Non-Cooperative Game of Coordinated Scheduling of Parallel Machine Production and Transportation in Shared Manufacturing

Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry,the coordinated scheduling problem of parallel machine production and transportation is investigated.The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance.A non-cooperative game model is established,considering the competition and self-interest behavior of jobs from different customers for machine resources.The job from different customers is mapped to the players in the game model,the corresponding optional processing machine and location are mapped to the strategy set,and the makespan of the job is mapped to the payoff.Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model.A Nash equilibrium solution algorithm based on the genetic algorithm(NEGA)is designed,and the effective solution of approximate Nash equilibrium for the game model is realized.The fitness function,single-point crossover operator,and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium.Rules are also designed to avoid the generation of invalid offspring chromosomes.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.Compared with other algorithms such as heuristic algorithms(FCFS,SPT,and LPT),the simulated annealing algorithm(SA),and the particle swarm optimization algorithm(PSO),experimental results show that the proposed NE-GA algorithm has obvious performance advantages.

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

Additive manufacturing of micropatterned functional surfaces:a review

Over the course of millions of years,nature has evolved to ensure survival and presents us with a myriad of functional surfaces and structures that can boast high efficiency,multifunctionality,and sustainability.What makes these surfaces particularly practical and effective is the intricate micropatterning that enables selective interactions with microstructures.Most of these structures have been realized in the laboratory environment using numerous fabrication techniques by tailoring specific surface properties.Of the available manufacturing methods,additive manufacturing(AM)has created opportunities for fabricating these structures as the complex architectures of the naturally occurring microstructures far exceed the traditional ways.This paper presents a concise overview of the fundamentals of such patterned microstructured surfaces,their fabrication techniques,and diverse applications.A comprehensive evaluation of micro fabrication methods is conducted,delving into their respective strengths and limitations.Greater emphasis is placed on AM processes like inkjet printing and micro digital light projection printing due to the intrinsic advantages of these processes to additively fabricate high resolution structures with high fidelity and precision.The paper explores the various advancements in these processes in relation to their use in microfabrication and also presents the recent trends in applications like the fabrication of microlens arrays,microneedles,and tissue scaffolds.

Advances and challenges in direct additive manufacturing of dense ceramic oxides

Ceramic oxides,renowned for their exceptional combination of mechanical,thermal,and chemical properties,are indispensable in numerous crucial applications across diverse engineering fields.However,conventional manufacturing methods frequently grapple with limitations,such as challenges in shaping intricate geometries,extended processing durations,elevated porosity,and substantial shrinkage deformations.Direct additive manufacturing(dAM)technology stands out as a state-of-the-art solution for ceramic oxides production.It facilitates the one-step fabrication of high-performance,intricately designed components characterized by dense structures.Importantly,dAM eliminates the necessity for post-heat treatments,streamlining the manufacturing process and enhancing overall efficiency.This study undertakes a comprehensive review of recent developments in dAM for ceramic oxides,with a specific emphasis on the laser powder bed fusion and laser directed energy deposition techniques.A thorough investigation is conducted into the shaping quality,microstructure,and properties of diverse ceramic oxides produced through dAM.Critical examination is given to key aspects including feedstock preparation,laser-material coupling,formation and control of defects,in-situ monitoring and simulation.This paper concludes by outlining future trends and potential breakthrough directions,taking into account current gaps in this rapidly evolving field.

A facile strategy for tuning the density of surface-grafted biomolecules for melt extrusion-based additive manufacturing applications

