Manufacturing System Architecture Research Articles

System architecture for small‐scale personalized production lines based on industrial Internet

AbstractThe rapid development of industrial Internet has resulted in significant changes in industrial manufacturing system architecture. In this study, a system architecture suitable for small‐scale personalized production lines is proposed. The core of this architecture is a three‐layer structure comprising the Internet layer, data layer, and field control layer. The system facilitates the transmission of user‐specific product demands from the Internet to the production line, where the production equipment achieves personalized production through motion control. This system architecture for production lines integrates industrial Internet and smart manufacturing technologies, reducing the complexity of the enterprise system architecture and enhancing the flexibility of the system compared to large‐scale personalized production systems. The effectiveness of this system architecture was validated using a flexible yogurt filling production line and lays a foundation for the transformation of industrial manufacturing from the tradition system to a personalized production.

Information system-theoretic view on the architecture of smart manufacturing systems: a case study in the Democratic People’s Republic of Korea

Information system-theoretic view on the architecture of smart manufacturing systems: a case study in the Democratic People’s Republic of Korea

A Fault-Tolerant and Reconfigurable Control Framework: Modeling, Design, and Synthesis

Manufacturing systems (MS) have become increasingly complex due to constraints induced by a changing environment, such as flexibility, availability, competition, and key performance indicators. This change has led to a need for flexible systems capable of adapting to production changes while meeting productivity and quality criteria and reducing the risk of failures. This paper provides a methodology for designing reconfigurable and fault-tolerant control for implementation in a Programmable Logic Controller (PLC). The main contribution of this methodology is based on a safe control synthesis founded on timed properties. If a sensor fault is detected, the controller switches from normal behavior to a degraded one, where timed information replaces the information lost from the faulty sensor. Switching between normal and degraded behaviors is ensured through reconfiguration rules. The primary objective of this method is to implement the obtained control into a PLC. In order to achieve this goal, a method is proposed to translate the controllers of the two behaving modes and the reconfiguration rules into different Grafcets. This approach relies on the modular architecture of manufacturing systems to avoid the combinatorial explosion that occurs in several approaches.

Control system architecture for matrix-structured manufacturing systems

Mass customization has in recent years required the manufacturing companies to become more flexible, while sustaining a high production throughput. To address this challenge, Matrix-Structured Manufacturing Systems (MMS) enable high levels of flexibility, scalability, and redundancy. However, current control system architectures do not sufficiently leverage these aspects that MMS enable, resulting in less efficient manufacturing systems. This paper therefore proposes a control system architecture for MMS, designed to leverage these benefits and increase the performance of this manufacturing system. To verify and validate the proposed control system architecture, a company case study with a Danish industrial automation company is presented, yielding potential performance increase and improved flexibility.

Graphic Model for Shop Floor Simulation and Control in the Context of Industry 5.0

Industry 5.0 changes the paradigm of the current production model, with repercussions throughout the value chain, and opens up opportunities for new approaches that include reducing waste to optimize the use of the planet’s resources. This paper proposes a functional and executable model that implements a Holonic Manufacturing System (HMS) architecture inspired by the I5.0 guidelines. This architecture presents the factory floor as a service provider for the product to be built, intending to make the manufacturing process adaptable to changes. The model uses Reference nets as the modeling language, a high-level class of Petri nets, Java programming language as the annotation language, and free tool support. The model can be used to perform software-level simulations and can also be interconnected to existing physical devices using Internet of things technologies, enabling interactions between Cyber–Physical Systems (CPSs). It thus allows for the control of the shop floor and the reuse of the current machine park to make its adoption more sustainable. The model was used to generate several simulation results, which are presented and analyzed, thus demonstrating the model’s usefulness.

Intelligent manufacturing system with human-cyber-physical fusion and collaboration for process fine control

There are some problems in non-ferrous metal industry, such as low production efficiency, separation of manufacturing information, disconnection between production, supply and marketing and so on, which highlight the need for fine process control and business collaborative decision-making with cost, efficiency, quality and safety as the core. On the basis of Human Cyber Physical fusion intelligence, this paper firstly discusses the mechanism of intelligent manufacturing system with HCP fusion and collaboration, and explores the HCP data fusion mechanism, collaboration mechanism and interaction mechanism. Then, an intelligent manufacturing system with HCP fusion and collaboration in the non-ferrous metal industry is established, in which a knowledge-graph based HCP data fusion framework and a cyber-physical system based HCP business collaborative decision model are discussed to realize HCP fusion and collaboration manufacturing. Finally, a case study for a copper smelting enterprise is explored, in which the intelligent manufacturing system architecture for the copper smelting enterprise is proposed, and validated through the collaborative decision-making of production, supply and marketing integration plan in the world’s largest monomer copper smelter enterprise.

