The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
Currently, little work has been devoted to the mediators and tools for multi-role production interactions in the mass individualization environment. This paper proposes a kind of hardware-software-integrated mediators...Currently, little work has been devoted to the mediators and tools for multi-role production interactions in the mass individualization environment. This paper proposes a kind of hardware-software-integrated mediators called social sensors （S2ensors） to facilitate the production interactions among customers, manufacturers, and other stakeholders in the social manufacturing systems （SMS）. The concept, classification, operational logics, and for- malization of S2ensors are clarified. S2ensors collect sub- jective data from physical sensors and objective data from sensory input in mobile Apps, merge them into meaningful information for decision-making, and finally feed the decisions back for reaction and execution. Then, an S2en- sors-Cloud platform is discussed to integrate different S2- ensors to work for SMSs in an autonomous way. A demonstrative case is studied by developing a prototype system and the results show that S2ensors and S2ensors- Cloud platform can assist multi-role stakeholders interact and collaborate for the production tasks. It reveals the mediator-enabled mechanisms and methods for production interactions among stakeholders in SMS.展开更多
In-space 3D printing is transforming the manufacturing paradigm of space structures from ground-based production to in-situ space manufacturing,effectively addressing the challenges of high costs,long response times,a...In-space 3D printing is transforming the manufacturing paradigm of space structures from ground-based production to in-situ space manufacturing,effectively addressing the challenges of high costs,long response times,and structural size limitations associated with traditional rocket launches.This technology enables rapid on-orbit emergency repairs and significantly expands the geometric dimensions of space structures.High-performance polymers and their composites are widely used in in-space 3D printing,yet their implementation faces complex challenges posed by extreme space environmental conditions and limited energy or resources.This paper reviews the state-of-the-art in 3D printing of polymer and composites for on-orbit structure manufacturing.Based on existing research activities,the review focuses on three key aspects including the impact of extreme space environments on forming process and performance,innovative design and manufacturing methods for space structures,and on-orbit recycling and remanufacturing of raw materials.Some experiments that have already been conducted on-orbit and simulated experiments completed on the ground are systematically analyzed to provide a more comprehensive understanding of the constraints and objectives for on-orbit structure manufacturing.Furthermore,several perspectives requiring further research in future are proposed to facilitate the development of new in-space 3D printing technologies and space structures,thereby supporting increasingly advanced space exploration activities.展开更多
Solid propellants are essential energy sources for rockets and other aerospace vehicles,and improvements in their performance have significant implications for the aerospace industry.The application of additive manufa...Solid propellants are essential energy sources for rockets and other aerospace vehicles,and improvements in their performance have significant implications for the aerospace industry.The application of additive manufacturing(AM)in the production of solid propellants promises a substantial leap in the design and fabrication of solid propellant grains.This review summarizes recent research on AM techniques for solid propellant manufacturing,evaluates current applications,and explores development trends.This review highlights that AM technology for solid propellants offers unparalleled advantages in terms of propellant design flexibility and functional gradient loading compared with traditional processes.This study presents a new perspective for the future manufacturing of intelligent and controllable solid propulsion systems.展开更多
Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have g...Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have garnered considerable attention due to their softness,conformability,and biocompatibility.However,several challenges remain,including imperfect skin-device interfaces,limited breathability,and insufficient mechanoelectrical stability.On-skin epidermal electronics,distinguished by their excellent conformability,breathability,and mechanoelectrical robustness,offer a promising solution for high-fidelity,long-term health monitoring.These devices can seamlessly integrate with the human body,leading to transformative advancements in future personalized healthcare.This review provides a systematic examination of recent advancements in on-skin epidermal electronics,with particular emphasis on critical aspects including material science,structural design,desired properties,and practical applications.We explore various materials,considering their properties and the corresponding structural designs developed to construct high-performance epidermal electronics.We then discuss different approaches for achieving the desired device properties necessary for long-term health monitoring,including adhesiveness,breathability,and mechanoelectrical stability.Additionally,we summarize the diverse applications of these devices in monitoring biophysical and physiological signals.Finally,we address the challenges facing these devices and outline future prospects,offering insights into the ongoing development of on-skin epidermal electronics for long-term health monitoring.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
