Fused deposition modeling (FDM), one of the most widely used additive manufacturing (AM) processes, is used for fabrication of 3D models from computer-aided design data using various materials for a wide scope of applications. The principle of FDM or, in general, AM plays an important role in minimizing the ill effects of manufacturing on the environment. Among the various available reinforcements, short glass fiber (SGF), one of the strong reinforcement materials available, is used as a reinforcement in the acrylonitrile butadiene styrene (ABS) matrix. At the outset, very limited research has been carried out till date in the analysis of the impact and flexural strength of the SGF-reinforced ABS polymer composite developed by the FDM process. In this regard, the present research investigates the impact and flexural strength of SGF–ABS polymer composites by the addition of 15 and 30 wt % SGF to ABS. The tests were conducted as per ASTM standards. Increments in flexural and impact properties were observed with the addition of SGF to ABS. The increment of 42% in impact strength was noted for the addition of 15 wt % SGF and 54% increase with the addition of 30 wt % SGF. On similar lines, flexural properties also showed improved values of 44 and 59% for the addition of 15 and 30 wt % SGF to ABS. SGF addition greatly enhanced the properties of flexural and impact strength and has paved the path for the exploration of varied values of reinforcement into the matrix.
The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function.
In the present study, the surface composite Al359/Si3N4/Eggshell is prepared by friction stir processing (FSP). The effect of reinforced particle volume fraction on the microstructural and tribological properties of the Al359/Si3N4/Eggshell surface composites was investigated and compared with the friction stir processed (FSPed) Al359 alloy. The microstructural properties were further investigated by light microscopy, FESEM, and EDS mapping. The tribological properties of the developed composite and FSPed Al359 were investigated using a reciprocating ball-on-plate universal tribometer. The microstructural results showed that defect-free composite surfaces are produced due to improved physical properties, severe plastic deformation, and better grain refinement. Moreover, the mean value of the friction coefficient (µ) for the developed composite and FSPed alloy are 0.36 µ and 0.47 µ, respectively. The obtained results indicated that Si3N4/Eggshell is a promising reinforced particle for improving microstructural and tribological performance in journal bearing, rotors, and machinery applications.
This study reports on the tribological behavior of Indian rail track and wheel materials under different contaminants. A pin-on-disc tribometer was selected for the experimental analysis in ambient conditions (temperature of 24.9 °C and relative humidity of 66%). Sand, mist, leaves, and grease were the contaminants used in this investigation. The railway track was used to make the pin, and the wheel was used to make the disc. The acquired results were analyzed using frictional force and wear depth as a function of time as the variables. These pollutant effects were compared to no-contaminant conditions. It was observed that the sand increased the friction force and wear depth, whereas oil decreased friction and wear. Mist and leaves also reduced friction and wear. The effect of leaves was higher than the mist. The effect of load on various contaminants was also investigated. The results showed that as the load increased, the friction force and wear also increased for all contaminants. The results of this study can help in understanding the wear phenomenon of wheels and rail tracks in different parts of India.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.