Knowledge-based engineering approach for defining robotic manufacturing system architectures

Robotic manufacturing systems have proven to be an effective solution for modern manufacturing enterprises to deal with increasing in customer demands and market competition. However, these systems may be unable to completely satisfy user requirements because of the difference between user and design perspectives. Thus, designing robotic manufacturing systems requires iterative processes that significantly increase development costs and lead time. A user-customised design approach is needed that enables users to customise robotic manufacturing systems as well as alleviate the burden on designers of eliciting user requirements. However, most users may not be able to customise their systems because of a lack of engineering knowledge. The authors propose a knowledge-based engineering approach to aid users in customising the architectures of robotic manufacturing systems. Two models — an ontological knowledge model and a multi-attribute decision-making model — are defined and integrated in the proposed KBE architecture definition method. A rule-based reasoning process is proposed in the ontological knowledge model based on explicit semantic descriptions of users’ unstructured or semi-structured requirements and the components of robotic manufacturing systems, which infers the possible architecture of the required system. The MADM model is adopted to evaluate the architecture alternatives to determine the optimal solution.

Service-oriented industrial internet of things gateway for cloud manufacturing

• Proposed a generic system architecture of IIoT-supported cloud manufacturing as a strategic guideline for integrating IIoT technologies in a cloud manufacturing environment. • Proposed a service-oriented PnP IIoT gateway for efficient data acquisition, communication, query, analysis, and visualisation of manufacturing equipment. • Developed two generic service-oriented data schemas for machine tools and 3D printers that allow efficient data management and query for decision-making tasks in a cloud manufacturing platform. • Developed two practical PnP IIoT gateways for a 3D printer and a CNC machine tool that validate the feasibility of the proposed approach. Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and on-demand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a service-oriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool. Our research suggests that purposely developed service-oriented data schemas that capture the essential information for high-level cloud manufacturing decision-making via PnP IIoT technologies are a good solution for connecting field-level manufacturing equipment to a cloud manufacturing platform.

Development of Innovative Operational Flexibility Measurement Model for Smart Systems in Industry 4.0 Paradigm

The exponential growth of cutting-edge technologies continuously pushing the manufacturing industry into the paradigm of smart manufacturing. Smart manufacturing provides the pace of highly competitive market demand and customized intensive production. The goal of smart manufacturing technologies embedded systems in the Industry 4.0 paradigm and lean production is to enhance the flexibility in all tears of the enterprise. It is a big challenge, to measure the flexibility of smart systems for decision-making and adaptation of the new manufacturing technologies. The conceptual architecture of smart manufacturing systems has been proposed to solve the problem. Operational flexibility has been measured using a mathematical model for smart manufacturing in the Open Platform Communication Unified Architecture enabled Cyber-Physical Production System at shop floor level and validated. The results obtained from experimentation depict the operational flexibility, maximum capacity, and breakeven point of the manufacturing system have been improved by using smart manufacturing technologies. The proposed model improves the product manufacturing using Smart Computer Numeric Control Machining, Smart Autonomous Robotic Machining, Smart Additive Manufacturing and Smart Hybrid Additive & Subtractive Manufacturing up to 30.4%, 53.6%, 55% and 65% respectively. It will also help the decision-makers to overcome the challenges of transformation from conventional to smart manufacturing industry 4.0 paradigm.

