Explicit topology optimization through moving node approach: beam elements recognition

TL;DR

This paper introduces a novel explicit topology optimization method using a moving node approach that recognizes beam structures with fewer design variables, streamlining the design process.

Contribution

It adapts a flow-inspired Moving Node Approach for topology optimization, decoupling discretization from material distribution and enabling beam recognition with fewer variables.

Findings

Validated on classical beam problems like Cantilever and L-Shape.

Achieved effective beam recognition with reduced computational complexity.

Demonstrated advantages over pixel-based methods.

Abstract

Structural optimization (topology, shapes, sizing) is an important tool for facilitating the emergence of new concepts in structural design. Normally, topology optimization is carried out at the early stage of design and then shape and sizing design are performed sequentially. Unlike traditional topology optimization method, explicit methodologies have attracted a great deal of attention because of the advantages of shortcuting the costly CAD/CAE processes while dealing with low order number of design variables compared to implicit method (such as SIMP). This paper aims at presenting an adaptation of a flow-inspired approach so-called Moving Node Approach (MNA) in topology optimization. In this approach, the discretization is decoupled from the material distribution and the final objective is to recognize the best beam assembly while minimizing compliance. The paradigm has here changed and new design variables are used such as nodes location, elements length/orientation and size providing a lower number of design variables than pixels-based. The methodology is validated using 2 classical testcases in the field of Topology Optimization: the Cantilever beam and the L-Shape problem.