Cloud edge integrated security architecture of new cloud manufacturing system

With the rapid development of cloud manufacturing technology and the new generation of artificial intelligence technology,the new cloud manufacturing system(NCMS)built on the connotation of cloud manufacturing 3.0 presents a new business model of“Internet of everything,intelligent leading,data driving,shared services,cross-border integration,and universal innovation”.The network boundaries are becoming increasingly blurred,NCMS is facing security risks such as equipment unauthorized use,account theft,static and extensive access control policies,unauthorized access,supply chain attacks,sensitive data leaks,and industrial control vulnerability attacks.Traditional security architectures mainly use information security technology,which cannot meet the active security protection requirements of NCMS.In order to solve the above problems,this paper proposes an integrated cloud-edge-terminal security system architecture of NCMS.It adopts the zero trust concept and effectively integrates multiple security capabilities such as network,equipment,cloud computing environment,application,identity,and data.It adopts a new access control mode of“continuous verification+dynamic authorization”,classified access control mechanisms such as attribute-based access control,rolebased access control,policy-based access control,and a new data security protection system based on blockchain,achieving“trustworthy subject identity,controllable access behavior,and effective protection of subject and object resources”.This architecture provides an active security protection method for NCMS in the digital transformation of large enterprises,and can effectively enhance network security protection capabilities and cope with increasingly severe network security situations.

QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is needed.Opti-mizing the plethora of cloud services has thus become a top priority.Cloud ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some users.To resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms.Untrusted data are assessed by an information correction method.The weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service quality.The manufacturing cloud service optimization algorithm thus provides a quantitative reference for service selection.In experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory

Cloud manufacturing is a specific implementation form of the "Internet + manufacturing" strategy. Why and how to develop cloud manufacturing platform(CMP), however, remains the key concern of both platform operators and users. A microscopic model is proposed to investigate advantages and diffusion forces of CMP through exploration of its diffusion process and mechanism. Specifically, a three-stage basic evolution process of CMP is innovatively proposed. Then, based on this basic process, a more complex CMP evolution model has been established in virtue of complex network theory, with five diffusion forces identified. Thereafter, simulations on CMP diffusion have been conducted. The results indicate that, CMP possesses better resource utilization,user satisfaction, and enterprise utility. Results of simulation on impacts of different diffusion forces show that both the time required for CMP to reach an equilibrium state and the final network size are affected simultaneously by the five diffusion forces. All these analyses indicate that CMP could create an open online cooperation environment and turns out to be an effective implementation of the "Internet + manufacturing" strategy.

An Approach for Enabling Intelligent Edge Gateway Based on Microservice Architecture in Cloud Manufacturing

Cloud manufacturing has become a reality. It requires sensing and capturing heterogeneous manufacturing resources and extensive data analysis through the industrial internet. However,the cloud computing and serviceoriented architecture are slightly inadequate in dynamic manufacturing resource management. This paper integrates the technology of edge computing and microservice and develops an intelligent edge gateway for internet of thing(IoT)-based manufacturing. Distributed manufacturing resources can be accessed through the edge gateway,and cloud-edge collaboration can be realized. The intelligent edge gateway provides a solution for complex resource ubiquitous perception in current manufacturing scenarios. Finally,a prototype system is developed to verify the effectiveness of the intelligent edge gateway.

Research on Cloud Manufacturing Resource-Aware and Access Technology Using RFID

With the continuous development of cloud manufacturing technology,in order to solve more complex manufacturing problem and conduct large-scale networked manufacturing,combining with the characteristic of discrete manufacturing enterprise's demands and RFID( Radio Frequency Identification),a kind of RFIDbased cloud manufacturing resource-aware and access technology is proposed. Firstly,the architecture of the cloud manufacturing system and RFID system is briefly introduced. Then,the key technologies of manufacturing resource-aware and access technology are analyzed,including anti-collision technology,reader management technology and so on. Finally,taking the manufacturing of the key components in discrete manufacturing enterprise as an example,the practicality and feasibility of the technology is verified. The results show that the application of this technology provides a strong guarantee for the sharing and collaboration of manufacturing resources and capacity in the discrete manufacturing industry.

Introduction to Cloud Manufacturing

Cloud manufacturing is a new, networked and intelligent manufacturing model that is service-oriented. know edge based, high performance, and energy efficient. In this model, state-of-the-art technologies such as informatized manufacturing, cloud computing, intemet of Things, semantic Web, aria high-performance computing are integrated in oroer to provide secure, reliabte. and high quality on-demand services at low prices for those involved in the whole manufacturing lifecycie. As an important part of cioud manufacturing, cloud simulation technology based on the COSIM-CSP platform has primarily been aoplied in thedesign of a multidisciplinary virtual prototype of a flight vehicle. This lays the foundation for further research into cloud manufacturina.

