Strain wave gears are widely used in applications that require high reduction ratios, compact designs, and high positioning accuracy. However, increasing torque capacity remains a key challenge, particularly due to the high stresses at the flex spline tooth root. This study presents the development and investigation of a new strain wave gear variant. The tooth profile is defined using a quadruple-arc geometry with tangential transitions. A two-dimensional tooth engagement analysis evaluates the meshing behavior under flex spline deflection and identifies potential collisions during the design phase. Consequently, an FE simulation was conducted to investigate stress distribution. In the final step, the developed strain wave gear was assessed for overall performance and load capacity. Performance evaluations, including hysteresis loss, showed that the developed strain wave gear performed equally well or better than a leading strain wave gear manufacturer across seven key performance metrics. In terms of load capacity, the maximum torque at 50% failure probability (T50%) reached 106.7 Nm, which is 30% higher than the development target of 82 Nm, thereby confirming the validity of the tooth-profile design.

This paper presents the development of a design optimization module for achieving the best performance of hydrostatic bearings. The design optimization module consists of two components: a bearing performance analysis module and an optimization module that utilizes optimization algorithms. Widely recognized global search methods, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were employed as the optimization algorithms. The design optimization problem was defined for hydrostatic bearings. Optimization design processes were carried out to improve load capacity, stiffness, and flow rate. Subsequent experimental validation was conducted through the fabrication of a practical experimental setup. The design optimization model demonstrated superior performance compared to the initial model while satisfying design conditions and constraints. This confirms the practical applicability of the design optimization module developed in this study.