Social Sensors (S^2ensors): A Kind of Hardware-Software-Integrated Mediators for Social Manufacturing Systems Under Mass Individualization

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.

Integrated and Intelligent Manufacturing： Perspectives and Enablers

With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership （AMP） in 2011 and the National Network for Manufacturing Innovation （NNMI） in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further ＂leverage existing resources... to nurture manufacturing innovation and accelerate commercialization＂ by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10- year plan and roadmap toward manufacturing： Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features： integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing （i2M） systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Intemet of Things and Services （IoTS）, cyber-physical systems （CPSs）, and cloud computing are discussed. Challenges are addressed with applica- tions that are based on commercially available platforms such as General Electric （GE）＇s Predix and PTC＇s ThingWorx.

RFID Based e-quality Tracking in Service-oriented Manufacturing Execution System

The method of acquiring the real-time data has influenced the implementation of the manufacturing execution system （MES）. Accompanied with turning the MES into service-oriented manufacturing execution system （so-MES）, real-time e-quality tracking （e-QT）, in which real-time data are computed, has played more and more important roles in manufacturing. This paper presents an e-QT model through the study of real-time status data tracking and quality data collecting. An implementing architecture of the e-QT model is constructed on the basis of radio frequency identification devices （RFID） data-tracking network. In order to develop the e-QT system, some key enabling technologies, such as configuration, data collection, and data processing, etc, are studied. The relation schema between hardware is built for the RFID data-tracking network based on the configuration technique. Real-time data are sampled by using data collecting technique. Furthermore, real-time status and quality data in a shop-floor can be acquired in terms of using the real-time data computing method. Finally, a prototype system is developed and a running example is given so as to verify the feasibility of methods proposed in this paper. The proposed research provides effective e-quality tracking theoretical foundation through the use of RFID technology for the discrete manufacturing.

Robust Deadlock Avoidance Policy for Automated Manufacturing System With Multiple Unreliable Resources

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.

Tunable 3D Nanofiber Architecture of Polycaprolactone by Divergence Electrospinning for Potential Tissue Engineering Applications

The creation of biomimetic cell environments with micro and nanoscale topographical features resembling native tissues is critical for tissue engineering. To address this challenge, this study focuses on an innovative electrospinning strategy that adopts a symmetrically divergent electric field to induce rapid self-assembly of aligned polycaprolactone(PCL) nanofibers into a centimeter-scale architecture between separately grounded bevels. The 3D microstructures of the nanofiber scaffolds were characterized through a series of sectioning in both vertical and horizontal directions. PCL/collagen(type I)nanofiber scaffolds with different density gradients were incorporated in sodium alginate hydrogels and subjected to elemental analysis. Human fibroblasts were seeded onto the scaffolds and cultured for 7 days. Our studies showed that the inclination angle of the collector had significant effects on nanofiber attributes, including the mean diameter, density gradient, and alignment gradient. The fiber density and alignment at the peripheral area of the 45°-collector decreased by 21% and 55%, respectively, along the z-axis,while those of the 60°-collector decreased by 71% and 60%, respectively. By altering the geometry of the conductive areas on the collecting bevels, polyhedral and cylindrical scaffolds composed of aligned fibers were directly fabricated. By using a four-bevel collector, the nanofibers formed a matrix of microgrids with a density of 11%. The gradient of nitrogen-to-carbon ratio in the scaffold-incorporated hydrogel was consistent with the nanofiber density gradient. The scaffolds provided biophysical stimuli to facilitate cell adhesion, proliferation, and morphogenesis in 3D.

Shape control on probability density function in stochastic systems

A novel strategy of probability density function （PDF） shape control is proposed in stochastic systems. The control er is designed whose parameters are optimal y obtained through the improved particle swarm optimization algorithm. The parameters of the control er are viewed as the space position of a particle in particle swarm optimization algorithm and updated continual y until the control er makes the PDF of the state variable as close as possible to the expected PDF. The proposed PDF shape control technique is compared with the equivalent linearization technique through simulation experiments. The results show the superiority and the effectiveness of the proposed method. The control er is excellent in making the state PDF fol ow the expected PDF and has the very smal error between the state PDF and the expected PDF, solving the control problem of the PDF shape in stochastic systems effectively.

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

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.

Roll-to-roll imprint for high precision grating manufacturing

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.