Melt extrusion-based additive manufacturing(ME-AM)is a promising technique to fabricate porous scaffolds for tissue engi-neering applications.However,most synthetic semicrystalline polymers do not possess the intrinsic biological activity required to control cell fate.Grafting of biomolecules on polymeric surfaces of AM scaffolds enhances the bioactivity of a construct;however,there are limited strategies available to control the surface density.Here,we report a strategy to tune the surface density of bioactive groups by blending a low molecular weight poly(ε-caprolactone)5k(PCL5k)containing orthogonally reactive azide groups with an unfunctionalized high molecular weight PCL75k at different ratios.Stable porous three-dimensional(3D)scaf-folds were then fabricated using a high weight percentage(75 wt.%)of the low molecular weight PCL 5k.As a proof-of-concept test,we prepared films of three different mass ratios of low and high molecular weight polymers with a thermopress and reacted with an alkynated fluorescent model compound on the surface,yielding a density of 201-561 pmol/cm^(2).Subsequently,a bone morphogenetic protein 2(BMP-2)-derived peptide was grafted onto the films comprising different blend compositions,and the effect of peptide surface density on the osteogenic differentiation of human mesenchymal stromal cells(hMSCs)was assessed.After two weeks of culturing in a basic medium,cells expressed higher levels of BMP receptor II(BMPRII)on films with the conjugated peptide.In addition,we found that alkaline phosphatase activity was only significantly enhanced on films contain-ing the highest peptide density(i.e.,561 pmol/cm^(2)),indicating the importance of the surface density.Taken together,these results emphasize that the density of surface peptides on cell differentiation must be considered at the cell-material interface.Moreover,we have presented a viable strategy for ME-AM community that desires to tune the bulk and surface functionality via blending of(modified)polymers.Furthermore,the use of alkyne-azide“click”chemistry enables spatial control over bioconjugation of many tissue-specific moieties,making this approach a versatile strategy for tissue engineering applications.

Repositioning fertilizer manufacturing subsidies for improving food security and reducing greenhouse gas emissions in China

China removed fertilizer manufacturing subsidies from 2015 to 2018 to bolster market-oriented reforms and foster environmentally sustainable practices.However,the impact of this policy reform on food security and the environment remains inadequately evaluated.Moreover,although green and low-carbon technologies offer environmental advantages,their widespread adoption is hindered by prohibitively high costs.This study analyzes the impact of removing fertilizer manufacturing subsidies and explores the potential feasibility of redirecting fertilizer manufacturing subsidies to invest in the diffusion of these technologies.Utilizing the China Agricultural University Agri-food Systems model,we analyzed the potential for achieving mutually beneficial outcomes regarding food security and environmental sustainability.The findings indicate that removing fertilizer manufacturing subsidies has reduced greenhouse gas(GHG)emissions from agricultural activities by 3.88 million metric tons,with minimal impact on food production.Redirecting fertilizer manufacturing subsidies to invest in green and low-carbon technologies,including slow and controlled-release fertilizer,organic-inorganic compound fertilizers,and machine deep placement of fertilizer,emerges as a strategy to concurrently curtail GHG emissions,ensure food security,and secure robust economic returns.Finally,we propose a comprehensive set of government interventions,including subsidies,field guidance,and improved extension systems,to promote the widespread adoption of these technologies.

Recent innovations in laser additive manufacturing of titanium alloys

Titanium(Ti)alloys are widely used in high-tech fields like aerospace and biomedical engineering.Laser additive manufacturing(LAM),as an innovative technology,is the key driver for the development of Ti alloys.Despite the significant advancements in LAM of Ti alloys,there remain challenges that need further research and development efforts.To recap the potential of LAM high-performance Ti alloy,this article systematically reviews LAM Ti alloys with up-to-date information on process,materials,and properties.Several feasible solutions to advance LAM Ti alloys are reviewed,including intelligent process parameters optimization,LAM process innovation with auxiliary fields and novel Ti alloys customization for LAM.The auxiliary energy fields(e.g.thermal,acoustic,mechanical deformation and magnetic fields)can affect the melt pool dynamics and solidification behaviour during LAM of Ti alloys,altering microstructures and mechanical performances.Different kinds of novel Ti alloys customized for LAM,like peritecticα-Ti,eutectoid(α+β)-Ti,hybrid(α+β)-Ti,isomorphousβ-Ti and eutecticβ-Ti alloys are reviewed in detail.Furthermore,machine learning in accelerating the LAM process optimization and new materials development is also outlooked.This review summarizes the material properties and performance envelops and benchmarks the research achievements in LAM of Ti alloys.In addition,the perspectives and further trends in LAM of Ti alloys are also highlighted.

