For this present research investigation, hybrid natural fiber reinforced Nano SiC particles composite was fabricated and their mechanical properties has been studied. Experiments have been planned as per Taguchi’s Method. After preparing the composite material by hand layup technique and then the mechanical characterizations are performed. Multi-response optimization has been carried out by employment of a recently developed method for multi-criteria decision making (MCDM), i.e. combined compromise solution (CoCoSo) method. For defining the relative significance of measured norms, pairwise comparison matrix was used. Optimal results have been verified through confirmatory experiments. Based on the experimental observations ultimate tensile strength (UTS), density and flexural strength, ANOVA result indicated that density has highest impact for a better mechanical behaviour. This can be useful in different field of application for fabrication of different structures, parts etc.

In this investigation, the influence of WEDM process constraints on tool wear rate, kerf width, surface roughness of SS 304 grade stainless steel was studied. Fifteen experimental runs were carried out based on Box-Behnken method of Response surface methodology and TOPSIS method was used for finding an optimum parameter setting. From the ANOVA results, pulse ON time was found as most significant factor for tool wear rate, kerf width and surface roughness. Genetic Algorithm and Simulated Annealing was also used for the calculation of the optimum setting along with the forecast of fitness values. It was found that every optimization technique gives similar factor setting.

Electrical discharge machining is a versatile machining technology that uses precisely directed sparks within the range of the dielectric fluid and between the electrodes to make electrically conductive products. The influence of different processing parameters on diverse metal matrix composites, including new reinforcing elements (silicon carbides, aluminium oxides and nitrides), and the modifications that occur as a consequence of each of these components are discussed in this article. As a result, new reinforcing materials and ways for optimising performance have emerged. The goal of this study is to provide analysts with research data that will allow them to effectively use EDM principles and, more particularly, to investigate process characteristics in order to enhance the process of advanced PMEDM operation.

Colossal materials do not provide a wide range of applications due to their mechanical property limitations. Fiber reinforced polymer composites are now considered as an important class of engineering materials. The processing and mechanical characterization of a new class of multi-phase composites consisting of polyester resin reinforced with jute-glass-silk fiber. This research investigation deals with mechanical properties of hybrid polymer composite. Woven palm fiber, teak wood dust and SiC Nano particles have been used for fabrication of the composites. Experiments have been planned as per Response Surface Method. After preparing the composite material by hand layup technique and then the mechanical characterizations are performed. Multi-response optimization has been carried out using Multi objective optimization on the basis of the ratio analysis method (MOORA) and Whale Optimization Algorithm. Optimal results have been verified through confirmatory experiments. Based on the experimental observations density, flexural and Ultimate Tensile Strength, it is concluded that teak wood powder influences the mechanical properties more than that of other two reinforcements but Nano particle filled composites shows better properties.