This work studies the robust deadlock control of automated manufacturing systems with multiple unreliable resources. Our goal is to ensure the continuous production of the jobs that only require reliable resources. To...This work studies the robust deadlock control of automated manufacturing systems with multiple unreliable resources. Our goal is to ensure the continuous production of the jobs that only require reliable resources. To reach this goal, we propose a new modified Banker's algorithm(MBA) to ensure that all resources required by these jobs can be freed. Moreover,a Petri net based deadlock avoidance policy(DAP) is introduced to ensure that all jobs remaining in the system after executing the new MBA can complete their processing smoothly when their required unreliable resources are operational. The new MBA together with the DAP forms a new DAP that is robust to the failures of unreliable resources. Owing to the high permissiveness of the new MBA and the optimality of the DAP, it is tested to be more permissive than state-of-the-art control policies.展开更多
As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symboli...As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.展开更多
In this paper,a novel roll-to-roll imprint process is proposed to fabricate gratings in large area. The key challenges in roll-to-roll imprint process,such as the roller mold preparation,imprint system,misalignment of...In this paper,a novel roll-to-roll imprint process is proposed to fabricate gratings in large area. The key challenges in roll-to-roll imprint process,such as the roller mold preparation,imprint system,misalignment of imprint roller and backup roller,and also the imprint pressure,are investigated. Various gratings with different periods and profiles are obtained by the roll-to-roll process. To calibrate the measuring ability of the self-made grating by imprint,a calibration system is built with a dual-frequency laser interferometer. By the calibration,the self-made gratings can achieve ±4μm accuracy in 45mm measuring distance.展开更多
Introduction During the last two decades,organic light-emitting diodes (OLED) have attracted considerable interest owing to their promising applications by replacing cathode ray tubes (CRTs) or liquid crystal displays...Introduction During the last two decades,organic light-emitting diodes (OLED) have attracted considerable interest owing to their promising applications by replacing cathode ray tubes (CRTs) or liquid crystal displays (LCDs).展开更多
This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-pl...This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-play (PnP) application modules of the dc-IUS are presented in the fields of machining process and quality control,material flow and inventory control,and factory resource tracking. Different schemes are discussed about how and where to apply these functions. Then some running examples are studied to demonstrate the feasibility and reliability of dc-IUS. At last,the challenges of applying WM are discussed and a conclusion is given.展开更多
Ceramic matrix composites(CMCs)structural components encounter the dual challenges of severe mechanical conditions and complex electromagnetic environments due to the increasing demand for stealth technology in aerosp...Ceramic matrix composites(CMCs)structural components encounter the dual challenges of severe mechanical conditions and complex electromagnetic environments due to the increasing demand for stealth technology in aerospace field.To address various functional requirements,this study integrates a biomimetic strategy inspired by gradient bamboo vascular bundles with a novel dual-material 3D printing approach.Three distinct bamboo-inspired structural configurations Cf/SiC composites are designed and manufactured,and the effects of these different structural configurations on the CVI process are analyzed.Nanoindentation method is utilized to characterize the relationship between interface bonding strength and mechanical properties.The results reveal that the maximum flexural strength and fracture toughness reach 108.6±5.2 MPa and 16.45±1.52 MPa m1/2,respectively,attributed to the enhanced crack propagation resistance and path caused by the weak fiber-matrix interface.Furthermore,the bio-inspired configuration enhances the dielectric loss and conductivity loss,exhibiting a minimum reflection loss of−24.3 dB with the effective absorption band of 3.89 GHz.This work introduces an innovative biomimetic strategy and 3D printing method for continuous fiber-reinforced ceramic composites,expanding the application of 3D printing technology in the field of CMCs.展开更多
Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exa...Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exacerbates this challenge by rendering the process vulnerable to environmental changes and unexpected factors,resulting in defects and inconsistent product quality,particularly in unmanned long-term operations or printing in extreme environments.To address these issues,we developed a process monitoring and closed-loop feedback control strategy for the 3D printing process.Real-time printing image data were captured and analyzed using a well-trained neural network model,and a real-time control module-enabled closed-loop feedback control of the flow rate was developed.The neural network model,which was based on image processing and artificial intelligence,enabled the recognition of flow rate values with an accuracy of 94.70%.The experimental results showed significant improvements in both the surface performance and mechanical properties of printed composites,with three to six times improvement in tensile strength and elastic modulus,demonstrating the effectiveness of the strategy.This study provides a generalized process monitoring and feedback control method for the 3D printing of continuous fiber-reinforced composites,and offers a potential solution for remote online monitoring and closed-loop adjustment in unmanned or extreme space environments.展开更多
As surgical procedures transition from conventional resection to advanced tissue-regeneration technologies,human disease therapy has witnessed a great leap forward.In particular,three-dimensional(3D)bioprinting stands...As surgical procedures transition from conventional resection to advanced tissue-regeneration technologies,human disease therapy has witnessed a great leap forward.In particular,three-dimensional(3D)bioprinting stands as a landmark in this setting,by promising the precise integration of biomaterials,cells,and bioactive molecules,thus opening up a novel avenue for tissue/organ regeneration.Curated by the editorial board of Bio-Design and Manufacturing,this review brings together a cohort of leading young scientists in China to dissect the core functionalities and evolutionary trajectory of 3D bioprinting,by elucidating the intricate challenges encountered in the manufacturing of transplantable organs.We further delve into the translational pathway from scientific research to clinical application,emphasizing the imperativeness of establishing a regulatory framework and rigorously enforcing quality-control measures.Finally,this review outlines the strategic landscape and innovative achievements of China in this field and provides a comprehensive roadmap for researchers worldwide to propel this field collectively to even greater heights.展开更多
Gecko-inspired van der Waals force-based adhesion technology demonstrates significant potential for robotic operations.While superior adhesion is achieved under parallel contact during testing,engineering operations o...Gecko-inspired van der Waals force-based adhesion technology demonstrates significant potential for robotic operations.While superior adhesion is achieved under parallel contact during testing,engineering operations often involve non-parallel contact,weakening adhesion,and compromising task stability and efficiency.Stable attachment under such non-parallel contacts remains challenging.Inspired by the soft muscle and rigid bone in the gecko’s sole,this study proposes a self-adaptive core-shell dry adhesive by embedding a thin,rigid piece into a soft,thick elastomer comprising a top adhesion tip with a mushroom-like geometry for interfacial adhesion based on the van der Waals force and a bottom core-shell configuration for interface stress regulation.Unlike traditional core-shell structures with a fixed“dead core,”the proposed“live core”rotates within the soft shell,mimicking skeletal joints.This enables stress equalization at the interface and facilitates adaptive contact to macroscopic interfacial angle errors.This innovative core-shell configuration demonstrates an adhesion strength 100 times higher than conventional homogeneous structures under non-parallel contact and offers anti-overturning ability by mitigating torsional effects.The proposed strategy can advance the development of gecko-inspired adhesion-based devices and systems.展开更多
Planar-structured amorphous InGaZnO(a-IGZO)film-based UV photodetectors with different ITO interdigitated electrode spacings were developed on flexible PI substrates via radio frequency magnetron sputtering and non-li...Planar-structured amorphous InGaZnO(a-IGZO)film-based UV photodetectors with different ITO interdigitated electrode spacings were developed on flexible PI substrates via radio frequency magnetron sputtering and non-lithographic fabrication processes.The effects of oxygen flow rate on the surface morphology,electrical transport,and chemical bonding properties of the a-IGZO films were systematically investigated to optimize the performance of the flexible detector.The average transmittance of the flexible a-IGZO photodetector is over 90%in the visible spectral range with a large photo-to-dark current ratio of 3.9×10^(3)under 360 nm UV illumination.The photocurrent of the detectors increases with decreasing the electrode spacing,which is attribute to formation of higher electrical field and drifting more electron-hole pairs to the electrode with shortening the electrode spacing.Under a UV illumination intensity of 9.1 mW/cm~2,the highest responsivity and detectivity of the photodetector with the electrode spacing of 0.4 mm reach 62.1 mA/W and2.83×10^(11)cm·Hz^(1/2)·W^(-1)at 11 V bias voltage,respectively.The flexible detector exhibits enhanced photoresponse performance with the rise and decay time of 2.02 and 0.94 s,respectively.These results can be used in a practical scheme to design and realize the a-IGZO based UV photodetectors with excellent transparency and flexibility for wearable UV monitoring applications.展开更多
