Topology Optimization of an Additive Manufactured UAV Catapult Bracket Using SIMP and BESO Methods

Catapult systems enable Unmanned Aerial Vehicles (UAVs) to take off without runways and must therefore meet key requirements such as safety, reliability, portability, efficiency, and durability. These systems contain mechanically complex components that are subjected to substantial dynamic loads during operation. This study examines the topology optimization of a UAV catapult launch bracket using the SIMP and BESO methods to achieve a structurally efficient design compatible with additive manufacturing. The bracket was analyzed under axial compression and bending moments representative of launch conditions. SIMP was implemented through the Abaqus Tosca infrastructure, while the BESO optimization employed an Abaqus (2018)–Matlab (2024B) interface supported by automated Python (3.13.0) scripts. The optimized design achieved a 51% reduction in volume, with a 52% increase in maximum von Mises stress and an 8% increase in displacement, yet all stresses remained below the yield strength of AA7075. Manufacturability was assessed in terms of wall thickness, overhang angles, and support requirements, confirming suitability for SLM-based production. The results show that SIMP produces smoother material distribution, whereas BESO converges more quickly, and both approaches provide manufacturable and structurally reliable solutions for lightweight aerospace components.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-11-25
DOI
https://doi.org/10.3390/app152312477
Primary Topic
Topology Optimization in Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Topology Optimization of an Additive Manufactured UAV Catapult Bracket Using SIMP and BESO Methods

Neslihan Top, İsmail Şahin, Harun Gökçe
Applied Sciences
Topology Optimization in Engineering
article

Topology Optimization of an Additive Manufactured UAV Catapult Bracket Using SIMP and BESO Methods

Neslihan Top, İsmail Şahin, Harun Gökçe
article en

Abstract

Catapult systems enable Unmanned Aerial Vehicles (UAVs) to take off without runways and must therefore meet key requirements such as safety, reliability, portability, efficiency, and durability. These systems contain mechanically complex components that are subjected to substantial dynamic loads during operation. This study examines the topology optimization of a UAV catapult launch bracket using the SIMP and BESO methods to achieve a structurally efficient design compatible with additive manufacturing. The bracket was analyzed under axial compression and bending moments representative of launch conditions. SIMP was implemented through the Abaqus Tosca infrastructure, while the BESO optimization employed an Abaqus (2018)–Matlab (2024B) interface supported by automated Python (3.13.0) scripts. The optimized design achieved a 51% reduction in volume, with a 52% increase in maximum von Mises stress and an 8% increase in displacement, yet all stresses remained below the yield strength of AA7075. Manufacturability was assessed in terms of wall thickness, overhang angles, and support requirements, confirming suitability for SLM-based production. The results show that SIMP produces smoother material distribution, whereas BESO converges more quickly, and both approaches provide manufacturable and structurally reliable solutions for lightweight aerospace components.

Applied SciencesVol. 15(23)
Gazi University (TR)
Openalex Percentile: Top 62%
Topology Optimization in Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.