Digital manufacturing of personalized magnesium implants through binder jet additive manufacturing and automated post machining

While magnesium(Mg)is a promising material for personalized temporary implants,the lack of a digital manufacturing solution for Mg implants impedes its potential progress.This study introduces a hybrid manufacturing process that integrates binder jet additive manufacturing with automated dry post-machining to enable end-to-end digital manufacturing of personalized Mg implants.Spherical cap-shaped Mg implants were additively manufactured through binder jetting.These implants were placed on graphite flakes during sintering as a potential non-reactive support material,allowing unrestricted shrinkage of 15.2%to a relative density of 87%.Microstructural and dimensional analysis revealed consistent interconnected porous microstructures with a shrinkage distortion within±0.2 mm of the original digital drawing.High-speed dry milling of the sintered samples,assessed via an orthogonal cutting test,identified the optimized cutting parameters.A three-step machining process for automated 5-axis machining,along with clamping strategies,referencing,and an adaptive plug-in,were successfully implemented.The automated dry machining on binder-jet printed Mg implants resulted in an average roughness of<1.3μm with no defects.In summary,this work introduces a robust digital manufacturing solution to advance the transformative landscape of Mg implants and scaffolds.©2024 Chongqing University.Publishing services provided by Elsevier B.V.on behalf of KeAi Communications Co.Ltd.

Ballistic performance of additive manufacturing 316l stainless steel projectiles based on topology optimization method

Material and structure made by additive manufacturing(AM)have received much attention lately due to their flexibility and ability to customize complex structures.This study first implements multiple objective topology optimization simulations based on a projectile perforation model,and a new topologic projectile is obtained.Then two types of 316L stainless steel projectiles(the solid and the topology)are printed in a selective laser melt(SLM)machine to evaluate the penetration performance of the projectiles by the ballistic test.The experiment results show that the dimensionless specific kinetic energy value of topologic projectiles is higher than that of solid projectiles,indicating the better penetration ability of the topologic projectiles.Finally,microscopic studies(scanning electron microscope and X-ray micro-CT)are performed on the remaining projectiles to investigate the failure mechanism of the internal structure of the topologic projectiles.An explicit dynamics simulation was also performed,and the failure locations of the residual topologic projectiles were in good agreement with the experimental results,which can better guide the design of new projectiles combining AM and topology optimization in the future.

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

Solvent-Free Manufacturing of Lithium-Ion Battery Electrodes via Cold Plasma

Slurry casting has been used to fabricate lithium-ion battery electrodes for decades,which involves toxic and expensive organic solvents followed by high-cost vacuum drying and electrode calendering.This work presents a new manufacturing method using a nonthermal plasma to create inter-particle binding without using any polymeric binding materials,enabling solvent-free manufacturing electrodes with any electrochemistry of choice.The cold-plasma-coating technique enables fabricating electrodes with thickness(>200 pm),high mass loading(>30 mg cm^(-2)),high peel strength,and the ability to print lithium-ion batteries in an arbitrary geometry.This crosscutting,chemistry agnostic,platform technology would increase energy density,eliminate the use of solvents,vacuum drying,and calendering processes during production,and reduce manufacturing cost for current and future cell designs.Here,lithium iron phosphate and lithium cobalt oxide were used as examples to demonstrate the efficacy of the cold-plasma-coating technique.It is found that the mechanical peel strength of cold-plasma-coating-manufactured lithium iron phosphate is over an order of magnitude higher than that of slurry-casted lithium iron phosphate electrodes.Full cells assembled with a graphite anode and the cold-plasma-coating-lithium iron phosphate cathode offer highly reversible cycling performance with a capacity retention of 81.6%over 500 cycles.For the highly conductive cathode material lithium cobalt oxide,an areal capacity of 4.2 mAh cm^(-2)at 0.2 C is attained.We anticipate that this new,highly scalable manufacturing technique will redefine global lithium-ion battery manufacturing providing significantly reduced plant footprints and material costs.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

