The effect of graphene as the matrix strengthener in fiber reinforced polymer composites was studied for the improvement of several properties of flax/epoxy composites. Physical and mechanical characteristics of the hybrid composite along with flammability and water absorption properties are thoroughly studied for varied quantities of graphene added to the epoxy resin. The modified epoxy resin was infused on the flax fabric using vacuum resin infusion technique. The presence of graphene in flax/epoxy composites was found to act as a flame retardant and the flammability is found to decrease with increase in graphene content. The water absorption rate was also reduced significantly to 0.1 wt% of graphene. The tensile and flexural strengths of the composites increased by 61% and 71% at 0.1 wt% of graphene but reduced for higher addition of graphene. Fractography reveals that the main mechanisms of failure were matrix fracture and interfacial debonding, predominant for higher graphene content.

Current trends in the progress of technology demand availability of materials resources ahead of the advancing fronts of the application areas. During the last couple of decades, significant progress has been made in computational and experimental design of materials. Among the potential computational techniques, soft computing stands in distinction due to the inherent flexibility in capturing the complexity of the problem in global scale. Since 1990s remarkable success has been achieved in soft computing activities in different facets of materials science and engineering. Extensive efforts have been devoted in design of metals and alloys based on composition–process–microstructure–property correlation. The present review aims to address the contribution of soft computing in the field of structural metals and alloys including processing and joining. The critical issues concerning applicability of particular techniques in specific materials problem have been particularly emphasised encompassing the scope of integrating the gradual progress in different techniques in hybrid and tandem framework to address greater complexities in larger length and time scale. Attempt has also been made to emphasise on the evolution of newer knowledge and materials through soft computing activities. Finally, the potential of soft computing techniques in futuristic design approaches has been critically enumerated.

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A comparative study between the conventional goal attainment strategy and an evolutionary approach using a genetic algorithm has been conducted for the multiobjective optimization of the strength and ductility of low-carbon ferrite-pearlite steels. The optimization is based upon the composition and microstructural relations of the mechanical properties suggested earlier through regression analyses. After finding that a genetic algorithm is more suitable for such a problem, Pareto fronts have been developed which give a range of strength and ductility useful in alloy design. An effort has been made to optimize the strength ductility balance of thermomechanically-processed high-strength multiphase steels. The objective functions are developed from empirical relations using regression and neural network modeling, which have the capacity to correlate high number of compositional and process variables, and works better than the conventional regression analyses.