Fast and Accurate Additive Manufacturability Analysis

This paper focuses on the design optimization of complex 3D composites structures made by additive manufacturing processes. There are commercial CAD-CAM software solutions for detailed offline path programming, but there is a growing need for innovative tools and methodologies for doing trade oil studies very early at design stage. A new innovative solution has been developed on top of the CATFIBER■ software,allowing both designers and stress engineers to quickly analyze complex double-curved geometries. It also includes a variable stiffness approach with tow-steering, and structural analysis of the manufacturing defects using Digimat■ software.

Study on the Manufacturability Evaluation Based on Double-layer Model of Manufacturing Resources

Virtual organization is a new production patter and a principal part in advanced manufacturing systems such as agile manufacturing. Manufacturability evaluation is the necessary condition to form the virtual organization. A new manufacturability evaluation approach is described in this paper, which is carried out based on every process feature under the double-layer model of manufacturing resources proposed by authors. The manufacturing resources that build up the virtual organization are selected according to the results of manufacturability evaluation.

Generative Learning in VLSI Design for Manufacturability: Current Status and Future Directions

With the continuous scaling of integrated circuit technologies,design for manufacturability(DFM)is becoming more critical,yet more challenging.Alongside,recent advances in machine learning have provided a new computing paradigm with promising applications in VLSI manufacturability.In particular,generative learning-regarded among the most interesting ideas in present-day machine learning-has demonstrated impressive capabilities in a wide range of applications.This paper surveys recent results of using generative learning in VLSI manufacturing modeling and optimization.Specifically,we examine the unique features of generative learning that have been leveraged to improve DFM efficiency in an unprecedented way;hence,paving the way to a new data-driven DFM approach.The state-of-the-art methods are presented,and challenges/opportunities are discussed.

Manufacturability and mechanical reliability study for heterogeneous integration system in display(HiSID)

In this paper,the system on display panel(SoDP)architecture,the primary stage of heterogeneous integration system in display(HiSID),is introduced for the first time.In this architecture,the driving components of display,which are supposed to be on the display flexible print circuit(FPC)in traditional architecture,are innovatively integrated onto the backside of display panel.Through the SoDP architecture,the simulated impact strain in the panel fan-out region can decrease about 30%compared to the traditional architecture,and SoDP provides more the 10 mm extra space in the in-plane Y-direction for holding a larger battery.Also,the SoDP is compatible with the current organic laser emitted diode(OLED)and system in package(SiP)processes.Besides the primary stage,this paper also presents a comprehensive and extensive analysis on the challenges of the manufacturability for the advanced stage of HiSID from four key technologies perspectives:device miniaturization,massive manufacturing,driving technology,and advanced heterogeneous integration.

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.

Effect of fabrication temperature on the manufacturability of lateral ZnO nanowire array UV sensor

Fabrication temperature is an important factor affecting the manufacturability of electronic devices,especially for the bottom-up self-assembled nano-device.In this study,we used a lateral-bridged zinc oxide(ZnO)nanowire array UV sensor as a model to investigate the influence of temperature on device performance over the entire manufacturing process,from sensor fabrication to packaging.We found that annealing of the SiO2 substrate would make ZnO seed layer on top of it more compact and uniform,and hence improve the lateral orientation and uniformity of ZnO nanowires grown from the seed layer.With the annealed substrate,the light-to-dark current ratio increased by two orders of magnitude.On the contrary,annealing the ZnO seed layer would deteriorate the light-to-dark current ratio of the sensor,because annealing caused most of the grains in the seed layer to become vertically aligned,which in turn affected the lateral growth of ZnO nanowire arrays.During the packaging process,the surface structure of ZnO nanowires would change if the chip welded at a temperature of 230℃for 2 min,resulting in a decrease of light-to-dark current ratio by three orders of magnitude.

Robust isogeometric topology optimization for piezoelectric actuators with uniform manufacturability

Piezoelectric actuators have received substantial attention among the industry and academia due to quick responses, such as high output force, high stiffness, high accuracy, and precision. However, the design of piezoelectric actuators always suffers from the emergence of several localized hinges with only one-node connection, which have difficulty satisfying manufacturing and machining requirements (from the over- or under-etching devices). The main purpose of the current paper is to propose a robust isogeometric topology optimization (RITO) method for the design of piezoelectric actuators, which can effectively remove the critical issue induced by one-node connected hinges and simultaneously maintain uniform manufacturability in the optimized topologies. In RITO, the isogeometric analysis replacing the conventional finite element method is applied to compute the unknown electro elastic fields in piezoelectric materials, which can improve numerical accuracy and then enhance iterative stability. The erode–dilate operator is introduced in topology representation to construct the eroded, intermediate, and dilated density distribution functions by non-uniform rational B-splines. Finally, the RITO formulation for the design of piezoelectric materials is developed, and several numerical examples are performed to test the effectiveness and efficiency of the proposed RITO method.

