Industrial Internet Technologies Research Articles

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

A Robust Dual Watermarking using Grey Wolf Optimization, Selective Encryption and Fast Flexible De-Noising Convolution Neural Network

Digital data interchange in IoT systems has flourished with the advancement of industrial internet technologies. Particularly, more and more digital images created by intelligent and industrial equipment are sent there are security concerns related to the website, server, and cloud. To accomplish this issue, in this article a secure watermarking approach is suggested in this research to effectively improve security, invisibility, and resilience at the same time. The adequate coefficient for information embedding is first determined using an assortment of transform domain techniques Discrete-Wavelet-Transform (DWT), Heisenberg- decomposition (HD), and Tensor-singular-value-decomposition (T-SVD). Using the grey wolf optimization (GWO) approach, we estimated the appropriate embedding factors to provide a reasonable compromise between robustness and invisibility. To enable the suggested approach to offer an additional level of security, a selective encryption technique is used on the watermark image. Moreover, FFDNet—a quick and adaptable de-noising convolutional-neural–network is working to increase the robustness-of-the suggested algorithm. The results demonstrate that the recommended watermarking method generates exceptional imperceptibility, resilience, and security against standard attacks. Additionally, the comparison demonstrates that the suggested algorithm performs better than alternative strategies. The following metrics were reached: 51.6966 dB, 0.9944, 0.9961, and 0.2849 for the peak-signal- to-noise ratio (PSNR), Structural-Similarity-Index (SSIM), number of changing pixels per second (NPCR), and unified-averaged-changed-intensity (UACI) average scores.

Research and Development of a Big Data Application Platform for Intelligent Blast Furnace Intensive Management and Control.

The blast furnaces of Anshan Iron and Steel have completed large-scale modernization, and a large amount of information technology has been popularized and applied to the process of blast furnaces. This paper takes the Anshan Iron and Steel blast furnace group as the research background. Based on big data and industrial Internet technology, combining the smelting process mechanism of blast furnace production and using artificial intelligence, cloud analysis, and other technologies, the data management platform was used to effectively integrate the data of each process of the blast furnace and design the data asset catalogue. The big data application platform for the intensive control of the blast furnace was established. The data were in multidimensional in-depth mining, and the intelligent application model of the blast furnace was established. The visual intelligent monitoring of the safe production and operation of the blast furnace was realized, and the production operation of the blast furnace was guided. The overall information and intelligent level of production operation and management of the blast furnace have been improved.

The Effect of Digital Green Strategic Orientation On Digital Green Innovation Performance: From the Perspective of Digital Green Business Model Innovation

Digital technology is gradually penetrating into all kinds of business of green innovation and green development. However, China’s industrial Internet technology foundation is weak. Scholars’ researches on the impact of digital green strategic orientation (DGSO) on digital green innovation performance (DGIP) and the intermediary role of digital green business model innovation (DGBMI) is relatively backward. Therefore, it is particularly important to divide the dimensions of DGSO from the perspective of DGBMI to describe the mechanism of DGIP. In this study, multiple regression approach was used to test the effect of DGSO on DGIP. The mediating role of DGBMI was explored through 562 questionnaires. In addition, the study divides the DGSO into three dimensions: green market orientation (GMO), digital green technology orientation (DGTO), and government orientation. DGBMI is divided into two dimensions: efficiency type and integration type. The results are as follows. (1) GMO, DGTO and government orientation all have positive effects on the improvement of DGIP. (2) GMO has a greater impact on integration DGBMI (IDGBMI). The influence of DGTO on efficient DGBMI (EDGBMI) is more significant. (3) Government orientation has a positive impact on EDGBMI. The influence of DGSO on DGIP can be realized through the intermediary role of EDGBMI and IDGBMI. The essence of this research is to help enterprises understand the characteristics of internal and external environment and make digital-green strategy-oriented choices to improve their DGIP. This study has a certain theoretical contribution to elucidating the influence of DGSO on DGIP and the mediating role of DGBMI. This study also provides a practical basis for enterprises’ digital green innovation practice based on their own environmental optimization and matching of DGSO to improve competitiveness.

