Networked Manufacturing Research Articles

A multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning in hybrid cloud manufacturing

Cloud manufacturing (CMfg) is a new network manufacturing mode, where one of the essential issues is service composition and optimal selection (SCOS), attracting a wide range of scholars' attention and research. At present, scholars at home and abroad mainly focus on studying the SCOS model in a single type of cloud environment, which integrates and optimizes large group enterprises' internal resources and capabilities. However, since the characteristics and objectives of the SCOS problem are different in different cloud environments (private, public, and hybrid cloud), there are many dynamic switching scenarios of manufacturing resources in different cloud environments. At this time, SCOS models in a single type of cloud environment are no longer applicable. Thus, how to realize the maximization of multi-stakeholder benefits through the adaptive partitioning of manufacturing resources in different cloud environments is an urgent problem to be solved. To fill the gap, a multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning (SCOS-MUB-ARP) is proposed for quickly obtaining the optimal service composition that maximizes the benefits of multi-stakeholder in hybrid cloud manufacturing. To address the SCOS-MUB-ARP, a multi-objective optimization algorithm based on a combination of the NSGA-II algorithm and the mayfly algorithm (MMA-NSGA2) with several optimization strategies is presented, which contains several problem-specific optimization strategies. Numerical experiments and application cases show the effectiveness of the proposed MMA-NSGA2 in optimizing and balancing multi-objectives, compared with some well-known algorithms.

Analysis of the conceptual framework and subjectivity principle of a data-driven networked manufacturing system

Analysis of the conceptual framework and subjectivity principle of a data-driven networked manufacturing system

Deep Reinforcement Learning-Based Multi-Task Scheduling in Cloud Manufacturing Under Different Task Arrival Modes

Abstract Cloud manufacturing is a service-oriented networked manufacturing model that aims to provide manufacturing resources as services in an on-demand manner. Scheduling is one of the key techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is a critical problem in cloud manufacturing. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to address the issue, which, however, either are incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) as the combination of deep learning (DL) and reinforcement learning (RL) provides an effective technique in this regard. In view of this, we employ a typical DRL algorithm—Deep Q-network (DQN)—and propose a DQN-based approach for multitask scheduling in cloud manufacturing. Three different task arrival modes—arriving at the same time, arriving in random batches, and arriving one by one sequentially—are considered. Four baseline methods including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time (min-time) scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is effective and performs best among all approaches in addressing the multitask scheduling problem in cloud manufacturing.

When technologies become Industry 4.0 platforms: Defining the role of digital technologies through a boundary-spanning perspective

The implementation of Industry 4.0 is usually built around core technologies that integrate different complementary hardware and software to coordinate activities within and across the company's borders. These technologies can operate as platforms, becoming the base of technology integration in the digital transformation journey. However, the specialized literature has not yet addressed Industry 4.0 technologies from a system view by considering the types of platforms involved. Rather, most Industry 4.0 technologies are considered singular entities of digital transformation. Thus, we aim to define which and how Industry 4.0 technologies can operate as platforms. We propose four types of platforms that support Industry 4.0 features: (i) Networked Manufacturing Operations platforms; (ii) Vertical Integration platforms; (iii) End-to-End Engineering platforms; (vi) Horizontal Integration platforms. We adopt a Boundary-Spanning (BS) perspective to analyze technologies as Industry 4.0 platforms using four BS activities: information collection and processing, external representation; task coordination; and knowledge transformation. We employ a multiple-case approach by scrutinizing 40 cases and selecting seven in which Industry 4.0 technologies are used as platforms. Although only the internet of things and cloud-based systems are conceived as technology platforms in practice, we show that Industry 4.0 can embrace other technologies as platforms that contribute to building cyber-physical systems. We suggest that some of these technologies can operate either as stand-alone or platforms, depending on how their use is designed in the digital transformation process for Industry 4.0 implementation. We provide a framework explaining platform layering as a value chain for these technologies.

