Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm

Area of Science:

• Mechanical Engineering
• Structural Analysis
• Computational Mechanics

Background:

• Crane outriggers are critical for stability, but their structural integrity under load requires optimization.
• Buckling and stress analysis are essential for ensuring the safety and efficiency of crane leg designs.
• Previous research has focused on material properties, but structural geometry and reinforcement methods offer further optimization potential.

Purpose of the Study:

• To analyze and optimize the structural design of crane legs for improved performance and safety.
• To investigate the buckling behavior of outriggers with various cross-sectional shapes.
• To determine the optimal structural parameters and reinforcement strategies for enhanced load-bearing capacity and lightweighting.

Main Methods:

• Finite element analysis (FEA) using ANSYS software for linear eigenvalue and geometric nonlinear buckling analysis.
• Modeling and simulation using SolidWorks software.
• Optimization using an agent-based multi-island genetic algorithm.
• Analysis of stress distribution and buckling conditions under varying parameters.

Main Results:

• Curved crane leg designs demonstrate superior buckling resistance compared to other shapes.
• Transversely stiffened plate reinforcement significantly enhances bearing capacity and achieves structural lightweighting.
• High-order fitting curves identified an optimal gap value between movable and fixed legs for stress mitigation.

Conclusions:

• The study successfully optimized crane leg structure, enhancing buckling performance and load-bearing capacity.
• Transverse stiffening is a highly effective strategy for improving outrigger performance and reducing weight.
• FEA and genetic algorithms provide a robust framework for optimizing complex mechanical structures like crane legs.