This article introduces a novel hybrid method to enable the self-evolution of equipment digital twins (DTs), allowing them to continuously and accurately mirror their physical counterparts. Self-evolution is the process by which a DT autonomously updates its models using real-time sensor data, adapting to dynamic real-world behavior. To enhance this process, we propose a data-physics driven approach that synergistically integrates meta-learning and continual learning. Our method begins by designing an extended residual model using a Koopman autoencoder (KAE) neural network. This component bridges the gap between an imperfect analytical physics model and actual equipment behavior. Next, we employ the Reptile meta-learning algorithm to train offline a versatile foundation model on historical data, endowing it with strong adaptability for rapid learning from new information. A key innovation is a periodic event-triggered mechanism, which monitors the DT's simulation accuracy against a fixed time window. When a performance discrepancy is detected, it automatically triggers a self-evolution cycle. The foundation model is then updated through a fine-tuning strategy based on continual learning with random reinitialization. This fusion of offline meta-learning and online continual learning allows the DT to quickly adapt to new, unseen scenarios, ensuring it reflects the physical equipment's state in real-time. We validate the effectiveness and improved performance of our proposed framework through a comprehensive robot simulation case study.

Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.

This paper introduces a fine-grained video tag classification algorithm that integrates knowledge graphs to identify specific TV series. A multimodal pretraining model extracts visual and textual features, feeding them into a multitask prediction model to classify type, genre, and entity tags. A similarity task enhances feature compactness, while an entity correction model refines predictions using co-occurrence data from knowledge graphs. Using a knowledge graph built from the data of Douban, a leading platform in China, the model achieved a 3.70% improvement in Top-1 accuracy for type tags, 3.35% for genre tags, and 16.57% for entity tags. The global-local attention model further increased entity tag accuracy from 38.7% to 45.6%. This approach boosts classification accuracy and provides insights for handling limited data.

Product design involves the conceptualization and creation of products, where simulation workflows often require dynamic scheduling. Although deep reinforcement learning has shown competitive performance in addressing dynamic scheduling problems, they encounter challenges when dealing with multiple types of dynamic events in workflow scheduling. To this end, the paper introduces a novel meta-reinforcement learning-based dynamic scheduling algorithm (MDSA) to tackle multiple dynamic events. Four types of dynamic events are considered and modeled, including random insertion, loop execution, structural adjustments, and resource uncertainties. The Markov decision process is formulated for the workflow scheduling problem, defining the state, action, reward, and state transition. To capture time-sequential information, a context-aware discretized policy is introduced, and a meta-training algorithm is employed to extract common knowledge across various scheduling scenarios. The effectiveness of the proposed method is demonstrated through a case study in the semiconductor display industry. The simulation environments are constructed using industry-inspired synthetic data, with reference to deployment practices from BOE and task patterns derived from Alibaba cluster traces, to ensure practical relevance while maintaining experimental control. Experimental results indicate that the approach demonstrates strong generalization capabilities, especially in terms of its consistent performance across varying task data distribution and its robustness in adapting to multiple dynamic conditions, thereby enhancing its adaptability in practical applications.

Cloud Manufacturing (CMfg) serves as a pivotal platform, seamlessly integrating enterprise resources and consumer demands, thus playing a central role in task scheduling and service allocation. However, the dynamic nature of cloud environments, especially during service interruptions, necessitates systems capable of promptly responding to real-time changes and demands, presenting formidable challenges for task migration. Traditional methods often struggle to adapt swiftly, leading to sub-optimal real-time decision-making. To address these challenges, we introduce the DRL-GNN-FL SynergyNet model, a hybrid solution that prioritizes both time efficiency and cost-effectiveness. This model leverages advanced technologies, including Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), and Federated Learning (FL). DRL enables rapid adaptability and real-time decision-making, GNN effectively processes complex network structures, and FL facilitates distributed learning and knowledge sharing. This comprehensive approach effectively tackles the intricacies of task migration in CMfg environments. Experimental results demonstrate the model’s exceptional performance, adaptability, and scalability in real-time scenarios, showcasing its potential as a practical and effective solution to the dynamic task migration challenges encountered in CMfg. The DRL-GNN-FL SynergyNet model offers a promising avenue for enhancing the resilience and responsiveness of cloud manufacturing systems.

The equipment digital twins (EDTs) for discrete manufacturing should be calibrated quickly to avoid irreversible physical damage to the equipment caused by biased control commands. Therefore, an online credibility assessment method for EDTs is urgently needed. However, existing assessment approaches consume too much time, and thus could not reveal dynamic faults in time. In this paper, the dynamic relationship between online evolution and actual applications of EDTs is investigated. Then, two steps are proposed to accelerate the assessment process significantly. One involves pre-constructing a performance-deviation-agent (PDA), and the other involves dynamically fitting the application-time-window (ATW) probability distribution. The methodology is applicable to discrete manufacturing processes. The dynamic credibility of EDT evolution process can be updated after every iteration of the model evolution. Sorting manufacturing equipment was used as a case study to demonstrate the effectiveness of this method. The time consumption was reduced by 90% compared with traditional assessment methods in the case.

