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Recently, as the development of information technologies and personalized needs, the Industrial Internet platform based manufacturing service collaboration becomes the main method for manufacturing collaboration, where multiple interest-independent stakeholders involve. However, the distrust between stakeholders and the platform (distrust among collaborators and the doubts of data accuracy) hinders the development of it. Manufacturing service that are mainly encapsulated by static information cannot well adapt to changing physical conditions, which will disappoint the stakeholders for data accuracy. In addition, distrust among unfamiliar stakeholders is also the reason to keep users away from the platform. Digital twin (DT) and blockchain (BC) technologies have been widely studied in many fields, as they can provide a trustful method for the process of cyber-physical integration. In view of the insufficient interaction of physical and cyber spaces and distrust challenges encountered in the development of the current Industrial Internet platform based manufacturing service collaboration, this paper proposes a DT-BC enhanced manufacturing service collaboration mechanism towards the Industrial Internet platform. In addition, based on the analysis of manufacturing collaboration development, the DT-BC enhanced manufacturing service management, challenges, and future work of implementing DT-BC enhanced manufacturing service management for the Industrial Internet Platform are discussed.

As the new generation of information technology, blockchain is characterized by a set of unique qualities and features in data storage and computing infrastructure, such as data traceability, data security, decentralized consensus, and decentralized process execution. These characteristics make it to be an important enabling technology for smart manufacturing, especially in product lifecycle management (PLM). In this paper, key features of blockchain are summarized, and its development history is analyzed first. Then, the challenges of PLM in data management and data security among multi-stakeholders are investigated, and a potential way to address these challenges by employing blockchain is discussed. An assessment framework is designed to evaluate the viability of applying blockchain in specific PLM scenarios. The application or potential application of blockchain in each section of PLM is studied in detail. Finally, both technical and non-technical challenges of applying blockchain in PLM are pointed out to achieve more effective applications.

Smart manufacturing is a popular concept for smarter decision-making and more efficient production. Although distributed methods for data management and processing in smart manufacturing have many ...

As a series of industrial Internet platforms have been launched, manufacturing facilities in the physical world, although distributed in different enterprises, are interconnected in the form of manufacturing services (MSs) in cyberspace. In this context, it makes possible for on-demand sharing of MSs as well as corresponding cross-enterprise collaboration. However, many dynamic uncertainties of both MSs and the submitted demands occur unpredictably, which seriously hinders the platforms’ applications. To cope with the problem of MSs scheduling with supply–demand dual dynamic uncertainties, a three-stage approach based on an evolutionary hypernetwork model is proposed. In which, six of nine kinds of dynamic events and eighteen specific conditions are considered, and an event-condition-act mechanism is designed to guide local/global rescheduling if needed. Experimental results show the effectiveness of the proposed approach, as well as the potential of a platform employing the approach in response to different dynamic events in its application.

Recently, artificial intelligence (AI) technology receives extensive attention in the manufacturing field. As the core technology, it generates considerable interest among smart manufacturing and Industry 4.0 strategy. Product lifecycle management (PLM) copes with various kinds of engineering, business, and management activities concerning a product throughout its whole lifecycle—from the inception of an intangible concept through the recycling of a finished product. In the context of smart manufacturing, this paper reviews various theories, algorithms, and technologies of AI to different stages of PLM (i.e., product design, manufacturing, and service). A structured roadmap is presented to navigate the future research and application of AI in PLM. This paper also discusses the opportunities and challenges of applying AI for PLM.

The overheated region is an abnormal condition for photovoltaic (PV) module in the routine inspection of PV plant. Many studies have invited thermography images to identify overheated region problems (e.g., hotspot), since they are easy and cheap to be collected. But these studies fail to automatically recognize the specific types and the exact positions of different potential overheated region targets in a single thermography image of PV module. Moreover, some overheated regions of PV module are small in scale, which induces that many traditional approaches fail to identify some overheated regions effectively and efficiently. Accordingly, a deep learning-based framework is proposed to handle these problems. First, multiple large-scale images are transformed from thermography images with overheated regions to precisely detect overheated region targets. Then, regions of interest are extracted from these images to bound potential regions that may exist overheated regions. Finally, a deep joint learning model is used to recognize the overheated region type and position from these regions. To benchmark the proposed framework, categories of experiments are conducted over the collected dataset. It proves that the proposed approach outperforms benchmarked approaches in terms of effectiveness and efficiency.

As an enabling technology for smart manufacturing, digital twin has been widely applied in manufacturing shop-floor. A great deal of research focuses on the key issues in implementing digital twin shop-floor (DTS), including scheduling, production planning, fault diagnosis and prognostics. However, DTS puts forward higher requirements in terms of real-time interaction. Artificial intelligence (AI), as an effective approach to improve the intelligence of the physical shop-floor, provides a new method to meet the above requirements. In this paper, a framework of AI-enhanced DTS in interaction is proposed. AI-enhanced DTS improves the real-time interaction through predictive control. The implementation mechanism of AI-enhanced interaction in DTS is also presented in detail. Enabling technologies for interaction in DTS are introduced at last.

