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30 September 2021, Volume 5 Issue 3

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Topic: Industrial Internet and Smart Manufacturing

Development of digital intelligent networked manufacturing for flexible customized manufacturing

Zhongde SHAN, Jun WANG, Qian ZHANG

2021, 5(3):  1-09.  doi:10.11959/j.issn.2096-3750.2021.00241

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Digital intelligent networked manufacturing technology and equipment are important foundations for the construction of manufacturing power and Internet power.They are important tools for the construction of digital workshop and intelligent factory, enabling the transformation and upgrading and high-quality development of the manufacturing industry.Combined with the characteristics of multiple varieties and variable batches of products in the aerospace field, it was studied how to better conduct digital intelligent networked manufacturing, how to better build digital manufacturing workshops and flexible intelligent manufacturing factories.For single piece and small batch flexible manufacturing, the construction principle, implementation path and technical architecture of digital intelligent flexible manufacturing production line and workshop/factory were proposed, which could be used to guide the construction of digital intelligent workshop/factory for future flexible manufacturing standardization.Through the exploration and application practice of typical intelligent factory construction in the aerospace field, the innovative scenario applications were explored, such as flexible intelligent production equipment, automatic flexible precision measurement equipment, measurement feedback-based adaptive processing and industrial internet-based digital twin system.

Development and application of a lightweight five-axis parallel machining robot

Zenghui XIE, Bin MEI, Weiyao BI, Fugui XIE, Xin-Jun LIU

2021, 5(3):  10-20.  doi:10.11959/j.issn.2096-3750.2021.00237

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In the fields of national defense, aerospace, and energy, there exist plenty of complex surface core parts, for example, the aircraft structural components and turbine blades.These parts usually have the machining requirements of large material removal rate, high dimensional accuracy and high surface quality.The traditional five-axis serial machine centers face two problems in the machining process: (1) the tool will automatically rotate when it passes through the pole posture; (2)the driving axes’dynamic response does not match.It was tried to solve these problems from the view of machining equipments’ configuration.By making fully use of the advantages of parallel robots in compact structure, high stiffness, kinematic flexibility and dynamic response, a lightweight five-axis parallel machining robot with attitude coupling motion and uniform driving axes’ dynamic response was developed.The optimal design, motion control, and precision guarantee were carried out to realize the high-efficiency and high-quality machining of complex curved parts.Finally, the application verification in AVIC (Aviation Industry Corporation of China) Chengdu Aircraft Industrial (Group) Co., Ltd.was conducted.

Health-based network node importance assessment

Jianwei LIU, Bin JIANG, Pu YANG

2021, 5(3):  21-26.  doi:10.11959/j.issn.2096-3750.2021.00238

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For the identification of key nodes in network systems, a node importance assessment method based on the system health state was proposed.Firstly, the definition of the system health degree was analyzed.Then, according to the connection relationship between network nodes, the adjacency matrix was established to calculate the network capacity change under normal conditions and fault degradation conditons.And the relationship model between the system health degree and the node importance degree was established.Finally, simulations of network systems showed that this method could effectively identify the key nodes in the system and improve the reliability of the network.

Inspection method for cable assembly quality based on AR virtual-real image attention mechanism

Ganlin ZHAO, Chang YU, Jianfu ZHANG, Jianxin YANG, Pingfa FENG, Qun SHEN

2021, 5(3):  27-38.  doi:10.11959/j.issn.2096-3750.2021.00236

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Cable assembly in spacecraft occupies a large proportion in the final assembly process, and the quality of cable assembly directly affects the working performance of the whole product.The existing augmented reality-assisted cable assembly system lacks the ability to make real-time judgment and feedback on the assembly results, and cannot identify whether the currently assembled cable meets the quality requirements.In order to solve the above problems, a cable assembly quality inspection method based on AR virtual-real image attention mechanism was proposed.Critical areas in the captured images were located and detected by constructing virtual assist models to filter redundant image information.Based on YOLOv4-Tiny image detection model, the key node location of assembled cable was judged.The overlap of the laying path was obtained based on the method of calculating the average overlap of the neighborhood, and the bending radius of the cable was derived based on the method of camera inverse projection.Finally, an augmented reality-based cable assembly quality inspection system was developed using HoloLens2.The quality inspection was performed on the four designed cable assembly cases, and the feasibility and effectiveness of the method and system were experimentally verified.