Cloud manufacturing (CMfg) converts the traditional manufacturing system into an Internet-of-things-enabled (IoT-enabled) manufacturing system, where both manufacturing and computational tasks must be scheduled among distributed and heterogeneous resources. Deep reinforcement learning (DRL) has recently become a promising idea for task scheduling in CMfg. However, existing DRL-based methods depend heavily on problem-specific reward engineering and struggle to represent hybrid decision variables. To this end, this paper proposed the sparse-reward deep reinforcement learning (SDRL) method to solve the hybrid task scheduling problem in CMfg. First, the hybrid task scheduling model in CMfg is constructed to minimize the makespan. We reformulate the studied problem as a partially observable Markov decision process (POMDP). Then, the objective hindsight experience replay (objective HER) mechanism is proposed to alleviate the sparse reward issue, through which the scheduling policy can be effectively trained without problem-specific reward engineering. The continuous action space is defined to represent hybrid decision variables, and the implicit action-selection mapping is utilized to alleviate the boundary effect. Numerical experiments validated the effectiveness and superiority of our method compared to eleven popular scheduling algorithms including evolutionary algorithms and DRL. Compared to mainstream DRL scheduling methods, the proposed SDRL outperforms the second-best one at most by$23.6\%$regarding generalization, and a scheduling solution can be generated in$0.5$seconds.Note to Practitioners—With the intelligentization of the CMfg platform, hybrid tasks, including manufacturing and computational tasks, need to be scheduled simultaneously. However, this hybrid task scheduling problem is rarely considered by existing works. DRL exhibits many benefits in addressing scheduling problems, but the strong dependency on problem-specific reward engineering limits its application. Additionally, most DRL-based scheduling algorithms are discrete-action DRL, restricting their capacity to effectively represent hybrid decision variables. The studied problem originates from the CMfg platform, but the proposed method holds potential for broader application. The scheduling framework and the POMDP modeling can be applied to similar problems, including hybrid, manufacturing, or computational task scheduling problems. The proposed objective HER serves as a general approach to addressing challenges associated with sparse rewards, which can be extended to diverse combinatorial optimization problems aimed at optimizing an objective. We will open-source our codes to help others to apply the method to other fields.

The Industrial Internet of Things provides an opportunity for flexible and collaborative manufacturing, but introduces more risk and more communication overhead from the Internet to the industrial field. To avoid attacks from unreliable service providers and requesters, Industrial Demilitarized Zone (IDMZ) is introduced in conjunction with firewalls to provide new communication modes between edge servers and industrial devices. As the number of tasks being offloaded to the edge side increases, optimal task offloading to balance the risk and the communication overhead with limited demilitarized buffer size becomes a challenge. Therefore, this paper establishes a mathematical model for secure task offloading in the Industrial Internet-of-Things considering dense communication with different communication modes. Then, a Parallel Gbest-centric differential evolution (P-G-DE) is designed to solve this task offloading problem with a heuristic-embedded initialization strategy, a modified Gbest-centric differential evolutionary operator and a circular-rotated parallelization scheme. The experimental results verify that the proposed method is capable of providing a high-quality solution with a lower risk and a shorter execution time in seconds, compared to six state-of-the-art evolutionary algorithms.

Recently, foundation models (such as ChatGPT) have emerged with powerful learning, understanding, and generalization abilities, showcasing tremendous potential to revolutionarily promote modern industry. Despite significant advancements in various fields, existing general foundation models face challenges in industry when dealing with the data of specialized modalities, the tasks of varying-scenario with multiple processes, and the requirements of trustworthy output, which makes industrial foundation model (IFM) a necessity. This article proposes a system architecture of termed IFMsys, including model training, model adaptation, and model application. Specifically, in model training, a base model is constructed by pretraining on multimodal industrial data and fine-tuning with fundamental industrial mechanisms. In model adaptation, the base model is developed into a series of task-oriented and domain-specific IFMs through fine-tuning with representative tasks and domain knowledge. In model application, an industrial agent-centric collaboration system and a comprehensive application framework of IFM are proposed to enhance the industrial product lifecycle applications. In addition, a prototype system of the IFM, namely, MetaIndux, is delivered, with application examples presented in typical industrial tasks. Finally, future research directions and open issues of IFM are prospected. We hope this article will inspire the advancements in the theories, technologies, and applications in this emerging research field of IFM.

