Optimization of Structural Parameters for Robotic Arm to Enhance Load-Bearing Performance Based on MOPSO Algorithm

Abstract

The enhancement of mechanical arm load-bearing performance under constrained operational conditions represents a persisting conundrum in mechanical engineering, given the profound influence of structural parameters on stress distribution and displacement profiles. In response to this requirement, this investigation introduces a comprehensive optimization paradigm. Initially, computational simulations are executed via ANSYS Workbench, wherein cross-sectional geometry, area proportion, and support configuration are designated as control variables, while mean stress intensity and displacement magnitude serve as pivotal response metrics. Leveraging regression analysis techniques, empirical models are constructed to elucidate the quantitative correlations between structural attributes and mechanical performance. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm is employed to systematically search the solution space, with the objective of minimizing stress concentration and displacement variance. The proposed methodology yields a set of optimal structural parameters and a validated predictive model, facilitating expedient assessment of load-bearing capacity and informed design decisions. The study identified a balanced optimized structure: a rectangular cross-section combined with a medium-high area ratio (0.95-1.0) and a middle support position (1.5-1.8 meters), and clarified the risk control areas for different cross-sectional shapes. This optimization framework not only significantly augments the structural efficiency of mechanical arms but also offers actionable insights for the rational design of robotic manipulators.