Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 121, P. 105961 - 105961

Published: Feb. 14, 2023

Language: Английский

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The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101566 - 101566

Published: Nov. 3, 2023

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches resource (WRM) struggle with real-time data acquisition, analysis, intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) Big Data Analytics (BDA) at the forefront have potential revolutionize way managed. This paper reviews current applications AI BDA in WRM, highlighting their capacity overcome existing limitations. It includes investigation technologies, such as machine learning deep learning, diverse quality monitoring, allocation, demand forecasting. In addition, review explores role resources, elaborating on various sources that can be used, remote sensing, IoT devices, social media. conclusion, study synthesizes key insights outlines prospective directions for leveraging optimal allocation.

Language: Английский

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Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

Polymers, Journal Year: 2023, Volume and Issue: 15(1), P. 233 - 233

Published: Jan. 2, 2023

This study investigates the application of a coupled multi-layer perceptrons (MLP) model with Archimedes optimizer (AO) to predict characteristics dissimilar lap joints made polymethyl methacrylate (PMMA) and polycarbonate (PC). The were welded using laser transmission welding (LTW) technique equipped beam wobbling feature. inputs models power, speed, pulse frequency, wobble width; whereas, outputs seam width shear strength joint. was employed obtain optimal internal parameters perceptrons. In addition optimizer, conventional gradient descent technique, as well particle swarm (PSO), optimizers model. prediction accuracy three compared different error measures. AO-MLP outperformed other two models. computed root mean square errors MLP, PSO-MLP, are (39.798, 19.909, 2.283) (0.153, 0.084, 0.0321) for width, respectively.

Language: Английский

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Water distillation tower: Experimental investigation, economic assessment, and performance prediction using optimized machine-learning model

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 388, P. 135896 - 135896

Published: Jan. 9, 2023

Language: Английский

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Double layered combined convective heated flow of Eyring-Powell fluid across an elevated stretched cylinder using intelligent computing approach

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 54, P. 104009 - 104009

Published: Jan. 10, 2024

This study investigates the possessions of a dual stratified common on diverse convection barrier layer discharge an Eyring-Powell fluid (CBLFEPL) induced by prone extensive barrel. The temperature and concentration at exterior barrel are assumed to be larger than moving fluid. To solve resulting flow equations, brilliant numerical established aggregating solver is employed using Levenberg-Marquardt neural network scheme (LMNNS). governing equations partial differential converted into interconnected nonlinear ordinary appropriate transformations. First, dataset generated for two distinct cases, one with zero curvature parameter (plate) other non-zero (cylinder). behaviors skin-friction coefficient, Sherwood number Nusselt presented over chart obtained BVP4C technique. Subsequently, intelligent computing algorithm nftool, utilized training, validation, testing steps approximate solutions various cases. designed solver, LMNNS, applied CBLFEPL problem through regression, mean squared error (MSE), histogram studies, gradient analysis. double-layered combined around elevated stretched cylinder provides insights heat mass transfer characteristics, crucial applications in engineering dynamics. aims contribute that can dynamics, aiding optimization relevant processes applications. research methodology involves employing technique, three-stage Lobatto IIIa formula, cylinder, while systematically varying key parameters analyze their impact phenomena. speed experiences notable rise higher values K, M, mixed λm, ratio buoyancy forces N. Conversely, velocity profile exhibits contrasting behavior concerning thermal stratification ϵ1, solutal ϵ2, inclination angle α.

Language: Английский

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Novel Design of Double Slope Solar Distiller with Prismatic Absorber Basin, Linen Wicks, and Dual Parallel Spraying Nozzles: Experimental Investigation and Energic–Exergic-Economic Analyses

Water, Journal Year: 2023, Volume and Issue: 15(3), P. 610 - 610

Published: Feb. 3, 2023

Increasing the evaporation zone inside solar distiller (SD) is a pivotal method for augmenting its freshwater production. Hence, in this work, newly designed prismatic absorber basin covered by linen wicks was utilized instead of conventional flat to increase surface area vaporization double-slope (DSSD). Meanwhile, further enhancement modified DSSD performance, dual parallel spraying nozzles are incorporated underneath glass cover as saltwater feed supply minimize thickness film on wick, which enhances heating process wick and, consequently, and condensation processes improved. Two double slope distillers, namely with (DSSD-WPB&DPSN) traditional (TDSSD), made tested outdoor summer conditions Tanta, Egypt (31° E 30.5° N). A comparative energic–exergic-economic analysis two proposed stills also conducted, terms cumulative distillation yield, daily energy efficiency, exergy cost per liter distilled yield. The present results show that yield DSSD-WPB&DPSN 8.20 kg/m2·day, higher than TDSSD 49.64%. Furthermore, efficiencies were increased 48.51% 118.10%, respectively, relative TDSSD. Additionally, life assessment reveals decreased 11.13% compared

Language: Английский

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Innovative solar distillation system with prismatic absorber basin: Experimental analysis and LSTM machine learning modeling coupled with great wall construction algorithm

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 186, P. 1120 - 1133

Published: April 18, 2024

Language: Английский

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Enhancement and prediction of a stepped solar still productivity integrated with paraffin wax enriched with nano-additives

Case Studies in Thermal Engineering, Journal Year: 2023, Volume and Issue: 49, P. 103215 - 103215

Published: June 29, 2023

The present study deals with the emhancement of thermophysical properties paraffin wax using Silver nanoparticles and to feasibility its application in a stepped solar still through an experimental approach. Along experimentation, yield, temperature water are predicted machine learning such as melting temperature, latent heat, thermal conductivity stability different concentrations (1 2%) investigated compared that without nanoadditives. was enhanced by about 35.71% 78.57% nano-additives 1% 2%, respectively. Three SS namely, wax, doped Ag nanoparticles, fabricated tested for climatic conditions Coimbatore, India. From results fresh generation, it is identified nanocomposite PCM nanoadditives 75.65% 114.81% respectively, while any energy storage. In order estimate amount can be produced each three stills, single adaptive neuro-fuzzy inference system (ANFIS) hybrid system-particle swarm optimizer (PSO) were used models. According statistical assessment, ANFIS-PSO model had greater level accuracy than standalone ANFIS. very high determination coefficient ranged between 0.981 0.995 which indicates capability predict yield stills.

Language: Английский

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Application of machine learning modeling in prediction of solar still performance: A comprehensive survey

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101800 - 101800

Published: Jan. 18, 2024

Being a cheap, simple, and low-energy consumer, solar stills have been introduced by water energy scientists as an alternative desalination method to fossil fuel-based ones. A wide variety of designs modifications applied enhance the stills' performance, which may be associated with experimental works that require time cost. Therefore, coupling state-of-the-art machine learning is expected overcome these disadvantages work. Artificial intelligence models try build relationships between input output data similar human brains depending on given dataset. In light these, this study carries out literature review considers applications artificial in performance prediction. The covers most repeated methods employed for prediction, focusing principles, advantages, limitations, mathematical description each besides model evaluation criteria. Then, comprehensive analysis performed classifying them according design. work compares previous studies within gives reasons authors' findings, highlighting variation models' prediction findings. Accordingly, root mean square errors close zero are highlighted throughout review.

Language: Английский

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Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

Applied Mathematical Modelling, Journal Year: 2024, Volume and Issue: 130, P. 243 - 271

Published: March 11, 2024

Language: Английский

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