The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for resource scheduling,however,resource providers have resource utilization requirements for cloud manufacturing platforms.In the process of resource optimization scheduling,the interests of all parties have conflicts of interest,which makes it impossible to obtain better optimization results for resource scheduling.Therefore,amultithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling.The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization,using the negotiation algorithm based on the Stackelberg game,the cloud manufacturing platformnegotiates andmediateswith the participants’agents,to maximize self-interest by constantly changing one’s own plan,iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform.Through multiple rounds of negotiation and calculation,we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task.Finally,through experimental simulation and comparative analysis,the validity and rationality of the model are verified.

Dynamic Intelligent Supply-Demand Adaptation Model Towards Intelligent Cloud Manufacturing

As a new mode and means of smart manufacturing,smart cloud manufacturing(SCM)faces great challenges in massive supply and demand,dynamic resource collaboration and intelligent adaptation.To address the problem,this paper proposes an SCM-oriented dynamic supply-demand(SD)intelligent adaptation model for massive manufacturing services.In this model,a collaborative network model is established based on the properties of both the supply-demand and their relationships;in addition,an algorithm based on deep graph clustering(DGC)and aligned sampling(AS)is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused by the high complexity of spatiotemporal search in the collaborative network model.At the same time,an intelligent supply-demand adaptation method driven by the quality of service(QoS)is established,in which the experiences of adaptation are shared among adaptation subdomains through deep reinforcement learning(DRL)powered by a transfer mechanism to improve the poor adaptation results caused by dynamic uncertainty.The results show that the model and the solution proposed in this paper can performcollaborative and intelligent supply-demand adaptation for themassive and dynamic resources in SCM through autonomous learning and can effectively performglobal supply-demand matching and optimal resource allocation.

Multi-Strategy Improvement of Sparrow Search Algorithm for Cloud Manufacturing Service Composition

In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange.In this paper,we introduce an improved Sparrow Search Algorithm(ISSA)to address these issues.The fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target solutions.To enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation strategies.Furthermore,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively.Finally,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities.Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods.These results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition.

Intelligent Manufacturing in the Context of Industry 4.0： A Review

Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things （IoT）- enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the loT, cyber-physical systems （CPSs）, cloud computing, big data analytics （BDA）, and information and communications technology （ICT） that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm

Cloud manufacturing is a new manufacturing model with crowd-sourcing characteristics,where a cloud alliance composed of multiple enterprises,completes tasks that a single enterprise cannot accomplish by itself.However,compared with heterogeneous cloud tasks,there are relatively few studies on cloud alliance formation for homogeneous tasks.To bridge this gap,a novel method is presented in this paper.First,a homogeneous cloud task distribution model under cloud environment was constructed,where services description,selection and combination were modeled.An improved leapfrog algorithm for cloud task distribution(ILA-CTD)was designed to solve the proposed model.Different from the current alternatives,the initialization operator and the leapfrog operator in ILA-CTD can ensure that the algorithm always searches the optimal solution in the feasible space.Finally,the processing of task allocation for 1000 pieces of medical labeling machine bottom plates was studied as a case to show the feasibility of the proposed method.The superiority of ILA-CTD was also proven based on more optimal solutions found,compared with the three other methods.

基于边缘计算的软件定义云制造和柔性资源调度研究

本研究的重点是在云制造环境中实现快速重构、实现灵活的资源调度、开发资源潜力以应对各种变化。因此,本文首先提出了一种新的基于云和软件定义网络(SDN)的制造模型——软件定义云制造(SDCM),该模型将控制逻辑从自动化硬件转移到软件上。这种转变意义重大,因为软件可以充当制造系统的“大脑”,并且可以轻松更改或更新以支持快速系统重新配置、运营和演进。随后,边缘计算被引入,以接近终端的计算和存储能力来补充云。另一个关键问题是管理由不同服务质量(QoS)要求的大量物联网(IoT)数据传输而导致的严重网络拥塞。基于SDCM的虚拟化和灵活的网络能力,本研究形式化了面向复杂制造任务集的时间敏感性数据流量控制问题,并考虑了子任务分配和数据路由路径选择。为了解决这一优化问题,提出了一种将遗传算法(GA)、Dijkstra最短路径算法和排队算法相结合的方法。实验结果表明,该方法能有效地防止网络拥塞,减少SDCM中的总通信延迟。

Product platform architecture for cloud manufacturing

Cloud manufacturing is emerging as a new manufacturing paradigm and an integrated technology.To adapt to the increasing challenges of the traditional manufacturing industry transforming toward service-oriented and innovative manufacturing,this paper proposes a product platform architecture based on cloud manufacturing.Firstly,a framework for the product platform for cloud manufacturing was built.The proposed architecture is composed of five layers:resource,cloud technology,cloud service,application,and user layers.Then,several key enabling technologies for forming the product platform were studied.Finally,the product platform for cloud manufacturing built by a company was taken as an application example to illustrate the architecture and functions of the system.The validity and superiority of the architecture were verified.

