Further expansion from Smart Manufacturing System (SMS) to Smart Manufacturing Implementation System (SMIS): industrial application scenarios and evaluation

Zhang, Xianyu; Ming, Xinguo

doi:10.1007/s00170-021-07411-w

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…Its primary target is to excavate the data's potential laws and hidden information. DL contains the convolutional neural network (CNN), self‐coding neural network, and deep belief network (DBN) (Xianyu & Xinguo, 2021). After multiple layers are processed, the data are transformed from the low‐level feature representation to the high‐level feature representation, and the data are processed further with a simple model.…”

Section: Research Model and The Methodologymentioning

confidence: 99%

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Yu¹,

Li²,

Li³

et al. 2023

Expert Systems

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The present work aims to promote the modernization of the real economy and facilitate the manufacturing industry to construct an intelligent manufacturing system. Analysis and evaluation are performed on the intelligent manufacturing system (IMS) of enterprises under the Internet of Things (IoT). First, the connotation and characteristics of machine learning (ML) in the era of IoT are explained, followed by the introduction to neural networks. Second, the optimization process of the neural network is described. Since it has no memory, the traditional deep neural network (DNN) needs to be improved. Then, the Generative Adversarial Network (GAN) is utilized to expand the sample data. Moreover, the confusion matrix is used in the indicator evaluation. The backpropagation neural network (BPNN) is optimized by the genetic algorithm (GA) to improve the network configuration and model performance. In addition, the connotation and development status of IMS are analysed, and the feasibility of applying deep learning to the evaluation of IMS is discussed. Finally, the evaluation model of IMS is designed and built via a neural network, which is ultimately verified in the simulation experiment. The experimental results indicate that the BPNN‐GA algorithm has advantages in predicting fitting accuracy, taking up less memory, and shortening training time; it can achieve generalization and accuracy in the forefront of optimal Pareto value, and the fitting accuracy attains 95.3%. The horizontal evaluation of IMS proves the effectiveness and feasibility of the evaluation model. This shows that the BPNN‐GA algorithm has an excellent evaluation effect on IMS. And the IMS evaluation model is applied to assess the IMS of sample enterprises. The evaluation results demonstrate that there are problems in the current manufacturing industry. For example, the transactions are challenging to be completed smoothly, information technology is updated slowly, and IMS is dependent on collecting resources.

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Section: Research Model and The Methodologymentioning

confidence: 99%

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Yu¹,

Li²,

Li³

et al. 2023

Expert Systems

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“…Researchers must develop the framework for the implementation of security in smart manufacturing systems. Zhang et al [29,30] developed a three-dimensional framework for SMS. They extended each layer of the SMS and SMIS.…”

Section: Research Gapmentioning

confidence: 99%

Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach

Hammad,

Jillani,

Ullah

et al. 2023

Sensors

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Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based through the IoT, where integrated and collaborated manufacturing system responds in real time to meet demand fluctuations for personalized customization. Within the network-based manufacturing system (NBMS), mobile industrial robots (MiRs) are vital in increasing operational efficiency, adaptability, and productivity. However, with the advent of IoT-enabled manufacturing systems, security has become a serious challenge because of the communication of various devices acting as mobile nodes. This paper proposes the framework for a newly personalized customization factory, considering all the advanced technologies and tools used throughout the production process. To encounter the security concern, an IoT-enabled NBMS is selected as the system model to tackle a black hole attack (BHA) using the NTRUEncrypt cryptography and the ad hoc on-demand distance-vector (AODV) routing protocol. NTRUEncrypt performs encryption and decryption while sending and receiving messages. The proposed technique is simulated by network simulator NS-2.35, and its performance is evaluated for different network environments, such as a healthy network, a malicious network, and an NTRUEncrypt-secured network based on different evaluation metrics, including throughput, goodput, end-to-end delay, and packet delivery ratio. The results show that the proposed scheme performs safely in the presence of a malicious node. The implications of this study are beneficial for manufacturing industries looking to embrace IoT-enabled subtractive and additive manufacturing facilitated by mobile industrial robots. Implementation of the proposed scheme ensures operational efficiency, enables personalized customization, and protects confidential data and communication in the manufacturing ecosystem.

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“…Zhang et alargued that smart manufacturing systems aim to reconfigure different systems to achieve system intelligence based on advanced intelligence in their system lifecycle, with each smart system tailored to manufacturing resource constraints and optimization-level performance metrics [9]. Zhang proposed that in the context of smart manufacturing, the relationship between framework elements and specific application scenarios should be further extended and studied from a realistic perspective; based on the existing resources, elements, foundation, environment, standards, and norms, specific implementation plans should be planned from the top-level planning of smart manufacturing industry time [10]. Smart manufacturing has an impact on the way companies make decisions and concepts in the manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin-Assisted Collaborative Capability Optimization Model for Smart Manufacturing System Based on Elman-IVIF-TOPSIS

Tang

Chen

et al. 2023

IEEE Access

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Smart manufacturing is one of the key elements to realizing Industry 4.0, which helps to improve the production quality and efficiency of collaborative manufacturing companies. With the rapid development of information technology such as the Internet of Things, the application of digital twin technology in smart manufacturing is becoming more and more widespread. In the manufacturing process, companies still face problems such as slow data flow and a serious waste of idle equipment resources. The purpose of this paper is to carry out a digital twin-based smart manufacturing system that combines the concept of value co-creation to achieve the smart manufacturing goal of using fewer manufacturing resources to create greater system value. The system builds a multi-objective optimization model to enhance the value of the shared supply chain and then helps collaborative manufacturing enterprises to optimize their production capacity and use population intelligence algorithms to solve the problem. The results of the combined case study show that the system is effective in achieving good operation of dynamic equipment resources while improving the overall profit of the system by 13.26%. This study proposes a smart manufacturing system based on the digital twin and considering the value of the supply chain will effectively help collaborative manufacturing enterprises to respond to the market environment quickly, reduce the loss of manufacturing resources while also significantly reducing the operating costs of enterprises, and help to realize the value of the shared supply chain.

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Further expansion from Smart Manufacturing System (SMS) to Smart Manufacturing Implementation System (SMIS): industrial application scenarios and evaluation

Cited by 8 publications

References 44 publications

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach

A Digital Twin-Assisted Collaborative Capability Optimization Model for Smart Manufacturing System Based on Elman-IVIF-TOPSIS

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