Microscopic Quality Profile of Mice Liver (Mus musculus) with 2µm, 5µm and 8µm Thickness Sections

The cutting stage is the network manufacturing stage which needs to be considered the thickness scale. The standard scale that is commonly used is 2-7 µm, while the thickness of 5 µm which is used as a standard for cutting the thickness of the liver tissue shows that the cell is good for cutting liver tissue in mice (Mus musculus). Mice have many advantages, while the liver organ is one of the organs that are often used in research. The aim of this study was to describe the microscopic quality of liver preparations of mice (Mus musculus) by cutting tissue thickness of 2 µm, 5 µm and 8 µm. This type of research includes experimental research with descriptive research criteria. The study design used a purposive sampling study approach. The results of the quality of microscopic images of mice tissue (Mus musculus) at a thickness of 2 µm there were 12% not good, 20% not good and 68% good. The thickness of 5 µm is 100% good, while the thickness of 8 µm is 8%, the preparation is not good, 36% is not good, and 56% is good. The conclusions of this study were microscopic tissue mice (Mus musculus) preparations with good quality 68%, 8 µm 56%, and 100% at a thickness of 5 µm.

Co-learning of Extrusion Deposition Quality for Supporting Interconnected Additive Manufacturing Systems

Additive manufacturing systems are being deployed on a cloud platform for providing networked manufacturing services. This paper explores the value of interconnected printing systems that share process data on the cloud in improving quality control. We employed an example of quality learning for cloud printers by understanding how printing conditions impact printing errors. Traditionally, extensive experiments are necessary to collect data and estimate the relationship between printing conditions vs. quality. This research establishes a multi-printer co-learning methodology to obtain the relationship between the printing conditions and quality using limited data from each printer. Based on multiple interconnected extrusion-based printing systems, the methodology is demonstrated by learning the printing line variations and resultant infill defects induced by extruder kinematics. The method leverages the common covariance structures among printers for the co-learning of kinematics-quality models. This paper further proposes a sampling-refined hybrid metaheuristic to reduce the search space for solutions. The results showed significant improvements in quality prediction by leveraging data from data-limited printers, an advantage over traditional transfer learning that transfers knowledge from a data-rich source to a data-limited target. The research establishes algorithms to support quality control for reconfigurable additive manufacturing systems on the cloud.

Sustainable Value Creation of Networked Manufacturing Enterprises: Big Data Analytics Based Methodology

Enterprises are constantly under pressure from stakeholders and government restrictions to encourage ethical and transparent behavior in using natural resources, their influence on nearby and global ecosystems, and their protection of people and community development. This leads them to pay attention to many economic, environmental, and social issues, such as climate change, poverty, social development, etc. Manufacturing enterprises are the most concerned by these regulations due to their impacts on the environment and society. They are concerned with sustainable value creation (SVC) to gain competitive advantages. To this end, due to its potential in enhancing sustainability issues, in this paper, a big data analytics (BDA)-based methodology is developed for SVC of networked manufacturing enterprises. A simple example of manufacturing enterprises selected from the database proposed by impact-weighted accounts project (IWAP) is used to demonstrate the applicability of the methodology. The obtained results show the added value of our proposed methodology.

Research on Digital Manufacturing of Lithium Battery Pilot Production Line Based on Virtual Reality

In order to systematically study and verify the digital manufacturing archit ecture and its key technologies, to solve the problems that enterprises currently face in the process of transformation and upgrading of digital and network manufacturing, a lithium battery pilot production line was selected as the research object, using the virtual reality technology as the underlying foundation, integratedly tested and verified the technology contents of the production line including the virtual simulation technology, manufacturing execution construction, information acquisition, and equipment automated transformation required for the construction of a digital factory, tested and verified the inter-drive and mutual control of the pilot line digital twin with its physical entity. The results show that virtual reality technology can provide a virtual testing platform for digital manufacturing which improves the efficiency of automation transformation, digital production and network operation of the actual production lines. This can provide a useful reference for enterprises to achieve a higher degree of digital manufacturing and maintenance in the future.

A blockchain technology based trust system for cloud manufacturing.

