Under the social manufacturing context, geographically distributed and decentralized micro-and-small-scale manufacturing enterprises (MSMEs) self-organize into manufacturing communities (MCs), a type of decentralized autonomous organization (DAO). In MCs, MSMEs share their manufacturing resources for order-driven cross-enterprise production cooperation, which is supported through blockchain-based order tracking. However, the application of blockchain also brings concerns about data security and privacy protection to MSMEs, which leads to disputes between MSMEs about which data should be stored in the blockchain. For this problem, a granular-computing-based data-sharing decision-making (GrC-DSDM) method is proposed. In the GrC-DSDM method, a fuzzy proximity relationship is used to describe the familiarity between MSMEs in the same MC, and MC familiarity is obtained based on the granular space derived from the fuzzy proximity relation. A fuzzy preference relation is used to represent MSMEs’ preferences for all metadata related to the order, and a group decision-making method is applied to calculate the preference values for all metadata. Through constructing the mapping relationship between MC familiarity and preference values of all metadata, we can determine which metadata should be shared with the MC for blockchain-based order tracking. The GrC-DSDM method can support group decision-making on data sharing among MSMEs in the same MC. The implementation of the GrC-DSDM method is demonstrated through the example of a sheet metal parts processing MC. It is expected that the GrC-DSDM method will provide a basis for enabling blockchain-based order tracking in MCs.

An incubator is a kind of public or private organization that can provide multidimensional support to startup companies in exchange for profit or stake sharing. To date, huge numbers of incubators have been established all over the world, and many successful companies have been incubated by them. However, most of the existing incubators are more focused on incubating the target startup companies and pay relatively less attention to promoting the establishment of the supply chain networks and the ecosystems for the startup companies. In this regard, a new kind of socialized incubator model is established. Based on a kind of “Service-Product-Ecosystem” oriented configuration/operation mechanism, the socialized incubator can provide multidimension production services and value-added services during the entire incubation life-cycle of startup companies, transfer startup companies into ready-for-sold products, and construct multilayer vertical industrial ecosystems for the startup companies using socialized manufacturing resources. The socialized incubator model is verified through a case study on incubating a real startup company.

Interpreting the mechanism of machining errors' accumulation (MEA) is hard in multistage machining processes. This article addresses a novel three-dimensional graphical model named RFID-MEA for quantitating MEA inspired by the radio-frequency identification (RFID) graphical deduction computing model. RFID-MEA aims at revealing the MEA's mechanism instead regarding the machining process as a traditional “black box” model. First, Taylor's second-order expansion is employed to advise the mathematical representation of MEA and its calculation formula. Then, RFID data are collected to bond the MEA formula's variables with certain data silos in the right type at the right time. After that, raw not only structured query language (NoSQL) data computing is executed in edge nodes for assigning values to variables of the MEA formula accurately. Finally, machining errors are estimated by solving the MEA formula. By illustrating an industrial case, it is shown that the RFID-MEA has better performance in estimating machining errors, as well as with higher reliability and interpretability.

Under the consideration of massive energy consumption of machine tools, many approaches have been proposed, and state control method of machine tools has proved its effectiveness. In order to satisfy the demand of real-time production control, a deep learning methodology for energy-efficient control of CNC machine tools is proposed in RFID-enabled ubiquitous environment. First, the energy-efficient control strategies for multiple machine tools are proposed to reduce the carbon emission of the machining process. Then, through evaluating the process progress in the RFID-enabled environment, a deep learning methodology for energy-efficient strategies selection of CNC machine tools using deep belief networks (DBNs) is established to realize the real-time and accurate control of machine tools. Finally, comparisons between the proposed approach and some state-of-the-art ones are given, and the experiment results indicate that the proposed method is effective and efficient for the energy-efficient control problem of machine tools. The proposed method can realize the real-time control of CNC machine tools based on the interaction information in Industrial 4.0. Furthermore, the machine tools will be converted to smart machines, which can complete self-perception and self-adjustment automatically.

With this research, an embedded-web-based remote control system for RepRap-based open-source 3D printers is proposed to equip 3D printers with capabilities of the Internet access and remote control. The system architecture is presented and some key enabling technologies that implement the control system are discussed. Based on that, a prototype for the embedded-web-based remote control system is developed and the feasibility is verified. This project reveals the hardware configuration and embedded web remote control logic, and provides a novel insight for future work.

The purpose of this paper is to develop an enhanced radio frequency identification (RFID)-enabled graphical deduction model (rfid-GDM) for tracking the time-sensitive state, position, and other attributes of RFID-tagged objects in process flow. Concepts and definitions related to processes and RFID applications are first clarified, and enhanced state blocks are proposed to depict four kinds of RFID application scenarios. The implementation framework of rfid-GDM and its five steps are further addressed. Both mathematical formalization and graphical description of each step are involved. Finally, a case is studied to verify the feasibility of rfid-GDM. It is expected that rfid-GDM will provide instructions for modeling and tracking RFID-enabled process flows in diverse fields.

There is an increasing use of social interaction contexts in the cross-enterprise manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise manufacturing demand-capability matching, we present automated solutions to two phases: (1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and (2) matching group-level relationships among the entities in the established manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise.

