Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and newgeneration intelligent manufacturing. New-generation intelligent manufacturing represents an indepth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for ‘‘parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China.
Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.
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 Internet of Things and Services (IoTS), cyber-physical systems (CPSs), and cloud computing are discussed. Challenges are addressed with applications that are based on commercially available platforms such as General Electric (GE)’s Predix and PTC’s ThingWorx.
An increased global supply of minerals is essential to meet the needs and expectations of a rapidly rising world population. This implies extraction from greater depths. Autonomous mining systems, developed through sustained R&D by equipment suppliers, reduce miner exposure to hostile work environments and increase safety. This places increased focus on “ground control” and on rock mechanics to define the depth to which minerals may be extracted economically. Although significant efforts have been made since the end of World War II to apply mechanics to mine design, there have been both technological and organizational obstacles. Rock in situ is a more complex engineering material than is typically encountered in most other engineering disciplines. Mining engineering has relied heavily on empirical procedures in design for thousands of years. These are no longer adequate to address the challenges of the 21st century, as mines venture to increasingly greater depths. The development of the synthetic rock mass (SRM) in 2008 provides researchers with the ability to analyze the deformational behavior of rock masses that are anisotropic and discontinuous—attributes that were described as the defining characteristics of in situ rock by Leopold Müller, the president and founder of the International Society for Rock Mechanics (ISRM), in 1966. Recent developments in the numerical modeling of large-scale mining operations (e.g., caving) using the SRM reveal unanticipated deformational behavior of the rock. The application of massive parallelization and cloud computational techniques offers major opportunities: for example, to assess uncertainties in numerical predictions; to establish the mechanics basis for the empirical rules now used in rock engineering and their validity for the prediction of rock mass behavior beyond current experience; and to use the discrete element method (DEM) in the optimization of deep mine design. For the first time, mining—and rock engineering—will have its own mechanics-based “laboratory.” This promises to be a major tool in future planning for effective mining at depth. The paper concludes with a discussion of an opportunity to demonstrate the application of DEM and SRM procedures as a laboratory, by back-analysis of mining methods used over the 80-year history of the Mount Lyell Copper Mine in Tasmania.
The rapid development of additive manufacturing and advances in shape memory materials have fueled the progress of four-dimensional (4D) printing. With the right external stimulus, the need for human interaction, sensors, and batteries will be eliminated, and by using additive manufacturing, more complex devices and parts can be produced. With the current understanding of shape memory mechanisms and with improved design for additive manufacturing, reversibility in 4D printing has recently been proven to be feasible. Conventional one-way 4D printing requires human interaction in the programming (or shape-setting) phase, but reversible 4D printing, or two-way 4D printing, will fully eliminate the need for human interference, as the programming stage is replaced with another stimulus. This allows reversible 4D printed parts to be fully dependent on external stimuli; parts can also be potentially reused after every recovery, or even used in continuous cycles—an aspect that carries industrial appeal. This paper presents a review on the mechanisms of shape memory materials that have led to 4D printing, current findings regarding 4D printing in alloys and polymers, and their respective limitations. The reversibility of shape memory materials and their feasibility to be fabricated using three-dimensional (3D) printing are summarized and critically analyzed. For reversible 4D printing, the methods of 3D printing, mechanisms used for actuation, and strategies to achieve reversibility are also highlighted. Finally, prospective future research directions in reversible 4D printing are suggested.
The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include: multi-time scale, space-time scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.
The development of technologies such as big data and cyber-physical systems (CPSs) has increased the demand for product design. Product digital design involves completing the product design process using advanced digital technologies such as geometry modeling, kinematic and dynamic simulation, multi-disciplinary coupling, virtual assembly, virtual reality (VR), multi-objective optimization (MOO), and human-computer interaction. The key technologies of intelligent design for customized products include: a description and analysis of customer requirements (CRs), product family design (PFD) for the customer base, configuration and modular design for customized products, variant design for customized products, and a knowledge push for product intelligent design. The development trends in intelligent design for customized products include big-data-driven intelligent design technology for customized products and customized design tools and applications. The proposed method is verified by the design of precision computer numerical control (CNC) machine tools.
Selective laser melting (SLM) additive manufacturing (AM) technology has become an important option for the precise manufacturing of complex-shaped metallic parts with high performance. The SLM AM process involves complicated physicochemical phenomena, thermodynamic behavior, and phase transformation as a high-energy laser beam melts loose powder particles. This paper provides multiscale modeling and coordinated control for the SLM of metallic materials including an aluminum (Al)-based alloy (AlSi10Mg), a nickel (Ni)-based super-alloy (Inconel 718), and ceramic particle-reinforced Al-based and Ni-based composites. The migration and distribution mechanisms of aluminium nitride (AlN) particles in SLM-processed Al-based nanocomposites and the in situ formation of a gradient interface between the reinforcement and the matrix in SLM-processed tungsten carbide (WC)/Inconel 718 composites were studied in the microscale. The laser absorption and melting/densification behaviors of AlSi10Mg and Inconel 718 alloy powder were disclosed in the mesoscale. Finally, the stress development during line-by-line localized laser scanning and the parameter-dependent control methods for the deformation of SLM-processed composites were proposed in the macroscale. Multiscale numerical simulation and experimental verification methods are beneficial in monitoring the complicated powder-laser interaction, heat and mass transfer behavior, and microstructural and mechanical properties development during the SLM AM process.