Cloud manufacturing (CM) is a new networked manufacturing model that delivers various on-demand manufacturing capabilities to the consumers from the providers. In this model, the provider and consumer never meet each other, thus “trust” is the major enabler for starting a collaboration. In another word, a user must be sure that the requested capability will not be provided with malicious results, and the provider should ensure that the payment will be made on time. In this paper, a novel Blockchain Technology (BCT)-based trust system called “Blocktrust” is proposed to address the trust problem of the CM. First, the CM framework that contains the digital firm, capability pool, and digital certificate issuing units is developed, and then, the private blocktrust peer-to-peer network is proposed and implemented based on Hyperledger fabric. Finlay, the feasibility of the blocktrust is examined under different testing scenarios. The reason for using a private network instead of the public is placing restrictions on who is allowed to participate in the network and also enjoying a network with fast transaction speed. Experiments show that the proposed blocktrust embedded CM is credible and practical.

Scheduling of field service resources in cloud manufacturing based on multi-population competitive-cooperative GWO

Cloud manufacturing (CMfg) is a new networked manufacturing mode based on the big data and the Internet of Thing technologies, which can dynamically and flexibly allocate the manufacturing resources on demand. All the current researches and applications on CMfg focused on the factory manufacturing schema, the field manufacturing schema has not been concerned. Field manufacturing refers to the geographically dispersed manufacturing resources are arranged to different locations specified by customers to execute their manufacturing tasks, such as the assembly, measurement and maintenance of large equipments, this type of manufacturing is getting more and more applications with the increase of product complexity and the trend of manufacturing industry servicizing. At present, the massive complicated field manufacturing tasks bring urgent needs on the dynamic organization and global planning of the dispersed manufacturing resources, which can be achieved by the network technology. To this end, this paper studies the scheduling of field manufacturing resources in CMfg environment. Firstly, the detailed process of the field service resource scheduling in CMfg environment (FSRS-CMfg) is designed based on analyzing the practical field manufacturing process. After presenting the assumptions and constraints of field manufacturing, the optimization model for the FSRS-CMfg problem is established, in which the overall quality of service (QoS) is used as the optimization objective and its evaluation indicators and aggregation method are elaborately designed. Then, to solve the above optimization model, the encoding and decoding methods are designed and the discrete search operators are developed. For improving the solving speed and accuracy, a multi-population competitive-cooperative grey wolf optimizer (MPCCGWO) is built to avoid trapping in local optimum. Finally, two experiments are carried out to verify the correctness and effectiveness of the proposed model and algorithm. Results show that the overall QoS of the field manufacturing resource scheduling scheme can be markedly improved based on the FSRS-CMfg model, and MPCCGWO possesses higher convergence precision than the commonly-used intelligent optimization algorithms without obvious increase of time consumption, so it is suitable for solving the problems with large solution spaces such as the FSRS-CMfg problem.

Origins and consequences of economic globalization: moving beyond a flawed orthodoxy

Contemporary economic globalization is typically seen as a product of both trade and economic liberalization after the Second World War and of technological advances that have made it possible to overcome coordination and management of geographically dispersed production units. Trade liberalization and technological advances were certainly important variables, but I argue that it was neo-protectionist American policies of the early 1980s that provided the initial catalyst for globally networked production processes. American protectionism encouraged Japanese investment in the United States that allowed US car manufacturers to learn the essentials of network manufacturing as practiced by Japanese transplants in the United States. In the next stage of global network manufacturing, liberal trade played a much more pivotal role because the global supply chains could not obviously be maintained without liberal trade. In this paper, I also discuss the likelihood of a reversal and suggest that globalization is unlikely to reversed in a significant way. Liberal trade is essential to the integrity of global supply chain networks, but these new production processes have themselves created a firewall against future systemic protectionism.

