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

Cloud manufacturing systems (CMSs) are networked, distributed and loosely coupled, so they face great uncertainty and risk. This paper combines the complex network model with multi-agent simulation in a novel approach to the robustness analysis of CMSs. Different evaluation metrics are chosen for the two models, and three different robustness attack strategies are proposed. To verify the effectiveness of the proposed method, a case study is then conducted on a cloud manufacturing project of a new energy vehicle. The results show that both the structural and process-based robustness of the system are lowest under the betweenness-based failure mode, indicating that resource nodes with large betweenness are most important to the robustness of the project. Therefore, the cloud manufacturing platform should focus on monitoring and managing these resources so that they can provide stable services. Under the individual server failure mode, system robustness varies greatly depending on the failure behavior of the service provider: Among the five service providers (S1-S5) given in the experimental group, the failure of Server 1 leads to a sharp decline in robustness, while the failure of Server 2 has little impact. This indicates that the CMS can protect its robustness by identifying key servers and strengthening its supervision of them to prevent them from exiting the platform.

Cloud manufacturing is an emerging novel business paradigm for the manufacturing industry. In cloud manufacturing, distributed manufacturing resources are encapsulated into services and aggregated in a cloud manufacturing platform. Through centralized service management, cloud manufacturing is capable of dealing with multiple requirement tasks simultaneously. The ability to deal with multiple tasks at the same time is an important characteristic that distinguishes cloud manufacturing from the previous networked manufacturing models such as manufacturing grid. When it comes to multiple tasks in cloud manufacturing, a critical issue is how to schedule massive services to complete them with shortest makespan, lowest cost, and highest quality, etc. In order to facilitate the research on this issue, we in this paper propose a model for multitask-oriented service composition and scheduling in cloud manufacturing, in which key factures of cloud manufacturing such as service orientation, involvement of logistics, and dynamical change of service availability are taken into account. New concepts such as service efficiency, enterprise capability, and task workload are introduced, and various types of times including service time, logistics time, and waiting time are analyzed in detail. Moreover, this model can be conveniently extended by incorporating new elements such as task constraints, task priority, and continuous task arrival. An example that motivates the current model is presented. Simulation experiments with different numbers of tasks are performed to demonstrate the feasibility of the model.

Cloud manufacturing (CMfg) is a relatively new manufacturing paradigm which promotes easier collaboration among geographically-dispersed manufacturers. Various manufacturing resources can be shared via cloud manufacturing platform as services. The efficacy of such resource sharing and thus collaboration in this service-oriented mode of manufacturing is highly dependent to the efficiency of underlying mechanisms used for processing these services. Activities such as service matching, service retrieval, service composition and optimal selection, and service scheduling are among the most critical of those service processing mechanisms. In this paper, we introduce and implement a comprehensive service composition and optimal selection (SCOS) technique that takes advantage of a real-world manufacturing capability dataset, deep learning models as well as Word2Vec technique to retrieve appropriate candidate sets for each submitted manufacturing subtask in the cloud manufacturing platform. Then, a genetic algorithm is implemented to obtain a near-optimal composite service that achieves the highest Quality of Service (QoS). A series of experiments were conducted to prove the feasibility and efficacy of the approach and results were presented.

In the cloud manufacturing platform, virtualized manufacturing resources are complex to ensure the authenticity and security of transaction data during the transaction process and rely on the unified deployment of cloud manufacturing platform operators (CPOs). This centralized framework is prone to a single point of failure and risks leakage of private data. Because of these problems, combined with the advantages of decentralization and immutability of blockchain, we innovatively explore the application of blockchain technology in cloud manufacturing and propose a blockchain-based cloud manufacturing resource allocation method. Firstly, a blockchain-based decentralized cloud manufacturing trading platform framework is presented. The Elliptic Curve Digital Signature Algorithm (ECDSA) in manufacturing resource/demand release is studied, and the matching mechanism between manufacturing resources and demand is analyzed. Then, a manufacturing resource verification contract and a manufacturing resource transaction contract for cloud manufacturing are designed using smart contracts (SC). The experimental tests in the Remix platform are completed using Solidity. The test results show that the proposed method can provide a safe and reliable guarantee for virtualized manufacturing resource transactions. Finally, the game problem of supply-demand balance between manufacturing resource suppliers (MRS) and demanders (MRDs) under the decentralized architecture is explored, and simulation is carried out on MATLAB R2021b. The simulation results show the manufacturing resource supply and demand. The fair game can reach the Nash equilibrium and has a faster convergence rate than the existing research.

