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基于人工神经网络/模拟退火算法估计的用于扭矩和力控制的磁致动器的设计与制造

Design and Fabrication of a Magnetic Actuator for Torque and Force Control Estimated by the ANN/SA Algorithm.

作者信息

Kazemzadeh Heris Pooriya, Khamesee Mir Behrad

机构信息

Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

出版信息

Micromachines (Basel). 2022 Feb 19;13(2):327. doi: 10.3390/mi13020327.

Abstract

Magnetic manipulation has the potential to recast the medical field both from an operational and drug delivery point of view as it can provide wireless controlled navigation over surgical devices and drug containers inside a human body. The presented system in this research implements a unique eight-coil configuration, where each coil is designed based on the characterization of the working space, generated force on a milliscale robot, and Fabry factor. A cylindrical iron-core coil with inner and outer diameters and length of 20.5, 66, and 124 mm is the optimized coil. Traditionally, FEM results are adopted from simulation and implemented into the motion logic; however, simulated values are associated with errors; 17% in this study. Instead of regularizing FEM results, for the first time, artificial intelligence has been used to approximate the actual values for manipulation purposes. Regression models for Artificial Neural Network (ANN) and a hybrid method called Artificial Neural Network with Simulated Annealing (ANN/SA) have been created. ANN/SA has shown outstanding performance with an average , and a root mean square error of 0.9871 and 0.0153, respectively. Implementation of the regression model into the manipulation logic has provided a motion with 13 μm of accuracy.

摘要

从操作和药物输送的角度来看,磁操纵有潜力重塑医学领域,因为它可以对人体内部的手术器械和药物容器提供无线控制导航。本研究中提出的系统采用了独特的八线圈配置,其中每个线圈都是根据工作空间的特性、在微尺度机器人上产生的力以及法布里因子来设计的。内径、外径和长度分别为20.5毫米、66毫米和124毫米的圆柱形铁芯线圈是优化后的线圈。传统上,有限元法(FEM)的结果取自模拟并应用于运动逻辑;然而,模拟值存在误差;本研究中的误差为17%。本研究首次使用人工智能来近似实际操纵值,而不是对有限元法的结果进行修正。已经创建了人工神经网络(ANN)的回归模型以及一种名为带有模拟退火的人工神经网络(ANN/SA)的混合方法。ANN/SA表现出色,平均绝对误差和均方根误差分别为0.9871和0.0153。将回归模型应用于操纵逻辑,实现了精度为13微米的运动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/528b/8880473/45444f2dba6d/micromachines-13-00327-g001.jpg

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