The bioinert nature of polyether ether ketone(PEEK)material limits the widespread clinical application of PEEK implants.Although the porous structure is considered to improve osseointegration of PEEK implants,it is ha...The bioinert nature of polyether ether ketone(PEEK)material limits the widespread clinical application of PEEK implants.Although the porous structure is considered to improve osseointegration of PEEK implants,it is hardly used due to its mechanical properties.This study investigated the combined influence of the porous structure and in vivo mechanical stimulation on implantation safety and bone growth based on finite element analysis of the biomechanical behavior of the implantation system.The combined control of pore size and screw preloads allows the porous PEEK implant to achieve good osseointegration while maintaining a relatively high safety level.A pore size of 600μm and a preload of 0.05 N·m are the optimal combination for the long-term stability of the implant,with which the safety factor of the implant is>2,and the predicted percentage of effective bone growth area of the bone-implant interface reaches 97%.For further clinical application,PEEK implants were fabricated with fused filament fabrication(FFF)three-dimensional(3D)printing,and clinical outcomes demonstrated better bone repair efficacy and long-term stability of porous PEEK implants compared to solid PEEK implants.Moreover,good osteointegration performance of 3D-printed porous PEEK implants was observed,with an average bone volume fraction>40%three months after implantation.In conclusion,3D-printed porous PEEK implants have great potential for clinical application,with validated implantation safety and good osseointegration.展开更多
Quick and accurate detecting the error of NC machine tool and performing the error compensation are important to improve the machining accuracy of NC machine tool. Currently, there are many methods for detecting the g...Quick and accurate detecting the error of NC machine tool and performing the error compensation are important to improve the machining accuracy of NC machine tool. Currently, there are many methods for detecting the geometric accuracy of NC machine tool. However, these methods have deficiencies in detection efficiency and accuracy as well as in versatility. In the paper, a method with laser tracker based on the multi-station and time-sharing measurement principle is proposed, and this method can rapidly and accurately detect the geometric accuracy of NC machine tool. The machine tool is controlled to move in the preset path in a 3D space or 2D plane, and a laser tracker is used to measure the same motion trajectory of the machine tool successively at different base stations. The original algorithm for multi-station and time-sharing measurement is improved. The space coordinates of the measuring point obtained by the laser tracker are taken as parameter values, and the initial position of each base point can be determined. The redundant equation concerning the base point calibration can be established by the distance information of the laser tracker, and the position of each base point is further determined by solving the equation with least squares method, then the space coordinates of each measuring point can be calibrated. The singular matrix does not occur in calculation with the improved algorithm, which overcomes the limitations of the original algorithm, that the motion trajectory of machine tool is in a 3D space and there exits height difference between the base stations. Adopting the improved algorithm can expand the application of multi-station and time-sharing measurement, and can meet the quick and accurate detecting requirements for different types of NC machine tool.展开更多
Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smar...Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.展开更多
Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods dif...Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.展开更多
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
基金Supported by National Natural Science Foundation of China(Grant Nos.71571142,51275396)
文摘Currently, little work has been devoted to the mediators and tools for multi-role production interactions in the mass individualization environment. This paper proposes a kind of hardware-software-integrated mediators called social sensors （S2ensors） to facilitate the production interactions among customers, manufacturers, and other stakeholders in the social manufacturing systems （SMS）. The concept, classification, operational logics, and for- malization of S2ensors are clarified. S2ensors collect sub- jective data from physical sensors and objective data from sensory input in mobile Apps, merge them into meaningful information for decision-making, and finally feed the decisions back for reaction and execution. Then, an S2en- sors-Cloud platform is discussed to integrate different S2- ensors to work for SMSs in an autonomous way. A demonstrative case is studied by developing a prototype system and the results show that S2ensors and S2ensors- Cloud platform can assist multi-role stakeholders interact and collaborate for the production tasks. It reveals the mediator-enabled mechanisms and methods for production interactions among stakeholders in SMS.