CPS: Role, Characteristics, Architectures and Future Potentials

CPS entails the incorporation of four key characteristic technologies. CPS device technologies, CPS computational technologies, Cyber Physical System of Systems (CPSoS) and CPS networking and communication (CPS-ComNet) technologies. Multiple CPS architectures, of which the Industrial Internet Reference Architecture (IIRA), Reference Architecture Model for Industrie 4.0 (RAMI 4.0) and Intelligent Manufacturing System Architecture (IMSA) are the major referenced architectures and this demand to facilitate interoperability between such architectures for effective integration. This framework has evolved toward having smart CPS with intelligence and decision-making capabilities and highly networked and integrated components. CPSs have been categorized into two types namely Cyber Physical Production Systems (CPPS) for the development of products and Cyber Physical Service Systems (CPSS) for the servicing of products. These developments facilitate the adoption of the CPS in smart cities, smart farming systems, smart manufacturing, and smart supply chain systems, etc., while it gives CPS the importance as one of the core technologies making up Industry 4.0 era. Despite these advances, cybersecurity and privacy, real-time control of systems, interoperability of services, etc. are key challenges facing the development of CPS and they demand solutions overcoming such challenges. This paper, therefore, presents the role, evolution, characteristics, architectures, and applications of CPSs and their future potentials.

Modeling of reconfigurable manufacturing system architecture with geometric machines and in-stage gantries

Reconfigurable Manufacturing Systems (RMSs) are well recognized by their capability to achieve high responsiveness and flexibility and are receiving increasing interest in today’s versatile manufacturing environment. This paper aims to develop methods to study the performance of RMS architecture consisting of machines and in-stage gantries whose time to failure and time to repair follow geometric distributions. A discrete-time Markov chain model is built to perform an exact analysis for two-stage-one-buffer systems. A decomposition method is developed to analyze multi-stage systems. Moreover, both time-dependent failure and operation-dependent failure modes are considered. Numerical studies that compare the calculated production rate and work-in-process to the simulation results demonstrate that the proposed methods can provide accurate results, especially for balanced systems and for systems with a small number of stages. The impact of different parameters (e.g., buffer capacity, machine/gantry reliability) on the production rate of the two models is also studied. Finally, examples on the applications of the models in the manufacturing systems design and operations are presented.

Customer satisfaction service match and service quality-based blockchain cloud manufacturing

Blockchain cloud manufacturing is an emerging service-oriented paradigm that takes many advantages to cloud manufacturing. Previous researchers have studied from eradicating third party trust problem among service providers and customers by decentralized data record and involving smart contract. However, the existing paradigm still suffers from third party trust problem among service-related participants and cloud manufacturing provider since service is solely managed by cloud manufacturing provider without supervision and service related participants cannot know whether composed service provision which indeed is NP-hard problem is optimal. In this paper, the architecture of blockchain cloud manufacturing system is proposed with adoption of several cloud manufacturing providers and vote mechanism to vote for optimal composed service provision on candidate composed service scheme firstly calculated by one cloud manufacturing provider. According to customer satisfaction-based service match and quality control, a system framework of composed service performance evaluation is desifned to incorporate the vote mechanism. Firstly, customer satisfaction dimensions as evaluation dimensions are extracted from customer text reviews based on latent dirichlet allocation (LDA) and sub-dimensions under each customer satisfaction dimension are set up based on professional field knowledge. Secondly, overall customer satisfaction rating of reviews and sentiment of reviews towards customer satisfaction dimensions are analysed by three score scheme, bag of words (BOW) and support vector machine (SVM) classification. Thirdly, the sentiment effect and category of customer satisfaction dimension on overall customer satisfaction are identified with the application of neural network and Kano model. Fourthly, in consideration of category characteristic, overall performance of customer satisfaction dimension is measured as distance and using proposed Logit-SVM method. Service selection standard according to value of customer satisfaction is set up. Finally, one empirical study is presented to verify the feasibility of proposed system framework, and proposed model is further discussed.

An Edge Computing Node Deployment Method Based on Improved k-Means Clustering Algorithm for Smart Manufacturing

With the rapid development of the mobile Internet, Industrial Internet of Things, cyber-physical systems, and the emergence of edge computing has provided an opportunity to realize the high computing performance and low latency of intelligent devices in the smart manufacturing environment. In this paper, we propose and verify an edge computing node deployment method for smart manufacturing. First, the architecture of a smart manufacturing system used for implementing the edge computing node deployment methods is presented. Then, comprehensively balancing the network delay and computing resources deployment cost, and considering the influence of device spatial distribution, device function, and computing capacity of edge nodes on the above optimization objectives, the optimal deployment number of edge computing nodes is obtained by using an improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering algorithm. Finally, a prototype platform is developed to verify the proposed method experimentally, and compare the improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering deployment method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering deployment method, and random deployment method. The proposed method is superior to the other two methods regarding both network delay and computing resources deployment cost. The experimental results show that the proposed edge computing node deployment method can be easily applied to the intelligent manufacturing system; also, the effectiveness and efficiency of this method are verified.

