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

As well known, the solar distiller is one of the introduced solutions to the freshwater shortage problem, but it is demerited by the low freshwater output. In this paper, a design modification that includes the use of a convex dish absorber instead of a flat absorber liner was proposed. Also, a circular stepped surface was used instead of the flat absorber surface. The modified solar distiller is nominated by dish solar distiller and abbreviated by DSD. In addition, a cotton wick was used as a wetting material for facilitating the evaporation process inside the distiller. Besides, the effect of different water heights in the steps was investigated for 0.50, 1, 1.50, 2, and 3 cm. Finally, the space under the dish absorber is filled with a phase change material (PCM) of paraffin wax mixed with CuO nanoparticles. Experimental results revealed that the best water depth over the steps absorber of DSD that provided the highest freshwater productivity was 1.50 cm, where the average daily yields of DSD (at 1.50 cm) and conventional distillers were reported as 6525 and 2800 mL/m².day, respectively. Then, the productivity of DSD was improved by around 133% over that of the conventional distiller. In addition, when using the phase change material, the average daily distillate of stepped DSD was improved by approximately 178% compared to that of the conventional solar still, where the distillate of conventional still and DSD with PCM at 1.50 cm water depth over the steps absorber of DSD was 2950 and 8200 mL/m².day, respectively. The water productivity of the three established solar distillers has been predicted using machine learning algorithms. Besides, the maximum thermal efficiency of DSD was obtained when using PCM at 1.5 cm water depth over the steps absorber of DSD, where it was 67.62% compared to 31.71% for the conventional distiller. The proposed machine learning algorithms succeeded in predicting water productivity with a high correlation coefficient of 0.99. 

Machine learning-based prediction and augmentation of dish solar distiller performance using an innovative convex stepped absorber and phase change material with nanoadditives

In this study, an optimized artificial intelligence model is developed to predict the kerf quality characteristics in laser cutting of basalt fibers reinforced polymer composites. The model is composed of Long Short-Term Memory (LSTM) and Chimp Optimization Algorithm (CHOA). The latter is used as an internal optimizer to obtain the optimal parameters of the network model. The developed model was compared with three other models, namely standalone LSTM, LSTM optimized using Heap-Based Optimizer (HBO), and LSTM optimized using Manta Ray Foraging Optimization (MRFO). All models were trained and tested using experimental data considering five process control factors (cutting speed, air pressure, pulse frequency, pulse width and lamp current) and three process response (kerf width, kerf taper and kerf deviation). Response surface methodology was used to design the experimental plan. The accuracy of the models was evaluated and compared to each other using different statistical measures. LSTM-CHOA succeeded to predict kerf quality characteristics of the cut composites despite of their heterogeneous and anisotropic structure and it outperformed the three other models. The root mean squared error of the predicted kerf width, kerf deviation and kerf taper using LSTM-CHOA decreased by about 27.43%, 60% and 56.6%, respectively, compared with that of standalone LSTM. 

Predicting kerf quality characteristics in laser cutting of basalt fibers reinforced polymer composites using neural network and chimp optimization

In this study, a coupled multi-layer perceptrons (MLP) model with an artificial rabbits optimizer (ARO) is developed to predict the water productivity of different designs of solar stills (SSs). The investigated SSs are conventional, stepped, pyramid, and tubular SSs. The accuracy of the developed model was compared with three other MLP models optimized with conventional optimizers, namely gradient descent algorithm, particle swarm optimizer (PSO), and genetic algorithm (GA) optimizer. All models were trained and tested using the experimental data of the four SSs under the meteorological conditions of different cities. The performance of all models was evaluated using different statistical measures for all investigated cases. The MLP-ARO outperformed the other three models. The computed root mean square deviation values for all SSs’ designs are 130.79, 57.07, 40.28, and 2.82 ml for MLP, MLP-GA, MLP-PSO, and MLP-ARO models, respectively. 

A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills

Performance enhancement and economic analysis of pyramid solar still with corrugated absorber plate and conventional solar still: A case study

Modeling of a solar-powered thermoelectric air-conditioning system using a random vector functional link network integrated with jellyfish search algorithm

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Modeling of a solar-powered thermoelectric air-conditioning system using a random vector functional link network integrated with jellyfish search algorithm