基金supported by National Natural Science Foundation of China(Grant No.52205413)National Key Research and Development Program(Grant No.2022YFB3806101)+1 种基金K C Wong Education FoundationThe Youth Innovation Team of Shaanxi Universities。
文摘In-space 3D printing is transforming the manufacturing paradigm of space structures from ground-based production to in-situ space manufacturing,effectively addressing the challenges of high costs,long response times,and structural size limitations associated with traditional rocket launches.This technology enables rapid on-orbit emergency repairs and significantly expands the geometric dimensions of space structures.High-performance polymers and their composites are widely used in in-space 3D printing,yet their implementation faces complex challenges posed by extreme space environmental conditions and limited energy or resources.This paper reviews the state-of-the-art in 3D printing of polymer and composites for on-orbit structure manufacturing.Based on existing research activities,the review focuses on three key aspects including the impact of extreme space environments on forming process and performance,innovative design and manufacturing methods for space structures,and on-orbit recycling and remanufacturing of raw materials.Some experiments that have already been conducted on-orbit and simulated experiments completed on the ground are systematically analyzed to provide a more comprehensive understanding of the constraints and objectives for on-orbit structure manufacturing.Furthermore,several perspectives requiring further research in future are proposed to facilitate the development of new in-space 3D printing technologies and space structures,thereby supporting increasingly advanced space exploration activities.
基金supported by National Key Research and Development Program of China(Grant.No.2022YFB4603102)Insight Action(Grant.No.AA5F41D0).
文摘Solid propellants are essential energy sources for rockets and other aerospace vehicles,and improvements in their performance have significant implications for the aerospace industry.The application of additive manufacturing(AM)in the production of solid propellants promises a substantial leap in the design and fabrication of solid propellant grains.This review summarizes recent research on AM techniques for solid propellant manufacturing,evaluates current applications,and explores development trends.This review highlights that AM technology for solid propellants offers unparalleled advantages in terms of propellant design flexibility and functional gradient loading compared with traditional processes.This study presents a new perspective for the future manufacturing of intelligent and controllable solid propulsion systems.
基金supported by National Natural Science Foundation of China(Grant Nos.52025055,52375576,52350349)Key Research and Development Program of Shaanxi(Program No.2022GXLH-01-12)+2 种基金Joint Fund of Ministry of Education for Equipment Pre-research(No.8091B03012304)Aeronautical Science Foundation of China(No.2022004607001)the Fundamental Research Funds for the Central Universities(No.xtr072024031).
文摘Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have garnered considerable attention due to their softness,conformability,and biocompatibility.However,several challenges remain,including imperfect skin-device interfaces,limited breathability,and insufficient mechanoelectrical stability.On-skin epidermal electronics,distinguished by their excellent conformability,breathability,and mechanoelectrical robustness,offer a promising solution for high-fidelity,long-term health monitoring.These devices can seamlessly integrate with the human body,leading to transformative advancements in future personalized healthcare.This review provides a systematic examination of recent advancements in on-skin epidermal electronics,with particular emphasis on critical aspects including material science,structural design,desired properties,and practical applications.We explore various materials,considering their properties and the corresponding structural designs developed to construct high-performance epidermal electronics.We then discuss different approaches for achieving the desired device properties necessary for long-term health monitoring,including adhesiveness,breathability,and mechanoelectrical stability.Additionally,we summarize the diverse applications of these devices in monitoring biophysical and physiological signals.Finally,we address the challenges facing these devices and outline future prospects,offering insights into the ongoing development of on-skin epidermal electronics for long-term health monitoring.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金supported in part by the Fundamental Research Funds for the Central Universities(3102017OQD110)the Natural Science Basic Research Plan in Shaanxi Province of China(2019JQ-435)+3 种基金the Project Funded by China Postdoctoral Science Foundation(2019M663818)the National Key Research and Development Program of China(2019YFB1703800)Guangdong Basic and Applied Basic Research Foundation(2019A1515111076)the National Natural Science Foundation of China(71931007)。
文摘This work studies the robust deadlock control of automated manufacturing systems with multiple unreliable resources. Our goal is to ensure the continuous production of the jobs that only require reliable resources. To reach this goal, we propose a new modified Banker's algorithm(MBA) to ensure that all resources required by these jobs can be freed. Moreover,a Petri net based deadlock avoidance policy(DAP) is introduced to ensure that all jobs remaining in the system after executing the new MBA can complete their processing smoothly when their required unreliable resources are operational. The new MBA together with the DAP forms a new DAP that is robust to the failures of unreliable resources. Owing to the high permissiveness of the new MBA and the optimality of the DAP, it is tested to be more permissive than state-of-the-art control policies.