Industrial Internet-of-things (IIoT) is considered an emerging infrastructure for enhancing manufacturing efficiency by facilitating the sharing of resources across multiple factories. With increasing requirements on customized production in IIoT, the tasks and objectives of the flexible job shop scheduling problem for different orders vary greatly, leading to repetitive algorithm adjustment and time-consuming solver invocation. To accelerate the efficiency for the production orders in different scheduling scenarios, this paper proposed a transfer optimization method based on pointer networks improved by Transformer (Trans-Ptr-Nets). A historical solution selection strategy accompanied with a historical solution dataset to retrieve solutions similar to the current scenario are established. Then, the Trans-Ptr-Nets is designed to transfer the candidate solutions to new solutions that are feasible to the target scheduling scenario. Subsequently, the new solutions are introduced as the additional new population of evolutionary algorithm to accelerate the optimization process. Experimental results conducted on four transfer scenarios show that the proposed method can realize at most 50% reduction in running time while improving the solution quality by at least 10%, compared with six typical evolutionary algorithms and three typical transfer learning networks.

Digital twin (DT) has become a widely discussed emerging topic in last few years, which is expected to bring a new revolution to the whole life cycle of complex equipment, including Research and Development design, optimization control, and predictive maintenance. Using online real-time sensor data to realize model dynamic evolution, so as to map the state change of physical equipment faithfully, is the most significant feature of DT and is also one of the most important key technologies of DT. Lifelong learning is a feasible way to realize DT dynamic evolution. By using offline historical data and online real-time data, the DT model built in a data-driven modeling manner can evolve continuously, therefore, the accuracy of predictive simulation can be guaranteed and improved. Simultaneously, to make a balance between the computation cost and dynamic evolution performance, we introduce the event-triggered scheme during the online lifelong learning process. To sum up, a lifelong learning method based on event-triggered online Frozen-EWC Transformer Encoder for equipment DT dynamic evolution has been proposed in this article and the real flight data set of quadrotor aircraft is used to verify the effectiveness of the proposed method.

The integration of industrial Internet, cloud computing, and big data technology is changing the business and management mode of the industry chain. However, the industry chain is characterized by a wide range of fields, complex environment, and many factors, which creates a challenge for efficient integration and leveraging of industrial big data. Aiming at the integration of physical space and virtual space of the current industry chain, we propose an industry chain digital twin (DT) system framework for the industrial Internet. In addition, an industry chain information model based on a knowledge graph (KG) is proposed to integrate complex and heterogeneous industry chain data and extract industrial knowledge. First, the ontology of the industry chain is established, and an entity alignment method based on scientific and technological achievements is proposed. Second, the bidirectional encoder representations from Transformers (BERT) based multi-head selection model is proposed for joint entity–relation extraction of industry chain information. Third, a relation completion model based on a relational graph convolutional network (R-GCN) and a graph sample and aggregate network (GraphSAGE) is proposed which considers both semantic information and graph structure information of KG. Experimental results show that the performances of the proposed joint entity–relation extraction model and relation completion model are significantly better than those of the baselines. Finally, an industry chain information model is established based on the data of 18 industry chains in the field of basic machinery, which proves the feasibility of the proposed method.

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re- sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. Note to Practitioners—To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

Cloud manufacturing (CMfg) provides on-demand services offered by the cloud platform to satisfy the individual requirements of users. However, the dynamics and uncertainty of the CMfg environment pose significant challenges to implementation of efficient synchronous scheduling of processing and logistic services. This paper proposes a game theory-based collaborative scheduling approach for effective utilization of distributed manufacturing and logistic resources with fuzzy uncertain task migration in CMfg, and the Nash equilibrium in this game theory is realized by a decision tree optimization algorithm. With this model, manufacturing and logistic services can cope with unexpected events whether or not task migration occurs. Moreover, the proposed approach takes into account independent and shared logistics, as well as delayed logistics, to improve the efficiency of transportation along the same route. Simulation results demonstrate that this approach is not only effective for the relevant optimization objective but also can achieve great performance under dynamic CMfg environments.Note to Practitioners—To formulate optimal production planning, how to solve the optimization of manufacturing and logistic resources is the main focus of smart manufacturing systems. In this paper, game theory is introduced to address the collaborative service scheduling issues in cloud manufacturing. Previous dynamic scheduling methods cannot further distinguish between independent logistics and shared logistics. Also, they rarely evaluate the impact of the task migration strategy on the occurrence of unexpected events from game point of view. Therefore, in this paper, a unique two-layer scheduling method based on game theory model that is composed of a processing service scheduling sub-game and a logistic service scheduling sub-game is presented. This model is implemented by adopting a proposed decision tree optimization algorithm that settles constrained non-linear programming. Simulation experiments show that the proposed method outperforms existing scheduling methods in terms of operational efficiency and fuzzy task migration decisions. Additionally, this method can be readily implemented and incorporated into real production settings. Future work could improve the proposed method by analyzing the uncertainties in point-to-point and hub-and-spoke networks across different transportation routes.