Exploration and Reflection on the Construction of Intelligent Manufacturing Professional Clusters for Industrial Internet

In the trend of the transformation of the manufacturing industry towards intelligence promoted by industrial internet technology, the traditional curriculum system related to the manufacturing industry cannot meet the requirements of society and industry for talent knowledge structure and skill training, which puts forward new requirements for undergraduate major construction and development. This article relies on the advantages of the industrial internet location in the Yangtze River Delta region, combined with the needs of regional industrial development, and the problems in the current process of intelligent manufacturing professional construction. Taking the construction of the intelligent manufacturing major cluster at the Nanjing University of Science and Technology ZiJin College as an example, in response to the needs of the development of the intelligent manufacturing industry and the problems in the current process of cultivating intelligent manufacturing talents, this article focuses on the construction of the teaching staff, revision of the curriculum system, construction of scientific research and virtual simulation teaching platforms, multi-disciplinary and multi-angle collaborative education models, providing reference for the reform of applied engineering and technology talent cultivation models in the manufacturing industry in the era of industrial internet.

Implementation of industrial internet of things technologies using Edge, Fog, and Cloud computing

Having superior features of productivity of the Industrial Internet technology of things used in the establishment of clever cities and clever villages, optimity, etc. increases the need to use it. The low-capacity of the physical resources of Iot-based sensors allows it to integrate into cloud technology. Cloud-based infrastructures are not sufficient for the existing requirements of the products of the products. The two main issues, in terms of reconciliation and network expansion limits arise. Also, the breaks of the Internet connection, the low conductivity, etc., as such problems, allow IIOT to integrate the "Fog Computing" and "Edge Computing" technologies. Taking into account the coverage and development of IIOT technology, it is believed that in many cases taking advantage of its full potential will be possible to combine clouds, fog and edge calculations. In this article, Industrial Internet technology of things, its integrated "Cloud", Fog ", Edge technologies are being investigated and differences between them, distribution-based approaches between levels are discussed.

The Interaction and Coupling Between Enterprise Value Evaluation and Regional Ecological Environment

Abstract The development of industrial Internet technology enterprises plays an important supporting role in green water and green mountains, as a forestry industry with both economic value and ecological value. at present, it is facing a good opportunity for development. However, industrial Internet technology enterprises are faced with many risks, such as long investment cycle, low comparative income, and their ecological value is easy to be ignored. Based on FCFF model and LINEST function, this paper studies the enterprise value evaluation and ecological development. The main conclusions are as follows: (1) the economic value of industrial Internet technology enterprises is greatly affected by non-financial factors; (2) the excess income of industrial Internet technology enterprises is affected by ecological improvement. (3) under the background of green finance, the comprehensive operation ability of enterprises can be effectively improved through internal control of enterprises, formulation of government laws and supervision of social enterprises. This study has important theoretical and practical significance for China to scientifically and reasonably evaluate the value of industrial Internet technology enterprises.

A Hybrid Optimization-Based Medical Data Hiding Scheme for Industrial Internet of Things Security

With the development of the industrial internet technology, the medical data exchange in IoT systems has become more prosperous. Specially, more and more medical images produced by industrial and intelligent devices are outsourced to the cloud for convenient use. However, IoT systems deployment poses several medical data security challenges. To address this issue, in this article, a robust medical data hiding scheme based on secure hybrid optimization for industrial scenario image is presented. Specifically, the marked image is obtained through non-subsampled shearlet transform-multiresolution singular value decomposition. In order to generate the dual marks, we employ the Fisher–Yates permutation to produce the scrambled system watermark address for embedding into the mark image. Afterward, the generated mark image is embedded in the chosen coefficients of the cover in an invisible way. After the watermarking, a hybrid optimization-based encryption scheme is utilized to secure the marked image. Extensive experiments demonstrate the invisibility, security, and robustness of our scheme. Further, the superiority of the scheme is elaborated through making the comparison with the other similar algorithms. The solution not only performs the robust exchange of medical data but also protects the privacy of patients.

