With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. ...Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.
Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in ...the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
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.
Smart manufacturing is increasingly becoming the common goal of various national strategies. Smart interconnection is one of the most important issues for implementing smart manufacturing. However, ...current solutions are not tended to realize smart interconnection in dealing with heterogeneous equipment, quick configuration and implementation, and online service generation. To solve the issues, industrial Internet-of-Things hub (IIHub) is proposed, which consists of customized access module (CA-Module), access hub (A-Hub), and local service pool (LSP). A set of flexible CA-Modules can be configured or programed to connect heterogeneous physical manufacturing resources. Besides, the IIHub supports manufacturing services online generation based on the service encapsulation templates and also supports quick configuration and implementation for smart interconnection. Furthermore, related smart analysis and precise management have the potential to be achieved. Finally, a prototype is given to illustrate the functions of the proposed IIHub, and to show how IIHub realizes smart interconnection.
•CPS and digital twin are reviewed and analyzed from the multi-perspectives.•The differences and correlation between CPS and digital twin are discussed.•Digital twin can be considered as a necessary ...foundation and path to realize CPS.
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.
•An FC switch is designed and implemented on a FPGA at the speed greater than 1 Gbps.•FC protocol is adopted to solve data transmission bottleneck in smart manufacturing.•FC switch composing 3 ...modules is applied in a hydrovalve production line.•From experiments and comparisons, FC outperforms other data communication approaches.
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.
This study was aimed to elucidate the relationship between sperm DNA fragmentation index (DFI) and semen parameters, and to investigate the impact of these parameters on in vitro fertilization-embryo ...transfer (IVF-ET) outcomes. The study was conducted on 159 couples undergoing IVF-ET treatment at the Department of Reproductive Health from January 2019 to October 2023. The case group was comprised of 79 patients with sperm DFI of ≥15%, and the control group had 80 patients with <15% fragmentation index. Comprehensive data on semen parameters and the reproductive outcomes were collected and analysed. Comparisons of the case and control groups depicted no significant differences in key parameters including semen volume, sperm concentration, total sperm count, number of retrieved oocytes, rates of mature (MII) oocytes, normal fertilization, cleavage, blastocyst formation, high-quality blastocysts, human chorionic gonadotropin (HCG) positivity, clinical pregnancy, implantation and miscarriage (p > 0.05). However, marked differences were found in the rates of sperm progressive motility, total sperm motility, normal morphology, high-quality embryos, and transferable embryos (p < 0.05). The correlation analysis between sperm DFI and semen parameters exhibited positive correlation between sperm DFI and total sperm count (p < 0.05). The negative correlations were found between the sperm DFI and sperm progressive motility, total sperm motility, or normal morphology (p < 0.01). The findings demonstrated that incorporating sperm DFI as a standard component of semen analysis was advisable, and the sperm DFI as reference tool assisted in predicting the early embryonic development in IVF-ET patients.
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.
In real-world scenarios, the number of phishing and benign emails is usually imbalanced, leading to traditional machine learning or deep learning algorithms being biased towards benign emails and ...misclassifying phishing emails. Few studies take measures to address the imbalance between them, which significantly threatens people’s financial and information security. To mitigate the impact of imbalance on the model and enhance the detection performance of phishing emails, this paper proposes two new algorithms with undersampling: the Fisher–Markov-based phishing ensemble detection (FMPED) method and the Fisher–Markov–Markov-based phishing ensemble detection (FMMPED) method. The algorithms first remove benign emails in overlapping areas, then undersample the remaining benign emails, and finally, combine the retained benign emails with phishing emails into a new training set, using ensemble learning algorithms for training and classification. Experimental results have demonstrated that the proposed algorithms outperform other machine learning and deep learning algorithms, achieving an F1-score of 0.9945, an accuracy of 0.9945, an AUC of 0.9828, and a G-mean of 0.9827.
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