Many articles have been published on intelligent manufacturing, most of which focus on hardware, software, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a different perspective by examining relevant challenges and providing examples of some less-talked-about yet essential topics, such as hybrid systems, redefining advanced manufacturing, basic building blocks of new manufacturing, ecosystem readiness, and technology scalability. The first major challenge is to (re-)define what the manufacturing of the future will be, if we wish to: ① raise public awareness of new manufacturing’s economic and societal impacts, and ② garner the unequivocal support of policymakers. The second major challenge is to recognize that manufacturing in the future will consist of systems of hybrid systems of human and robotic operators; additive and subtractive processes; metal and composite materials; and cyber and physical systems. Therefore, studying the interfaces between constituencies and standards becomes important and essential. The third challenge is to develop a common framework in which the technology, manufacturing business case, and ecosystem readiness can be evaluated concurrently in order to shorten the time it takes for products to reach customers. Integral to this is having accepted measures of “scalability” of non-information technologies. The last, but not least, challenge is to examine successful modalities of industry–academia–government collaborations through public–private partnerships. This article discusses these challenges in detail.

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.

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).

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.

Using the theory of human blood circulation system, the authors explore the importance of remanufacturing in Industry 4.0. In this paper, they draw analogies between smart factory and human heart, between smart products and blood, and, between product function and nutrition and oxygen in the blood. Remanufacturing is analogous to the ingestion of oxygen and nutrition in lesser circulation or systemic circulation. Remanufacturing well supports recycling production, which is significant in realizing intelligent industry. Furthermore, this paper discusses the development direction of remanufacturing engineering in Industry 4.0 ages.

Understanding the holistic relationship between refinery production scheduling (RPS) and the cyber-physical production environment with smart scheduling is a new question posed in the study of process systems engineering. Here, we discuss state-of-the-art RSPs in the crude-oil refining field and present examples that illustrate how smart scheduling can impact operations in the high-performing chemical process industry. We conclude that, more than any traditional off-the-shelf RPS solution available today, flexible and integrative specialized modeling platforms will be increasingly necessary to perform decentralized and collaborative optimizations, since they are the technological alternatives closer to the advanced manufacturing philosophy.