New development on additive manufacturing technology and its applications

Additive manufacturing technology has been developed in Xi＇ an Jiaotong University for almost 20 years. Up to now~ it is still attracting the attentions of the researchers or manufacturers all over the world. Some in- novative processes and frontier application research are all being conducted here to catch up with the new develop- ment of this technology. In the paper, newly developed processes, such as ultraviolet-light emitting diode （UV- LED） stereolithography, ceramic stereolithography, and direct metal forming, were described. Some results of the frontier application researches, such as indirect fabrication of ceramic casting mould, wind-tunnel-testing models, photonic crystals and metamatcrials, were also briefly reviewed.

A Prediction Model of Effective Thermal Conductivity for Metal Powder Bed in Additive Manufacturing

In current research,many researchers propose analytical expressions for calculating the packing structure of spherical particles such as DN Model,Compact Model and NLS criterion et al.However,there is still a question that has not been well explained yet.That is:What is the core factors affecting the thermal conductivity of particles?In this paper,based on the coupled discrete element-finite difference(DE-FD)method and spherical aluminum powder,the relationship between the parameters and the thermal conductivity of the powder(ETC_(p))is studied.It is found that the key factor that can described the change trend of ETC_(p) more accurately is not the materials of the powder but the average contact area between particles(a_(ave))which also have a close nonlinear relationship with the average particle size d_(50).Based on this results,the expression for calculating the ETC_(p) of the sphere metal powder is successfully reduced to only one main parameter d_(50)and an efficient calculation model is proposed which can applicate both in room and high temperature and the corresponding error is less than 20.9%in room temperature.Therefore,in this study,based on the core factors analyzation,a fast calculation model of ETC_(p) is proposed,which has a certain guiding significance in the field of thermal field simulation.

Interaction of HLA and TCP/IP subsystems in C^3I system

Aiming to the fact that the distribution modules of the C3I simulation system originally developed based on TCP/IP protocol has to be compatible with the present HLA (High Level of Architecture) rules, three methods for implementing the interaction of HLA and TCP/IP subsystems are presented to make the original TCP/IP modules directly used in the HLA-based C3I system without redevelopment. The three methods are agent method, middleware method and gateway method. Moreover, the implementation of gateway method via VC++ as well as the key technology is discussed.

Soft Imprint Lithography for Pixel Array Manufacturing in OLED

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).

Ubiquitous data computing and information using in a smart factory with wireless manufacturing

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.

Corona noise model of high-voltage AC transmission lines and engineering applications

In order to predict the levels of corona noise from high-voltage alternating current （AC） transmission lines, the mechanism of corona noise and the corresponding theoretical prediction model are investigated. On the basis of Drnde model, the motion of positive and negative ions produced by high-voltage corona is analyzed, and the mechanism of corona noise is discovered. The theoretical prediction model is put forward by using Kirchhoff formula, which is verified by the well agreement between our result and others＇ , considering the case of three- phase single lines. Moreover, the calculation results show that for both single and bundled lines, the sound pres- sure level of the typical frequency, i.e. twice the power frequency, attenuates slowly and leads to an obviously in- terferential phenomenon near the transmission lines, but the level of the bundled lines is smaller than that of the single ones under the same transmission voltage. Based on the mechanism of corona noise and the prediction model, it is obvious that bundled lines and/or increased line radius can be adopted to reduce corona noise in the practical engineering applications effectively. This model can also provide a theoretical guidance for the high-volt- age AC transmission line design.

The Additive Manufacturing Process of Electric Power Fittings Fabricated by Metal Droplet Deposition

Metal droplet deposition is a kind of additive manufacturing(3D Printing)technique that fabricates near-net part through droplets deposition with lower cost and higher efficiency.This paper proposed a solution to problems of electric power fittings that large inventories,high procurement costs,low manufacturing efficiency and transportation cost.Using additive Manufacturing technique-metal droplet deposition,electric power fittings fabricated on power construction site.This paper describes the manufacturing process of typical thin-walled samples(the structure optimized based on additive manufacturing principle)and ball head rings of electric power fittings.Aiming at the integral AM forming for ball and ball socket electric power fitting workpiece,a novel easy removal forming support material(ceramics and gypsum mixed with UV cured resin)have been developed.Here this support material was used to fabricate nested integral workpieces.Dimensional accuracy and microstructure of the test pieces were analyzed.The error of the height and width of the forming workpiece is within 5%.No obvious overlap trace(such as overlap line and cracks)observed,and the internal microstructure is equiaxial crystal.The average density of the component is 99.51%,which measured by drainage method and 13.39%higher than the cast raw material.

