류제경 김지엽 박찬욱 김해윤 이득희 한경원 2026-03-04T07:00:12Z 2026-03-04T07:00:12Z 2026-02-12 2026-02-05 1975-6291 https://pubs.kist.re.kr/handle/201004/154396 Microneedles offer promising capabilities not only for minimally invasive drug delivery but also as effective bio-tissue grippers. However, achieving strong tissue fixation while minimizing tissue damage during insertion remains a significant challenge. In this study, we propose a novel microneedle geometry optimized for 3D printing, designed to maximize the Pull-Out-to-Penetration Ratio through a machine-learning-based optimization framework combined with finite element analysis. Experimental results show that the optimized geometry achieves a six-fold improvement in the objective metric relative to conventional conical designs, demonstrating enhanced tissue fixation while simultaneously reducing insertion-induced damage. This approach highlights the potential for customizable, low-pain microneedle designs across a broad range of biomedical applications. Korean 한국로봇학회 Development of a Machine-Learning-Driven Microneedle Design Methodology for Biological Tissue Grippers Conference 2 제21회 한국로봇 종합학술대회 제21회 한국로봇 종합학술대회 KO 알펜시아 컨벤션센터 2026-02-04 제21회 한국로봇 종합학술대회 논문집