In-situ additive manufacturing of high strength yet ductility titanium composites with gradient layered structure using N_(2)

It has always been challenging work to reconcile the contradiction between the strength and plasticity of titanium materials.Laser powder bed fusion(LPBF) is a convenient method to fabricate innovative composites including those inspired by gradient layered materials.In this work,we used LPBF to selectively prepare Ti N/Ti gradient layered structure(GLSTi)composites by using different N_(2)–Ar ratios during the LPBF process.We systematically investigated the mechanisms of in-situ synthesis Ti N,high strength and ductility of GLSTi composites using microscopic analysis,TEM characterization,and tensile testing with digital image correlation.Besides,a digital correspondence was established between the N_(2) concentration and the volume fraction of LPBF in-situ synthesized Ti N.Our results show that the GLSTi composites exhibit superior mechanical properties compared to pure titanium fabricated by LPBF under pure Ar.Specifically,the tensile strength of GLSTi was more than 1.5times higher than that of LPBF-formed pure titanium,reaching up to 1100 MPa,while maintaining a high elongation at fracture of 17%.GLSTi breaks the bottleneck of high strength but low ductility exhibited by conventional nanoceramic particle-strengthened titanium matrix composites,and the hetero-deformation induced strengthening effect formed by the Ti N/Ti layered structure explained its strength-plasticity balanced principle.The microhardness exhibits a jagged variation of the relatively low hardness of 245 HV0.2 for the pure titanium layer and a high hardness of 408 HV0.2 for the N_(2) in-situ synthesis layer.Our study provides a new concept for the structure-performance digital customization of 3D-printed Ti-based composites.

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.

In-situ deposition of apatite layer to protect Mg-based composite fabricated via laser additive manufacturing

Biodegradable magnesium(Mg) and its alloy show huge potential as temporary bone substitute due to the favorable biocompatibility and mechanical compatibility. However, one issue deserves attention is the too fast degradation. In this work, mesoporous bioglass(MBG)with high pore volume(0.59 cc/g) and huge specific surface area(110.78 m^(2)/g) was synthesized using improved sol-gel method, and introduced into Mg-based composite via laser additive manufacturing. Immersion tests showed that the incorporated MBG served as powerful adsorption sites, which promoted the in-situ deposition of apatite by successively adsorbing Ca2+and HPO42-. Such dense apatite film acted as an efficient protection layer and enhanced the corrosion resistance of Mg matrix, which was proved by the electrochemical impedance spectroscopy measurements. Thereby, Mg based composite showed a significantly decreased degradation rate of 0.31 mm/year. Furthermore,MBG also improved the mechanical properties as well as cell behavior. This work highlighted the advantages of MBG in the fabrication of Mg-based implant with enhanced overall performance for orthopedic application.

Performance-control-orientated hybrid metal additive manufacturing technologies:state of the art,challenges,and future trends

Metal additive manufacturing(AM)technologies have made significant progress in the basic theoretical field since their invention in the 1970s.However,performance instability during continuous processing,such as thermal history,residual stress accumulation,and columnar grain epitaxial growth,consistently hinders their broad application in standardized industrial production.To overcome these challenges,performance-control-oriented hybrid AM(HAM)technologies have been introduced.These technologies,by leveraging external auxiliary processes,aim to regulate microstructural evolution and mechanical properties during metal AM.This paper provides a systematic and detailed review of performance-control-oriented HAM technology,which is categorized into two main groups:energy field-assisted AM(EFed AM,e.g.ultrasonic,electromagnetic,and heat)technologies and interlayer plastic deformation-assisted AM(IPDed AM,e.g.laser shock peening,rolling,ultrasonic peening,and friction stir process)technologies.This review covers the influence of external energy fields on the melting,flow,and solidification behavior of materials,and the regulatory effects of interlayer plastic deformation on grain refinement,nucleation,and recrystallization.Furthermore,the role of performance-control-oriented HAM technologies in managing residual stress conversion,metallurgical defect closure,mechanical property improvement,and anisotropy regulation is thoroughly reviewed and discussed.The review concludes with an analysis of future development trends in EFed AM and IPDed AM technologies.

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.

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 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.

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.

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.

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.

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.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

A review on the multi-scaled structures and mechanical/thermal properties of tool steels fabricated by laser powder bed fusion additive manufacturing

The laser powder bed fusion(LPBF) process can integrally form geometrically complex and high-performance metallic parts that have attracted much interest,especially in the molds industry.The appearance of the LPBF makes it possible to design and produce complex conformal cooling channel systems in molds.Thus,LPBF-processed tool steels have attracted more and more attention.The complex thermal history in the LPBF process makes the microstructural characteristics and properties different from those of conventional manufactured tool steels.This paper provides an overview of LPBF-processed tool steels by describing the physical phenomena,the microstructural characteristics,and the mechanical/thermal properties,including tensile properties,wear resistance,and thermal properties.The microstructural characteristics are presented through a multiscale perspective,ranging from densification,meso-structure,microstructure,substructure in grains,to nanoprecipitates.Finally,a summary of tool steels and their challenges and outlooks are introduced.

Research progress in CALPHAD assisted metal additive manufacturing

Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM.The calculation of phase diagrams(CALPHAD)is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems.By combining with the element mobility data and non-equilibrium phase transition model,it has been widely used in the analysis of traditional metal materials.The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials,the reduction of metallurgical imperfections,and the control of microstructural attributes.This endeavor carries considerable theoretical and practical significance.This paper summarizes the important achievements of CALPHAD in additive manufacturing(AM)technology in recent years,including material design,process parameter optimization,microstructure evolution simulation,and properties prediction.Finally,the limitations of applying CALPHAD technology to MAM technology are discussed,along with prospective research directions.

Cloud edge integrated security architecture of new cloud manufacturing system

With the rapid development of cloud manufacturing technology and the new generation of artificial intelligence technology,the new cloud manufacturing system(NCMS)built on the connotation of cloud manufacturing 3.0 presents a new business model of“Internet of everything,intelligent leading,data driving,shared services,cross-border integration,and universal innovation”.The network boundaries are becoming increasingly blurred,NCMS is facing security risks such as equipment unauthorized use,account theft,static and extensive access control policies,unauthorized access,supply chain attacks,sensitive data leaks,and industrial control vulnerability attacks.Traditional security architectures mainly use information security technology,which cannot meet the active security protection requirements of NCMS.In order to solve the above problems,this paper proposes an integrated cloud-edge-terminal security system architecture of NCMS.It adopts the zero trust concept and effectively integrates multiple security capabilities such as network,equipment,cloud computing environment,application,identity,and data.It adopts a new access control mode of“continuous verification+dynamic authorization”,classified access control mechanisms such as attribute-based access control,rolebased access control,policy-based access control,and a new data security protection system based on blockchain,achieving“trustworthy subject identity,controllable access behavior,and effective protection of subject and object resources”.This architecture provides an active security protection method for NCMS in the digital transformation of large enterprises,and can effectively enhance network security protection capabilities and cope with increasingly severe network security situations.