Multiobjective optimization of hydrostatic thrust bearing operating with electrorheological lubricant using Taguchi-fuzzy technique

An electrorheological (ER) fluid falls in the category of a smart lubricant. In an ER lubricant, the dielectric microparticles are suspended in an insulating lubricant like silicon oil. The properties of ER fluids change when they encounter an external electric field. In the present study, the ER lubricant is modeled using a continuous Bingham model. The finite element method is utilized to solve the Reynolds equation to compute fluid film pressure and associated performance indices like load-carrying capacity, stiffness, and damping parameters of hydrostatic thrust bearing. In the present study, the influencing variables, that is, recess shapes, compensating elements, and strength of an electric field have been optimized using the fuzzy-based Taguchi optimization technique. The optimal combination of recess shape, compensating element, and electric field is required to enhance the performance of the hydrostatic thrust bearing. The authors attempt to see the interaction of the input variable by the trade-off study graph. A Taguchi fuzzy-based multipurpose optimization was proposed to optimize the performance indices and an optimal combination of compensating elements, recess shape, and electric field has been presented.