An Evaluation Model for Green Manufacturing Quality of Children’s Furniture Based on Artificial Intelligence

With the differentiation of the furniture market, there is a growing demand for children’s furniture. The design of children’s furniture must fully consider the special cognition and preference of children, highlight environmental friendliness and health, and benefit the physical and mental development of children. These design objectives are similar to those of green furniture. Therefore, it is necessary to accurately evaluate the quality of green manufacturing, the key link of green furniture production, with the aid of the excellent data processing technique of artificial intelligence (AI). Thus, this paper summarizes the AI applications in quality testing of children’s furniture and statistical analysis on its greenness, and constructs an evaluation model for green manufacturing quality of children’s furniture. Firstly, the authors introduced the architecture of the green manufacturing system for children’s furniture, and analyzed the product lifecycle and environmental pollutions. On this basis, a complete and scientific evaluation index system (EIS) was constructed. Next, the weight coefficients of the goal layer and criteria layer were determined by the entropy method, and the initial evaluation result were provided. Finally, a comprehensive evaluation model was established for the green manufacturing quality of children’s furniture, based on backpropagation neural network (BPNN), and genetic algorithm with adaptive mutation (AMGA). The proposed EIS and model were proved effective through experiments. The research results provide a reference for the quality evaluation in other fields.

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.

Perspective on holonic manufacturing systems: PROSA becomes ARTI

Looking back at 30 years of research into holonic manufacturing systems, these explorations made a lasting scientific contribution to the overall architecture of intelligent manufacturing systems. Most notably, holonic architectures are defined in terms of their world-of-interest (Van Brussel et al., 1998). They do not have an information layer, a communication layer, etc. Instead, they have components that relate to real-world assets (e.g. machine tools) and activities (e.g. assembly). And, they mirror and track the structure of their world-of-interest, which allows them to scale and adapt accordingly.This research has wandered around, at times learning from its mistakes, and progressively carved out an invariant structure while it translated and applied scientific insights from complex-adaptive systems theory (e.g. autocatalytic sets) and from bounded rationality (e.g. holons). This paper presents and discusses the outcome of these research efforts.At the top level, the holonic structure distinguishes intelligent beings (or digital twins) from intelligent agents. These digital twins inherit the consistency from reality, which they mirror. They are intelligent beings when they reflect what exists in the world without imposing artificial limitations in this reality. Consequently, a conflict with a digital twin is a conflict with reality.In contrast, intelligent agents typically transform NP-hard challenges into computations with low-polynomial complexity. Unavoidably, this involves arbitrariness (e.g. don’t care choices). Likewise, relying on case-specific properties, to ensure an outcome in polynomial time, usually renders the validity of an agent’s choices both short-lived and situation-dependent. Here, intelligent agents create conflicts by imposing limitations of their own making in their world-of-interest.Real-world smart systems are aggregates comprising both intelligent beings and intelligent agents. They are performers. Inside these performers, digital twins may constitute the foundations, supporting walls, support beams and pillars because these intelligent beings are protected by their real-world counterpart. Further refining the top-level of this architecture, a holonic structure enables these digital twins to shadow their real-world counterpart whenever it changes, adapts and evolves.In contrast, the artificial limitations, imposed by the intelligent agents, cannot be allowed to build up inertia, which would hamper the undoing of arbitrary or case-specific limitations. To this end, performers explicitly manage the rights over their assets. Revoking such rights from a limitation-imposing agent will free the assets. This will be at the cost of reduced services from the agent. When other service providers rely on this agent, their services may be affected as well; that’s how the inertia builds up and how harmful legacy is created. Thus, the services of digital twins are to be preferred over the services of an intelligent agent by developers of holonic manufacturing systems.Finally, digital twins corresponding to the decision making in the world-of-interest (a non-physical asset) allow to mirror the world-of-interest in a predictive mode (in addition to track and trace). It allows to generate short-term forecasts while preserving the benefits of intelligent beings. These twins are the intentions of the decision-making intelligent agents. Evidently, when intentions change, the forecasts needs to be regenerated (i.e. tracking the corresponding reality by the twin). This advanced feature can be deployed in a number of configurations (cf. annex).