Preface

The 3rd International Conference on Advanced Materials and Intelligent Manufacturing (ICAMIM 2022) was successfully held in Guangzhou, China from August 5-7, 2022. ICAMIM 2022 provided a high-level technical exchange platforms for experts, scholars, and entrepreneurs worldwide to communicate face-to-face with each other, strengthening the exchange of advanced materials and intelligent manufacturing technologies in various countries around the world.We were greatly honored to have both Prof. Kaiming Wu from Wuhan University of Science and Technology, China and Prof. Liejun Li from South China University of Technology, China to serve as our Conference Chairmen. 120 individuals attended the conference. The conference agenda was composed of keynote speeches and oral presentations. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors by turns.At the beginning of the conference, nine sophisticated keynote speakers were invited to deliver their speeches. Among them, Prof. Mingxin Huang from The University of Hong Kong, China made a report on the Hydrogen Embrittlement of 1500 and 2000 MPa Press-Hardened Steel. This presentation compared the hydrogen embrittlement of two PHS grades with ultimate tensile strength of 1500 and 2000 MPa respectively. And it was found that hydrogen embrittlement depends strongly on the deformation strain rate. In addition, Prof. Tongguang Zhai from University of Kentucky, USA showed us his research: Physical Models for Quantification of Fatigue Crack Initiation Life in Engineering Alloys Based on Microstructure. He introduced the relationships between fatigue crack initiation and 3-D microstructure discovered recently in high performance Al alloys and established two physical models to quantify the stochastic behaviors of fatigue crack initiation. Last but not least, Researcher. Yu Chen from Institute of Intelligent Manufacturing, GDAS delivered a speech on the Industrial Internet Technologies and Manufacturing Digital Transformation. This speech introduced the concepts, intension and features of Industrial Internet, expounded the close relationship between Intelligent Manufacturing and Industrial Internet, and illustrated the typical Industrial Internet application scenarios in the Intelligent Factory and Manufacturing Digital Transformation. The brilliant speeches of each keynote speaker had triggered heated discussion in the conference. Moreover, their speeches won the praise of every participant for disseminating useful and insightful knowledge.The proceedings of ICAMIM 2022 cover various topics, which include Non-ferrous Metal Materials, Ceramic Metallurgy, Biomimicry Mechanisms, Engineering Optimization, Digital Manufacturing, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.On behalf of the conference organizing committee, we’d like to show our congratulation and sincere gratitude to all the keynote speakers, peer reviewers, and all the participants. In addition, our special thanks go to the Journal of Physics: Conference Series, for all efforts of the colleagues in publishing this paper volume. The help and contribution of each individual and institution was instrumental in the success of the conference.Committee of ICAMIM 2022List of Committee member is available in this Pdf.

Application of Industrial Internet for Equipment Asset Management in Social Digitalization Platform Based on System Engineering Using Fuzzy DEMATEL-TOPSIS

In any industry, Equipment Asset Management (EAM) is at the core of the production activities. With the rapid development of Industrial Internet technologies and platforms, the EAM based on the Industrial Internet has become an important development trend. Meanwhile, the paradigm of EAM is changing, from a single machine to integrated systems, from the phase of using them to the end of their lifecycle, from breakdown maintenance to predictive maintenance, and from local decision-making to collaborative optimization. However, because of the lack of a unified understanding of the Industrial Internet platforms (IIPs) and the lack of a comprehensive reference architecture and detailed implementation framework, the implementation of EAM projects will face greater risks according to special needs in different industries. Based on the method of system engineering, this study proposes a general reference model and a reference architecture of implementation for the Industrial Internet Solution for Industrial Equipment Asset Management (I3EAM). Further, to help enterprise to evaluate and select their best-fit I3EAM scheme and platform partner, we proposed a set of performance indicators of I3EAM schemes and a quantitative decision-making method based on fuzzy DEMATEL-TOPSIS. Finally, a case study for an I3EAM in automated container terminals was conducted. In the multi-criteria decision environment with complex uncertainty, the project group identified the I3EAM metrics priorities and social digitalization platforms that were more in line with the actual needs of the automated container terminal and firms. The complexity and time of the decision-making process were dramatically reduced. In terms of feasibility and validity, the decision result was positively verified by the feedback from the enterprise implementation. The given model, architecture, and method in this study can create a certain reference value for various industrial enterprises to carry out the analysis and top-level planning of their I3EAM needs and choose the partner for co-implementation. In addition, the research results of this study have the potential to support the construction of standard systems and the planning and optimization of the cross-domain social platform, etc.