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.

Since composite components have been used in many fields with high-performance requirements, their quality is always of great concern. During production, improving temperature uniformity of the mold, which has close contact with the composites, is critical for reducing component deformation. However, the bottleneck is realizing the rapid and accurate prediction for the mold temperature distribution. Therefore, this paper designs a new hybrid modeling method for mold temperature prediction, which is driven by both physical and data models. The proposed method is applied in a case study of quality assurance for a plate component. Its advantages are also validated.

With the deeper application of sensor & cloud-based environment into manufacturing, deploying the industrial Internet platforms toward smart manufacturing has been more concerned. Based on the platforms, ubiquitous enterprises could participate in and support cross-enterprise collaboration, so that their distributed manufacturing facilities and capabilities could be shared and utilized in the form of manufacturing services (MSs). However, in order to achieve the successful application of the platforms, how to settle the supply demand matching (SDM) of the distributed manufacturing facilities and capabilities in the form of MSs, namely, MSs-SDM, becomes one of the most urgent problems to be solved. In addition, the trend of manufacturing socialization makes this problem much more scalable. In this context, this article aims to establish a set of hypernetwork-based models for the scalable MSs-SDM problem at first. An enterprises collaborative network is derived which is the projection of the underlying MSs-SDM situation to the upper-layer enterprises. Second, a method according to the evaluation on the cross-enterprise collaboration is proposed for this problem. In which, the created utilities, the rates of service invocation, and task allocation from both the global view of the overall network and the local view of each participated enterprise are evaluated. Finally, two steps of experiments introducing scalabilities illustrate the feasibility of the proposed models and the effectiveness of the derived method for MSs-SDM optimization, and further reveal five managerial implications to improve the operation and industrial practice of the platforms.

Industrial Internet or industrial cloud platforms based manufacturing collaboration in the form of services, namely manufacturing services collaboration (MSC), is being the new generation of networked manufacturing mode. In the process of MSC, a lot of failures would occur due to different reasons, and then propagate to cause other parts of failures. However, the classification of failures in MSC is unclear, the occurrence and propagation of failures are difficult to detect, and the control strategies for the failures and their corresponding propagation is insufficient, which lead to the lower users’ collaboration participation in the platforms. In order to solve the above problems, the failures detection, failures cascading propagation analysis and specific control strategies for the platform-based MSC are investigated in this study. Firstly, the model of manufacturing services collaboration network (MSC-Net) is established by introducing the collaborative relations aware community of complex network. Then six types of failures occurred in MSC-Net are defined, corresponding detection methods for the defined failures are presented. The specific cascading propagation characteristics of different failures are revealed. The control strategies for different failures as well as their cascading propagation in MSC-Net are proposed. Finally, a 3D Printing case is given to illustrate the feasibility of the proposed methods.

Manufacturing resources sharing and collaboration has become the inevitable trend to optimize the operation of manufacturing industry. The development of various advanced manufacturing systems (AMSs) and new-generated information technologies (New ITs) prompt the emergence of various industrial Internet platforms. In this context, it provides a brand new opportunity for enterprises to conduct manufacturing resources sharing and collaboration in the form of manufacturing services, namely, manufacturing services collaboration (MS-collaboration). However, the relevant research on MS-collaboration under the environment of industrial Internet platforms is still insufficient. Therefore, in this paper, the evolution from manufacturing collaboration to MS-collaboration toward industrial Internet platforms is analyzed at first, and the derived environment characteristics and challenges of MS-collaboration are discussed accordingly. After exploring the connotation of MS-collaboration, its implementation framework and corresponding enabling technologies are proposed. A 3D Printing case is presented to illustrate the application of the proposed methods. Finally, in order to promote the application of an industrial Internet platform, stimulate more enterprises to participate in, and conduct MS-collaboration in it, several future research issues are pointed out.

Unstable buckling leads to the failure of thin-walled cylinders under axial compression. Because of the manufacturing and loading imperfections, a significant variation was observed between experim...