Revisiting the United States Telecommunications Network Policy in a Post-Huawei World: Improving Economic Competitiveness, Addressing Security Weakness, and Building Alliances

The evolution of the network gear manufacturing market into a small number of global providers has made network service providers (NSP’s) and countries increasingly reliant on a small number of supplier firms – a process exacerbated by the need to exclude recognized national security threats. The original intent of the O-RAN Alliance to increase interoperability, reduce excessive reliance on monopolistic providers and create a common pool of IP around interoperable standards is reasonable. In the first instance, the intent of the legislation focused on stimulating economic efficiency and addressing valid national security threats. The wording of that legislation, however, provides no explicit guidance, requirements, or standards linking national security threats to telecoms gear. Between a combination of conflicting incentives, lack of factual understanding on the issues, and poor oversight of legislative intent, the US promotion of open radio network interoperability as currently organized stands only to provide China the dominant position in standard setting and network manufacturing. Policy measures individually and in combination need to decouple the United States telecommunications network market from China; provide explicit legislative guidance and exclusions at the intersection of national security and telecommunications; build data and verification capacity on US supply chains that feed into telecommunications networks; and collaborate with allies to increase technology security and providing assurances to firms about the direction and reliability of US policy. These are individual measures that have received broad bipartisan support across the legislative and executive branches, over recent years.

Ontology-Based Holonic Event-Driven Architecture for Autonomous Networked Manufacturing Systems

The current trends in personalized products drive the paradigms of production systems toward autonomous networked manufacturing systems. This article proposes an ontology-based holonic event-driven architecture for implementing loosely coupled, holonic, autonomous distributed systems. The event-driven architecture (EDA) enables the services provided by different organizations and their suborganizations to be autonomously configured and integrated, while ensuring that the predefined organizational event access rules are compulsorily followed. This is realized by organizing the event services in a holonic manager and modeling the knowledge of services via the ontology developed in this study. The ontology model and the corresponding autonomous configuration mechanism are introduced. We developed a demonstration case to elaborate our approach and implemented a testbed using Java-based technology for testing the effectiveness, efficiency, scalability, and reliability of the proposed approach. Note to Practitioners —The ever-changing customer demands, the volatile market, and the trend of mass personalization require manufacturing systems to be smart in the event of uncertainties. To this end, information sharing in the supply chain is a key factor. However, there are several barriers in implementing a cross-organizational information system, including security, access privileges, and system interoperability. To lift these barriers, this article proposes an ontology model and a hierarchical EDA. Compared with conventional architectures, our approach facilitates the management of access privileges, integration of interorganizational information systems, and their interoperation. Moreover, our approach is competitive compared with the conventional service-oriented architecture in terms of both scalability and reliability of the system. Before the proposed approach can be practically applied, the ontology model needs to be further extended, and the event registration and message exchange protocol need to be standardized.

An effective adaptive adjustment method for service composition exception handling in cloud manufacturing

With the increasing market features of globalization, service and customization, the way manufacturers conduct manufacturing business is changing. Under this background, Cloud Manufacturing (CMfg) emerges as a new networked manufacturing model. However, CMfg is immature in many aspects, especially in exception handling of service composition execution. Due to the complexity of the enterprise manufacturing process, there are a large number of unpredictable abnormal events in the dynamic open cloud manufacturing environment (such as user demand change, machine failure, etc.), so in order to ensure the smooth implementation of the service combination, it is indispensable to establish an effective service exception handling mechanism in CMfg. Moreover, when an exception occurs, in order to ensure the smooth execution of the downstream services after the exception point, the exception handling must satisfy the strict time constraints. To realize the exception-handing of service-composition with the strict deadline or strict time constraints, this paper proposes a service-composition exception adaptive adjustment model, considering the influences of the logistics transferring time and cost. And the occupied time of the cloud services and the valid replacement time range of the exception service are considered as the constraints in this model. In addition, the processing quality, the cost, and the quality of service are set as the optimal objectives. On the above basis, a service-composition exception handling adaptive adjustment (SCEHAA) algorithm based on the improved ant colony optimization algorithm (ACO) is proposed and applied to address the above model. Finally, to validate the performance of SCEHAA, a case study and the comparison experiment between SCEHAA and other algorithms (Particle Swarm Optimization and Artificial Bee Colony) are performed. The results show that the SCEHAA algorithm can perform the adaptive adjustment of the service-composition with strict time limit effectively, through the adaptive service execution path reconfiguration and has fast convergence effects.