Research and development of thick plate shape prediction system based on industrial big data

Yufei MA, Changxin LIU, Wei KONG, Jinliang DING

2021, 5(3):  39-48.  doi:10.11959/j.issn.2096-3750.2021.00239

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Thick plate shape is one of the important indicators to measure the quality of thick plate products.The timely prediction of the final plate shape in production is of great significance for adjusting the operation and control of thick plate production.In actual industrial production, thick plate data has many characteristics, such as multiple coupling information, large amount of redundant information, and multi-source heterogeneity of data.Combining the needs of thick plate shape prediction, a thick plate shape prediction system was designed and developed.The data dump function was used to filter and preprocess the industrial big data to remove the coupling information and redundant variables in the data.LSTM neural network, convolutional neural network and 3D convolutional neural network were used to extract data features from data of different dimensions, and the features were fused based on the maximum mutual information coefficient to establish an integrated learning prediction model, which effectively solved the modeling difficulties caused by multi-source heterogeneous data.The actual industrial data of a domestic thick plate production line was used for verification, and the results showed the effectiveness of the developed system.

Research on a lightweight framework of industrial Internet-oriented manufacturing service collaboration level agreement

Jinying CAI, Feng XIANG, Ying ZUO, Lei ZHONG, Ping ZHOU

2021, 5(3):  49-55.  doi:10.11959/j.issn.2096-3750.2021.00240

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The industrial Internet platform for service-oriented manufacturing has been developing rapidly and put into use.Its advantages of deep perception of industrial data, real-time transmission, rapid calculation and analysis, and high feedback response are gradually highlighted.It also provides platform support for the implementation of manufacturing service reliable collaboration.A lightweight framework for manufacturing service reliable collaboration was proposed based on service level agreement (SLA), including collaboration service communication and interface modules, service level agreement data management module, service collaboration quality of service (QoS) negotiation module, ＆quot;failure-distrust-trustworthiness＆quot; evaluation module, incentive and punishment mechanism module.The SLA-based manufacturing service collaboration process was also given.The reliability of manufacturing service collaboration was guaranteed from three aspects: reliable service release, effective execution of collaboration, and task trustworthy completion.

Theory and Technology

A survey on resource allocation in backscatter communication networks

Yongjun XU, Haoke YANG, Yinghui YE, Qianbin CHEN, Guangyue LU

2021, 5(3):  56-69.  doi:10.11959/j.issn.2096-3750.2021.00215

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With the development of Internet of things (IoT) technology, wireless networks have the characteristics of massive user access, high power consumption, and high capacity requirements.In order to meet the transmission requirements and reduce energy consumption, backscatter communication technology was considered to be one of the most effective solutions to the above problems.In the fact of complex network scenarios, the improvement of spectrum efficiency, system capacity, and energy management has become an urgent problem of resource allocation areas in backscatter communications.For this problem, resource allocation algorithms in backscatter communications were surveyed.Firstly, the basic concept and different network architectures of backscatter communication were introduced.Then, resource allocation algorithms in backscatter communication networks were analyzed according to different network types, optimization objectives, and the number of antennas.Finally, the challenges and future research trends of resource allocation problems in backscatter communication networks were prospected.

A NB-IoT access scheme based on Beta distribution

Mengya LI, Zhengquan LI

2021, 5(3):  70-77.  doi:10.11959/j.issn.2096-3750.2021.00216

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As one of the emerging technologies of low-power wide-area Internet of things, narrow band Internet of things (NB-IoT) faces the pressure and challenge of massive connections of terminals.Aiming at practical application scenarios, a preamble allocation scheme based on Beta distribution was proposed.In this scheme, the energy consumption of each time slot device was firstly calculated.The equipment was divided into high energy consumption according to two grouping methods: average energy consumption and cumulative energy consumption.The preamble was assigned to the high-energy-consuming device group first, and the remaining preamble and the conflicting preamble of the high-energy-consuming device group were assigned to the low-energy-consuming device group.The simulation results show that compared with the traditional random allocation scheme, the proposed scheme can effectively improve the average access success rate and average throughput of the NB-IoT system, and can effectively reduce the back off time.

Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing

Yiyang HU, Lina QI

2021, 5(3):  78-85.  doi:10.11959/j.issn.2096-3750.2021.00227

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Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity.

Task allocation in IoV-based crowdsensing combing clustering and CMAB

Xinxin FENG, Danying GUO, Zefeng LIU, Haifeng ZHENG

2021, 5(3):  86-96.  doi:10.11959/j.issn.2096-3750.2021.00224

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The crowdsening network based on Internet of vehicles (IoV) users has the advantages of extensive node coverage, complete and timely data.A major difficulty in the realization of this technology lies in how to fully mine and use the information of connected vehicular users (such as the user's geographic location, etc.) to select appropriate perception task participants, so as to carry out reasonable task assignments, thereby improving the completion quality of perception tasks and task publisher’s benefits.To solve the above problems, a task allocation method combining the trajectory features and the combinatorial multi-armed bandits (CMAB) algorithm was proposed.Firstly, users were clustered based on the similarity of their historical driving trajectories.Then, the CMAB model was adopted so that the trajectory clustering information could be used as the basis for deciding the optimal worker combination.Finally, the proposed algorithm was verified using the real taxi-trajectory dataset.The experimental results show that the task assignment algorithm considering the trajectory feature information has a higher accuracy and higher profit.At the same time, the selected workers have a high completion quality for tasks at the same location, and can effectively improve the quality of perceived data and the benefits of task publishers, which is suitable for practical application scenarios.

