Artificial Intelligence to Analyze the Performance of the Ceramic-Coated Diesel Engine Using Digital Filter Optimization

Abstract

The completeness of oil goods activates the barriers of lack of goods, inequality in the society, and surroundings impoverishment. Avoiding their use overnight and switching to clean electric motors are a challenge. Under all these conditions, researchers can launch their research on alternative fuels for a preeminent solution. Oxygenated fuel additives and thermal barrier coating (TBC) applications are essential to decrease the emission levels of exhaust and improve the performance of the vehicle. The main objective of this research is to analyze the performance of the ceramic-coated diesel engine. The ceramic particles use polymer coating to enhance the functionality and durability. Optimum outcomes are determined using Taguchi method. The impacts of various casting parameters of composites have been examined in detail. PSO-GA (Particle Swarm Optimization and Genetic Algorithm) is utilized to analyze the performance. Using an artificial neural network (ANN), the performance of diesel engine is examined to reduce time, cost, and experimental repetition. Thus, by using the artificial intelligence, the performance of the ceramic-coated diesel engine is analyzed and the polymeric substance and condition in coating ceramic engine is discussed.