Medical Additive Manufacturing: From a Frontier Technology to the Research and Development of Products

1.Research and development(R&D)and the challenges of raw materials for medical additive manufacturing Raw materials for medical additive manufacturing have a wide range of commonalities that are also seen in many other fields,making them an important basis in the field of three-dimensional(3D)printing.Problems and challenges related to material types,powder properties,formability,viscoelasticity,and so forth also share common features.For example,many metal materials are used in the field of aviation,while metals,polymers,and inorganic materials are used in the field of biomedicine.The most widely used materials in biomedicine are biocompatible.Various homogeneous and non-homogeneous composites are also available for 3D printing,and impose an additional challenge in additive manufacturing;the use of heterogeneous composites in 3D printing is particularly challenging.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

High-Efficiency Dynamic Scanning Strategy for Powder Bed Fusion by Controlling Temperature Field of the Heat-Affected Zone

Improvement of fabrication efficiency and part performance was the main challenge for the large-scale powder bed fusion(PBF)process.In this study,a dynamic monitoring and feedback system of powder bed temperature field using an infrared thermal imager has been established and integrated into a four-laser PBF equipment with a working area of 2000 mm×2000 mm.The heat-affected zone(HAZ)temperature field has been controlled by adjusting the scanning speed dynamically.Simultaneously,the relationship among spot size,HAZ temperature,and part performance has been established.The fluctuation of the HAZ temperature in four-laser scanning areas was decreased from 30.85℃to 17.41℃.Thus,the consistency of the sintering performance of the produced large component has been improved.Based on the controllable temperature field,a dynamically adjusting strategy for laser spot size was proposed,by which the fabrication efficiency was improved up to 65.38%.The current research results were of great significance to the further industrial applications of large-scale PBF equipment.

Revealing precipitation behavior and mechanical response of wire-arc directed energy deposited Mg-Gd-Y-Zr alloy by tailoring aging procedures

Mg-Gd-Y-Zr alloy,as a typical magnesium rare-earth(Mg-RE)alloy,is gaining popularity in the advanced equipment manufacturing fields owing to its noticeable age-hardening properties and high specific strength.However,it is extremely challenging to prepare wrought components with large dimensions and complex shapes because of the poor room-temperature processability of Mg-Gd-Y-Zr alloy.Herein,we report a wire-arc directed energy deposited(DED)Mg-10.45Gd-2.27Y-0.52Zr(wt.%,GW102K)alloy with high RE content presenting a prominent combination of strength and ductility,realized by tailored nanoprecipitates through an optimized heat treatment procedure.Specifically,the solution-treated sample exhibits excellent ductility with an elongation(EL)of(14.6±0.1)%,while the aging-treated sample at 200°C for 58 h achieves an ultra-high ultimate tensile strength(UTS)of(371±1.5)MPa.Besides,the aging-treated sample at 250°C for 16 h attains a good strength-ductility synergy with a UTS of(316±2.1)MPa and a EL of(8.5±0.1)%.Particularly,the evolution mechanisms of precipitation response induced by various aging parameters and deformation behavior caused by nanoprecipitates type were also systematically revealed.The excellent ductility resulted from coordinating localized strains facilitated by active slip activity.And the ultra-high strength should be ascribed to the dense nano-β'hampering dislocation motion.Additionally,the shearable nano-β1 contributed to the good strength-ductility synergy.This work thus offers insightful understanding into the nanoprecipitates manipulation and performance tailoring for the wire-arc DED preparation of large-sized Mg-Gd-Y-Zr components with complex geometries.

Carbon nanotube integrated circuit technology:purification,assembly and integration

As the manufacturing process of silicon-based integrated circuits(ICs)approaches its physical limit,the quantum effect of silicon-based field-effect transistors(FETs)has become increasingly evident.And the burgeoning carbon-based semiconductor technology has become one of the most disruptive technologies in the post-Moore era.As one-dimensional nanomaterials,carbon nanotubes(CNTs)are far superior to silicon at the same technology nodes of FETs because of their excellent electrical transport and scaling properties,rendering them the most competitive material in the next-generation ICs technology.However,certain challenges impede the industrialization of CNTs,particularly in terms of material preparation,which significantly hinders the development of CNT-based ICs.Focusing on CNT-based ICs technology,this review summarizes its main technical status,development trends,existing challenges,and future development directions.