Big Data Driven Edge-Cloud Collaboration Architecture for Cloud Manufacturing: A Software Defined Perspective

In the practice of cloud manufacturing, there still exist some major challenges, including: 1) cloud based big data analytics and decision-making cannot meet the requirements of many latency-sensitive applications on shop floors; 2) existing manufacturing systems lack enough reconfigurability, openness and evolvability to deal with shop-floor disturbances and market changes; and 3) big data from shop-floors and the Internet has not been effectively utilized to guide the optimization and upgrade of manufacturing systems. This paper proposes an open evolutionary architecture of the intelligent cloud manufacturing system with collaborative edge and cloud processing. Hierarchical gateways connecting and managing shop-floor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">things</i> at the “edge” side are introduced to support latency-sensitive applications for real-time responses. Big data processed both at the gateways and in the cloud will be used to guide continuous improvement and evolution of edge-cloud systems for better performance. As software tools are becoming dominant as the “brain” of manufacturing control and decision-making, this paper also proposes a new mode - “AI-Mfg-Ops” (AI enabled Manufacturing Operations) with a supporting software defined framework, which can promote fast operation and upgrading of cloud manufacturing systems with smart monitoring-analysis-planning-execution in a closed loop. This research can contribute to the rapid response and efficient operation of cloud manufacturing systems.

A Referenced Cyber Physical System for Compressor Manufacturing

Compressor is a typical high-end discrete product，with the shortening of product life cycle and the enhancement of the degree of product customization, the traditional compressor manufacturing system architecture cannot meet the requirements of comprehensive digital management of compressor from body scheme design to parts production line, logistics management, operation and maintenance monitoring and evaluation. This paper presents a compressor manufacturing system architecture based on digital twinning, and establishes an Internet platform for compressor industry oriented to remote coordination from three aspects of compressor design, production, operation and maintenance. The platform includes industrial Internet infrastructure layer, physical space entity model layer, virtual space multidimensional model layer, physical space and virtual space multidimensional model correlation and mapping layer, big data intelligent analysis decision-making layer, and digital twin application layer. Through the establishment of the compressor product design and simulation model of digital twin, compressor production process digital twin model, compressor fault diagnosis and remote operations digital twin model, implementation is based on the number of compressor collaboration in manufacturing industrial Internet platform twin system, leading the transformation and upgrading of intelligent manufacturing industry, compressor industry sustainable development ability and international competitiveness.

SME-oriented flexible design approach for robotic manufacturing systems

Continuously growing pressures due to the shorter development lead-time and the uncertainty of increasingly complex market require a high flexibility from production companies and their manufacturing systems. Comparing with large enterprises, small and medium-sized enterprises (SMEs) which lack concentrated market power and powerful original equipment manufacturers (OEMs) have difficulty and risk to develop flexible and customised robotic manufacturing systems. Nowadays, although various design methods for robotic manufacturing systems have been made to fix this problem, however, there still exist two main challenges: flexibility in design and high degree of customisation.To deal with the existing challenges, this paper proposes an SME-oriented design approach based on a configuration design paradigm. This method can offer decision support to designers to form flexible architecture for robotic manufacturing systems and, at the same time, give more interactive configuration freedom to customers so as to achieve a high productivity and flexibility for customisation but with less risk and cost of product development. To validate the proposed method, two design cases of robotic manufacturing systems are presented for demonstration.

An overall framework and subsystems for smart manufacturing integrated system (SMIS) from multi-layers based on multi-perspectives

In the tide of smart manufacturing in the world, many countries have put forward their own reference frameworks for smart manufacturing system. Based on the framework of smart manufacturing system (SMS) proposed by China, a reference smart manufacturing integration system (SMIS) from multi-level and multi-perspective was proposed. The reference model of SMIS was analyzed by means of system engineering. Based on this model, the overall architecture of SMIS was proposed. Then, the physics subsystem, information integration subsystem, network integration subsystem, data integration subsystem, and visualization integration subsystem for SMIS were proposed through the overall architecture of SMIS. Finally, the above integrated subsystems were collected together to deduce the implementation path of SMIS. Four reference subsystems for SMIS proposed in this paper have achieved good effects in the projects we participated in, which were organized by the state. The research results of this paper can be used as a reference for industry and government to design, set, and carry out SMIS. At the same time, it has a certain reference value for the improvement and supplement of national smart manufacturing system architecture.