Within the cloud manufacturing paradigm, Cloud Manufacturing Service Composition (CMSC) is a core technology for intelligent resource orchestration in Cloud Manufacturing Platforms (CMP). However, existing research faces critical limitations in real-world CMP operations: single-task-centric optimization ignores resource sharing/competition among coexisting manufacturing tasks (MTs), causing performance degradation and resource “starvation”; traditional heuristics require full re-execution for new scenarios, failing to support real-time online decision-making; single-agent reinforcement learning (RL) lacks mechanisms to balance global efficiency and inter-task fairness, suffering from inherent fairness defects. To address these challenges, this paper proposes a fairness-aware multi-task CMSC method based on Multi-Agent Reinforcement Learning (MARL) under the Centralized Training with Decentralized Execution (CTDE) framework, targeting the symmetry-breaking issue of uneven resource allocation among MTs and aiming to achieve symmetry restoration by restoring relative balance in resource acquisition. The method constructs a multi-task CMSC model that captures real-world resource sharing/competition among concurrent MTs, and integrates a centralized global coordination agent into the MARL framework (with independent task agents per MT) to dynamically regulate resource selection probabilities, overcoming single-agent fairness defects while preserving distributed autonomy. Additionally, a two-layer attention mechanism is introduced—task-level self-attention for intra-task subtask correlations and global state self-attention for critical resource features—enabling precise synergy between local task characteristics and global resource states. Experiments verify that the proposed method significantly enhances inter-task fairness while maintaining superior global Quality of Service (QoS), demonstrating its effectiveness in balancing efficiency and fairness for dynamic multi-task CMSC.

Cloud manufacturing as a new service-oriented business model transforms manufacturing resources into manufacturing services and manages them centrally via Cloud manufacturing platforms. Using distributed manufacturing resources is an opportunity to decrease the logistics and production costs by allocating tasks to manufacturing resources toward task destination nodes. Decomposing ordered tasks to several subtasks, allocating distributed manufacturing resources to tasks, scheduling manufacturing tasks, and select appropriate logistics strategies to deliver tasks to service providers are important activities that must be considered to improve the performance of a Cloud manufacturing system. This paper proposes a mathematical model to improve the performance of the Cloud manufacturing systems. To that end, task decomposition is implemented based on the alternative process routing concept. Decomposing each task to several manufacturing plans results in enhancing system flexibility and consequently improve the performance of cloud manufacturing. Besides, a consolidated production-distribution toward destination nodes strategy is employed to allocate and deliver tasks to manufacturing resources at minimum costs. The impacts of the alternative process routing concept and consolidated production-distribution toward destination nodes strategy are discussed by considering different scenarios and the capabilities of the proposed model have been demonstrated.

In modern manufacturing industry, resource efficiency is negatively affected by the high fluctuations of demands within the global market. In this work, an intelligent cloud manufacturing platform is proposed to increase resource efficiency, productivity, and utilization rates in a smart manufacturing network by dynamically matching manufacturing services offers and requests through the broad sharing and on-demand delivery of distributed computational, software, digital, and physical manufacturing resources. The cloud platform is developed with particular reference to the sheet metal cutting sector and includes several modules. A database module is employed for users data input and storage. An intelligent assessment and optimization module performs the functional and geometrical assessments of the sheet metal cutting instances entered by customers and suppliers and makes use of a genetic algorithm to optimize the manufacturing solutions with specific attention to the surface utilization rate, as key performance index of resource efficiency. A decision-making module supports the supplier in the selection of the best production strategy and the customer in the evaluation and comparison of the best manufacturing solutions ranked according to the preferred criteria. To demonstrate the implementation of the proposed cloud manufacturing platform in a manufacturing network scenario, a case study including several customer and supplier instances is presented, showing the multiple manufacturing solutions proposed by the platform and the advantages in terms of industrial resource efficiency improvement.