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.

Industrial Internet of Things enables various manufacturing processes executed in distributed production lines and flexible workshops. With different cloud–edge–device collaboration ways, interconnected manufacturing tasks and computational tasks are cooperatively completed in manufacturing cells, cloud resources, and edge resources. Large-scale decision variables and complex precedence constraints make the scheduling problem intractable. To this end, this article proposed a discretized soft actor–critic configured differential evolution algorithm to find a stable solution for the cloud–edge–device collaborative task-scheduling problem. A mathematical model is established to describe the relationship between different tasks, the variables, the main constraints in collaboration, and the scheduling targets. A decentralized partially observable Markov decision process is modeled with five neural networks and three discretized loss functions to formulate the discretized soft actor–critic policy efficiently and enable it to find the best differential evolution configurations for different scheduling cases. Experimental analysis of four cloud–edge–device scheduling instances indicates that the proposed method trained in one case is adaptable to the other three cases. In the four cases, the proposed method reduces the total objective by 30.82% and 44.35% at most compared to five deep-reinforcement-learning-based differential evolution algorithms and seven typical evolutionary algorithms, respectively.

Industrial Internet of Things is moving toward an intelligent level with large-scale collaborative cloud and edge resources, making it possible for online supervision, fast analysis, and precise control for many manufacturing job shops. However, online processing of large-scale industrial computation brings huge communication overhead and energy consumption among cloud, edge, and end devices. To improve the performance of the cloud–edge collaboration, this article establishes a practical model of task scheduling considering two kinds of cloud–edge collaborative modes. We propose a parallel group-merge evolutionary algorithm to assign thousands of tasks in seconds. The algorithm separates tasks into weakly correlated groups and applies modified evolutionary operators to find a subsolution for each group. Then, the subsolutions are merged to form a complete solution for fine-tuning based on the cross-use of heuristics. Experimental results show that the proposed method could assign thousands of tasks to cloud servers and edge servers in seconds, reduce the overall task computing time by 36.97%, and save the overall energy by 23.71% at most.

Service composition enables the flexible and agile collaboration of multiple services to complete personalized manufacturing tasks in cloud manufacturing. Compared with traditional manufacturing mode and cloud computing, trust problems become more serious and crucial in cloud manufacturing because of the nontransparency and short-term cooperation mode. A trust evaluation method for service composition in cloud manufacturing is proposed in this article. To quantitatively calculate the trust, a trust evaluation index system is established that comprehensively considers the influencing factors in the production, transaction, and collaboration processes of cloud manufacturing services. The trust value is synthesized based on all index values using the criteria importance through intercriteria correlation method. To extract the temporal information in historical trust data, a time-aware predictive trust evaluation method based on gated recurrent unit is proposed to learn the changing pattern of trust value over time. The trust data are trained together with their timestamps to predict the trust in the scheduled transaction time. The correlation between services in service composition is modeled to mine the correlation information by association analysis. The trust of service composition depends on the trust values of all component services and the correlations between them. The experiments demonstrate the effectiveness of the proposed method through case studies and performance comparisons with other state-of-art methods.

Computer vision (CV) techniques have played an important role in promoting the informatization, digitization, and intelligence of industrial manufacturing systems. Considering the rapid development of CV techniques, we present a comprehensive review of the state of the art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling. A system framework of CV in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, CV algorithms, a decision-making module, and an actuator. Applications of CV to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data preprocessing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for nonannotated data processing, developing effective data preprocessing mechanisms, customizing CV models, and opportunities aroused by 5G.

Soft sensor, as an important paradigm for industrial intelligence, is widely used in industrial production to achieve efficient monitoring and prediction of production status including product quality. Data-driven soft sensor methods have attracted attention, which still have challenges because of complex industrial data with diverse characteristics, nonlinear relationships, and massive unlabeled samples. In this article, a data-driven self-supervised long short-term memory–deep factorization machine (LSTM-DeepFM) model is proposed for industrial soft sensor, in which a framework mainly including pretraining and finetuning stages is proposed to explore diverse industrial data characteristics. In the pretraining stage, an LSTM-autoencoder is first unsupervised pretrained. Then, based on two self-supervised mask strategies, LSTM-deep can explore the interdependencies between features as well as the dynamic fluctuation in time series. In the finetuning stage, relying on pretrained representation, the temporal, high-dimensional, and low-dimensional features can be extracted from the LSTM, deep, and FM components, respectively. Finally, experiments on the real-world mining dataset demonstrate that the proposed method achieves state of the art comparing with stacked autoencoder-based models, variational autoencoder-based models, semisupervised parallel DeepFM, etc.