A critical review of direct laser additive manufacturing ceramics

The urgent need for integrated molding and sintering across various industries has inspired the development of additive manu-facturing(AM)ceramics.Among the different AM technologies,direct laser additive manufacturing(DLAM)stands out as a group of highly promising technology for flexibly manufacturing ceramics without molds and adhesives in a single step.Over the last decade,sig-nificant and encouraging progress has been accomplished in DLAM of high-performance ceramics,including Al_(2)O_(3),ZrO_(2),Al_(2)O_(3)/ZrO_(2),SiC,and others.However,high-performance ceramics fabricated by DLAM face challenges such as formation of pores and cracks and resultant low mechanical properties,hindering their practical application in high-end equipment.Further improvements are necessary be-fore they can be widely adopted.Methods such as field-assisted techniques and post-processing can be employed to address these chal-lenges,but a more systematic review is needed.This work aims to critically review the advancements in direct selective laser sintering/melting(SLS/SLM)and laser directed energy deposition(LDED)for various ceramic material systems.Additionally,it provides an overview of the current challenges,future research opportunities,and potential applications associated with DLAM of high-perform-ance ceramics.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Robust interface and excellent as-built mechanical properties of Ti–6Al–4V fabricated through laser-aided additive manufacturing with powder and wire

The feasibility of manufacturing Ti-6Al-4V samples through a combination of laser-aided additive manufacturing with powder(LAAM_(p))and wire(LAAM_(w))was explored.A process study was first conducted to successfully circumvent defects in Ti-6Al-4V deposits for LAAM_(p) and LAAM_(w),respectively.With the optimized process parameters,robust interfaces were achieved between powder/wire deposits and the forged substrate,as well as between powder and wire deposits.Microstructure characterization results revealed the epitaxial prior β grains in the deposited Ti-6Al-4V,wherein the powder deposit was dominated by a finerα′microstructure and the wire deposit was characterized by lamellar α phases.The mechanisms of microstructure formation and correlation with mechanical behavior were analyzed and discussed.The mechanical properties of the interfacial samples can meet the requirements of the relevant Aerospace Material Specifications(AMS 6932)even without post heat treatment.No fracture occurred within the interfacial area,further suggesting the robust interface.The findings of this study highlighted the feasibility of combining LAAM_(p) and LAAM_(w) in the direct manufacturing of Ti-6Al-4V parts in accordance with the required dimensional resolution and deposition rate,together with sound strength and ductility balance in the as-built condition.

Mechanical and corrosion properties of full liquid phase sintered WE43 magnesium alloy specimens fabricated via binder jetting additive manufacturing

This study investigates full liquid phase sintering as a process of fabrication parts from WE43(Mg-4wt.%Y-3wt.%RE-0.7wt.%Zr)alloy using binder jetting additive manufacturing(BJAM).This fabrication process is being developed for use in producing structural or biomedical devices.Specifically,this study focused on achieving a near-dense microstructure with WE43 Mg alloy while substantially reducing the duration of sintering post-processing after BJAM part rendering.The optimal process resulted in microstructure with 2.5%porosity and significantly reduced sintering time.The improved sintering can be explained by the presence of Y_(2)O_(3)and Nd_(2)O_(3)oxide layers,which form spontaneously on the surface of WE43 powder used in BJAM.These layers appear to be crucial in preventing shape distortion of the resulting samples and in enabling the development of sintering necks,particularly under sintering conditions exceeding the liquidus temperature of WE43 alloy.Sintered WE43 specimens rendered by BJAM achieved significant improvement in both corrosion resistance and mechanical properties through reduced porosity levels related to the sintering time.

Influence of manufacturing process-induced geometrical defects on the energy absorption capacity of polymer lattice structures

Modern additive manufacturing processes enable fabricating architected cellular materials of complex shape,which can be used for different purposes.Among them,lattice structures are increasingly used in applications requiring a compromise among lightness and suited mechanical properties,like improved energy absorption capacity and specific stiffness-to-weight and strength-to-weight ratios.A dedicated modeling strategy to assess the energy absorption capacity of lattice structures under uni-axial compression loading is presented in this work.The numerical model is developed in a non-linear framework accounting for the strain rate effect on the mechanical responses of the lattice structure.Four geometries,i.e.,cubic body centered cell,octet cell,rhombic-dodecahedron and truncated cuboctahedron 2+,are investigated.Specifically,the influence of the relative density of the representative volume element of each geometry,the strain-rate dependency of the bulk material and of the presence of the manufacturing process-induced geometrical imperfections on the energy absorption capacity of the lattice structure is investigated.The main outcome of this study points out the importance of correctly integrating geometrical imperfections into the modeling strategy when shock absorption applications are aimed for.

