A novel comparative analysis between the experimental and numeric methods on viscosity of zirconium oxide nanofluid: Developing optimal artificial neural network and new mathematical model

Dynamics Modeling of Industrial Robotic Manipulators: A Machine Learning Approach Based on Synthetic Data

Obtaining a dynamic model of the robotic manipulator is a complex task. With the growing application of machine learning (ML) approaches in modern robotics, a question arises of using ML for dynamic modeling. Still, due to the large amounts of data necessary for this approach, data collection may be time and resource-intensive. For this reason, this paper aims to research the possibility of synthetic dataset creation by using pre-existing dynamic models to test the possibilities of both applications of such synthetic datasets, as well as modeling the dynamics of an industrial manipulator using ML. Authors generate the dataset consisting of 20,000 data points and train seven separate multilayer perceptron (MLP) artificial neural networks (ANN)—one for each joint of the manipulator and one for the total torque—using randomized search (RS) for hyperparameter tuning. Additional MLP is trained for the total torsion of the entire manipulator using the same approach. Each model is evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE), with 10-fold cross-validation applied. With these settings, all individual joint torque models achieved R2 scores higher than 0.9, with the models for first four joints achieving scores above 0.95. Furthermore, all models for all individual joints achieve MAPE lower than 2%. The model for the total torque of all joints of the robotic manipulator achieves weaker regression scores, with the R2 score of 0.89 and MAPE slightly higher than 2%. The results show that the torsion models of each individual joint, and of the entire manipulator, can be regressed using the described method, with satisfactory accuracy.

A Review on Thermophysical Property Assessment of Metal Oxide-Based Nanofluids: Industrial Perspectives

Energy consumption in the industrial sector can be significantly reduced by improving heat transfer rates in heat exchanger circuits, pool boiling, metal cutting industries, etc. Numerous energy-related issues can be overcome to a large extent by improving heat flow properties by utilizing nanofluids. The present contribution reviews the improvement in thermophysical properties of metal oxide-based nanofluids. Key parameters affecting the thermophysical properties of nanofluids, such as particle volume fraction, temperature, particle size and various stabilizers, were reviewed. The importance of DLVO theory and zeta potential to control the electrostatic repulsion and pH values of nanofluids for stable nanofluid formulations were discussed. It has been observed that classical theories of thermal conductivity and viscosity cannot predict exact values for a wide range of variables. Therefore, various extensive correlations have been introduced to predict the thermophysical properties of nanofluids. In these correlations, individual dependent variables such as particle size, temperature, nanofluid layer thickness, and Brownian velocity of nanoparticles, etc. were considered for more accurate prediction. The heat transfer efficiencies of nanofluids to base fluids in the laminar and turbulent regimes have been discussed using various figures of merits. Finally, the scope of industrial applications of metal oxide-based nanofluids and future research opportunities have been discussed.

Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling

In current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg-Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.