基金Supported in part by Natural Science Foundation of China(Grant Nos.51835009,51705398)Shaanxi Province 2020 Natural Science Basic Research Plan(Grant No.2020JQ-042)Aeronautical Science Foundation(Grant No.2019ZB070001).
文摘As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.
基金The Major Special Projects of Ministry of Science and Technology of China(No.2011ZX04014-071)National Natural Science Foundation of China(No.51275400)
文摘In this paper,a novel roll-to-roll imprint process is proposed to fabricate gratings in large area. The key challenges in roll-to-roll imprint process,such as the roller mold preparation,imprint system,misalignment of imprint roller and backup roller,and also the imprint pressure,are investigated. Various gratings with different periods and profiles are obtained by the roll-to-roll process. To calibrate the measuring ability of the self-made grating by imprint,a calibration system is built with a dual-frequency laser interferometer. By the calibration,the self-made gratings can achieve ±4μm accuracy in 45mm measuring distance.
基金This work was supported by the National Science Support Program （2006BAE04B05-3）MOE Program for Changjiang Scholars and Innovative Research Team （grant No. 0629）+2 种基金National Science Foundation of China （grant No. 50505037）863 Hi-Teeh Program （grant No. 2006AA04Z322 ）＂Qing Lan＂ Talent Engineering Funds by Lanzhou Jiaotong University.
文摘Introduction During the last two decades,organic light-emitting diodes (OLED) have attracted considerable interest owing to their promising applications by replacing cathode ray tubes (CRTs) or liquid crystal displays (LCDs).
基金National Natural Science Foundation of China(No.50875204)National Basic Research "973" Project(No.2011CB706805)
文摘This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-play (PnP) application modules of the dc-IUS are presented in the fields of machining process and quality control,material flow and inventory control,and factory resource tracking. Different schemes are discussed about how and where to apply these functions. Then some running examples are studied to demonstrate the feasibility and reliability of dc-IUS. At last,the challenges of applying WM are discussed and a conclusion is given.
基金supported by The National Key Research and Development Program of China(No.2019YFB1901001).
文摘Ceramic matrix composites(CMCs)structural components encounter the dual challenges of severe mechanical conditions and complex electromagnetic environments due to the increasing demand for stealth technology in aerospace field.To address various functional requirements,this study integrates a biomimetic strategy inspired by gradient bamboo vascular bundles with a novel dual-material 3D printing approach.Three distinct bamboo-inspired structural configurations Cf/SiC composites are designed and manufactured,and the effects of these different structural configurations on the CVI process are analyzed.Nanoindentation method is utilized to characterize the relationship between interface bonding strength and mechanical properties.The results reveal that the maximum flexural strength and fracture toughness reach 108.6±5.2 MPa and 16.45±1.52 MPa m1/2,respectively,attributed to the enhanced crack propagation resistance and path caused by the weak fiber-matrix interface.Furthermore,the bio-inspired configuration enhances the dielectric loss and conductivity loss,exhibiting a minimum reflection loss of−24.3 dB with the effective absorption band of 3.89 GHz.This work introduces an innovative biomimetic strategy and 3D printing method for continuous fiber-reinforced ceramic composites,expanding the application of 3D printing technology in the field of CMCs.
