Abdullah Ahmed Choukri, Ahmadinejad Erfan, Tasoglu Savas
Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkiye.
Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkiye.
ACS Meas Sci Au. 2024 Aug 6;4(5):504-514. doi: 10.1021/acsmeasuresciau.4c00021. eCollection 2024 Oct 16.
Microneedles (MNs), that is, a matrix of micrometer-scale needles, have diverse applications in drug delivery, skincare therapy, and health monitoring. MNs offer a minimally invasive alternative to hypodermic needles, characterized by rapid and painless procedures, cost-effective fabrication methods, and reduced tissue damage. This study explores four MN designs, cone-shaped, tapered cone-shaped, pyramidal with a square base, and pyramidal with a triangular-shaped base, and their optimization based on predefined criteria. The workflow encompasses three loading conditions: compressive load during insertion, critical buckling load, and bending loading resulting from incorrect insertion. Geometric parameters such as base radius/width, tip radius/width, height, and tapered angle tip influence the output criteria, namely, total deformation, critical buckling loads, factor of safety (FOS), and bending stress. The comprehensive framework employing a design of experiment approach within the ANSYS workbench toolbox establishes a mathematical model and a response surface fitting model. The resulting regression model, sensitivity chart, and response curve are used to create a multiobjective optimization problem that helps achieve an optimized MN geometrical design across the introduced four shapes, integrating machine learning (ML) techniques. This study contributes valuable insights into a potential ML-augmented optimization framework for MNs via needle designs to stay durable for various physiologically relevant conditions.
微针（MNs），即微米级针的阵列，在药物递送、皮肤护理治疗和健康监测等方面有多种应用。微针为皮下注射针提供了一种微创替代方案，其特点是操作快速无痛、制造方法经济高效且组织损伤小。本研究探讨了四种微针设计，即锥形、锥形渐缩形、方形基底金字塔形和三角形基底金字塔形，并根据预定义标准对其进行优化。工作流程包括三种加载条件：插入过程中的压缩载荷、临界屈曲载荷以及不正确插入导致的弯曲载荷。诸如基底半径/宽度、尖端半径/宽度、高度和尖端锥角等几何参数会影响输出标准，即总变形、临界屈曲载荷、安全系数（FOS）和弯曲应力。在ANSYS工作台工具箱内采用实验设计方法的综合框架建立了数学模型和响应面拟合模型。所得的回归模型、灵敏度图和响应曲线用于创建一个多目标优化问题，该问题有助于通过整合机器学习（ML）技术，针对所引入的四种形状实现微针几何设计的优化。本研究通过针设计为微针潜在的机器学习增强优化框架提供了有价值的见解，以使其在各种生理相关条件下保持耐用。
