Health monitoring of structures and people requires the integration of sensors and devices on various 3D curvilinear,hierarchically structured,and even dynamically changing surfaces.Therefore,it is highly desirable to...Health monitoring of structures and people requires the integration of sensors and devices on various 3D curvilinear,hierarchically structured,and even dynamically changing surfaces.Therefore,it is highly desirable to explore conformal manufacturing techniques to fabricate and integrate soft deformable devices on complex 3D curvilinear surfaces.Although planar fabrication methods are not directly suitable to manufacture conformal devices on 3D curvilinear surfaces,they can be combined with stretchable structures and the use of transfer printing or assembly methods to enable the device integration on 3D surfaces.Combined with functional nanomaterials,various direct printing and writing methods have also been developed to fabricate conformal electronics on curved surfaces with intimate contact even over a large area.After a brief summary of the recent advancement of the recent conformal manufacturing techniques,we also discuss the challenges and potential opportunities for future development in this burgeoning field of conformal electronics on complex 3D surfaces.展开更多
The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furn...The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.展开更多
Real-time proprioception presents a significant challenge for soft robots due to their infinite degrees of freedom and intrinsic compliance.Previous studies mostly focused on specific sensors and actuators.There is st...Real-time proprioception presents a significant challenge for soft robots due to their infinite degrees of freedom and intrinsic compliance.Previous studies mostly focused on specific sensors and actuators.There is still a lack of generalizable technologies for integrating soft sensing elements into soft actuators and mapping sensor signals to proprioception parameters.To tackle this problem,we employed multi-material 3D printing technology to fabricate sensorized soft-bending actuators(SBAs)using plain and conductive thermoplastic polyurethane(TPU)filaments.We designed various geometric shapes for the sensors and investigated their strain-resistive performance during deformation.To address the nonlinear time-variant behavior of the sensors during dynamic modeling,we adopted a data-driven approach using different deep neural networks to learn the relationship between sensor signals and system states.A series of experiments in various actuation scenarios were conducted,and the results demonstrated the effectiveness of this approach.The sensing and shape prediction steps can run in real-time at a frequency of50 Hz on a consumer-level computer.Additionally,a method is proposed to enhance the robustness of the learning models using data augmentation to handle unexpected sensor failures.All the methods are efficient,not only for in-plane 2D shape estimation but also for out-of-plane 3D shape estimation.The aim of this study is to introduce a methodology for the proprioception of soft pneumatic actuators,including manufacturing and sensing modeling,that can be generalized to other soft robots.展开更多
A touch sensor is an essential component in meeting the growing demand for human-machine interfaces.These sensors have been developed in wearable,attachable,and even implantable forms to acquire a wide range of inform...A touch sensor is an essential component in meeting the growing demand for human-machine interfaces.These sensors have been developed in wearable,attachable,and even implantable forms to acquire a wide range of information from humans.To be applied to the human body,sensors are required to be biocompatible and not restrict the natural movement of the body.Ionic materials are a promising candidate for soft touch sensors due to their outstanding properties,which include high stretchability,transparency,ionic conductivity,and biocompatibility.Here,this review discusses the unique features of soft ionic touch point sensors,focusing on the ionic material and its key role in the sensor.The touch sensing mechanisms include piezocapacitive,piezoresistive,surface capacitive,piezoelectric,and triboelectric and triboresistive sensing.This review analyzes the implementation hurdles and future research directions of the soft ionic touch sensors for their transformative potential.展开更多
The soft robotics field is on the rise. The highly adaptive robots provide the opportunity to bridge the gap between machines and people. However, their elastomeric nature poses significant challenges to the perceptio...The soft robotics field is on the rise. The highly adaptive robots provide the opportunity to bridge the gap between machines and people. However, their elastomeric nature poses significant challenges to the perception, control, and signal processing. Hydrogels and machine learning provide promising solutions to the problems above. This review aims to summarize this recent trend by first assessing the current hydrogel-based sensing and actuation methods applied to soft robots. We outlined the mechanisms of perception in response to various external stimuli. Next, recent achievements of machine learning for soft robots’ sensing data processing and optimization are evaluated. Here we list the strategies for implementing machine learning models from the perspective of applications. Last, we discuss the challenges and future opportunities in perception data processing and soft robots’ high level tasks.展开更多
基金This research is supported by the National Science Foundation(Grant No.ECCS-1933072)the Doctoral New Investigator grant from the American Chemical Society Petro-leum Research Fund(59021-DNI7)the National Heart,Lung,And Blood Institute of the National Institutes of Health under Award Number R61HL154215,and Penn State University.