Background The impact of antiviral therapy on long-term survival outcomes in patients with small HBV-related hepatocellular carcinoma (HBV-related HCC) after liver resection is still controversial, as the impact can be overshadowed by tumor-related factors. This study investigated this impact on recurrence and survival in patients with HCC of less than 3 cm. Objective This study was designed to further determine the impact of antiviral treatment on prognosis of patients with HCC after liver resection, to verify whether patients with cirrhosis still benefited from antiviral treatment, to study the impact of antiviral treatment on post-operative HCC recurrence, and to determine whether patients with a low preoperative HBV-DNA viral load should receive antiviral therapy. Methods The clinical data on patients who underwent curative liver resection for histopathologically confirmed small HCC (≤3 cm in diameter) were analyzed to determine factors which were related with HCC recurrence and survival. The disease-free and overall survival outcomes were estimated by the Kaplan-Meier method. Univariate and multivariate analyses were performed to identify the risk factors of long-term survival. Results Of the 795 patients in this study, patients with high preoperative HBV-DNA levels had significantly worse DFS and OS outcomes at 1-, 3- and 5- year after liver resection when compared with those with low HBV-DNA levels (86.1%, 60.8%, 46.6% vs 90.5%, 71.3%, 51.4%; and 98.5%, 89.3%, 75.2% vs 98.8%, 91.5%, 84%, respectively). Patients who received antiviral therapy had significantly better DFS and OS outcomes at 1-, 3- and 5- year after liver resection when compared with those without (91.6%, 69.5%, 55% vs 80.2%, 56%, 44.2%; and 99.6%, 93.5%, 87% vs 96.1%, 80.5%, 61.3%, respectively). Antiviral therapy significantly improved the OS but not DFS outcomes in patients with low HBV-DNA levels. The corresponding 1-, 3- and 5- year DFS and OS outcomes were 92.6%, 73%, 59.1% vs 87.1%, 68.5%, 57.9%; and 99.5%, 95.1%, 91.1% vs 97.6%, 85.5%, 72.4%, respectively. Antiviral treatment significantly prolonged DFS and OS in patients with cirrhosis. The corresponding 1-, 3- and 5- year DFS and OS were 90.2%, 66%, 49% vs 73.9%, 46.6%, 32.8%; and 100%, 93.6%, 85% vs 93.8%, 73.3%, 52.6%, respectively. Conclusion Antiviral therapy improved the prognosis of small HBV-related HCC of less than 3 cm. The survival benefit was also detected in patients with cirrhosis. Antiviral therapy should be considered a routine post-operative therapy for patients with HBV-related HCC.

To improve the energy efficiency of the manufacturing industry, it is vital to correlate the energy consumption of machine tools with the machinery data. This paper proposes an OPC-UA based framework for predicting the energy consumption of machine tools, which includes the off-line learning stage and on-line exploitation stage. By using the proposed method, the gap between machinery data and energy data could be bridged. The experiment conducted on a milling machine has been exploited to validate the proposed framework. The results show that the proposed framework is feasible to predict the energy consumption of machine tools.

In the new era of Industry 4.0, traditional manufacturing plants are forced to transform into smart factories with cyber-physical product creation systems. Robots are believed to be one of the key components of such systems. This paper presents an architecture of using cloud-based ubiquitous robotic systems for smart manufacturing of the customized product. A framework for designing a cloud-based ubiquitous robotic system (URS) is developed, which consists of the function, structure and behavior of a URS. After that, a procedure for the development of such a URS is provided. Finally, the implementation of a cloud-based ubiquitous robotic system for smart producing and assembly of a customized product shows that the proposed approach can achieve the goal of smart manufacturing of customized product by developing cloud-based ubiquitous robotic systems.

Integrated and collaborative manufacturing system develops as massive data are obtained by the Internet of Things (IoT) technology. However, the “trust tax” imposed on manufacturers during their countless collaborations with customers, suppliers, distributors, governments, service providers, and other manufacturers is very high. Blockchain is an emerging technology that can lead to more transparent, secure, and efficient transactions. It represents a new paradigm, as well as new thinking, of how data can be securely stored, integrated, and communicated among different stakeholders, organizations, and systems that unnecessarily trust each other. Blockchain is greatly useful for reducing the “trust tax,” especially beneficial for the small- and medium-sized enterprises that must tolerate much heavier trust tax than the established manufacturers. This paper investigates the blockchain-based security and trust mechanism and elaborates a particular application of blockchain for quality assurance, which is one of the strategic priorities of smart manufacturing. Data generated in a smart manufacturing process can be leveraged to retrieve material provenance, facilitate equipment management, increase transaction efficiency, and create a flexible pricing mechanism. The dairy industry is used to instantiate the value propositions of blockchain for quality assurance.

Background: CBX3 is a key gene that is involved in immune cell regulation, however, its prognostic values and its correlation with infiltrating lymphocytes in various cancers have not been clearly established. This study aims to investigate the role CBX3 in hepatocellular carcinoma (HCC).Methods: We first reviewed the expression of CBX3 in different cancers and adjacent tissues using oncomine database. Next, the authors focus on the expression of CBX3 in hepatocellular carcinoma. Therefore, the expression of CBX3 in hepatocellular carcinoma were analyzed through UALCAN online analysis website and the Human Protein Atlas (www.proteinatlas.org) website. In addition, we further found that CBX3 can be identified as an effective marker for the prognostic guidance of hepatocellular carcinoma according to the Kaplan-Meier plotter database and the Bioinformatics analysis online websites (www.aclbi.com). Next, we used the Bioinformatics analysis online websites to explore whether the expression level of CBX3 in liver cancer is related to the infiltration of certain immune cells. In addition, we also predicted the correlation between immune checkpoint and CBX3 in liver cancer.Results: The analysis results preliminarily show that CBX3 be expressed abnormally in many cancers, and CBX3 was significantly up-regulated in HCC. The high expression of CBX3 indicated survival outcomes and it showed a huge potential as a effective marker for the prognostic guidance of hepatocellular carcinoma. Furthermore, we found that CBX3 in liver cancer is related to the infiltration of certain immune cells, including CD4+ T cells, macrophages and B cells. In addition, the results showed HAVCR2 is most likely to become an effective immune checkpoint for HCC patients immunotherapy with high CBX3 expression.Conclusions: CBX3 is a potential diagnostic and prognostic marker in HCC and related to the infiltration of certain immune cells. It is expected to become a breakthrough point in immunotherapy in the future.