Research on control and management mechanisms in Time-sensitive networks

In recent years, with the development of the new generation of network information and communication technology, the internet of things, the internet of vehicles and the industrial internet technology have been booming. At the same time, many time-sensitive businesses have sprung up. The demand of these businesses for data transmission is developing towards the direction of high rate, low delay and low jitter. The current internet technology is facing great challenges. However, due to the inherent problems of forwarding mode and control management mechanism, traditional ethernet is difficult to meet the real-time, deterministic and reliability demands of time-sensitive flows. In the face of the urgent demand for the deterministic data transmission of time-sensitive businesses, time-sensitive network technology emerges at the historic moment.

Stream-of-Quality methodology for industrial Internet-based manufacturing system

Modern production systems still use traditional quality management methods, even in the Industry 4.0 transformation. With the advent of industrial internet technologies, more advancements are needed to improve the quality management systems. This paper introduces the Stream-of-QualityTM (SoQ) methodology, which defines the 3 T's Transparency, Traceability, and Trendability for the industrial internet-based manufacturing environment.

An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data.

Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.

Empirical Analysis of the Impact of Industrial Internet Development Environment on Open Green Innovation of Manufacturing Enterprises

This study aims to expand the research perspective from the micro-enterprise level to the regional environment level to identify changes in the regional industrial Internet environment. The development and application of Industrial Internet technologies formed by these changes have spillover effects on Industrial Internet innovation. Sample data from 30 provinces and big cities of China from 2006 to 2018 were used to verify the network externality characteristics of industrial Internet development. The nonlinear impact of environmental factors, such as the proportion of Internet users and intellectual property protection on the open green innovation of manufacturing enterprises, was investigated through the panel threshold model. Meanwhile, the development level of the industrial Internet in eastern and western China is compared and analyzed. This study contributes to existing knowledge and guides practitioners to help manufacturing organizations develop industrial Internet environments.

Enabling Real-Time Quality-of-Service and Fine-Grained Aggregation for Wireless TSN.

Wireless Time-Sensitive Networking (WTSN) has emerged as a promising technology for Industrial Internet of Things (IIoT) applications. To meet the latency requirements of WTSN, wireless local area network (WLAN) such as IEEE 802.11 protocol with the time division multiple access (TDMA) mechanism is shown to be a practical solution. In this paper, we propose the RT-WiFiQA protocol with two novel schemes to improve the latency and reliability performance: real-time quality of service (RT-QoS) and fine-grained aggregation (FGA) for TDMA-based 802.11 systems. The RT-QoS is designed to guarantee the quality-of-service requirements of different traffic and to support the FGA mechanism. The FGA mechanism aggregates frames for different stations to reduce the physical layer transmission overhead. The trade-off between the reliability and FGA packet size is analyzed with numerical results. Specifically, we derive a critical threshold such that the FGA can achieve higher reliability when the aggregated packet size is smaller than the critical threshold. Otherwise, the non-aggregation scheme outperforms the FGA scheme. Extensive experiments are conducted on the commercial off-the-shelf 802.11 interface. The experiment results show that compared with the existing TDMA-based 802.11 system, the developed RT-WiFiQA protocol can achieve deterministic bounded real-time latency and greatly improves the reliability performance.

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing.