Comprehensive Survey of the Landscape of Digital Twin Technologies and Their Diverse Applications

The concept of the digital twin,also known colloquially as the DT,is a fundamental principle within Industry 4.0 framework.In recent years,the concept of digital siblings has generated considerable academic and practical interest.However,academia and industry have used a variety of interpretations,and the scientific literature lacks a unified and consistent definition of this term.The purpose of this study is to systematically examine the definitional landscape of the digital twin concept as outlined in scholarly literature,beginning with its origins in the aerospace domain and extending to its contemporary interpretations in the manufacturing industry.Notably,this investigationwill focus on the research conducted on Industry 4.0 and smartmanufacturing,elucidating the diverse applications of digital twins in fields including aerospace,intelligentmanufacturing,intelligent transportation,and intelligent cities,among others.

Additive manufactured osseointegrated screws with hierarchical design

Bone screws are devices used to fix implants or bones to bones.However,conventional screws are mechanically fixed with thread and often face long-term failure due to poor osseointegration.To improve osseointegration,screws are evolving from solid and smooth to porous and rough.Additive manufacturing(AM)offers a high degree of manufacturing freedom,enabling the preparation of predesigned screws that are porous and rough.This paper provides an overview of the problems currently faced by bone screws:long-term loosening and screw breakage.Next,advances in osseointegrated screws are summarized hierarchically(sub-micro,micro,and macro).At the sub-microscale level,we describe surface-modification techniques for enhancing osseointegration.At the micro level,we summarize the micro-design parameters that affect the mechanical and biological properties of porous osseointegrated screws,including porosity,pore size,and pore shape.In addition,we highlight three promising pore shapes:triply periodic minimal surface,auxetic structure with negative Poisson ratio,and the Voronoi structure.At the macro level,we outline the strategies of graded design,gradient design,and topology optimization design to improve the mechanical strength of porous osseointegrated screws.Simultaneously,this paper outlines advances in AM technology for enhancing the mechanical properties of porous osseointegrated screws.AM osseointegrated screws with hierarchical design are expected to provide excellent long-term fixation and the required mechanical strength.

Microstructure and thermal properties of dissimilar M300–CuCr1Zr alloys by multi-material laser-based powder bed fusion

Multi-material laser-based powder bed fusion (PBF-LB) allows manufacturing of parts with 3-dimensional gradient and additional functionality in a single step. This research focuses on the combination of thermally-conductive CuCr1Zr with hard M300 tool steel.Two interface configurations of M300 on CuCr1Zr and CuCr1Zr on M300 were investigated. Ultra-fine grains form at the interface due to the low mutual solubility of Cu and steel. The material mixing zone size is dependent on the configurations and tunable in the range of0.1–0.3 mm by introducing a separate set of parameters for the interface layers. Microcracks and pores mainly occur in the transition zone.Regardless of these defects, the thermal diffusivity of bimetallic parts with 50vol% of CuCr1Zr significantly increases by 70%–150%compared to pure M300. The thermal diffusivity of CuCr1Zr and the hardness of M300 steel can be enhanced simultaneously by applying the aging heat treatment.

Mechanical behavior and response mechanism of porous metal structures manufactured by laser powder bed fusion under compressive loading

Al Si10Mg porous protective structure often produces different damage forms under compressive loading,and these damage modes affect its protective function.In order to well meet the service requirements,there is an urgent need to comprehensively understand the mechanical behavior and response mechanism of AlSi10Mg porous structures under compressive loading.In this paper,Al Si10Mg porous structures with three kinds of volume fractions are designed and optimized to meet the requirements of high-impact,strong-energy absorption,and lightweight characteristics.The mechanical behaviors of AlSi10Mg porous structures,including the stress-strain relationship,structural bearing state,deformation and damage modes,and energy absorption characteristics,were obtained through experimental studies at different loading rates.The damage pattern of the damage section indicates that AlSi10Mg porous structures have both ductile and brittle mechanical properties.Numerical simulation studies show that the AlSi10Mg porous structure undergoes shear damage due to relative misalignment along the diagonal cross-section,and the damage location is almost at 45°to the load direction,which is the most direct cause of its structural damage,revealing the damage mechanism of AlSi10Mg porous structures under the compressive load.The normalized energy absorption model constructed in the paper well interprets the energy absorption state of Al Si10Mg porous structures and gives the sensitive location of the structures,and the results of this paper provide important references for peers in structural design and optimization.

