Developing biocomposites by hybridization, which is the combination of two or more materials, can be a potential solution for improving material recyclability and sustainability. This study focuses on creating a hybrid biocomposite reinforced with cotton-blended pineapple leaf fibre (PALF) fabric (174 GSM) and jute fibre fabric (265 GSM) which are thrown away by textile factories. The mechanical, moisture absorption, and vibration characteristics of four stacking sequences of hybrid composites and two unhybridized composites were analyzed. Results indicated that hybridization improved tensile and flexural characteristics compared to pineapple leaf fibre reinforced polymer (PFRP) composites. The jute fibre reinforced polymer (JFRP) composite exhibited the maximum tensile strength of 35.16 MPa, while the hybrid composites achieved a maximum of 32.16 MPa. Among the hybrid composites, jute layers on the outer plies (4P5J-2) demonstrated the maximum tensile modulus of 1.315 GPa. Additionally, the hybrid composite with three layers of jute plies between alternating layers of jute-pineapple plies showed the highest elongation at 15.94%. Among the hybrids, alternate stacking of jute/PALF plies (4P5J-1) gave a maximum flexural strength of 44.36 MPa, which is similar to JFRP (44.91 MPa) and a 78.57% increase in flexural modulus compared to PFRP composite, despite having the lowest tensile strength. Although the JFRP composite exhibited the highest impact strength, the hybrids still outperformed the PFRP composites. With hybridization, moisture absorption decreased, with a maximum of 29.50% compared to the JFRP composite. Furthermore, due to the spiral-like orientation of the yarns, stacking PALF plies on the outside can cause critical damping. Therefore, it is shown in this paper that both hybridization and stacking sequence can significantly influence composite performance. These findings also implies the utilization of textile industry's natural fibres to develop hybrid composites for automotive applications, like brake and accelerator pedals, for a greener future and effective waste material utilization.

In manufacturing industries, selecting the appropriate cutting parameters is essential to improve the product quality. As a result, the applications of optimization techniques in metal cutting processes is vital for a quality product. Due to the complex nature of the machining processes, single objective optimization approaches have limitations, since several different and contradictory objectives must be simultaneously optimized. Multi-objective optimization method is introduced to find the optimum cutting parameters to avoid this dilemma. The main objective of this paper is to develop a multi-objective optimization algorithm using the hybrid Whale Optimization Algorithm (WOA). In order to perform the multi-objective optimization, grey analysis is integrated with the WOA algorithm. In this paper, Stainless Steel 304 is utilized for turning operation to study the effect of machining parameters such as cutting speed, feed rate and depth of cut on surface roughness, cutting forces, power, peak tool temperature, material removal rate and heat rate. The output parameters are obtained through series of simulations and experiments. Then by using this hybrid optimization algorithm the optimum machining conditions for turning operation is achieved by considering unit cost and quality of production. It is also found that with the change of output parameter weightage, the optimum cutting condition varies. In addition to that, the effects of different cutting parameters on surface roughness and power consumption are analysed.