With the rapid developments of the high-speed railway in China, a great number of long-span bridges have been constructed in order to cross rivers and gorges. At present, the longest main span of a constructed high-speed railway bridge is only 630 m. The main span of Hutong Yangtze River Bridge and of Wufengshan Yangtze River Bridge, which are under construction, will be much longer, at 1092 m each. In order to overcome the technical issues that originate from the extremely large dead loading and the relatively small structural stiffness of long-span high-speed railway bridges, many new technologies in bridge construction, design, materials, and so forth have been developed. This paper carefully reviews progress in the construction technologies of multi-function combined bridges in China, including combined highway and railway bridges and multi-track railway bridges. Innovations and practices regarding new types of bridge and composite bridge structures, such as bridges with three cable planes and three main trusses, inclined main trusses, slab-truss composite sections, and steel-concrete composite sections, are introduced. In addition, investigations into high-performance materials and integral fabrication and erection techniques for long-span railway bridges are summarized. At the end of the paper, prospects for the future development of long-span high-speed railway bridges are provided.
In this study, the flow characteristics and behaviors of virgin and recycled Inconel powder for powder-bed additive manufacturing (AM) were studied using different powder characterization techniques. The results revealed that the particle size distribution (PSD) for the selective laser melting (SLM) process is typically in the range from 15 μm to 63 μm. The flow rate of virgin Inconel powder is around 28 s·(50 g)-1. In addition, the packing density was found to be 60%. The rheological test results indicate that the virgin powder has reasonably good flowability compared with the recycled powder. The inter-relation between the powder characteristics is discussed herein. A propeller was successfully printed using the powder. The results suggest that Inconel powder is suitable for AM and can be a good reference for researchers who attempt to produce AM powders.
The additive design (AD) and additive manufacturing (AM) of jet engine parts will revolutionize the traditional aerospace industry. The unique characteristics of AM, such as gradient materials and micro-structures, have opened up a new direction in jet engine design and manufacturing. Engineers have been liberated from many constraints associated with traditional methodologies and technologies. One of the most significant features of the AM process is that it can ensure the consistency of parts because it starts from point(s), continues to line(s) and layer(s), and ends with the competed part. Collaboration between design and manufacturing is the key to success in fields including aerodynamics, thermodynamics, structural integration, heat transfer, material development, and machining. Engineers must change the way they design a part, as they shift from the traditional method of “subtracting material” to the new method of “adding material” in order to manufacture a part. AD is not the same as designing for AM. A new method and new tools are required to assist with this new way of designing and manufacturing. This paper discusses in detail what is required in AD and AM, and how current problems can be solved.
Mineral consumption is increasing rapidly as more consumers enter the market for minerals and as the global standard of living increases. As a result, underground mining continues to progress to deeper levels in order to tackle the mineral supply crisis in the 21st century. However, deep mining occurs in a very technical and challenging environment, in which significant innovative solutions and best practice are required and additional safety standards must be implemented in order to overcome the challenges and reap huge economic gains. These challenges include the catastrophic events that are often met in deep mining engineering: rockbursts, gas outbursts, high in situ and redistributed stresses, large deformation, squeezing and creeping rocks, and high temperature. This review paper presents the current global status of deep mining and highlights some of the newest technological achievements and opportunities associated with rock mechanics and geotechnical engineering in deep mining. Of the various technical achievements, unmanned working-faces and unmanned mines based on fully automated mining and mineral extraction processes have become important fields in the 21st century.
Medical models, or “phantoms,” have been widely used for medical training and for doctor-patient interactions. They are increasingly used for surgical planning, medical computational models, algorithm verification and validation, and medical devices development. Such new applications demand high-fidelity, patient-specific, tissue-mimicking medical phantoms that can not only closely emulate the geometric structures of human organs, but also possess the properties and functions of the organ structure. With the rapid advancement of three-dimensional (3D) printing and 3D bioprinting technologies, many researchers have explored the use of these additive manufacturing techniques to fabricate functional medical phantoms for various applications. This paper reviews the applications of these 3D printing and 3D bioprinting technologies for the fabrication of functional medical phantoms and bio-structures. This review specifically discusses the state of the art along with new developments and trends in 3D printed functional medical phantoms (i.e., tissue-mimicking medical phantoms, radiologically relevant medical phantoms, and physiological medical phantoms) and 3D bio-printed structures (i.e., hybrid scaffolding materials, convertible scaffolds, and integrated sensors) for regenerated tissues and organs.