Optimization of complex part-machining services based on feature decomposition in cloud manufacturing

ABSTRACT Cloud manufacturing (CM) is a new service-oriented networked manufacturing mode. The optimal configuration of manufacturing services is one of most challenging topics in CM. Most research focuses on service composition optimization algorithms. However, for different manufacturing tasks, the configuration mode of the manufacturing services is different. For complex parts, effectively using the appropriate optimization strategy to solve the optimization of machining services is still rare in CM. To solve the above problem, a new machining task decomposition and service optimization strategy is proposed. Under this mode, the features of the complex part are defined as the basic task granularity. Four machining service optimization modes are constructed, and a mathematical model of machining service optimization under the four modes is established. Subsequently, a particle swarm optimization algorithm based on simulated annealing (PSOBSA) is designed by combining the particle swarm optimization (PSO) and simulated annealing (SA). Finally, three groups of simulation experiments are conducted to simulate the optimization mode of complex parts machining services based on feature decomposition. The simulation results demonstrate the feasibility of the service optimization mode and the effectiveness of the PSOBSA. The research results presented in this paper provide an machining service outsourcing method for complex parts.

Theory, supporting technology and application analysis of cloud manufacturing: a systematic and comprehensive literature review

PurposeCloud manufacturing (CMfg) is a networked manufacturing mode that promotes the agile, service-oriented, green and intelligent development of the manufacturing industry. Although some scholars have reviewed related studies of CMfg from multiple perspectives, these reviews are not fully systematic or well justified and fail to fully reveal the key characteristics in the development process of CMfg. The purpose of this paper is to systematically review the relevant research on CMfg via identification of key characteristics of definition, architecture, supporting technology and application of CMfg to provide critical information in decision support for the innovation and development of CMfg.Design/methodology/approachThis study systematically reviews the relevant research on CMfg across theoretical methods to technical applications by integrating quantitative and qualitative methods. Word cloud method is used to quantitatively analyse the structure and feature of different definitions of CMfg. The principle of System Science is used to explore the basic components and functions of various CMfg architectures and their common and differing characteristics. A multi-level technology framework is developed to explore the development status of CMfg supporting technologies. A multi-stage application classification is proposed to reveal the application status of CMfg.FindingsThrough literature review, this study found that CMfg architecture is currently dominated by general architectures and lacks architectures that fit the actual enterprise characteristics; CMfg supporting technology is mature in the traditional cloud computing-based technology, but it is still weak in the development of virtualization and servitization technology, service scheduling technology; CMfg application is still in the initial stage and still lacks a relatively complete system application. By analysing the development status of CMfg, this study also identified potential research directions of CMfg in information management, service composition and evaluation, system application and sustainable development and other aspects.Research limitations/implicationsThis paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. Chinese articles account for more than half of the total. The reason is that CMfg was proposed by the Chinese and CMfg is suitable for the development of China's manufacturing industry because of China's intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg.Originality/valuePrior literature reviews ignore the identification and analysis of key feature identification for the current development of CMfg, including common and unique feature identification of different CMfg architectures and functions, multi-layer analysis and interpretation of CMfg technology and different stage analysis of CMfg applications. This study addresses these limitations and provides a comprehensive literature review.

Interpretive framework by analysing the enablers for implementation of Industry 4.0: an ISM approach

Today, industries are compelled to manufacture the products of excellent quality to meet the ever-increasing consumer demands competitively. Hence, implementing Industry 4.0 is becoming essentially the new age solution for industrial operations. This study attempts to categorise the important technological enablers for the Indian manufacturing industry in implementing Industry 4.0. In this context, 14 selected and identified enablers, namely Visual Computing, Big Data, Product Life Cycle Management, Supply Chain, Top Level Management, Collaborative Productivity, Internet of Things, Horizontal and Vertical Integration, Mass Customisation, Cyber-Physical System, Additive Manufacturing, Professional Training and Development, Networked Manufacturing Systems and Operational Efficiency, are identified through the review of literature and expert’s opinion for implementing Industry 4.0. Interpretive structural modelling (ISM) methodology is employed to appreciate the mutual relationships among the identified enablers. Furthermore, contextual interactions among the enablers are established by brainstorming with experts. The identified 14 technological enablers are further classified as dependent and driving factors. Later, the validation of the factors is done by MICMAC analysis. The interactions amongst these enablers will help the manufacturing organisations to prioritise and administer these factors efficiently and effectively to reap benefits during the implementation of Industry 4.0. Therefore, this study analyses the enablers to implement Industry 4.0 in the manufacturing industries. Also, it develops an interpretive framework that will help understand the interdependence of the enablers.