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely smart manufacturing (SM) and intelligent manufacturing (IM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While SM and IM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM. A bibliometric analysis of publication sources, annual publication numbers, keywords frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies of SM and IM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM is provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

Long noncoding RNA (lncRNA) urothelial carcinoma associated 1 (UCA1) has been reported to be highly expressed in many kinds of cancers. This meta-analysis summarized its potential prognostic value in digestive system malignancies. A meta-analysis was performed through a comprehensive search in PubMed, EMBASE, the Cochrane Library, Web of Science and Chinese National Knowledge Infrastructure (CNKI) for suitable articles on the prognostic impact of UCA1 in digestive system malignancies from inception to June 27, 2019. Pooled hazard ratios (HRs) with 95% confidence interval (95%CI) were calculated to summarize the effect. Sixteen studies were included in the study, with a total of 1504 patients. A significant association was observed between UCA1 abundance and poor overall survival (OS), and shorter disease-free survival (DFS) for patients with digestive system malignancies, with pooled HR of 2.07 (95%CI: 1.74-2.47), and of 2.50 (95%CI: 1.62-3.86). Subgroup analysis and sensitivity analysis suggested the reliability of our findings. It is suggested that UCA1 abundance may serve as a reliable predictive factor for poor prognosis in patients with digestive system malignancies.

Task allocation (TA) problem is of critical importance in manufacturing industry, and determines the effectiveness and efficiency of advanced manufacturing systems. A proper TA approach can give an optimized arrangement of existing resources, enable manufacturing system's flexibility, thus improve both economic performance and social benefits. However, there is still no uniform analysis on TA to date, while it has been paid more attention from the view of manufacturing resource allocation. With the application of advanced information and manufacturing technologies, the TA process improved with intelligence or even smartness could respond to demand changes rapidly and maintain a good balance for supply-demand matching issues. In this paper, TA and its intelligent improvements are picked and investigated. The general workflow of TA is divided into six stages: task description and modelling, analysis and modelling of TA process, algorithm design and selection for TA, decision-making of TA, simulation, and task execution. Each stage is separately analyzed at first. In particular, the decision-making process of TA consists of two approaches: the traditional way of system-oriented process (SoP), and the task-oriented process (ToP). Researches show that the latter one can better suit current systems and their manufacturing environment. At last, future directions of TA are pointed out to make systems achieve much more intelligence.

The rapid development of information and communication technologies (ICTs) and cyber–physical systems (CPSs) has paved the way for the increasing popularity of smart products. Context-awareness is an important facet of product smartness. Unlike artifacts, various bio-systems are naturally characterized by their extraordinary context-awareness. Biologically inspired design (BID) is one of the most commonly employed design strategies. However, few studies have examined the BID of context-aware smart products to date. This paper presents a structured design framework to support the BID of context-aware smart products. The meaning of context-awareness is defined from the perspective of product design. The framework is developed based on the theoretical foundations of the situated function–behavior–structure ontology. A structured design process is prescribed to leverage various biological inspirations in order to support different conceptual design activities, such as problem formulation, structure reformulation, behavior reformulation, and function reformulation. Some existing design methods and emerging design tools are incorporated into the framework. A case study is presented to showcase how this framework can be followed to redesign a robot vacuum cleaner and make it more context-aware. Keywords: Design method, Biologically inspired design, Context-awareness, Intelligent design

State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber–physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber–physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber–physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber–physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives. Keywords: Cyber–physical systems (CPS), Digital twin (DT), Smart manufacturing, Correlation and comparison

With the advances in new-generation information technologies (New IT), such as internet of things (IoT), cloud computing, and big data, etc., the big data-driven smart manufacturing era is coming. The volume of data generated and collected in manufacturing process is explosively growing, and big data need to be transmitted from data resources to a fog or a cloud platform. However, some practical limitations, such as overfull bandwidth, and data loss, confine the promotion of smart manufacturing. The limiting capacity of current data communication technologies becomes the bottleneck for smart manufacturing systems. In this paper, fibre channel (FC) switch based on field programmable gate array (FPGA) is designed and implemented due to its high speed, low latency, and high performance transmission capacities. Categories of comparative experiments were conducted and a case study is presented, which indicate that the designed FC switch meets the need of big data transmission for smart manufacturing. Its advanced capacity of transmitting and processing big data opens a bright perspective for smart manufacturing.