Bionic lightweight design of limb leg units for hydraulic quadruped robots by additive manufacturing and topology optimization

Galloping cheetahs,climbing mountain goats,and load hauling horses all show desirable locomotion capability,which motivates the development of quadruped robots.Among various quadruped robots,hydraulically driven quadruped robots show great potential in unstructured environments due to their discrete landing positions and large payloads.As the most critical movement unit of a quadruped robot,the limb leg unit(LLU)directly affects movement speed and reliability,and requires a compact and lightweight design.Inspired by the dexterous skeleton–muscle systems of cheetahs and humans,this paper proposes a highly integrated bionic actuator system for a better dynamic performance of an LLU.We propose that a cylinder barrel with multiple element interfaces and internal smooth channels is realized using metal additive manufacturing,and hybrid lattice structures are introduced into the lightweight design of the piston rod.In addition,additive manufacturing and topology optimization are incorporated to reduce the redundant material of the structural parts of the LLU.The mechanical properties of the actuator system are verified by numerical simulation and experiments,and the power density of the actuators is far greater than that of cheetah muscle.The mass of the optimized LLU is reduced by 24.5%,and the optimized LLU shows better response time performance when given a step signal,and presents a good trajectory tracking ability with the increase in motion frequency.

A survey of composite robot for aviation intelligent manufacturing application

With the rapid development of the aviation industry,the development of intelligent manufacturing equipment represented by composite robots has been paid close attention by the aviation industry.Based on the analysis of the background and main structure function of composite robots,this paper focuses on the analysis of key technologies such as composite robot hardware design,visual sensing and planning system,integrated control of‘hands,feet,and eyes',multi-robot collaborative operation,and safety.The typical applications of composite robots in aviation intelligent manufacturing such as automatic drilling and connection of aircraft,aircraft surface spraying and finishing,parts handling,aircraft measurement,and inspection are presented.The development trends such as standardization of composite robots,integration of‘5G+cloud computing+AI',and fusion of intelligent sensors are proposed.

Design and additive manufacturing of bionic hybrid structure inspired by cuttlebone to achieve superior mechanical properties and shape memory function

Lightweight porous materials with high load-bearing,damage tolerance and energy absorption(EA)as well as intelligence of shape recovery after material deformation are beneficial and critical for many applications,e.g.aerospace,automobiles,electronics,etc.Cuttlebone produced in the cuttlefish has evolved vertical walls with the optimal corrugation gradient,enabling stress homogenization,significant load bearing,and damage tolerance to protect the organism from high external pressures in the deep sea.This work illustrated that the complex hybrid wave shape in cuttlebone walls,becoming more tortuous from bottom to top,creates a lightweight,load-bearing structure with progressive failure.By mimicking the cuttlebone,a novel bionic hybrid structure(BHS)was proposed,and as a comparison,a regular corrugated structure and a straight wall structure were designed.Three types of designed structures have been successfully manufactured by laser powder bed fusion(LPBF)with NiTi powder.The LPBF-processed BHS exhibited a total porosity of 0.042% and a good dimensional accuracy with a peak deviation of 17.4μm.Microstructural analysis indicated that the LPBF-processed BHS had a strong(001)crystallographic orientation and an average size of 9.85μm.Mechanical analysis revealed the LPBF-processed BHS could withstand over 25000 times its weight without significant deformation and had the highest specific EA value(5.32 J·g^(−1))due to the absence of stress concentration and progressive wall failure during compression.Cyclic compression testing showed that LPBF-processed BHS possessed superior viscoelastic and elasticity energy dissipation capacity.Importantly,the uniform reversible phase transition from martensite to austenite in the walls enables the structure to largely recover its pre-deformation shape when heated(over 99% recovery rate).These design strategies can serve as valuable references for the development of intelligent components that possess high mechanical efficiency and shape memory capabilities.