In modern transportation systems, more and more vehicles result in severe traffic congestion, affecting our daily life and modern logistics. Well-designed traffic infrastructures play an important role in implementing safe and efficient traffic systems. From subjective experience, roundabouts are more efficient than conventional signalized intersections because vehicles do not wait for traffic signals, decreasing which is the main reason for time-loss at signalized crossroads. This paper aims to further investigate the differences between intersections and roundabouts in the capacity of vehicle passing and other performance. Two scenarios with different traffic volumes are considered, including a large volume of traffic flow (2.44 vehicles per second) and a small volume of traffic flow (0.52 vehicles per second). In each scenario, we build six junction models including four intersections with different traffic light time and two roundabouts with different numbers of lanes. Multiple evaluation metrics (i.e., number of passing vehicles over time, mean speed of passing vehicles, mean number of halts per vehicle, mean time-loss per vehicle, and total time for all vehicles to pass) are considered to compare these models’ performance. Results illustrate that roundabouts have a higher capability of vehicle passing than intersections, especially for a large traffic volume. But roundabouts bring more halts per vehicle than signalized intersections when the traffic volume is large. When the volume of traffic flow is small, there is no significant efficiency difference between intersections and roundabouts. These results tend to be applied to future traffic junction designs to improve system efficiency.

In the modern industry, the information has been sufficiently shared among the production equipment, intelligent subsystems, and mobile devices via advanced network technology. For this purpose, many challenges on plant-wide performance evaluation such as product quality prediction have been received considerable attention in complex industrial Internet of Things systems. In this article, an efficient and effective soft sensor based on the semisupervised parallel deepFM model is proposed for the product quality prediction. First, a label broadcasting method is presented to augment labeled samples from unlabeled samples. Then, a data binning method is introduced to discretize process variables for an unbiased estimation. Based on the modified deepFM model, quality information can be separately extracted from different components of the model while high- and low-dimensional features can be obtained. Manifold regularization is embedded into the back propagation algorithm, in which unlabeled samples issue can be further resolved. Experiments on a real-world dataset demonstrate the effectiveness and performance of the proposed methods.

Virtualization is a crucial technology of cloud computing to enable the flexible use of a significant amount of distributed computing services on a pay-as-you-go basis. As the service demand continuingly increases to a global scale, efficient virtual machine consolidation becomes more and more imperative. Existing heuristic algorithms targeted mostly at minimizing either the rate of service level agreement violations or the energy consumption of the cloud. However, the communication overhead among different virtual machines and the decision time of virtual machine consolidation are rarely considered. To reduce both the over-utilized nodes and the under-utilized nodes with the consideration of migration cost, communication overhead, and energy consumption, this paper presents a new iterative budget algorithm in which a budget heuristic and a multi-stage selection strategy are designed to find suitable migration objects and targets simultaneously. Experiments show that the proposed algorithm provides a substantial improvement over other typical heuristics and metaheuristic algorithms in reducing the energy consumption, the number of migrated virtual machines, the overall communication overhead, as well as the decision time.

Cloud manufacturing is a service-oriented manufacturing model with the aim to provide consumers with on-demand manufacturing services over the Internet. Scheduling is a key technology for cloud manufacturing to achieve the aim. However, scheduling in cloud manufacturing faces a series of challenges. How to ensure the credibility of scheduling solutions (i.e. a scheduling solution is said to be credible if it is able to function as expected by consumers) is one of the challenges. IoT and blockchain provide key enabling technologies for addressing this challenge. This work provides a systematic framework for credible scheduling in cloud manufacturing based on the aforementioned two technologies. A framework for credible scheduling in cloud manufacturing based on IoT and blockchain is first proposed. Then, the credibility of manufacturing resources and scheduling services are discussed. Finally, a case study taking the car industry as an example is given to validate the effectiveness and feasibility of the framework proposed.

The industrial fault text data are the special type of short texts, and they come from the records of faults in the factory. Clustering the industrial fault text data can reduce the redundant data and find out the hidden information, which is of great significance to improve the utilization of the industrial fault text data. The industrial fault text data are unstructured and irregular, so the clustering faces quite a few challenges. This paper introduces some existing algorithms for the clustering of short texts, and the shortcomings of them are briefly analyzed. This paper indicates that the main problem of the clustering of the industrial fault text data is the contradiction between the requirements and the setup of parameters, and it leads to low accuracy when cluster the corpus of different sizes. To increase the accuracy of clustering, an improved clustering algorithm is proposed which can solve this contradiction. The results of the comparative experiments show that the improved clustering algorithm has better performance than DBSCAN in corpus of different sizes on the industrial fault text data.

