An Evolutionary Multi-Objective Topology Optimization Framework for Welded Structures

Design of multi-component structures can be a challenging task. While having multiple components in a complex structure is often necessary in order to reduce the manufacturing cost, multiple components need joining operations. Optimal design of joints is not a decoupled problem from designing the base structure, and often comes at balancing trade-offs in assembly cost, weight and structural performance. Thus, the problem is posed in a multi-objective framework. Since some of the objectives are inherently discrete, non-gradient optimization methods are needed. Previous work has adopted a Kriging-interpolated level-set (KLS) formulation for implicit definition of the base topology as well as its decomposition into multiple components. While the number of design variables in KLS formulation is significantly smaller than explicit formulations, it can still be a challenge for general-purpose non-gradient multi-objective algorithms. This paper proposes a systematic approach for the problem in order to efficiently generate a well-seeded initial population to be used in multi-objective evolutionary algorithms. A multi-component cantilever is used as a basis for comparison between a basic NSGA-II algorithm, versus the proposed optimization framework. The results demonstrate its superiority and capability in obtaining multi-component complex topologies with desirable quality, which are not achieved by the basic algorithm.