基金supported by National Key Research and Development Program of China(Grant No.2023YFB4604100)National Key Research and Development Program of China(Grant No.2022YFB3806104)+4 种基金Key Research and Development Program in Shaanxi Province(Grant No.2021LLRH-08-17)Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)K C Wong Education Foundation of ChinaYouth Innovation Team of Shaanxi Universities of ChinaKey Research and Development Program of Shaanxi Province(Grant 2021LLRH-08-3.1).
文摘Ensuring the consistent mechanical performance of three-dimensional(3D)-printed continuous fiber-reinforced composites is a significant challenge in additive manufacturing.The current reliance on manual monitoring exacerbates this challenge by rendering the process vulnerable to environmental changes and unexpected factors,resulting in defects and inconsistent product quality,particularly in unmanned long-term operations or printing in extreme environments.To address these issues,we developed a process monitoring and closed-loop feedback control strategy for the 3D printing process.Real-time printing image data were captured and analyzed using a well-trained neural network model,and a real-time control module-enabled closed-loop feedback control of the flow rate was developed.The neural network model,which was based on image processing and artificial intelligence,enabled the recognition of flow rate values with an accuracy of 94.70%.The experimental results showed significant improvements in both the surface performance and mechanical properties of printed composites,with three to six times improvement in tensile strength and elastic modulus,demonstrating the effectiveness of the strategy.This study provides a generalized process monitoring and feedback control method for the 3D printing of continuous fiber-reinforced composites,and offers a potential solution for remote online monitoring and closed-loop adjustment in unmanned or extreme space environments.
基金supported by the National Natural Science Foundation of China(Nos.52325504,52235007,and T2121004).
文摘As surgical procedures transition from conventional resection to advanced tissue-regeneration technologies,human disease therapy has witnessed a great leap forward.In particular,three-dimensional(3D)bioprinting stands as a landmark in this setting,by promising the precise integration of biomaterials,cells,and bioactive molecules,thus opening up a novel avenue for tissue/organ regeneration.Curated by the editorial board of Bio-Design and Manufacturing,this review brings together a cohort of leading young scientists in China to dissect the core functionalities and evolutionary trajectory of 3D bioprinting,by elucidating the intricate challenges encountered in the manufacturing of transplantable organs.We further delve into the translational pathway from scientific research to clinical application,emphasizing the imperativeness of establishing a regulatory framework and rigorously enforcing quality-control measures.Finally,this review outlines the strategic landscape and innovative achievements of China in this field and provides a comprehensive roadmap for researchers worldwide to propel this field collectively to even greater heights.
基金supported by the National Natural Science Foundation(52025055,52175546,and 52405624)the Shaanxi University Youth Innovation Team.
文摘Gecko-inspired van der Waals force-based adhesion technology demonstrates significant potential for robotic operations.While superior adhesion is achieved under parallel contact during testing,engineering operations often involve non-parallel contact,weakening adhesion,and compromising task stability and efficiency.Stable attachment under such non-parallel contacts remains challenging.Inspired by the soft muscle and rigid bone in the gecko’s sole,this study proposes a self-adaptive core-shell dry adhesive by embedding a thin,rigid piece into a soft,thick elastomer comprising a top adhesion tip with a mushroom-like geometry for interfacial adhesion based on the van der Waals force and a bottom core-shell configuration for interface stress regulation.Unlike traditional core-shell structures with a fixed“dead core,”the proposed“live core”rotates within the soft shell,mimicking skeletal joints.This enables stress equalization at the interface and facilitates adaptive contact to macroscopic interfacial angle errors.This innovative core-shell configuration demonstrates an adhesion strength 100 times higher than conventional homogeneous structures under non-parallel contact and offers anti-overturning ability by mitigating torsional effects.The proposed strategy can advance the development of gecko-inspired adhesion-based devices and systems.