Node clustered deployment of emergency Internet of things based on UAV with equipment access restriction

Wei WANG, Yajing LIANG, Li PENG, Zhongcheng WEI, Jijun ZHAO

2021, 5(3):  97-105.  doi:10.11959/j.issn.2096-3750.2021.00220

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Lack of ground base station in emergency Internet of things for public security application scenarios, the unmanned aerial vehicle (UAV) as the air-based base station was adopted to solve the problem of node cluster deployment under the condition of limited access of emergency Internet of things ground terminal equipment.A clustering deployment method based on on-demand K-means (ODKM) was proposed.Firstly, base station selection and equipment clustering were carried out according to the equipment access ability of different UAV air-based base stations through two clustering steps.Secondly, by solving a relaxed non convex nonlinear optimization model, the UAV air-based base station deployment based on energy efficiency optimization was realized.The experimental results show that the proposed method can meet the constraints of UAV base station access ability, and has a good performance in balancing the transmission energy consumption of each cluster, and realizes the goal of communication energy saving when the ground terminal equipment needs to access.

Research and implementation of safety authentication technology in Internet of vehicles

Manzhu WANG, Ziqi LI, Yifei CHEN, Gaofeng HONG, Wei SU

2021, 5(3):  106-114.  doi:10.11959/j.issn.2096-3750.2021.00212

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Nowadays, safety certification technology for Internet of vehicles (IoV) cannot well defend the various attacks, and there are also many shortcomings when considering the balance between safety and performance.Based on the in-depth analysis of the research status and security threats for IoV, a hierarchical vehicle-to-vehicle (V2V) safety authentication scheme was proposed.Furthermore, an L-type safety authentication scheme which based on more than one trusted third parties was proposed.Aimed at ensuring the information safety of the communication process and preventing the communication data from being stolen, the schemes were more suitable to IoV.

Service and Application

Real-time diagnosis of multi-category skin diseases based on IR-VGG

Ling TAN, Shanshan RONG, Jingming XIA, Sarker SAJIB, Wenjie MA

2021, 5(3):  115-125.  doi:10.11959/j.issn.2096-3750.2021.00217

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Malignant skin lesions have a very high cure rate in the early stage.In recent years, dermatological diagnosis research based on deep learning has been continuously promoted, with high diagnostic accuracy.However, computational resource consumption is huge and it relies on large computing equipment in hospitals.In order to realize rapid and accurate diagnosis of skin diseases on Internet of things (IoT) mobile devices, a real-time diagnosis system of multiple categories of skin diseases based on inverted residual visual geometry group (IR-VGG) was proposed.The contour detection algorithm was used to segment the lesion area of skin image.The convolutional block of the first layer of VGG16 was replaced with reverse residual block to reduce the network parameter weight and memory overhead.The original image and the segmented lesion image was inputed into IR-VGG network, and the dermatological diagnosis results after global and local feature extraction were outputed.The experimental results show that the IR-VGG network structure can achieve 94.71% and 85.28% accuracy in Skindata-1 and Skindata-2 skin diseases data sets respectively, and can effectively reduce complexity, making it easier for the diagnostic system to make real-time skin diseases diagnosis on IoT mobile devices.

Analysis and prospects of the development of the industrial Internet in the petrochemical industry

Siqi SUN

2021, 5(3):  126-132.  doi:10.11959/j.issn.2096-3750.2021.00242

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In recent years, many countries have vigorously developed the transformation and upgrading of the manufacturing industry.China has promulgated a number of policies to encourage the development of the industrial Internet in various industries and regions, and it has begun to take shape.Based on the current development needs of the petrochemical industry, and the overall transformation and upgrading challenges, the problems faced by the upstream, midstream and downstream were analyzed, including upstream reliance on imports, inadequate midstream refining and chemical core technology and system capabilities, and downstream product structure layout.Several suggestions based on the development of the industrial Internet were put forward, including underlying data analysis, core technology integration, industry ecological environment, and security protection system.Finally, the prospects and research directions for the future development of the industrial Internet in the petrochemical industry were put forward.