Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges

Efficient container virtualization-based digital twin simulation of smart industrial systems

Cloud manufacturing (CMfg) is a networked manufacturing mode that promotes the agile, service-oriented, green and intelligent development of the manufacturing industry. Although some scholars have reviewed related studies of CMfg from multiple perspectives, these reviews are not fully systematic or well justified and fail to fully reveal the key characteristics in the development process of CMfg. The purpose of this paper is to systematically review the relevant research on CMfg via identification of key characteristics of definition, architecture, supporting technology and application of CMfg to provide critical information in decision support for the innovation and development of CMfg.,This study systematically reviews the relevant research on CMfg across theoretical methods to technical applications by integrating quantitative and qualitative methods. Word cloud method is used to quantitatively analyse the structure and feature of different definitions of CMfg. The principle of System Science is used to explore the basic components and functions of various CMfg architectures and their common and differing characteristics. A multi-level technology framework is developed to explore the development status of CMfg supporting technologies. A multi-stage application classification is proposed to reveal the application status of CMfg.,Through literature review, this study found that CMfg architecture is currently dominated by general architectures and lacks architectures that fit the actual enterprise characteristics; CMfg supporting technology is mature in the traditional cloud computing-based technology, but it is still weak in the development of virtualization and servitization technology, service scheduling technology; CMfg application is still in the initial stage and still lacks a relatively complete system application. By analysing the development status of CMfg, this study also identified potential research directions of CMfg in information management, service composition and evaluation, system application and sustainable development and other aspects.,This paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. Chinese articles account for more than half of the total. The reason is that CMfg was proposed by the Chinese and CMfg is suitable for the development of China's manufacturing industry because of China's intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg.,Prior literature reviews ignore the identification and analysis of key feature identification for the current development of CMfg, including common and unique feature identification of different CMfg architectures and functions, multi-layer analysis and interpretation of CMfg technology and different stage analysis of CMfg applications. This study addresses these limitations and provides a comprehensive literature review.

Theory, supporting technology and application analysis of cloud manufacturing: a systematic and comprehensive literature review

With the integration of new-generation information and communication technology (ICT) in the manufacturing industry, many advanced manufacturing modes have been proposed. Cloud manufacturing (CMfg)...

Cloud manufacturing in China: a review

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification.

/pdf/dfcanet-a-novel-lightweight-convolutional-neural-network-29roda16.pdf

DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

Application technology of cloud manufacturing for aerospace complex products

Intelligent Manufacturing Plan Management Based on Digital Twins

CAMFFNet: A novel convolutional neural network model for tobacco disease image recognition

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Application technology of cloud manufacturing for aerospace complex products

Intelligent Manufacturing Plan Management Based on Digital Twins

CAMFFNet: A novel convolutional neural network model for tobacco disease image recognition