This paper proposes a new type of product service system (PSS) design method named Service-oriented Design (SOD), the design method aim at building a service-based modeling system to totally satisfy customer demand. The mainline of SOD is customer demand acquire, service function and performance, service structure and service activity. Based on this method and its mainline, we propose a CAD system (Service Design System) that designer is able to design a service using this visible system. Through its application to business case such as hydraulic fracturing, the Service Design System was proven to support designer to design a steady and efficient service.

In recent years, with significantly increasing pressures from both energy price and the scarcity of energy resources have dramatically raised sustainability awareness in the industrial sector where the effective energy efficient process planning and scheduling are urgently demanded. To response this trend, development of a low cost, high accuracy, great flexibility and distributed real time energy monitoring platform is imperative. This paper presents the design, implementation, and testing of a remote energy monitoring system to support energy efficient sustainable manufacturing in an industrial workshop based on a hierarchical network architecture by integrating WSNs with Internet communication into a knowledge and information services platform. In order to verify the feasibility and effectiveness of the proposed system, the system has been implemented in a real shop floor to evaluate with various production processes. The assessing results showed that the proposed system has significance in practice of discovering energy relationships between various manufacturing processes which can be used to support for machining scheme selection, energy saving discovery and energy quota allocation in a shop floor.

Manufacturing outsourcing has been regarded as a strategic approach to enhance competitiveness of manufacturing enterprises. However, making an outsourcing decision based on historical data and subjective experience in one-of-a-kind production (OKP) is deficient and unpersuasive. Meanwhile, manufacturing capability (MC), comprised of processing capability and production capacity, is the most important factor for outsourcing decision-making (ODM). In this paper, a three-phase description, match and evaluation model of MC is proposed to provide outsourcing decision-making support for manufacturing enterprises. Firstly, a description model of MC is developed based on ontology; then, a four-step match method is put forward to match the processing capability; finally, the evaluation of production capacity is carried out by dealing with the dynamic flexible job-shop problem using the nondominated sorting genetic algorithm II (NSGA-II). A simulation study is analyzed to validate the proposed model.

With the enhancement of people's environmental awareness, low-carbon and energy-efficiency in manufacturing industry has been drawing much attention due to the huge consumption of raw materials and energy during machining processes. And it is a complex problem to evaluate part machining processes from the view point of carbon emission because there are many factors involved in it, such as machine tools, raw materials, energy, workshop, and so on. For solving this problem, a web-based architecture is established which mainly contains six parts, i.e., process system, process flow, data acquisition network, data transmission network, calculation of carbon emission and optimization of machine energy consumption. By introducing network technology into low-carbon manufacturing, the architecture forms a closed-loop low-carbon manufacturing system which can not only calculate the carbon emission of machining processes, but also reduce the carbon emission of a workshop with time by means of optimizing the machine energy consumption. What's more, three key enabling technologies are described in detail to make sure the evaluation architecture runs smoothly, that is, calculation of the carbon footprint, low-carbon data processing and prediction of part energy consumption.

In a collaborative design process, design activities, people and design tools are connected each other for collaboratively finishing design tasks. A method based on complex network theory is proposed in this paper to analyze the performance of the process. Firstly, the collaborative design process is abstracted as a network named collaborative design network (CDN), and its elements of CDN, such as nodes, edges and weight of edges are defined. Then the topological and physical characteristics of the CDN are defined and analyzed for revealing its performances. Finally, a steam turbine rotor design process is studied as an example to illustrate the feasibility and availability of the proposed method.

To avoid repetitiveness and invalidation in product configuration, in this paper, a new approach to configuration design based on customer requirement’s priority is presented. Firstly, a configuration model is constructed and translated into CSP model. Secondly, in the stage of interactive configuration, customer configured more important issue earlier than other issues. The Check Configuration process has to reduce the variant space provided by the CSP model with regard to the outcome of the previous configuration. As a result, customer demands with consistency and reduced configuration structure are achieved. Finally, under the optimization conditions of customer wish requirement, the reduced configuration structure is optimized to gain final BOM of the configured product.

In order to change the serial service in conventional manuals, dynamic online manuals (DOA) are designed. DOMs meet the requirements of sustainable development and benefit users and producers, which provide just-in-time and high-quality services to users, and offer new design ideas to producers. The DOM architecture for CNC mill center is then proposed after services content is presented, and key technologies of the architecture are outlined. Finally, it is concluded that the DOM has some support for future research on product service.

In order to generate the shortest and appropriate tool motion trajectories and reducing the nonproductive tool positioning time, the tool motion trajectories of pocket milling with contour-parallel should be optimized. A chaos ant colony algorithm (CACA) is presented to solve the optimization problem in this paper. In the CACA, ant colony optimization (ACO) is used to find out the local optimal solution of current position and chaos iterative is used to extend the searching spaces. To demonstrate the efficiency of the CACA, a numerical experiment is put forward. The results show that the algorithm could realize the optimization of tool motion trajectories effectively and rapidly in pocket milling with contour-parallel.