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.

Customized scaffolds for large bone defects using 3D‑printed modular blocks from 2D‑medical images

Additive manufacturing(AM)has revolutionized the design and manufacturing of patient-specific,three-dimensional(3D),complex porous structures known as scaffolds for tissue engineering applications.The use of advanced image acquisition techniques,image processing,and computer-aided design methods has enabled the precise design and additive manufacturing of anatomically correct and patient-specific implants and scaffolds.However,these sophisticated techniques can be timeconsuming,labor-intensive,and expensive.Moreover,the necessary imaging and manufacturing equipment may not be readily available when urgent treatment is needed for trauma patients.In this study,a novel design and AM methods are proposed for the development of modular and customizable scaffold blocks that can be adapted to fit the bone defect area of a patient.These modular scaffold blocks can be combined to quickly form any patient-specific scaffold directly from two-dimensional(2D)medical images when the surgeon lacks access to a 3D printer or cannot wait for lengthy 3D imaging,modeling,and 3D printing during surgery.The proposed method begins with developing a bone surface-modeling algorithm that reconstructs a model of the patient’s bone from 2D medical image measurements without the need for expensive 3D medical imaging or segmentation.This algorithm can generate both patient-specific and average bone models.Additionally,a biomimetic continuous path planning method is developed for the additive manufacturing of scaffolds,allowing porous scaffold blocks with the desired biomechanical properties to be manufactured directly from 2D data or images.The algorithms are implemented,and the designed scaffold blocks are 3D printed using an extrusion-based AM process.Guidelines and instructions are also provided to assist surgeons in assembling scaffold blocks for the self-repair of patient-specific large bone defects.

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.

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.

Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.

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.

Emerging Trends in Damage Tolerance Assessment:A Review of Smart Materials and Self-Repairable Structures

The discipline of damage tolerance assessment has experienced significant advancements due to the emergence of smart materials and self-repairable structures.This review offers a comprehensive look into both traditional and innovative methodologies employed in damage tolerance assessment.After a detailed exploration of damage tolerance concepts and their historical progression,the review juxtaposes the proven techniques of damage assessment with the cutting-edge innovations brought about by smart materials and self-repairable structures.The subsequent sections delve into the synergistic integration of smart materials with self-repairable structures,marking a pivotal stride in damage tolerance by establishing an autonomous system for immediate damage identification and self-repair.This holistic approach broadens the applicability of these technologies across diverse sectors yet brings forth unique challenges demanding further innovation and research.Additionally,the review examines future prospects that combine advanced manufacturing processes with data-centric methodologies,amplifying the capabilities of these‘intelligent’structures.The review culminates by highlighting the transformative potential of this union between smart materials and self-repairable structures,promoting a sustainable and efficient engineering paradigm.

Optimization of the Physical Aperture of the Parabolic Reflector Antenna

In fields like astronomy and radar technology, high-gain antennas are required for long-distance communication. Due to its relatively large gain, the use of parabolic antennas has become very popular over time, because they can easily achieve gains of above 30 dB at microwave and higher frequencies. Today, most systems’ success depends on how well the antennas perform. These antennas are available in different types and sizes. Each antenna’s effective area usually has less than the actual physical area of the antenna surface. This means that the unused area of the antenna is massive, and a waste. The aim of the research is to show that the actual physical aperture of a parabolic antenna can be reduced as much as possible to equal the effective area, as given by the antenna formula, thereby saving manufacturing costs, improve the aesthetics. In other words, the focus of this work is to experimentally show that reflector antenna can be made of smaller sizes but better performance. Measurements were taken from different positions from a parabolic antenna, the signal level measured and compared with signal levels for optimal performance.

Research on the Innovative Decisions of Supermarket Private Brands and Designated Manufacturers

One of the core competencies of a supermarket lies in its branding.With the continuous development of the market economy and the ongoing evolution of consumer demand,private brands have progressively emerged as significant contributors to supermarket growth.However,a pivotal developmental challenge for supermarkets is navigating the innovative decision-making process between private brands and designated manufacturers.This paper aims to investigate the innovative decisions between private brands and designated manufacturers,along with the relevant promotional strategies employed during entry into the United States market.