Background & Aims Genetic variability in the hepatitis B virus X gene (HBx) is frequently observed and is associated with hepatocellular carcinoma (HCC) progression. However, a genotype classification based on the full-length HBx sequence and the impact of genotypes on hepatitis B virus (HBV)-related HCC prognosis remain unclear. We therefore aimed to perform this genotype classification and assess its clinical impact. Methods We classified the genotypes of the full-length HBx gene through sequencing and a cluster analysis of HBx DNA from a cohort of patients with HBV-related HCC, which served as the primary cohort (n = 284). Two independent HBV-related HCC cohorts, a validation cohort (n = 171) and a serum cohort (n = 168), were used to verify the results. Protein microarray assay analysis was performed to explore the underlying mechanism. Results In the primary cohort, the HBx DNA was classified into 3 genotypes: HBx-EHBH1, HBx-EHBH2, and HBx-EHBH3. HBx-EHBH2 (HBx-E2) indicated better recurrence-free survival and overall survival for patients with HCC. HBx-E2 was significantly correlated with the absence of liver cirrhosis, a small tumor size, a solitary tumor, complete encapsulation and Barcelona Clinic Liver Cancer (BCLC) stage A-0 tumors. Additionally, HBx-E2 served as a significant prognostic factor for patients with BCLC stage B HCC after hepatectomy. Mechanistically, HBx-E2 is unable to promote proliferation in HCC cells and normal hepatocytes. It also fails to activate the Janus kinase 1 (JAK1)/signal transducer and activator of transcription 3 (STAT3)/STAT5 pathway. Conclusion Our study identifies a novel HBx genotype that is unable to promote the proliferation of HCC cells and suggests a potential marker to preoperatively predict the prognosis of patients with BCLC stage B, HBV-associated, HCC. Lay summary We classified a novel genotype of the full-length hepatitis B virus X gene (HBx), HBx-E2. This genotype was identified in tumor and nontumor tissues from patients with hepatitis B virus-related hepatocellular carcinoma. HBx-E2 could preoperatively predict the prognosis of patients with intermediate stage hepatocellular carcinoma, after resection.

The structure of Printed Circuit board (PCB) is very complicated because it consists of woven composites and custom defined conducting layers. To improve the reliability of PCB, it is critical to p...

Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.

The state-of-the-art technologies in new generation information technologies (New IT) greatly stimulate the development of smart manufacturing. In a smart manufacturing environment, more and more devices would be connected to the Internet so that a large volume of data can be obtained during all phases of the product lifecycle. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. However, different factors, such as the network unavailability, overfull bandwidth, and latency time, restrict its availability for high-speed and low-latency real-time applications. Fog computing and edge computing extended the compute, storage, and networking capabilities of the cloud to the edge, which will respond to the above-mentioned issues. Based on cloud computing, fog computing, and edge computing, in this paper, a hierarchy reference architecture is introduced for smart manufacturing. The architecture is expected to be applied in the digital twin shop floor, which opens a bright perspective of new applications within the field of manufacturing.

Human-machine interaction (HMI) is a key technology for implementing smart manufacturing, which primarily focuses on the issues of communication, interaction, and cooperation between humans and machines. HMI has been widely studied in product lifecycle including product design, manufacturing, and service, but the efficiency and safety still cannot meet the new requirements for smart manufacturing along with new emerging technologies (e.g., big data, artificial intelligence, augmented reality, etc.). Digital twin (DT), as a new technology to realize cyber-physical integration, provides a new chance to address the above problems. Using a five-dimension DT model driven by real-time data, HMI will generate a more flexible interactive mode to improve machine efficiency and operation safety. In this paper, HMI in product lifecycle is investigated first, and the shortness and new requirements are analyzed, then a DT enhanced HMI framework is proposed to address the shortness.

The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively. Funding Agencies|National Research Foundation (NRF), SingaporeSingapore National Research Foundation; Delta Electronics International (Singapore) Pte., Ltd., through the Corporate Laboratory@ University Scheme, Nanyang Technological University, Singapore [RCA-16/434]

In the big data era, a variety of new data can be collected from various sources and analyzed through different techniques to support the decision-making regarding product customization. Based on a thorough investigation of relevant data, this paper presents a new customization framework that is greatly driven by data. The framework includes three interrelated components: data module, design module, and service module. It is intended to promote smart customization in terms of product designing, manufacturing, pricing, marketing, and service. A case study of product customization in the footwear industry is presented to instantiate the implementation of the proposed framework.

Data acquisition of manufacturing equipment and process is the foundation to achieve intelligent shop-floor. Due to the instantaneity and suddenness of sudden small-volume data (SSVData), sensitive perception and high frequency acquisition are necessary. However, continuous high frequency acquisition will result in a large amount of data, which brings great difficulties to data storage and analysis. Meanwhile, the existing low frequency acquisition for conventional large-volume data (CLVData) cannot meet the requirements of SSVData. Therefore, this paper systematically analyzes the differences and relationship between CLVData and SSVData, and investigates state-of-the-art of both in shop-floor. Then a novel variable frequency sampling method is proposed, which solves the contradiction between low frequency acquisition for CLVData and high frequency acquisition for SSVData.

