Design Optimization of Angular Contact Ball Bearing Using Genetic Algorithm and Grid Search Method

Abstract

Abstract The present work proposes an approach to design and optimize the internal geometry of angular contact ball bearings (ACBBs) with the help of the genetic algorithm (GA) and the grid search method (GSM). Rolling bearings have very complex internal geometry and have varied types. In literature among other types of bearings, very few authors attempted optimization of ACBBs, and subjected to very few constraints. Additionally, none of them considered the free contact angle as a design parameter, which is very important. So, the main motivation of the present work is to see the effect of inclusion of free contact angle in the optimum design of ACBBs. To optimize the geometry of ACBBs, in present study, we have taken a set of 11 design variables out of which 5 are geometric design variables and the rest are constraint parameters. The solution space has been bounded using a set of 20 realistic constraints. This way performing the same analysis with more realistic constraint model of the bearing, increases the feasibility and reliability of the optimized solutions. The results obtained from the genetic algorithm and the grid search method for the dynamic capacity of the bearing were compared to make a evaluate performance of two approaches. The simulation is performed on a series of selected ACBBs. Using the GA, for 7006 ACBB 83% improvement in its dynamic capacity over its rated values has been achieved, while using the GSM improvement is 43%. However, time taken by the GA over the GSM is one-sixth. A sensitivity analysis is done to check the most affecting parameters for the selected objective function. The present methodology helps the designers to reduce the design time, and select the optimum values of design parameters for manufacturing the custom bearing by prioritizing a specific objective.