文摘Health monitoring of structures and people requires the integration of sensors and devices on various 3D curvilinear,hierarchically structured,and even dynamically changing surfaces.Therefore,it is highly desirable to explore conformal manufacturing techniques to fabricate and integrate soft deformable devices on complex 3D curvilinear surfaces.Although planar fabrication methods are not directly suitable to manufacture conformal devices on 3D curvilinear surfaces,they can be combined with stretchable structures and the use of transfer printing or assembly methods to enable the device integration on 3D surfaces.Combined with functional nanomaterials,various direct printing and writing methods have also been developed to fabricate conformal electronics on curved surfaces with intimate contact even over a large area.After a brief summary of the recent advancement of the recent conformal manufacturing techniques,we also discuss the challenges and potential opportunities for future development in this burgeoning field of conformal electronics on complex 3D surfaces.
基金Project supported by the National Natural Science Founda-tion of China(Nos.62003301,61933013,and 61833014)the Natural Science Foundation of Zhejiang Province,China(No.LQ21F030018)the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang Univer-sity,China(Nos.ICT2022B30 and ICT2022B08)。
文摘The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.
基金supported by International Cooperation Program of the Natural Science Foundation of China(Grant No.52261135542)Zhejiang Provincial Natural Science Foundation of China(Grant No.LD22E050002)+1 种基金Zhejiang University Global Partnership Fundgrateful to the Russian Science Foundation(Grant No.23-43-00057)for financial support。
文摘Real-time proprioception presents a significant challenge for soft robots due to their infinite degrees of freedom and intrinsic compliance.Previous studies mostly focused on specific sensors and actuators.There is still a lack of generalizable technologies for integrating soft sensing elements into soft actuators and mapping sensor signals to proprioception parameters.To tackle this problem,we employed multi-material 3D printing technology to fabricate sensorized soft-bending actuators(SBAs)using plain and conductive thermoplastic polyurethane(TPU)filaments.We designed various geometric shapes for the sensors and investigated their strain-resistive performance during deformation.To address the nonlinear time-variant behavior of the sensors during dynamic modeling,we adopted a data-driven approach using different deep neural networks to learn the relationship between sensor signals and system states.A series of experiments in various actuation scenarios were conducted,and the results demonstrated the effectiveness of this approach.The sensing and shape prediction steps can run in real-time at a frequency of50 Hz on a consumer-level computer.Additionally,a method is proposed to enhance the robustness of the learning models using data augmentation to handle unexpected sensor failures.All the methods are efficient,not only for in-plane 2D shape estimation but also for out-of-plane 3D shape estimation.The aim of this study is to introduce a methodology for the proprioception of soft pneumatic actuators,including manufacturing and sensing modeling,that can be generalized to other soft robots.
基金supported by the National Research Foundation of Korea(NRF)(No.2021R1C1C2009703)the Gachon University Research Fund of 2022(GCU-202300890001).
文摘A touch sensor is an essential component in meeting the growing demand for human-machine interfaces.These sensors have been developed in wearable,attachable,and even implantable forms to acquire a wide range of information from humans.To be applied to the human body,sensors are required to be biocompatible and not restrict the natural movement of the body.Ionic materials are a promising candidate for soft touch sensors due to their outstanding properties,which include high stretchability,transparency,ionic conductivity,and biocompatibility.Here,this review discusses the unique features of soft ionic touch point sensors,focusing on the ionic material and its key role in the sensor.The touch sensing mechanisms include piezocapacitive,piezoresistive,surface capacitive,piezoelectric,and triboelectric and triboresistive sensing.This review analyzes the implementation hurdles and future research directions of the soft ionic touch sensors for their transformative potential.
基金supported in part by the National Natural Science Foundation of China under Grant 62104034the Natural Science Foundation of Hebei Province under Grant F2020501033Fundamental Research Fund for Central University under grant N2223032.
文摘The soft robotics field is on the rise. The highly adaptive robots provide the opportunity to bridge the gap between machines and people. However, their elastomeric nature poses significant challenges to the perception, control, and signal processing. Hydrogels and machine learning provide promising solutions to the problems above. This review aims to summarize this recent trend by first assessing the current hydrogel-based sensing and actuation methods applied to soft robots. We outlined the mechanisms of perception in response to various external stimuli. Next, recent achievements of machine learning for soft robots’ sensing data processing and optimization are evaluated. Here we list the strategies for implementing machine learning models from the perspective of applications. Last, we discuss the challenges and future opportunities in perception data processing and soft robots’ high level tasks.