Tasks in the field of service-oriented manufacturing (SOM) such as cloud manufacturing have the characteristics of complexity, heterogeneity, uncertainty, and geographically distribution, which make scheduling them nontrivial and challenging, especially in the fuzzy environment. A fuzzy multicriteria modeling is of importance for the problem of fuzzy scheduling in SOM. In this article, four comprehensive models are proposed, which are different in the uncertain degree of considered performance criteria and/or defuzzification timepoints of fuzzy values. For each model, all weighted criteria are aggregated by using an exponential benefit function. For solving the models, three scheduling algorithms, namely one-level fuzzy ant colony optimization (OFACO), two-level single optimization fuzzy ACO (TSFACO), and two-level double optimization fuzzy ACO (TDFACO), are proposed. OFACO takes the view of the whole set of tasks on the SOM platform only whereas TSFACO and TDFACO consider both the view of the whole set of tasks and the view of individual task. The performance and effectiveness of the proposed fuzzy models and scheduling schemes are compared, respectively, using test datasets with varying sizes. The test results show that the first model with fuzzy objective is better for type-1 fuzzy uncertainty whereas the second model with defuzzified objective is better for type-2 fuzzy uncertainty and TDFACO outperforms the other two scheduling schemes in terms of the proposed integrated fuzzy multicriteria performance both from the individual task perspective and the whole task perspective.

Cloud manufacturing (CMfg) is an emerging paradigm that aims to provide on-demand manufacturing services over the Internet. Service composition as an important means for generating value-added services plays an important role in achieving the aim of CMfg. Most of previous works focused on exploring techniques of service composition for a single composite task using meta-heuristic algorithms. However, the issue of service composition for multiple composite tasks has rarely been considered. Meta-heuristic algorithms suffer from cumbersome parameter tuning as well as the tendency of getting into local optima. In addition, the effectiveness of different algorithms has not yet been fully explored when different degrees of constraints are imposed. Different from approaches in most of the previous works, this paper proposes an extended Gale-Shapley (GS) algorithm-based approach for service composition that allows generation of multiple service composition solutions effectively. Requirements with different constraints are considered. Experimental results indicate that: 1) meta-heuristic algorithms can be used in various scenarios with different degrees of constraints. However, they are incapable of finding the optimal solutions in situations with relatively loose constraints, and moreover, the failure rate of finding solutions for a batch of multiple tasks is high; 2) the dynamic programming (DP) is a method that is the most sensitive to constraints. It performs better only under loose constraints and in the case of a single requirement; and 3) the application range of the GS method proposed is wider than that of the DP method. It can achieve better performance when constraints are relaxed irrespective of task status (i.e., a single task or multiple tasks), and moreover, it can make more tasks find solutions in the multitask scenario without service reuse.

With the rapid development of 3D printing technology and the continuous breakthrough of new material technologies, 3D printing has received more and more attention in the fields of industry, medical, sports, and education. In the cloud manufacturing environment, how to efficiently manage 3D printing models is a critical issue that needs to be solved urgently. The paper begins with a study of 3D printing model aggregation and retrieval mechanism based on this problem. Firstly, we devised a set of 3D printing model management framework with high scalability. Under this framework, a meta-model library and feature library are established to realize the aggregation of 3D printing models. Then, we developed a sketch-based 3D model retrieval method, which can help platform users to create and retrieve personalized 3D printing models easily. This study provides new ideas and a reference pattern for the model design and retrieval methods of existing commercial 3D printing platforms.

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

In service-oriented manufacturing modes, service scheduling is important for providing just-in-time delivery of manufacturing services to customers. In this paper, the mathematical model of the dynamic cloud manufacturing scheduling problem is constructed, and a scheduling method based on dynamic data-driven simulation is proposed to improve the scheduling performance. The framework, scheduling rules and simulation strategies are discussed in detail. Specifically, three single scheduling rules and three combined scheduling rules are designed based on service time, logistics time, and subtask queue status of candidate services. The real-time information of tasks and services is involved in the simulation strategy to select better scheduling rules. The simulation models of proposed scheduling strategies are constructed and simulated in the Simio software, which is connected to a real-time information database. The proposed method is tested through a case study of numerical control machining in cloud manufacturing, and the results show that the proposed method is promising.

Efficient service scheduling in the dynamic cloud manufacturing environment is very important for optimal supply-demand matching and timely delivery of products. This paper presents a systematic and deep analysis of the scheduling process and problem characteristics for dynamic service scheduling in cloud manufacturing. A simulation model is established for the dynamic service scheduling process of cloud manufacturing from aspects of demanders, tasks, services and path models. Both the attributes and activities of different models are studied, and a systematic scheduling simulation model is established. This work is probably valuable for the future simulation-based dynamic scheduling research in cloud manufacturing.