Wearable electronics have been attracting significant attention in various applications such as consumer electronics, healthcare monitoring, localization and navigation and so on. The demand for advanced wearable electronics brings new challenges for the wearable technologies, which impose the limitations of the development of the current wearable electronics. The next generation of wearable electronics calls for special attention on several major challenges, which features more convenient, more energy-efficient and more precise sensing. In this dissertation, in order to tackle these challenges, three solutions are proposed and the application of ECG monitoring is selected as the validation of our solutions. For the convenience of the wearable ECG monitoring, we propose a new design and manufacturing approach for the embroidered textile circuits to achieve the fully flexible system integrated into cloth, which is called System-on-Cloth (SoCl). A prototype of embroidered ECG sensor is fabricated and tested based on the proposed approach. The testing results of the embroidered ECG sensor show that the cloud manufacturing platform can be considered as an effective tool for design and manufacturing the textile circuits based wearable electronics. For the energy efficiency of the ECG monitoring system, a new ECG signal compression method is proposed for the improvement of energy efficiency via reducing the energy consumption of wireless transmission. The simulation results of the ECG compression show that the new ECG compression method is promising to greatly improve the energy efficiency for the ECG monitoring system. For the precise ECG sensing, a new denoising method is developed to enable the high quality ECG sensing for the embroidered ECG sensor. The experimental results for the ECG denoising method present a better performance than the state of the art methods.

Smart cloud manufacturing platform for resource efficiency improvement of additive manufacturing services

In the cloud manufacturing (CMfg) platform, there are numerous manufacturing services with same or similar functions. Due to the inconsistency of service quality, how to effectively evaluate the quality of services is a fundamental problem in CMfg, and which aims to reduce risk and increase the benefits of users. Under these contexts, this study first constructs a comprehensive three-dimensional trust evaluation system that considers the trust of service demanders, resource providers, and cloud platform operators in CMfg. And then, a manufacturing services trust evaluation and preprocessing model is proposed, and the optimization process is described as following: (1) superior and inferior manufacturing services are identified by the first stage filtration; and (2) inferior manufacturing services are further classified by the second stage filtration. After that, to deal with the concerned problem, an improved multi-objective non-dominated sorting genetic algorithm III (IMO-NSGA-III) is developed to find the Pareto-optimal solutions. Furthermore, nine random instances are designed to show that the proposed IMO-NSGA-III outperforms other three state-of-the-art algorithms in terms of convergence and diversity. Finally, three case studies that comes from an automotive parts assembly company is employed, and the effectiveness of the proposed model and IMO-NSGA-III algorithm is further demonstrated.

3D printing, as a symbol for the 4th of industrial revolution, is now changing many aspects of our life, ranging from rapid manufacturing, mass customization to health care devices. There is no 3D printer that can produce a 3D object without a digital 3D model. Thus, the construction of the 3D model to achieve mass customization inevitably involves service matching and task scheduling in Cloud manufacturing (CMfg) platform. However, such a complex and dynamic CMfg environment poses a challenge to traditional service matching and selection. In this research, a non-cooperative dynamic game model based selection is presented. To obtain Nash equilibrium point, an evolution solving algorithm is designed to find an optimal solution. Experimental results indicate that the proposed method is practical and valid.

