The significance of producing Carbon nanomaterials (CNMs) reinforced polymer composites are increasing in manufacturing trades due to their exceptional performances. CNM modified composites are primarily employed in structural component needs due to expanded physicomechanical properties. This paper highlights a coherent approach of the VIšekriterijumsko KOmpromisno Rangiranje(VIKOR) and Teaching learning-based optimization algorithm (TLBO) to evaluatethe Milling efficiency. The machining was performed for the Milling process of0-D carbon nano onion (CNO) reinforced polymer (Epoxy) composite at four different levels of Box Behnken Design (BBD). The Milling performances such as Material Removal Rate (MRR) and Surface roughness (SR) were optimized to enhance product quality and productivity. The control of varying process constraints, viz. Weight % of CNO filler content(A), cutting speed (B), feed rate (C) and depth of cut (D), was used to optimize the machining response. The conflicting response is aggregated through the VIKOR method to develop the fitness function for an algorithm. The process constraints play a significant role in influencing the cost and productivity ofthe machined components. The objective function derived from VIKOR was supplied as input into the TLBO algorithm. The results demonstrated that the spindlespeed, feed rate, and weight % of CNO filler are the most contributing factors for machining indices. Also, the hybrid VIKOR-TLBO module shows a lower error percentage than the conventional VIKOR method. The microstructural investigation of the machined surface reveals the feasibility of the proposed hybrid module in a production environment.

In this study, Carbon, Glass, and Aramid fiber reinforced composite and their hybridized forms were fabricated using five different stacking sequences of the fabrics. Using the Vacuum Assisted Resin Transfer Molding (VARTM) procedure, epoxy resin was injected into these fabrics and allowed to cure at room temperature. From these five stacking sequences, a standard specimen with four different orientations viz. 0/90°, 15/75°, 30/60°, 45/-45° orientations were obtained using the Abrasive Water Jet Machining(AWJM) Process. The influence of stacking order and fiber orientation on tensile and flexural properties of composite was investigated. From the result of tensile testing, the highest and lowest tensile strength values were observed for neat carbon fiber reinforced composite at 0/90° orientation and at 45/-45° orientation respectively. The highest flexural strength was achieved in a hybrid combination of two layers of carbon, glass and aramid fabric for 0/90° whereas the lowest flexural strength was found in glass reinforced composite for the 45/-45° orientation.

The application of Fused Deposition Modeling (FDM) is restricted due to limited information about the mechanical properties of printed parts. Therefore, it is required to determine the mechanical properties of the FDM properties to avail the full benefit of the FDM process. In the present study, Classic Laminate Theory (CLT) has been employed at the different configurations of layer thickness and raster width. The required elastic constant of material for CLT has been experimentally obtained through FDM printed Polylactic Acid (PLA) unidirectional specimens at 0°, 45° and 90° for different combinations of layer height and raster width. For these different combinations of layer height and raster width, constitutive models were developed to predict the tensile properties of the PLA parts. Tensile strength of the FDM printed bi-directional specimens has been experimentally obtained to validate the proposed CLT model results. The experimental tensile strength data is in good agreement with the data predicted by the proposed CLT model. Higher tensile strength and modulus were achieved with 0° raster angle compared to 90° raster angle. In the case of a bi-directional printed specimen, higher tensile strength was obtained with 45°/-45° raster angle followed by 30°/-60° and 0°/90° raster angle.