The problem of task allocation and service selection in the complex dynamic cloud manufacturing (CMfg) environment is complex for different types of manufacturing resources. With the rapid development of 3D printing technology, the supply-demand matching problem of 3D printing tasks and services in CMfg needs to be modelled specifically. In this paper, the service attributes of 3D printing services are analyzed, including model size, printing material, printing preciseness, cost, time and logistics. The service transaction model of 3D printing services is built. To reduce delivery time of tasks from service suppliers to service demanders, a 3D printing service matching and selection method (MST) is proposed to generate the optimal solutions. Experimental results show that the average task completion time with MST is less than that of the typical method when the amounts of tasks change. Besides, MST can balance the task assignment among different service providers.

This paper studies the leader-following consensus problem for linear and Lipschitz nonlinear multiagent systems where the communication topology has a directed spanning tree with the leader as the root. Due to the constraints of communication bandwidth and storage space, agents can only receive uniform quantized information. We first consider the leader-following consensus problem for linear multiagent systems via quantized control. Then, in order to reduce the communication load, an event-triggered control strategy is investigated to solve the consensus problem for linear multiagent systems with uniform quantization. It is shown that leader-following practical consensus can be achieved and no Zeno behavior occurs in this case. Furthermore, the proposed control strategies are extended to investigate the leader-following consensus problem for multiagent systems with Lipschitz nonlinear dynamics. Simulation results are given to demonstrate the feasibility and effectiveness of the theoretical analysis.

With the increase of dimensions and complexity of current engineering problems, parallel evolutionary algorithm which take advantage of population division and information exchange among processors has been introduced for years. However, low solution ability of each sub-group and high communication load between them are always seen as the biggest bottlenecks which hinder parallel evolutionary algorithm to be more efficient. To overcome this two problems, a multi operators-based partial connected parallel evolutionary algorithm, i.e. MO-PCPEA is proposed. By combining multiple evolutionary operators, an adaptive strategy for operator configuration inside each parallel group is designed to ensure the searching ability of the algorithm for wider range of problems. More importantly, a partial connection topology is proposed to guide the periodic communication between each group. Computational results in two typical permutation combinatorial optimization benchmarks and one practical case study demonstrate that MO-PCPEA is highly competitive compared with most tailored serial and parallel evolutionary algorithms in terms of not only searching time, but also solution quality.

Abstract Real‐time streaming of shape deformations in a shared distributed virtual environment is a challenging task due to the difficulty of transmitting large amounts of 3D animation data to multiple receiving parties at a high frame rate. In this paper, we present a framework for streaming 3D shape deformations, which allows shapes with multi‐resolutions to share the same deformations simultaneously in real time. The geometry and motion of deformingmeshorpoint‐sampledsurfaces are compactly encoded, transmitted, and reconstructed using the spectra of the manifold harmonics. A receiver‐based multi‐resolution surface reconstruction approach is introduced, which allows deforming shapes to switch smoothly between continuous multi‐resolutions. On the basis of this dynamic reconstruction scheme, a frame rate control algorithm is further proposed to achieve rendering at interactive rates. We also demonstrate an efficient interpolation‐based strategy to reduce computing of deformation. The experiments conducted on bothmeshandpoint‐sampledsurfaces show that our approach achieves efficient performance even if deformations of complex 3D surfaces are streamed. Copyright © 2013 John Wiley & Sons, Ltd.

With growing number of high quality 3D models published online, the technique of generating efficient and economical 3D model previews has raised increasing concerns. Although several previous work has been done on the preview of static mesh, that of animated mesh is different and more complex due to the difficulty in describing the animation. In this paper, we present a novel method of automatic preview generation for 3D mesh animation. A new measure named inter-frame surface saliency, which evaluates both inter-frame motions and surface saliency in each frame, is introduced. Given an animated mesh, an energy function combining this measure and camera smoothness is constructed for the representative viewpoints selection in key frames, and then an optimal camera path is generated. Finally, a brief but informative preview could be created by moving the camera along this path with frame rate control.

Recently, Internet of Things (IoT) and cloud computing (CC) have been widely studied and applied in many fields, as they can provide a new method for intelligent perception and connection from M2M (including man-to-man, man-to-machine, and machine-to-machine), and on-demand use and efficient sharing of resources, respectively. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IoT and CC in manufacturing are investigated in this paper first. Then, a CC- and IoT-based cloud manufacturing (CMfg) service system (i.e., CCIoT-CMfg) and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed. The technology system for realizing the CCIoT-CMfg is established. Finally, the advantages, challenges, and future works for the application and implementation of CCIoT-CMfg are discussed.