A classification matching method for manufacturing resource in cloud manufacturing environment

Cloud manufacturing(CMfg),combining the idea and technologies of cloud computing and Internet of Things,is an emerging service-oriented manufacturing model.The supply–demand matching of manufacturing resources is one of the key technologies for implemention.However,resources in CMfg system are geographically distributed,functional of similar and dynamically changeable,and these features make it difficult to obtain higher accuracy for existing matching methods.In order to select the most satisfied resources in CMfg,a semantics-based supply–demand classification matching method(SDCM)is proposed.Firstly,the implementing framework of SDCM is constructed.Then,combined with the theories of ontology and dynamic description logic,a semantics-based SDCM algorithm is designed,which includes four implementation stages,respectively,basic information matching,IOPE parameters(Input,Outputs,Preconditions,Effects)matching,QoS(Quality of Service)matching and comprehensive matching.Finally,a case verifies the feasibility and effectiveness of the proposed method.

Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing

Cloud manufacturing is a new kind of networked manufacturing model.In this model,manufacturing resources are organized and used on demand as marketoriented services.These services are highly uncertain and focus on users.The information between service demanders and service providers is usually incomplete.These challenges make the resource scheduling more difficult.In this study,an iterative double auction mechanism is proposed based on game theory to balance the individual benefits.Resource demanders and providers act as buyers and sellers in the auction.Resource demanders offer a price according to the budget,the delivery time,preference,and the process of auction.Meanwhile,resource providers ask for a price according to the cost,maximum expected profit,optimal reservation price,and the process of auction.A honest quotation strategy is dominant for a participant in the auction.The mechanism is capable of guaranteeing the economic benefits among different participants in the market with incomplete information.Furthermore,the mechanism is helpful for preventing harmful market behaviors such as speculation,cheating,etc.Based on the iterative double auction mechanism,manufacturing resources are optimally allocated to users with consideration of multiple objectives.The auction mechanism is also incentive compatibility.

Rough set based optimization method of machine tools in cloud manufacturing

Cloud Manufacturing(CMfg),combining with the technologies of Cloud computing and Internet of Things,is an intelligent networked manufacturing model,which can quickly integrate various distributed manufacturing resources for collaboratively completing the complex and customized manufacturing tasks.One of the key technologies supporting this model is the optimal manufacturing resources in the CMfg systems,typically machine tools(MTs).In this paper,the attributes of MTs in cloud environment are analyzed,the constraint relationship between the attributes and the optimization criteria of MTs is established,and an optimization method of MTs based on rough set is proposed.Finally,a case study is discussed to validate the feasibility and effectiveness of the proposed method.

Agent-based simulation platform for cloud manufacturing

Cloud manufacturing is a new manufacturing paradigm which creates an open environment for transactions among the enterprises.Research on transaction modes and regularities in a cloud manufacturing environment is important for promoting the applications of cloud manufacturing.To this end,we design and implement a simulation platform according to the typical transaction processes of enterprises in the cloud manufacturing environment.In the simulation platform,enterprises are encapsulated into Service Agents,and thus the activities of service agents can be used to describe enterprise behaviors.By defining different rules,simulations for different business models can be conducted.Detailed descriptions of the platform architecture,functions,and key technologies are presented.The feasibility of the simulation platform is verified through a case study.

3D printing process selection model based on triangular intuitionistic fuzzy numbers in cloud manufacturing

The distributed and customized 3D printing can be realized by 3D printing services in a cloud manufacturing environment.As a growing number of 3D printers are becoming accessible on various 3D printing service platforms,there raises the concern over the validation of virtual product designs and their manufacturing procedures for novices as well as users with 3D printing experience before physical products are produced through the cloud platform.This paper presents a 3D model to help users validate their designs and requirements not only in the traditional digital 3D model properties like shape and size,but also in physical material properties and manufacturing properties when producing physical products like surface roughness,print accuracy and part cost.These properties are closely related to the process of 3D printing and materials.In order to establish the 3D model,the paper analyzes the model of the 3D printing process selection in the cloud platform.Triangular intuitionistic fuzzy numbers are applied to generate a set of 3D printers with the same process and material.Based on the 3D printing process selection model,users can establish the 3D model and validate their designs and requirements on physical material properties and manufacturing properties before printing physical products.

Big data analytics with applications

In this paper,recent developments on the Internet of Things(IoT)and its applications are surveyed,and the impact of newly developed Big Data(BD)on manufacturing information systems is especially discussed.Big Data analytics(BDA)has been identified as a critical technology to support data acquisition,storage,and analytics in data management systems in modern manufacturing.The purpose of the presented work is to clarify the requirements of predictive systems,and to identify research challenges and opportunities on BDA to support cloudbased information systems.