Designing a product service system begins with understanding customer voices. Compared to the traditional methods such as survey, interview, customer review represents a particular kind of big data that contain rich information that is useful for the design of product service system. This paper presents a new framework that integrates a variety of artificial intelligence and machine learning techniques. All proposed operations of the framework can be realized based on the Google Cloud Platform. A case study is conducted to showcase the practical applicability of some key operations such as opinion mining. More than 40,000 customer reviews about the product Kindle White E-reader were analyzed.

Recently, along with the wide application of new generation information technologies (New IT) in manufacturing, many countries issued their national advanced manufacturing development strategies, such as Industrial Internet, Industry 4.0, and Made in China 2025. One common aim of these strategies is to achieve smart manufacturing, which demands the interoperation, integration, and fusion of the physical world and the cyber world of manufacturing. As well, New IT [such as Internet of Things (IoT), cloud computing, big data, mobile Internet, and cyber-physical systems (CPS)] have played pivotal roles in promoting smart manufacturing. Data generated in the physical world can be sensed and transfered to the cyber world through IoT and the Internet, and be processed and analyzed by cloud computing, big data technologies to adjust the physical world. The physical world and the cyber world of manufacturing are integrated based on CPS. On the other hand, servitization has become a prominent trend in the manufacturing. Embracing the concept of “Manufacturing-as-a-Service,” manufacturing is provided as service for users. Because of the characteristics of interoperability and platform independence, services pave the way for large-scale smart applications and manufacturing collaboration. Combining New IT and services, this paper proposes a framework—New IT driven service-oriented smart manufacturing (SoSM). SoSM aims at facilitating the visions of smart manufacturing by making full use of New IT and services. Complementary to the framework of SoSM, the New IT driven typical characteristics of SoSM are also investigated and discussed, respectively.

COVID-19, which is rampant around the world, has seriously disrupted people's normal work and living. To respond to public urgent needs such as COVID-19, emergency supplies are essential. However, due to the special requirements of supplies, when an emergency occurs, the supply reserve mostly cannot cope with the high demand. Given the importance of emergency supplies in public emergencies, rapid response manufacturing of emergency supplies is a necessity. The faster emergency supplies and facilities are manufactured, the more likely the pandemic can be controlled and the more human lives are saved. Besides, new generation information technology represented by cloud computing, IoT, big data, AI, etc. is rapidly developing and can be widely used to address such situations. Therefore, rapid response manufacturing enabled by New IT is presented to quickly meet emergency demands. And some policy suggestions are presented.

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

International audience; Digital twin (DT) technology aims to create a virtual model of a physical entity and efficiently analyze the intelligent manufacturing system. Based on the DT, human-machine interaction (HMI) is a typical application. Deep learning technology is employed in the digital twin to realize and strengthen HMI while analyzing the physical and virtual data. The convolutional neural network (CNN) is used for analyzing visual information. For dealing with the HMI task in DT, two CNN models, Visual Geometry Group Network (VGG) and Residual Network (ResNet), are adopted. Modified 3D-VGG and 3D-ResNet models are proposed in this paper, which is an improvement over existing VGG and ResNet models. The models focus on humans’ information in videos that are captured with the HMI system’s sensors. The information, which can be regarded as the digital twin data, includes the action and the position of the human skeleton. Additionally, the proposed models are end-to-end. The experiments show that both models perform well on the human motion recognition task. The model can effectively generate skeletal data from video data. With the generated information, the human and the machine can interact well with the aid of the digital twin data analysis.

Infrared(IR)photo detectors based on InAs/GaSb type II superlattice have developed quickly in recent years. Many groups show great interest in InAs/GaSb superlattice detector for its superiors as high quantum efficient, high working temperature, high uniformity and low dark current densities. Inductively coupled plasma(ICP) etching of GaSb and InAs/ GaSb superlattices were performed using Cl2/Ar/CH4/H2. This paper introduceste inductively coupled plasma( ICP) etching of Inas/GaSb with SiO2 mask by the Cl2/Ar/CH4/H2 mixed-gas process. The effects of process parameters such as gas combination, ICP and RF power on the etch rate and quality of InAs/GaSb It is found that the ratio of Cl2 flow rate significantly affects the etch rate, due to the trade-off between physical and chemical component of etching. The etch rate of InAs/GaSb increases with the increase of percent of Cl2, there will InClx remains in the etching channel when the etching depth exceeded 2μm, which can stop the etching going on. This phenomenon can be eliminated by decrease the Cl2 ratio,to make sure the etching depth reach 6μm under a certain low etching rate. The surface morphology and SEM of the superlattice material after etching shows that dry etching morphology is better than wet etching.After the electrode is grown, the superlattice chip have a good diode characteristic curve.