Energy-saving and emission-reduction (ESER), carbon footprint, carbon labeling, and carbon trading have attracted much attention recently due to severe environmental challenges. One key technology for implementing the above concepts is how to realize the effective quantitative evaluation of ESER. In this paper, the existing ESER evaluation technology and systems are summarized first. It is found that the existing ESER evaluation technology and systems are almost isolated from the existing enterprise information systems, such as enterprise resource planning (ERP), product data management (PDM), and customer relationship management (CRM), which results in the expanding of the enterprise information islands. In order to address this problem, a new method for ESER life cycle assessment (LCA) based on Internet of Things (IoT) and bill of material (BOM) is proposed in this paper. A four-layered structure (i.e., perception access layer, data layer, service layer, and application layer) ESER LCA system based on IoT and BOM is designed and presented, as well as the key technologies and the functions in each layer. A prototype application system is developed to validate the proposed method. The main contributions of the proposed method are: 1) facilitate real-time intelligent perception, and the collection of energy consumption and environmental impact data generated in the entire life cycle of manufacturing by using IoT technologies; and 2) realize effective data integration between the ESER evaluation system and the existing enterprise information systems based on BOM.

Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.

In order to realize the full-scale sharing, free circulation and transaction, and on-demand-use of manufacturing resource and capabilities in modern enterprise systems (ES), Cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm has been proposed recently. Compared with cloud computing, the services that are managed in CMfg include not only computational and software resource and capability service, but also various manufacturing resources and capability service. These various dynamic services make ES more powerful and to be a higher-level extension of traditional services. Thus, as a key issue for the implementation of CMfg-based ES, service composition optimal-selection (SCOS) is becoming very important. SCOS is a typical NP-hard problem with the characteristics of dynamic and uncertainty. Solving large scale SCOS problem with numerous constraints in CMfg by using the traditional methods might be inefficient. To overcome this shortcoming, the formulation of SCOS in CMfg with multiple objectives and constraints is investigated first, and then a novel parallel intelligent algorithm, namely full connection based parallel adaptive chaos optimization with reflex migration (FC-PACO-RM) is developed. In the algorithm, roulette wheel selection and adaptive chaos optimization are introduced for search purpose, while full-connection parallelization in island model and new reflex migration way are also developed for efficient decision. To validate the performance of FC-PACO-RM, comparisons with 3 serial algorithms and 7 typical parallel methods are conducted in three typical cases. The results demonstrate the effectiveness of the proposed method for addressing complex SCOS in CMfg.

Cloud Manufacturing provides a new concept and model for manufacturing informatization, which has become a hot research topic in modern networked manufacturing area. Cloud manufacturing systems generate huge amounts of sensor data from distributed manufacturing devices, and how to manage them efficiently becomes one of the main challenges of Cloud Manufacturing. Various software frameworks have been developed in Cloud Computing to handle massive data management, and the Hadoop framework has proved an effective solution. In this paper we introduce Hadoop to deal with the sensor data management in Cloud manufacturing systems. First we analyze the requirement of massive sensor data management in Cloud Manufacturing and the defects of traditional RDBMS (Relational Database Management System), and then present a framework supporting parallel storage and processing of massive sensor data in Cloud manufacturing systems based on Hadoop. This framework can provide a promising solution for massive sensor data management in Cloud Manufacturing.

The new networked manufacturing mode, cloud manufacturing (CMfg), is provided with a new operation and transaction mode. To support the research, development and application of the mode and service platform of CMfg, manufacturing resource and capability cloud service transaction (CST) of the tripartite users (i.e., provider, operator and consumer) is described briefly, and the detailed transaction flow is provided. With the characteristics of different cloud services (CSs), considering the multi-layer of logistics, information flow and capital flow, the transactions on hardware-class, software-class, product-class and capability-class CSs are analyzed respectively. Finally, the important and difficult problems urgently to be solved in the whole CST process are pointed out.

Energy saving and emission reduction (ESER) has caught attention around the world due to the severe environmental challenges. An Evaluation System to assess the consumption and emission of a product during its life cycle is proposed in this paper. The system integrates with the enterprise information systems by adding the consumption and emission data into BOM(bill of material). Based on ESER database, the whole system takes product life cycle as the main line and improves the accuracy of evaluation on ESER. Besides, design and optimization of green manufacturing can also be accomplished by using this system. The protype and framework of designed ESER system is described in this paper.

Optimal deployment of simulation virtual machines is an important issue in Cloud Simulation. Challenges involve resource cost prediction for simulation tasks as well as host physical machine selection for simulation virtual machines. In this paper we propose a novel approach using knowledge curves (i.e., curves as knowledge base) to solve this problem. First we present a resource cost estimation algorithm using empirical load curves synthesis, and then discuss a deployment target host selection algorithm by curves matching. This approach can provide a promising solution for intelligent deployment of virtual machines in Cloud Simulation. In addition, the proposed approach will be increasingly precise and effective as curve knowledge base increases.