On the Shape Optimization of Self-Adaptive Grooves

Abstract

Microscale mechanical self-adaptive bearings are a recent development in the field that offer promising features such as enhanced load-carrying capacity compared to conventional bearings. This study aims to improve the load-carrying capacity of these bearings by introducing novel deformable groove designs. The objective is to find the optimum groove's top surface shape that maximizes the load-carrying support. Assuming that the thickness can vary along the groove's length, three different thickness patterns including constant, linear, and spline are considered. A hybrid optimization algorithm based on the harmony search (HS) algorithm and sequential quadratic programming (SQP) is utilized to find the optimum shape for each thickness pattern. A benchmark problem is considered to show the performance of new designs. Results show that the optimally designed grooves with spline thickness profile can have load-carrying capacities up to 45% larger than the original ones.

KEY WORDS:

• Self-Adaptive Grooves
• Shape Optimization
• Load-Carrying Capacity
• Harmony Search Algorithm
• Mathematical Programming

Acknowledgments

Review led by Gregory Kostrzewsky