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.

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.

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.

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.

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.

基于FAHP-CM的舰船电力系统韧性评价研究

针对当前评价舰船电力系统韧性具有较大的不确定性和主观性问题,在建立舰船电力系统韧性评价指标体系的基础上,将云模型(cloud model,CM)和模糊层次分析法(fuzzy analytic hierarchy process,FAHP)相结合,提出FAHP-CM的舰船电力系统韧性评价方法。该方法利用FAHP-CM计算评价指标的权重,使权重更加客观准确,同时简化计算过程,提高评估效率。实例分析结果表明,所建立的评价方法能够很好地反映舰船电力系统的韧性,并能有效解决评价的不确定性和主观性问题,使评价结果更加科学合理。

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.

A Framework for Cloud Validation in Pharma

The pharmaceutical industry’s increasing adoption of cloud-based technologies has introduced new challenges in computerized systems validation (CSV). This paper explores the evolving landscape of cloud validation in pharmaceutical manufacturing, focusing on ensuring data integrity and regulatory compliance in the digital era. We examine the unique characteristics of cloud-based systems and their implications for traditional validation approaches. A comprehensive review of current regulatory frameworks, including FDA and EMA guidelines, provides context for discussing cloud-specific validation challenges. The paper introduces a risk-based approach to cloud CSV, detailing methodologies for assessing and mitigating risks associated with cloud adoption in pharmaceutical environments. Key considerations for maintaining data integrity in cloud systems are analyzed, particularly when applying ALCOA+ principles in distributed computing environments. The article presents strategies for adapting traditional Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ) models to cloud-based systems, highlighting the importance of continuous validation in dynamic cloud environments. The paper also explores emerging trends, including integrating artificial intelligence and edge computing in pharmaceutical manufacturing and their implications for future validation strategies. This research contributes to the evolving body of knowledge on cloud validation in pharmaceuticals by proposing a framework that balances regulatory compliance with the agility offered by cloud technologies. The findings suggest that while cloud adoption presents unique challenges, a well-structured, risk-based approach to validation can ensure the integrity and compliance of cloud-based systems in pharmaceutical manufacturing.

CWM-CM模型在采空区遗煤自燃风险评价中的应用

为更科学分析遗煤自燃危险性状态,建立基于组合赋权法-正态云模型(CWM-CM模型)进行采空区遗煤自燃风险评价。首先,选取了8个典型性影响因素,构建采空区遗煤自燃风险评价体系;其次,利用G1-云模型、改进CRITIC-云模型和CWM-CM模型,计算指标综合权重,并通过计算综合确定度,以最大隶属度为判别准则,综合评估风险等级;最后,应用该模型对山西省某煤矿3个采空区遗煤自燃风险实例分析,并与物元可拓和未确知测度模型评估结果比较。结果显示:3个采空区遗煤自燃风险均为易燃等级,3种模型评估结果完全一致,验证了该模型在采空区遗煤自燃风险评价方面具有可行性和适用性,可为遗煤自燃风险评估提供新思路。

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.

Integrated and Intelligent Manufacturing： Perspectives and Enablers

With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership （AMP） in 2011 and the National Network for Manufacturing Innovation （NNMI） in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further ＂leverage existing resources... to nurture manufacturing innovation and accelerate commercialization＂ by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10- year plan and roadmap toward manufacturing： Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features： integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing （i2M） systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Intemet of Things and Services （IoTS）, cyber-physical systems （CPSs）, and cloud computing are discussed. Challenges are addressed with applica- tions that are based on commercially available platforms such as General Electric （GE）＇s Predix and PTC＇s ThingWorx.

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.

From Mind to Products:Towards Social Manufacturing and Service

After reviewing the development of industrial manufacturing, a novel concept called social manufacturing(SM) and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer's requirements of "from mind to products", and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend,and is expected to become popular in more and more industries.First, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies,like Internet of Things(Io T), social networks, cloud computing,3 D printing, and intelligent systems, are introduced and analyzed,and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3 D printing are suggested for different user cases.

Social Manufacturing for High-end Apparel Customization

Social manufacturing(SM), a novel distributed,collaborative and intelligent manufacturing mode, is proposed and developed for high-end apparel customization. The main components of SM cloud are designed, and its research topics are summarized. Then, SM's key technologies are studied. 3D technologies for apparel customization, like 3D modeling, 3D fitting mirror and 3D customization, are developed to improve the customization precision and user experience. Information based collaborative management is realized to share, communicate,and handle the information efficiently among all groups and individuals of SM cloud. Suppliers' evaluation mechanism is designed to support the optimal decisions making. Next, SM cloud is constructed in five layers for high-end apparel customization.By using SM cloud based crowd-sourcing, social resources can be allocated rationally and utilized efficiently, consumer can customize the product in any processes like innovation, design,making, marketing and service, and traditional apparel enterprise can be upgraded into SM mode for keeping it competitive in the future customization markets.

The Optimization of Manufacturing Resources Allocation Considering the Geographical Distribution

From the perspective of the geographical distribution, considering production fare, supply chain information and quality rating of the manufacturing resource(MR), a manufacturing resource allocation(MRA) model considering the geographical distribution in cloud manufacturing(CM) environment is built. The model includes two stages, preliminary selection stage and optimal selection stage. The membership function is used to select MRs from cloud resource pool(CRP) in the first stage, and then the candidate resource pool is built. In the optimal selection stage, a multi-objective optimization algorithm, particle swarm optimization(PSO) based on the method of relative entropy of fuzzy sets(REFS_PSO), is used to select optimal MRs from the candidate resource pool, and an optimal manufacturing resource supply chain is obtained at last. To verify the performance of REFS_PSO, NSGA-Ⅱ and PSO based on random weighting(RW_PSO) are selected as the comparison algorithms. They all are used to select optimal MRs at the second stage. The experimental results show solution obtained by REFS_PSO is the best. The model and the method proposed are appropriate for MRA in CM.

基于CM-AHP的石羊河流域水质评价

为准确描述石羊河流域水环境质量状态,明晰流域水质的主要污染因子,根据甘肃省石羊河流域典型断面水质数据的分析结果,选取溶解氧、化学需氧量、氨氮、总磷、氟化物5项污染因子作为水质评价指标,建立了基于CM-AHP、熵权法的组合赋权综合评价模型,对石羊河流域主要污染因子进行研究。结果表明,总磷、氨氮污染组合权重占比最大,分别为0.438、0.296,是石羊河流域主要污染因子;石羊河流域从上游至中下游,水质逐步变差。

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.