by taking the experimental results as the selection criterion. The results show that the 2.5 wt.% wood waste added PLA biocomposite proved to be the best solution with optimal physical, mechanical, and wear properties. The validation with other decision-making models supports the robustness of the proposed approach in that the 2.5 wt.% wood waste added PLA biocomposite is the most dominating. This study contributes by providing preferences for the selection criteria and assessing the best alternative from the available PLA biocomposites.

Based on the criteria importance through inter-criteria correlation (CRITIC) and the multi-attributive border approximation area comparison (MABAC), a decision-making algorithm was developed to select the optimal biocomposite material according to several conflicting attributes. Poly(lactic acid) (PLA)-based binary biocomposites containing wood waste and ternary biocompos-ites containing wood waste/rice husk with an overall additive content of 0, 2.5, 5, 7.5 and 10 wt.% were manufactured and evaluated for physicomechanical and wear properties. For the algorithm, the following performance attributes were considered through testing: the evaluated physical (density, water absorption), mechanical (tensile, flexural, compressive and impact) and sliding wear proper-ties. The water absorption and strength properties were found to be the highest for unfilled PLA, while modulus performance remained the highest for 10 wt.% rice husk/wood-waste-added PLA biocomposites. The density of PLA biocomposites increased as rice husk increased, while it decreased as wood waste increased. The lowest and highest density values were recorded for 10 wt.% wood waste and rice husk/wood-waste-containing PLA biocomposites, respectively. The lowest wear was exhibited by the 5 wt.% rice husk/wood-waste-loaded PLA biocomposite. The experimental results were composition dependent and devoid of any discernible trend. Consequently, prioritizing the performance of PLA biocomposites to choose the best one among a collection of alternatives became challenging. Therefore, a decision-making algorithm, called CRITIC–MABAC, was used to select the optimal composition. The importance of attributes was determined by assigning weight using the CRITIC method, while the MABAC method was employed to assess the complete ranking of the biocomposites. The results achieved from the hybrid CRITIC–MABAC approach demonstrated that the 7.5 wt.% wood-waste-added PLA biocomposite exhibited the optimal physicomechanical and wear properties.