Aramid Fibre Reinforced Plastic composites are difficult to be drilled due to anisotropic material properties. Currently, soft computing techniques are used as alternatives to conventional mathematical models, which is robust and can deal with inaccuracy and uncertainty. In this paper, drilling of Aramid Fibre Reinforced Plastics (AFRPs) was carried out using Taguchi L54 experimental layout. Drilling tool used in this experiment was solid carbide. The purpose of this study was to find optimum combination of drilling parameters to obtain minimum thrust and torque force to reduce the delamination. Also, this paper proposed a prediction model of Multilayer Perception Neural Network optimized by Genetic Algorithm (MLPNN-GA). Moreover, RSM technique was used to evaluate the influence of process parameters (spindle speed, feed rate, drill point angle and drill diameter on thrust force and torque. The prediction capability of both RSM and MLPNN-GA was compared with Response optimizer for thrust force and torque. The investigation demonstrated that drill point angle is the primary factor affecting thrust force and drill diameter influences the torque force on the drill bit. Overall, this study recommends the use of high speed and low feed combination and drill point angles of 90°–118° to reduce the delamination of the materials in the drilling of AFRP composites.
The connecting rod big end bearings are under dynamic lubrication during working cycle, and in most of the time, the con rod is subjected to compressive stress. The conventional method of performing an EHL analysis on a bearing involves development of complex mathematical equations and simplification of actual physical model. This paper presents a methodology to model and simulate the elastohydrodynamic lubrication and wear study of connecting rod big end bearings of off‐highway application engine using the application of computational fluid dynamics (CFD) and computational structural dynamics (CSD) approaches. The pressure field for a full journal bearing operating under laminar flow regime with various eccentricities was obtained by CFD, and fluid pressure distribution and deformation in the bearing liner due to pressure were evaluated using FSI approach. Relevant parameters of lubrication characteristics were analyzed to optimize the eccentricity value. The maximum bearing load value of 21 kN was noticed at TDC position for the optimum eccentricity. The load distribution indicated critical points in the bearing, and the data obtained from bearing load and sliding velocity of journal were used in Archard's wear relation to determine the wear depth along the bearing width. The simulated wear results were compared with three‐cylinder off‐highway application engine con rod big end bearings, which ran for 1000 hours at full load condition, and satisfactory agreement was observed between experiment and simulation values.
Corrosion of the piping system is a genuine problem in the oil and gas industry. Most oil and gas industries used a carbon steel pipeline for the transportation of crude oil, which is affected by CO 2 corrosion. Now a day, the computational approach and artificial neural network approach will be used to study the corrosion rate. Therefore, in this work, Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) studies on piping systems were made to determine the corrosion rate induced by CO 2 saturated aqueous solutions on carbon steel pipeline. In CFD study, corrosion rates were computed by modeling the electrochemical processes occurring at the metal substrate from cathodic reductions of the carbonic acid and hydrogen ions, and the anodic oxidation of the metal component. Also, an artificial neural network study was made using a multilayer perceptron neural network method; and, computational fluid dynamics and artificial neural network simulations were validated with in-house built experiment set-up. The experimental study had been carried out for more than 200-h to find the corrosion rate on the pipeline, and satisfactory trends were observed between computational fluid dynamics, artificial neural network, and experimental values. In the end, corroded pipes were observed under a scanning electron microscope and x-ray spectroscopy, and the corroded zones were viewed as against the non-corroded pipe.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.