A Novel Truthful and Fair Resource Bidding Mechanism for Cloud Manufacturing

Cloud manufacturing is an emerging service-oriented and market-oriented networked manufacturing paradigm. Resource scheduling of cloud manufacturing is different from those of other manufacturing paradigms due to some distinctive characteristics, such as high concurrency of multi-service and multi-task, high heterogeneity and high dynamics of resources, incomplete information of cloud services and user-centric services. These characteristics make the resource scheduling problem of cloud manufacturing more complicated. Efficient resource scheduling and economic benefit are the major concerns of users in cloud manufacturing environment. A novel market-based continuous bidding mechanism is proposed in this paper by applying game theory. In this mechanism, the overall benefit of both the cloud service provider and the cloud service demander is considered as the optimization target. The constraints are the task delivery time and budget. For decomposable tasks, the constraints for each subtask are also considered in resource bidding. Service demanders can bid continuously until the corresponding service is obtained or the bidding constraint is violated. Experiments demonstrate the proposed resource bidding mechanism is efficient in cloud service scheduling and can ensure the cloud manufacturing market is truthful and fair.

A dynamic and static data based matching method for cloud 3D printing

Abstract 3D printing is widely used in such sectors as industry, medical, sports and education with the rapid development 3D printing technology and continual breakthrough of new material technology. Faced with the continual expansion of 3D printing market and the diversity and rapid growth of the scale of 3D printing devices, efficiently manage 3D print resources in the environment of distributed network manufacturing is a critical problem urgently to resolve. As a novel business paradigm, Cloud manufacturing can effectively integrate and manage manufacturing resources. Therefore, based on the cloud manufacturing paradigm, this study focuses on dynamic and static data based matching method for cloud 3D printing. In this paper, we propose a modeling framework to describe two models of the print task and print resource by model-based systems engineering. This modeling framework can support the efficient matching of the two types of models. Finally, the dynamic and static data based matching method can realistically simulate the supply-demand matching process of cloud 3D printing platform and provide a technical solution for quick supply-demand matching of large-scale resources in the environment of cloud manufacturing. During in the modeling process, we not only consider the static characteristics of 3D printers and analyze quantitatively all the parameter indicators of static characteristics, but also consider the dynamic characteristics of 3D printers to establish a universal dynamic data acquisition system, which can be used for real-time monitoring and automatic diagnosis of the health status of 3D printers. Therefore, this matching method has important theoretical significance and engineering value.

Sistem Kontrol Robot Pengintai Berbasis Video Sender

The development of industrial technology has caused the transition of the use of human and animal power to be replaced by mechanical technology and robots. One of them is the fourth industrial revolution or industry 4.0 that occurred in 2012, industry 4.0 introduced the Cyber-Physical production process, this Industry 4.0 leads to an internet-based or wireless network manufacturing process, the use of this technology is not only limited to communication but also remote control that is quite far away. Therefore, it is a challenge for the nation's children who are already engaged in this technology, so researchers designed a tank-shaped surveillance device that can be controlled remotely which has several advantages, one of which is that it can be used in a dangerous place or place which is at high risk for humans when entering it, this robot can be controlled remotely using telemetry as its control and equipped with a monitoring camera or monitiring to complete the performance of the surveillance robot connected to the sender audio video module as the sender and receiver of audio video data displayed on the screen monitor, in carrying out the tasks of this robot is more flexible and also for the controller does not have to go directly to the location because the system has been controlled remotely so it does not take time and energy, and also the robot is equipped with a clamp or gripper that can take and put goods. The level of success in this design is 97.50%