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Multi-component Topology Optimization Considering Joint Distance

조인트 최소거리를 고려한 다중구조물 위상최적설계 기법

  • Jun Hwan, Kim (Department of Mechanical Convergence Engineering, Hanyang University) ;
  • Gil Ho, Yoon (Department of Mechanical Engineering, Hanyang University)
  • 김준환 (한양대학교 융합기계공학과) ;
  • 윤길호 (한양대학교 기계공학과)
  • Received : 2022.09.27
  • Accepted : 2022.10.31
  • Published : 2022.12.31

Abstract

This paper proposes a new topology optimization scheme to determine optimized joints for multi-component models. The joints are modeled as zero-length high-stiffness spring elements. The spring joints are considered as mesh-independent springs based on a joint-element interpolation scheme. This enables the changing of the location of the joints regardless of the connected nodes during optimization. Because the joints are movable, the locations of the optimized joints should be aggregated at several points. In this paper, the novel joint dispersal (JD) constraint to prevent joint clustering is proposed. With the joint dispersal constraint, it is possible to determine the optimized joint location as well as optimized topologies while maintaining the minimum distance between each joint. The mechanical compliance value is considered as the objective function. Several topology optimization examples are solved to demonstrate the effect of the joint dispersal constraint.

본 논문에서는 구조물이 다중 구조물로 연결되는 경우 연결부의 조인트 위치를 기존의 위상최적설계 기법을 활용해 설계하는 기법을 개발하였다. 조인트는 길이가 0이고 강성이 매우 강한 스프링으로 모델링되었으며, 조인트는 유한요소 메시 형상과 무관하게 이동할 수 있도록 모델링되었다. 최적화 과정에서 조인트가 서로 뭉치는 현상을 방지하기 위해 조인트 최소거리 조건을 추가해 조인트간의 최소거리가 확보된 설계를 얻었다. 최적설계 시 목적함수로는 전체 구조물의 compliance 값이 사용되었으며, 조인트 최소거리 조건에 따른 결과를 비교하기 위해 2개의 수치예제를 해석하였다. 위상최적설계 결과 조인트 최소거리 조건의 변화에 따라 조인트 및 구조물의 최적 형상을 얻을 수 있었다.

Keywords

Acknowledgement

This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE). (2021202080026D, Development of platform technology and operation management system for design and operating condition di-agnosis of fluid machinery with variable devices based on AI/ICT).

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