Soft computing-based predictive modeling of flexible electrohydrodynamic pumps

作     者：Zebing Mao Yanhong Peng Chenlong Hu Ruqi Ding Yuhei Yamada Shingo Maeda 

作者机构：Department of Mechanical EngineeringTokyo Institute of TechnologyTokyo 152-8550Japan Department of Information and Communication EngineeringGraduate School of EngineeringNagoya UniversityNagoya 464-8603Japan Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Key Laboratory of Conveyance and EquipmentMinistry of EducationEast China Jiaotong UniversityNanchang 330013China Living Systems Materialogy(LiSM)Research GroupInternational Research Frontiers Initiative(IRFI)Tokyo Institute of TechnologyYokohama 226-8501Japan 

出 版 物：《Biomimetic Intelligence & Robotics》 (仿生智能与机器人（英文）)

年 卷 期：2023年第3卷第3期

页      面：30-37页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Grant-in-Aid for Early-Career Scientists from the Japan Society for the Promotion of Science(23K13290) Japan 

主　　题：Electrohydrodynamic pumps Neural network Rigid regression Random forest regression 

摘      要：Flexible electrohydrodynamic(EHD)pumps have been developed and applied in many fields due to no transmission structure,no wear,easy manipulation,and no *** simulation is often used to predict the output performance of flexible EHD ***,this method neglects fluid–solid interaction and energy loss caused by flexible materials,which are both difficult to calculate when the flexible pumps ***,this study proposes a flexible pump output performance prediction using machine learning *** used three different types of machine learning:random forest regression,ridge regression,and neural network to predict the critical parameters(pressure,flow rate,and power)of the flexible EHD ***,angle,gap,overlap,and channel height are selected as five input data of the neural *** addition,we optimized essential parameters in the three ***,we adopt the best predictive model and evaluate the significance of five input parameters to the output performance of the flexible EHD *** the three methods,the MLP model has exceptionally high accuracy in predicting pressure and *** work is beneficial for the design process of fluid sources in flexible soft actuators and soft hydraulic sources in microfluidic chips.