The particulate observing scanning polarimeter (POSP) measurement spatial response function (SRF) relates to the weighted contribution of each location within the measurement footprint, which is determined by the percentage of the dwell time of each location on the Earth surface to the overall sampling integration time. The SRF resulting from a combination of the equally weighted instantaneous field of view (IFOV) during integration is required for an accurate modeling. Simply using a mean value SRF assuming an equivalent weight at each sampling position instead of the actual SRF will inevitably introduce errors. Considering the data fusion between POSP and high spatial resolution sensors, a discrete integration method that takes the effect of actual weights into account is proposed in this paper. The simulation results of the integral model and the mean value model show that the larger the intensity change in the sampling area covered by the IFOV of the POSP during a single sampling, the more significant the difference between the two results. Meanwhile, the integration SRF is validated by resampling the simultaneous imaging polarization camera (SIPC) data, which is compared with POSP data acquired at the same time in an aerial experiment. The results show that the integration SRF model is more accurate to characterize the details of POSP measurement than the mean value SRF model. The proposed SRF reduces the root mean square error (RMSE) of convolved results and measurements by 5∼30% with different radiance contrast scene.

Urban functional area (UFA) recognition is one of the most important strategies for achieving sustainable city development. As remote-sensing and social-sensing data sources have increasingly become available, UFA recognition has received a significant amount of attention. Research on UFA recognition that uses a single dataset suffers from a low update frequency or low spatial resolution, while data fusion-based methods are limited in efficiency and accuracy. This paper proposes an integrated model to identify UFA using satellite images and taxi global positioning system (GPS) trajectories in four steps. First, blocks were generated as spatial units in the study area, and the spatiotemporal information entropy of the taxi GPS trajectory (STET) for each block was calculated. Second, a 24-hour time-frequency series was formed based on the pick-up and drop-off points extracted from taxi trajectories and used as the interpretation indicator of the blocks. The K-Means++ and k-Nearest Neighbor (kNN) algorithm were used to identify their social functions. Third, a multilabel classification method based on the residual neural network (MLC-ResNets) and “You Only Look Once” (YOLO) target detection algorithms were used to identify the features of the typical and atypical spatial textures, respectively, of the satellite images in the blocks. The confidence scores of the features of the blocks were categorized by the decision tree algorithm. Fourth, to find the best way to integrate the two sub-models for UFA identification, the 10-fold cross-validation method based on stratified random sampling was applied to determine the most optimal STET thresholds. The results showed that the average accuracy reached 82.0%, with an average kappa of 73.5%—significant improvements over most existing studies. This paper provides new insights into how the advantages of satellite images and taxi trajectories in UFA identification can be fully exploited to support sustainable city management.

International audience; Soft Computing are facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of Soft Computing such as image processing, computer vision and pattern recognition combined with Cloud Computing, the construction of smart cities could be maintained and greatly enhanced. In this article, we focus on the ODAS (Online Detection of Action Start) task in video understanding and analysis which is critical to the multimedia security in smart cities. We propose a novel model to tackle this problem and achieves state-of-the-art results on the benchmark THUMOS14 dataset.

Artificial neural network (ANN) models are used to learn the nonlinear constitutive laws based on indirectly measurable data. The real input and output of the ANN model are derived from indirect data using a mechanical system, which is composed of several subsystems including the ANN model. As the ANN model is coupled with other subsystems, the input of the ANN model needs to be determined during the training. This approach integrates measurable data, mechanics, and ANN models so that the ANN models can be trained without direct data which is usually not available from experiments. Two examples are provided as an illustration of the proposed approach. The first example uses two-dimensional (2D) finite element (FE) analysis to train an ANN model to learn the nonlinear in-plane shear constitutive law. The second example applies a continuum damage model to train an ANN model to learn the damage accumulation law. The results show that the trained ANN models achieve great accuracy based on the proposed approach.

The failure prognosis is crucial for industrial equipment in prognostics and health management field. The vibration signal is the commonly used data for failure prognosis. The conventional prognostic approaches have limitations to handle the features extracted from the vibration signal because of the large data quantity, complex feature relations, and limited degeneration mechanisms. In this paper, a deep learning-based approach is proposed to predict the failure of the complex equipment. To supply plenty of features, three different domain features are extracted from vibration signals. Next, these features are preprocessed and reconstructed by arctangent normalization and data stream, respectively. Finally, a deep neural network, namely, multistream deep recurrent neural network (MS-DRNN) is built to fuse these features for failure target. The presented deep neural network is hybrid, involving recurrent layer, fusion layer, fully connected layer, and linear layer. To benchmark the proposed approach, several prognosis approaches are evaluated with the testing data from six large bearing datasets. Simulation results demonstrate that the prediction performance of the MS-DRNN-based approach is effective and reliable.

Computerized numerical control machine tools (CNCMTs) are fundamental facilities in the manufacturing field. It is essential to establish a precise virtual prototype of CNCMTs for simulation in order to save time and ensure safety. Considering the design, operation, and maintenance processes of CNCMTs, there are two requirements for establishing the virtual prototype: (1) the virtual prototype should reflect the comprehensive performance of multidomain systems of CNCMTs. (2) The virtual prototype should be aware of performance changes of CNCMTs and update itself autonomously. Therefore this chapter establishes a multidomain virtual prototype of CNCMTs on a unified platform and introduces the concept of digital twin (DT) into prototype to achieve real-time and accurate mapping between the virtual and real worlds. First, a basic virtual prototype of CNCMTs is built by establishing its mechanical, electrical, and control subsystems. Then, an updating strategy that contains mapping and consistency maintenance is designed for dynamic and accurate model update. Taking advantage of the cyber-physical mapping characteristics of DT, the designed virtual prototype can truly reflect the actual processing state. On this basis, the accuracy of simulation using the DT-based CNCMTs virtual prototype can be improved, which will benefit the various stages of CNCMTs such as design and commissioning.