Cloud manufacturing is characterized by large uncertainties and disturbances due to its networked, distributed, and loosely coupled features. To target the problem of frequent cloud resource node failure, this paper proposes (1) three resource substitution strategies based on node redundancy and (2) a new robustness analysis method for cloud manufacturing systems based on a combination of the complex network and multi-agent simulation. First, a multi-agent simulation model is constructed, and simulation evaluation indexes are designed to study the robustness of the dynamic cloud manufacturing process (CMP). Second, a complex network model of cloud manufacturing resources is established to analyze the static topological robustness of the cloud manufacturing network. Four types of node failure modes are defined, based on the initial and recomputed topologies. Further, three resource substitution strategies are proposed (i.e., internal replacement, external replacement, and internal–external integration replacement) to enable the normal operation of the system after resource node failure. Third, a case study is conducted for a cloud manufacturing project of a new energy vehicle. The results show that (1) the proposed robustness of service index is effective at describing the variations in CMP robustness, (2) the two node failure modes based on the recalculated topology are more destructive to the robustness of the CMP than the two based on the initial topology, and (3) under all four failure modes, all three resource substitution strategies can improve the robustness of the dynamic CMP to some extent, with the internal–external integration replacement strategy being most effective, followed by the external replacement strategy, and then the internal replacement strategy.

Supply-demand matching degree evaluation is an effective way to reduce the transaction risk of manufacturing supply-demand subjects. Cloud manufacturing provides a platform for supply-demand entities through the network. Based on the characteristics of cloud manufacturing, this paper proposes an intelligent evaluation method of supply-demand matching degree based on ELECTRE III and VIKOR. Firstly, the demand side uploads a bill of material on the cloud manufacturing platform, and the supplier provides a supply plan. The demand side forms an evaluation team to evaluate the quality of the supplied product. Secondly, the weight of the evaluators is calculated in cloud manufacturing platform based on the similarity measurement method. Then, the cloud manufacturing platform carries out the weighted aggregation of evaluation information and uses the ELECTRE III method to build the weak advantage relationship among indicators. Finally, the cloud manufacturing platform performs a VIKOR compromise calculation and selects the optimal supply-demand matching degree. At the end of the paper, an example is taken to evaluate the matching degree of supply-demand of robot machined parts, to demonstrate the practicability of the new method. Based on Canny algorithm, a robot action stability test platform is built to verify the calculation results of the new method. INDEX TERMS Canny algorithm, cloud manufacturing platform, ELECTRE III, supply-demand matching degree evaluation, VIKOR.

Industrial Parks thrive in China in the last few decades, but they face the problems of weak accumulative effect, low utilization rate of idle resources and loose regional bonds. To solve these problems, a cloud manufacturing platform is proposed to share services in Industrial Parks. Specially, the principles, architecture and operational model of CMPSS are discussed and investigated to facilitate the inter-enterprises collaboration. Furthermore, four key technologies to implement CMPSS including resource servitization, task decomposition, optimal service allocation and service trust evaluation are conducted to lay a foundation for further research and applications.

There is a growing need of knowledge description of manufacturing equipment and their capabilities for users, in order to efficiently obtain the on-demand services of manufacturing equipment in cloud manufacturing, and the understanding of the manufacturing capability of equipment is the most important basis for optimizing the cloud service management. During the manufacturing processes, a number of uncertain incidents may occur, which could degrade the manufacturing system performance or even paralyze the production line. Hence, all aspects about the equipment should be reflected within the knowledge description, and the static and dynamic information are both included in the knowledge model of manufacturing equipment. Unification and dynamics are the most important characteristics of the framework of knowledge description. The primary work of this study is fourfold. First, three fundamental ontologies are built, namely, basic information ontology, functional ontology, and manufacturing process ontology. Second, the correlation between the equipment ontology and the fundamental ontology that forms the unified description framework is determined. Third, the mapping relationship between the real-time condition data and the model of manufacturing equipment capability ontology is established. On the basis of the mapping relationship, the knowledge structure of the manufacturing equipment capability ontology is able to update in real-time. Finally, a prototype system is developed to validate the feasibility of the proposed dynamic modeling method. The system implementation demonstrates that the proposed knowledge description framework and method are capable of reflecting the current conditions and the dynamic capability of manufacturing equipment.