Increasing demand for weight reduction and greater fuel efficiency continues to spur the use of composite materials in commercial aircraft structures. Subsequently, as composite aerostructures become larger and more complex, traditional autoclave manufacturing methods are becoming prohibitively expensive. This has prompted renewed interest in out-of-autoclave processing techniques in which resins are introduced into a reinforcing preform. However, the success of these resin infusion methods is highly dependent upon operator skill and experience, particularly in the development of new manufacturing strategies for complex parts. Process modeling, as a predictive computational tool, aims to address the issues of reliability and waste that result from traditional trial-and-error approaches. Basic modeling attempts, many of which are still used in industry, generally focus on simulating fluid flow through an isotropic porous reinforcement material. However, recent efforts are beginning to account for the multiscale and multidisciplinary complexity of woven materials, in simulations that can provide greater fidelity. In particular, new multi-physics process models are able to better predict the infusion behavior through textiles by considering the effect of fabric deformation on permeability and porosity properties within the reinforcing material. In addition to reviewing previous research related to process modeling and the current state of the art, this paper highlights the recent validation of a multi-physics process model against the experimental infusion of a complex double dome component. By accounting for deformation-dependent flow behavior, the multi-physics process model was able to predict realistic flow behavior, demonstrating considerable improvement over basic isotropic permeability models.
In the past decades, many underwater tunnels have been constructed in the mainland of China, and great progress has been made in related tunneling technologies. This paper presents the history and state of the art of underwater tunnels in the mainland of China in terms of shield-bored tunnels, drill-and-blast tunnels, and immersed tunnels. Typical underwater tunnels of these types in the mainland of China are described, along with innovative technologies regarding comprehensive geological prediction, grouting-based consolidation, the design and construction of large cross-sectional tunnels with shallow cover in weak strata, cutting tool replacement under limited drainage and reduced pressure conditions, the detection and treatment of boulders, the construction of underwater tunnels in areas with high seismic intensity, and the treatment of serious sedimentation in a foundation channel of immersed tunnels. Some suggestions are made regarding the three potential great strait-crossing tunnels—the Qiongzhou Strait-Crossing Tunnel, Bohai Strait-Crossing Tunnel, and Taiwan Strait-Crossing Tunnel—and issues related to these great strait-crossing tunnels that need further study are proposed.
The ball-screw feed drive has varying high-order dynamic characteristics due to flexibilities of the slender screw spindle and joints between components, and an obvious feature of non-collocated control when a direct position measurement using a linear scale is employed. The dynamic characteristics and non-collocated situation have long been the source of difficulties in motion and vibration control, and deteriorate the achieved accuracy of the axis motion. In this study, a dynamic model using a frequency-based substructure approach is established, considering the flexibilities and their variation. The position-dependent variation of the dynamic characteristics is then fully investigated. A corresponding control strategy, which is composed of a modal characteristic modifier (MCM) and an intelligent adaptive tuning algorithm (ATA), is then developed. The MCM utilizes a combination of peak filters and notch filters, thereby shaping the plant dynamics into a virtual collocated system and avoiding control spillover. An ATA using an artificial neural network (ANN) as a smooth parameter interpolator updates the parameters of the filters in real time in order to cope with the feed drive’s dynamic variation. Numerical verification of the effectiveness and robustness of the proposed strategy is shown for a real feed drive.
Electron beam selective melting (EBSM) is a promising additive manufacturing (AM) technology. The EBSM process consists of three major procedures: ① spreading a powder layer, ② preheating to slightly sinter the powder, and ③ selectively melting the powder bed. The highly transient multi-physics phenomena involved in these procedures pose a significant challenge for in situ experimental observation and measurement. To advance the understanding of the physical mechanisms in each procedure, we leverage high-fidelity modeling and post-process experiments. The models resemble the actual fabrication procedures, including ① a powder-spreading model using the discrete element method (DEM), ② a phase field (PF) model of powder sintering (solid-state sintering), and ③ a powder-melting (liquid-state sintering) model using the finite volume method (FVM). Comprehensive insights into all the major procedures are provided, which have rarely been reported. Preliminary simulation results (including powder particle packing within the powder bed, sintering neck formation between particles, and single-track defects) agree qualitatively with experiments, demonstrating the ability to understand the mechanisms and to guide the design and optimization of the experimental setup and manufacturing process.
Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work.
This paper reviews the development history of alkali element doping on Cu(In,Ga)Se2 (CIGS) solar cells and summarizes important achievements that have been made in this field. The influences of incorporation strategies on CIGS absorbers and device performances are also reviewed. By analyzing CIGS surface structure and electronic property variation induced by alkali fluoride (NaF and KF) post-deposition treatment (PDT), we discuss and interpret the following issues: ① The delamination of CIGS thin films induced by Na incorporation facilitates CuInSe2 formation and inhibits Ga during low-temperature co-evaporation processes. ② The mechanisms of carrier density increase due to defect passivation by Na at grain boundaries and the surface. ③ A thinner buffer layer improves the short-circuit current without open-circuit voltage loss. This is attributed not only to better buffer layer coverage in the early stage of the chemical bath deposition process, but also to higher donor defect (CdCu+) density, which is transferred from the acceptor defect (VCu−) and strengthens the buried homojunction. ④ The KF-PDT-induced lower valence band maximum at the absorber surface reduces the recombination at the absorber/buffer interface, which improves the open-circuit voltage and the fill factor of solar cells.