With the advent of new generation information technologies (new IT) in industry and product design, the big data–driven product design era has arrived. However, current research mainly places emphasis on the analysis of physical data rather than the virtual models. In other words, the convergence between product’s physical and virtual spaces is usually absent. Fortunately, digital twin (DT), an emerging and fast-growing technology, provides a promising way to connect and integrate the physical and virtual spaces seamlessly, thus to solve the previous problem efficiently. Therefore this chapter introduces DT into product design and presents a new design method based on the DT approach. First, related theories and technologies of product design are reviewed and the concept of DT is explored. Then a five-dimension DT and its abilities enabled by new IT are proposed. Based on this, a DT-driven smart product design (DTPD) framework is discussed, in terms of key processes and related technologies. Finally, DTPD applications in bicycle and landing gear are presented as two case studies to illustrate the proposed method.

In comparison to conventional conceptual design, capability of big data analysis, machine learning ability, and physical–virtual simulations, the digital twin (DT) assists designers to obtain earlier stage feedbacks, more reliable concept generations and evaluations. This chapter will envision the evolution of conceptual design supported by DT technology. Conceptual design methodologies are introduced first, including axiomatic design, systematic design, and function–behavior–structure ontology. Mainly based on the axiomatic design theory, the chapter clarifies how DT technology assists the functional modeling, concept generation, and concept evaluation. In addition, contradiction resolution and constraints management are also part of the discussion. The DT-driven conceptual design is illustrated with a robot vacuum cleaner as an example in each procedure.

An increasing number of cities are implementing bicycle sharing systems to reduce traffic congestion. Determining the locations of bicycle stations is one of the fundamental challenges in planning of such systems. This paper provides a novel solution on it. The min-plus algebra is introduced to model transport systems for accessibility analysis. A unified model in the sense of the min-plus algebra for an integrated system with both buses and bicycles is presented to dynamically describe the state transitions of passengers in the system. A new accessibility is proposed with regards to a general index $\omega $ such as the geographical distance and the travel time. A necessary and sufficient condition on the accessibility is then provided. The minimization of bicycle stations under the accessibility is formulated to be a 0–1 integer programming problem. The case studies on two cities, Ningbo and Hangzhou, were performed, which show that compared with the current layouts of urban transportation networks, the proposed public bicycle sharing systems have remarkable advantages in topological characteristics and robustness against failures. Note to Practitioners —This paper reports a practical solution for station allocation for public bicycle sharing systems (PBSSs). PBSSs have increased tremendously in recent years and become popular in many countries. They complement the bus and subway systems and contribute to better environment and lifestyle. On the other hand, they cause some serious issues with their planning and operations. The station allocation for PBSSs is one of its fundamental challenges as it greatly affects investment and running costs as well as customer satisfaction. Note that the station allocation for PBSSs should be addressed based on a unified dynamic model taking into consideration the transition of passengers, PBSS, and urban public transportation system together. Thus, a dynamically synthesized, physically realizable, and satisfactory solution for station allocation of PBSSs is significant for the success of practitioners. This paper proposes a framework to analytically obtain the station allocation of bicycle sharing systems based on a new accessibility criterion associated with detailed planning requirements and on a hybrid min-plus model corresponding to an integrated public transportation system. Furthermore, an algorithm for obtaining the minimum number of bicycle stations is presented for ease of actual implementation in applications and demonstrated with numerical simulation on the systems in two cities, Ningbo and Hangzhou. It should be pointed out that our optimization formulation is indicative, and alternative formulations with other objective functions and constraints are possible. But, the proposed accessibility as the most fundamental constraint of transportation systems should be included in any formulation.

The Off-axis Three-mirror Simultaneous Imaging Polarimeter (OTSIP) is a kind of polarimetric remote sensor with high spatial resolution. In OTSIP, simultaneous measurements were performed by means of prism dividing amplitude. Due to various equipped polarizers and complex polarimetric characteristics of OTSIP, its instrument matrix will deviate from the ideal value. In order to ensure the polarimetric accuracy of OTSIP, the development of an efficient polarimetric calibration is indispensable. In this paper, a calibration method using a standard linear polarization light source and circular polarization light source was proposed. The first three columns of the instrument matrix were firstly calibrated by a linear polarimetric calibration source to obtain the calibration coefficients via the least-squares fitting algorithm, and then the fourth column of the instrument matrix was calibrated by a circular polarimetric calibration source. Moreover, the nonideality of circular polarization state light was significantly improved by averaging measured results at 0 and 90° azimuths. As for the full field of view polarization calibration, a linear fitting method to each element of the instrument matrixes at multiple field of view angles was used. The resulting polarimetric measurement accuracy showed that the linear and circular polarization measurement accuracy was better than 1% (DOP

The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.