Artificial neural network in the prediction of surface roughness: A comparative study

Surface roughness is a key parameter to consider in the machining of aluminum alloy. It is rendered as one of the important determinants of the performance of mechanical instruments or components. Owing to its excellent mechanical properties, and ease of machinability, Aluminum 6061 (Al6061) is rendered a popular choice in many industries. Achieving a desired surface finish is crucial for the performance and longevity of machined components. This study aimed to compare the predictive performance of the artificial neural network (ANN) model versus the response surface methodology (RSM) in the prediction of surface roughness in the turning process of Al6061. ANN performed better than RSM in the prediction of surface roughness (A20 index 0.93 and 0.86 for ANN and RSM models respectively). MAPE and sMAPE were also found to be lower in the ANN model compared with the RSM model (8.06 versus 9.69, and 0.039 versus 0.047 respectively) indicating that the ANN model had a better predictive performance compared with the RSM model. Both ANN and RSM models showed that cutting speed and feed rate were the most important determinants of surface roughness in the turning process of Al6061 in other words to achieve a smoother surface during the turning process of Al6061 high cutting speed and low feed rate should be used. The findings of this study reflect the potential utility of ANN in the prediction and subsequently optimizing cutting parameters to achieve a smoother surface

On the Performance of Hybrid PV/Unitized Regenerative Fuel Cell System in the Tropics

Solar hydrogen system is a unique power system that can meet the power requirements for future energy demands. Such a system uses the hydrogen as the energy carrier, which produces energy through the electrolyzer with assistance of the power from the PV during the sunny hours, and then uses stored hydrogen to produce energy through the fuel cell after sunset or on cloudy days. The current study has used premanufactured unitized regenerative fuel cells in which the electrolyzer and the fuel cell function within one cell at different modes. The system components were modeled and the one-day real operational and simulated data has been presented and compared. The measured results showed the ability of the system to meet the proposed load, and the total efficiency was about 4.5%

Antimalarial use in managing COVID-19 in the context of Glucose-6-Phosphate-Dehydrogenase G6PD deficiency

BackgroundThe use of certain medications in G6PD deficient patients can trigger an oxidative stress which can lead to haemolytic anaemia. Recently, in a few countries, Chloroquine and Hydroxychloroquine; drugs that are indicated for prevention and treating malaria have been used in the management of COVID-19 patients. Evidently, the use of chloroquine and hydroxychloroquine can cause negative impact to the haemolytic status of COVID-19 G6PD deficient patients.AimsThe aim of this mini review was to provide an overview of the use of antimalarial agents in the management of COVID-19 G6PD deficient patients.Methods We conducted a review of the literature that has examined the use of antimalarial agents in the management of COVID-19 G6PD deficient patients.Results Chloroquine and hydroxychloroquine have been found to exhibits an antiviral activity against several viral infections including human coronaviruses. Many countries have implemented the use of Chloroquine and Hydroxychloroquine in managing COVID-19 patients. However, according to published case reports, the use of Chloroquine and hydroxychloroquine have been shown to worsen the haemolytic status in G6PD deficient patients.ConclusionCOVID‐19 infection can trigger severe acute haemolytic crisis in G6PD‐deficient patients which can be worsened by chloroquine and hydroxychloroquine. Thus, physicians should be aware to this possible adverse event particularly in countries with high prevalence of G6PD deficiency

Cutting force prediction model by FEA and RSM when machining Hastelloy C-22HS with 90° holder

421-427Finite element (FEA) method and response surface method (RSM) are used to find the effect of milling parameters (cutting speed, feed rate and axial depth) on cutting force when milling Hastelloy C-22HS. Based on variance analyses of First- and Second-Order RSM models, most influential design variable is feed rate. Optimized cutting force values are subsequently obtained from model equations. FEA model shows distribution of cutting force

Experimental study of heat transfer augmentation in non-circular duct using combined nanofluids and vortex generator

This study presents the effect of nanofluid and vortex generators compound on heat transfer and friction factor in an equilateral triangular duct experimentally. Two different types of nanofluids namely, Al2O3 and SiO2 nanoparticles suspended in distilled water (DW) with two particle concentrations were successfully prepared and tested. A wide range of Reynolds number was covered from 500 to 8000, approximately. The results of smooth triangular duct using water as a working fluid were validated with literatures and a good agreement was obtained. The present results show a good enhancement in heat transfer by using vortex generator with base fluid while a significant enhancement was registered by using the compound of vortex generator and nanofluids accomplished with a moderate increase in the friction factor. The maximum enhancement in the Nusselt number obtained in this study is 44.64% and 41.82% at 1 vol.% and Re ≈ 4000 for SiO2–DW and Al2O3–DW nanofluid, respectivel