基金Funded by the Research Project of Shenzhen Science and Technology Innovation Committee(No.JCYJ20180306170801080)。
文摘Planar-structured amorphous InGaZnO(a-IGZO)film-based UV photodetectors with different ITO interdigitated electrode spacings were developed on flexible PI substrates via radio frequency magnetron sputtering and non-lithographic fabrication processes.The effects of oxygen flow rate on the surface morphology,electrical transport,and chemical bonding properties of the a-IGZO films were systematically investigated to optimize the performance of the flexible detector.The average transmittance of the flexible a-IGZO photodetector is over 90%in the visible spectral range with a large photo-to-dark current ratio of 3.9×10^(3)under 360 nm UV illumination.The photocurrent of the detectors increases with decreasing the electrode spacing,which is attribute to formation of higher electrical field and drifting more electron-hole pairs to the electrode with shortening the electrode spacing.Under a UV illumination intensity of 9.1 mW/cm~2,the highest responsivity and detectivity of the photodetector with the electrode spacing of 0.4 mm reach 62.1 mA/W and2.83×10^(11)cm·Hz^(1/2)·W^(-1)at 11 V bias voltage,respectively.The flexible detector exhibits enhanced photoresponse performance with the rise and decay time of 2.02 and 0.94 s,respectively.These results can be used in a practical scheme to design and realize the a-IGZO based UV photodetectors with excellent transparency and flexibility for wearable UV monitoring applications.
基金supported by the National Key R&D Program of China(No.2023YFB4603500)the Program for Innovation Team of Shaanxi Province(No.2023-CX-TD-17)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Shaanxi Province Qinchuangyuan“Scientist+Engineer”Team Construction Project(No.2022KXJ-106).
文摘The bioinert nature of polyether ether ketone(PEEK)material limits the widespread clinical application of PEEK implants.Although the porous structure is considered to improve osseointegration of PEEK implants,it is hardly used due to its mechanical properties.This study investigated the combined influence of the porous structure and in vivo mechanical stimulation on implantation safety and bone growth based on finite element analysis of the biomechanical behavior of the implantation system.The combined control of pore size and screw preloads allows the porous PEEK implant to achieve good osseointegration while maintaining a relatively high safety level.A pore size of 600μm and a preload of 0.05 N·m are the optimal combination for the long-term stability of the implant,with which the safety factor of the implant is>2,and the predicted percentage of effective bone growth area of the bone-implant interface reaches 97%.For further clinical application,PEEK implants were fabricated with fused filament fabrication(FFF)three-dimensional(3D)printing,and clinical outcomes demonstrated better bone repair efficacy and long-term stability of porous PEEK implants compared to solid PEEK implants.Moreover,good osteointegration performance of 3D-printed porous PEEK implants was observed,with an average bone volume fraction>40%three months after implantation.In conclusion,3D-printed porous PEEK implants have great potential for clinical application,with validated implantation safety and good osseointegration.
基金supported by National Hi-tech Research and Development Program of China (863 Program,Grant No. 2008AA042404)
文摘Quick and accurate detecting the error of NC machine tool and performing the error compensation are important to improve the machining accuracy of NC machine tool. Currently, there are many methods for detecting the geometric accuracy of NC machine tool. However, these methods have deficiencies in detection efficiency and accuracy as well as in versatility. In the paper, a method with laser tracker based on the multi-station and time-sharing measurement principle is proposed, and this method can rapidly and accurately detect the geometric accuracy of NC machine tool. The machine tool is controlled to move in the preset path in a 3D space or 2D plane, and a laser tracker is used to measure the same motion trajectory of the machine tool successively at different base stations. The original algorithm for multi-station and time-sharing measurement is improved. The space coordinates of the measuring point obtained by the laser tracker are taken as parameter values, and the initial position of each base point can be determined. The redundant equation concerning the base point calibration can be established by the distance information of the laser tracker, and the position of each base point is further determined by solving the equation with least squares method, then the space coordinates of each measuring point can be calibrated. The singular matrix does not occur in calculation with the improved algorithm, which overcomes the limitations of the original algorithm, that the motion trajectory of machine tool is in a 3D space and there exits height difference between the base stations. Adopting the improved algorithm can expand the application of multi-station and time-sharing measurement, and can meet the quick and accurate detecting requirements for different types of NC machine tool.
基金Supported by Shaanxi Provincial Overall Innovation Project of Science and Technology,China(Grant No.2013KTCQ01-06)
文摘Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.
基金National Natural Science Foundation of China (Grant Nos. 51835009, 52105116)China Postdoctoral Science Foundation (Grant Nos. 2021M692557, 2021TQ0263)。
文摘Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et.
