• Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of ‘Internet plus AI’, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent man-ufacturing technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the appli-cation of AI in intelligent manufacturing in China are presented.
  • Complementing our previous publications, this paper presentsthe information schema constructs (ISCs) that underpin the programmingof specific system manifestation feature (SMF) orientated informationmanagement and composing system models. First, we briefly present(1) the general process of pre-embodiment design with SMFs, (2) theprocedures of creating genotypes and phenotypes of SMFs, (3) the specificprocedure of instantiation of phenotypes of SMFs, and (4) the procedureof system model management and processing. Then, the chunks of informationneeded for instantiation of phenotypes of SMFs are discussed, andthe ISCs designed for instantiation presented. Afterwards, the informationmanagement aspects of system modeling are addressed. Methodologically,system modeling involves (1) placement of phenotypes of SMF in themodeling space, (2) combining them towards the desired architectureand operation, (3) assigning values to the parameters and checkingthe satisfaction of constraints, and (4) storing the system modelin the SMFs-based warehouse database. The final objective of the reportedresearch is to develop an SMFs-based toolbox to support modeling ofcyber-physical systems (CPSs).
  • Our long-term objective is to develop a software toolbox for pre-embodiment design of complex and heterogeneous systems, such as cyber-physical systems. The novelty of this toolbox is that it uses system manifestation features (SMFs) for transdisciplinary modeling of these systems. The main challenges of implementation of the toolbox are functional design- and language-independent computational realization of the warehouses, and systematic development and management of the various evolving implements of SMFs (genotypes, phenotypes, and instances). Therefore, an information schema construct (ISC) based approach is proposed to create the schemata of the associated warehouse databases and the above-mentioned SMF implements. ISCs logically arrange the data contents of SMFs in a set of relational tables of varying semantics. In this article we present the ISCs necessary for creation of genotypes and phenotypes. They increase the efficiency of the database development process and make the data relationships transparent. Our follow-up research focuses on the elaboration of the SMF instances based system modeling methodology.
  • This paper establishes a new framework for modeling electrical cyber-physical systems (ECPSs), integrating both power grids and communication networks. To model the communication network associated with a power transmission grid, we use a mesh network that considers the features of power transmission grids such as high-voltage levels, long-transmission distances, and equal importance of each node. Moreover, bidirectional links including data uploading channels and command downloading channels are assumed to connect every node in the communication network and a corresponding physical node in the transmission grid. Based on this model, the fragility of an ECPS is analyzed under various cyber attacks including denial-of-service (DoS) attacks, replay attacks, and false data injection attacks. Control strategies such as load shedding and relay protection are also verified using this model against these attacks.
  • With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.
  • Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

  • Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are cost-effective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

  • It is costly and time consuming to use machining processes, such as grinding, polishing and lapping, to produce optical glass lenses with complex features. Precision glass molding (PGM) has thus been developed to realize an efficient manufacture of such optical components in a single step. However, PGM faces various technical challenges. For example, a PGM process must be carried out within the super-cooled region of optical glass above its glass transition temperature, in which the material has an unstable non-equilibrium structure. Within a narrow window of allowable temperature variation, the glass viscosity can change from 105 to 1012 Pa·s due to the kinetic fragility of the super-cooled liquid. This makes a PGM process sensitive to its molding temperature. In addition, because of the structural relaxation in this temperature window, the atomic structure that governs the material properties is strongly dependent on time and thermal history. Such complexity often leads to residual stresses and shape distortion in a lens molded, causing unexpected changes in density and refractive index. This review will discuss some of the central issues in PGM processes and provide a method based on a manufacturing chain consideration from mold material selection, property and deformation characterization of optical glass to process optimization. The realization of such optimization is a necessary step for the Industry 4.0 of PGM.

  • Manufacturing is the foundation of a nation’s economy. It is the primary industry to promote economic and social development. To accelerate and upgrade China’s manufacturing sector from “precision manufacturing” to “high-performance and high-quality manufacturing”, a new breakthrough should be found in terms of achieving a “leap-frog development”. Unlike conventional manufacturing, the fundamental theory of “Manufacturing 3.0” is beyond the scope of conventional theory; rather, it is based on new principles and theories at the atomic and/or close-to-atomic scale. Obtaining a dominant role at the international level is a strategic move for China’s progress.

  • This paper addresses the problem of condition assessment of bridge expansion joints using long-term measurement data under changing environmental conditions. The effects of temperature, traffic loading and wind on the expansion joint displacements are analyzed and interpreted, which reveal that measured displacements are observed to increase with an increase in temperature and to decrease with increased traffic loading, while the correlation between displacement and wind speed is very weak. Two regression models are developed to simulate the varying displacements under the changes in temperature and traffic loadings. Based on these models, the effects of the environmental conditions are removed to obtain the normalized displacement. Statistical process control using mean value control charts is further used to detect damage to the bridge expansion joints. The results reveal that the proposed method had a good capability for detecting the damage-induced 1.0% variances of the annual changes in the expansion joint displacements.
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