This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast ...This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast iron(CI)filler particles.Hybrid G-E composite with added cast iron(CI)filler particles enhances stiffness,strength,and vibration damping,offering enhanced performance for vibration-sensitive engineering applications.Unlike conventional approaches,this work simultaneously employs Artificial Neural Networks(ANN)for highaccuracy property prediction and Response Surface Methodology(RSM)for in-depth analysis of factor interactions and optimization.A total of 24 experimental test data sets of varying input factors(granite weight%,epoxy weight%,and CI filler weight%)were utilized to train and test the prediction models using an ANN approach and further analyze the interaction effects using RSM.Mechanical properties,including tensile,compressive,and flexural strength,elastic modulus,density and damping properties measured under various testing conditions,were set as output parameters for prediction.This study analyzed and optimized the performance of the ANN model using Bayesian Regularization and Levenberg-Marquardt algorithms to identify the best performing number of neurons in the hidden layer for achieving the highest prediction accuracy.The proposed ANN framework achieved an exceptional average determination coefficient(R2)exceeding 99%,with Bayesian Regularization demonstrating remarkable stability in the 22-neuron range and minimal variation across all properties.RSM and ANN form a powerful framework for predicting and optimizing hybrid G-E composite properties,enabling efficient design for vibration-critical applications with reduced experimental effort and performance optimization.展开更多
Current system focuses on design&construction of a multistage reciprocating evaporative cooling test rig.Four packings that are used will undergo the reciprocating action,powered by the cam follower mechanism main...Current system focuses on design&construction of a multistage reciprocating evaporative cooling test rig.Four packings that are used will undergo the reciprocating action,powered by the cam follower mechanism maintained at an actual rotation speed.Inlet dry bulb temperature&humidity values are varied to replicate the varying climatic conditions,and the output parameters such as cooling load,evaporation rate(ER),humidification efficiency(HE),coefficient of performance(COP)are obtained.Output results showed that as inlet humidity raises,the performance of system drops.With the rise in the camshaft speed or packing velocity,there is an upsurge in the performance until a cam speed of 10 r/min or packing velocity of 0.083 m/s and with further increases in the value,overall performance drops.The system gave a maximum COP,ER,and HE equal to 3.80,73.87%,and 0.53 g/kg respectively.A rise in the inlet air temperature yielded a maximum change in dry bulb temperature of 8.2℃.Overall,results indicated that evaporative cooling is more effective in arid climates than the cold and humid climates.Air quality test is performed to measure CO_(2),Total Volatile Organic Compound(TVOC),and Formaldehyde(HCHO),and it is found that humidified air entering the defined space is of excellent quality.展开更多
文摘This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite-epoxy(G-E)composites reinforced with cast iron(CI)filler particles.Hybrid G-E composite with added cast iron(CI)filler particles enhances stiffness,strength,and vibration damping,offering enhanced performance for vibration-sensitive engineering applications.Unlike conventional approaches,this work simultaneously employs Artificial Neural Networks(ANN)for highaccuracy property prediction and Response Surface Methodology(RSM)for in-depth analysis of factor interactions and optimization.A total of 24 experimental test data sets of varying input factors(granite weight%,epoxy weight%,and CI filler weight%)were utilized to train and test the prediction models using an ANN approach and further analyze the interaction effects using RSM.Mechanical properties,including tensile,compressive,and flexural strength,elastic modulus,density and damping properties measured under various testing conditions,were set as output parameters for prediction.This study analyzed and optimized the performance of the ANN model using Bayesian Regularization and Levenberg-Marquardt algorithms to identify the best performing number of neurons in the hidden layer for achieving the highest prediction accuracy.The proposed ANN framework achieved an exceptional average determination coefficient(R2)exceeding 99%,with Bayesian Regularization demonstrating remarkable stability in the 22-neuron range and minimal variation across all properties.RSM and ANN form a powerful framework for predicting and optimizing hybrid G-E composite properties,enabling efficient design for vibration-critical applications with reduced experimental effort and performance optimization.
文摘Current system focuses on design&construction of a multistage reciprocating evaporative cooling test rig.Four packings that are used will undergo the reciprocating action,powered by the cam follower mechanism maintained at an actual rotation speed.Inlet dry bulb temperature&humidity values are varied to replicate the varying climatic conditions,and the output parameters such as cooling load,evaporation rate(ER),humidification efficiency(HE),coefficient of performance(COP)are obtained.Output results showed that as inlet humidity raises,the performance of system drops.With the rise in the camshaft speed or packing velocity,there is an upsurge in the performance until a cam speed of 10 r/min or packing velocity of 0.083 m/s and with further increases in the value,overall performance drops.The system gave a maximum COP,ER,and HE equal to 3.80,73.87%,and 0.53 g/kg respectively.A rise in the inlet air temperature yielded a maximum change in dry bulb temperature of 8.2℃.Overall,results indicated that evaporative cooling is more effective in arid climates than the cold and humid climates.Air quality test is performed to measure CO_(2),Total Volatile Organic Compound(TVOC),and Formaldehyde(HCHO),and it is found that humidified air entering the defined space is of excellent quality.
