Application of artificial neural networks in the blade coating process using viscoelastic nanofluid model with magnetohydrodynamics and slip effects

The blade coating process is highly significant due to its extensive use in producing items like newspapers, photographic films, fibers, catalogs, and magnetic storage media. Its economic impact has increased the trend to understand the involved physical mechanisms to optimize the process. This study utilizes Levenberg–Marquardt algorithm combined with a backpropagated artificial neural network to analyze the steady, incompressible heat transfer analysis in the blade coating process using viscoelastic nanofluid model with magnetohydrodynamics (MHD) and non-linear slip effects. The mathematical equations are modeled with the use of lubrication approximation theory, and then, these normalized ordinary differential equations (ODEs) are solved numerically using boundary value problem fourth-order collocation (bvp4c) method. The coated thickness is calculated iteratively with the help of the Regula–Falsi method by imposed zero pressure at entering and exiting points of the sheet. This work explores how changes in physical parameters affect flow characteristics and mechanical properties of the blade coating process are investigated with help of various graphs and tables. With increasing MHD parameter value, the coating thickness increased 65.97% and 63.66% for plane and exponential coaters as compared to the Newtonian value. Additionally, as the slip parameter values increased the blade load increased by 601.79% and 599.48% for plane and exponential coater from Newtonian value.