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

Incorporating Environmental Protection Requirement in Industrial IoT Access Control Security Using Arduino Technology MQ2 and DHT11 Sensor Networks

This paper presents the development of environmental monitoring mechanism based on Arduino Sensor Technology for Industrial Internet of Things (Industrial IoT) Critical Infrastructure (CI) Protection in an Access Control role. Access control of hazardous substances is one of the most vulnerable aspects of Industrial IoT CI. The idea behind this study is to harden environmental security through live ecological monitoring of Temperature, Humidity and emitted hazardous substances or gases due to Industrial activities and processes in Smart CI places such as Nuclear Processing Plant, Thermal Generation Power Industries, Fossil Processing Plant, Military Chemical warfare plant, food processing factory, Agriculture Chemical Manufacturing Plant and many other CI industries. Live monitoring is one of the most important security requirements for any cyber-Access Control Mechanism. The literature revealed that similar systems were developed and implemented in different organizations and with different System Requirements and specifications. However, most CI industries have not automated environmental monitoring requirements and integrated the requirement into a broad institution security continuum. The consequence of neglecting environmental security is climate change. Furthermore, Toxic gas pollution affects millions of people around the world and contributes to about 5.4 percent of deaths worldwide. Pollution kills more people than the combination of malaria, AIDS, and tuberculosis hence the development of security mechanisms to monitor pollutants in the atmosphere. The main objective of this study was to develop a computer-based application for monitoring environmental hazardous substances such as extreme temperature, humidity, carbon dioxide (CO2), carbon monoxide (CO), and Smoke in the atmosphere surrounding Industrial IoT CI Industries. The system development approach employed was the Arduino engineering process model. To achieve this objective, cheaper ecological sensor Networks that include MQ2 and DHT11 Arduino Sensors are connected to Arduino Uno Microcontroller through a solderless breadboard Arduino component. The backend database was MySQL Relational database management system. The developed prototype application produced a number of results including live monitoring of temperature, humidity, CO2, CO, Smoke, and many others. A system evaluation process was conducted and the result indicated 99.999% accuracy levels. The results, it evidence that the developed prototype application can improve environmental security in Industrial IoT CI institutions.

ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing

The photovoltaic industry is a strategic and sunrise industry with international competitive advantages. Driven by policy guidance and market demand, the new energy industry represented by the photovoltaic industry has been a significant emerging industry in developing the national economy and people’s livelihood. Stable photovoltaic power generation capacity supply is a critical issue in the photovoltaic industry. With the popularization of industrial Internet technology and Internet of things technology, more and more academic and industrial circles begin to introduce new technologies to provide the latest research results and solutions for the photovoltaic industry. Electroluminescence is a standard detection method for photovoltaic production in the application of solar energy production. This method uses human vision to detect whether the solar silicon unit is defective. In this article, due to the three core pain points in traditional electroluminescence detection: low efficiency of offline identification, low accuracy and accuracy of data detection, and no online diagnosis and prediction, we carry out ISEE research based on edge computing unit. ISEE uses the edge device to collect the real-time video image of the solar panel through the camera. Then it uses the powerful neural network processing unit module of the edge computing unit, combined with the convolutional neural network algorithm transplanted to the edge, to detect the defects of solar panels in real time. It completes the research on intelligent detection of photovoltaic power generation production defects based on the Internet of Things. After a large number of experimental design verification, ISEE effectively improves the automation degree and identification accuracy in the production and detection process of solar photovoltaic cells and reduces the cost of operation and maintenance. The accuracy rate reaches 93.75%, which has significant theoretical research significance and practical application value.

Industrial internet technologies and their impact on economic growth

Industrial internet technologies and their impact on economic growth

Research on key technologies for industrial field communication quality analysis

With the widespread application of intelligent manufacturing and industrial internet technologies, higher requirements for the reliability, stability and robustness of industrial communication networks have been put forward. To achieve these requirements, the detecting and evaluating diagnostic technology for industrial field communication is essential. This paper analyzes the key technologies and difficulties of detecting and evaluating diagnostic technology from the four aspects of access, collection, communication and evaluation & diagnosis and proposes some feasible solutions to provide a new perspective for further in-depth research.