Cyber-physical Manufacturing Research Articles

Internet of Things-enabled Sustainability, Industrial Big Data Analytics, and Deep Learning-assisted Smart Process Planning in Cyber-Physical Manufacturing Systems

Internet of Things-enabled Sustainability, Industrial Big Data Analytics, and Deep Learning-assisted Smart Process Planning in Cyber-Physical Manufacturing Systems

Cyber-Physical Security Evaluation in Manufacturing Systems with a Bayesian Game Model

Cyber-physical security has been taken more seriously recently in manufacturing systems because of highly increasing integration of cyber and network systems with physical production known as cyber-physical manufacturing systems (CPMS). This paper presents a novel proactive approach to forecast the interrelation of attackers and cyber-physical manufacturing systems to suggest a proper defense strategy to the system managerial. In this research, the zero-sum game payoff function developed in our previous studies is transformed into an epistemic type Bayesian game that addresses uncertainties on types of players. The risk preference behavior also is considered as a characteristic of different types of players and to form the Bayesian utility function. Finally, a numerical example is analyzed with a structured method by developing the Bayesian game, computing Bayes-Nash equilibria, and finding quantal response equilibrium. This approach enables managers to decide a proper defense strategy in advance of experiencing cyber-physical dilemma when there are not enough previous data.

Sensing, Smart, and Sustainable Technologies in Cyber-Physical Manufacturing Systems

Sensing, Smart, and Sustainable Technologies in Cyber-Physical Manufacturing Systems

Digital Twin in 5G Digital era developed through Cyber Physical Systems

The paper Cyber Physical Systems integrated in VIPRO- Platform through intelligent control interfaces and genetic algorithms to build “digital-twins” robots vectors tools for cyber-physical manufacturing presents. Intelligent control interfaces are useful for simulating robots vectors’ capabilities in a safe and cost-effective way, but it is challenging to accurately emulate the behavior of the physical tools. When an unmanned autonomous vehicles breaks down or malfunctions, engineers can always go back to check the digital traces using the “digital-twins” VIPRO- Platform for diagnosis and prognosis. This paper presents an integration of Cyber Physical Systems using intelligent control interfaces and genetic algorithms into developing “digital-twins” VIPRO- Platform in 5G Digital era to improve their accountability and capabilities for cyber-physical manufacturing. The intelligent control interfaces data are used to extract the machining characteristics profiles of a digital-twins machine tool, with which the tool can better reflect the actual status of its physical counterpart in its various applications. The modelling techniques and decision making in complex systems for controlling the position of a unmanned autonomous vehicles engaged in a mission are discussed, and analytical techniques of data and genetic algorithms are presented for modeling and developing “digital-twins ”VIPRO- Platform.

Integration of Existing Cyber-Physical Manufacturing Systems into a Common Information Model

In order to be able to serve constantly new customer requirements, manufacturing systems must be able to adapt to frequent changes. In addition, repeatedly objects are removed or added to the network. To control and monitor such a constantly changing system a mapping of existing manufacturing systems into a common information model is necessary. This model describes information that is produced and stored in different entities of the complete system. To create a common address space and expose the relations between the devices an aggregation of every element in the system is needed. This paper describes a methodology for the creation of an information model for a complete manufacturing environment, followed by an approach for the aggregation of the singular system entities. The concept of this paper is illustrated with a demonstrator. The results of this approach have been discussed in the following sections along with the proposal for further directions.

Cognitive Decision-Making Algorithms, Real-Time Sensor Networks, and Internet of Things Smart Devices in Cyber-Physical Manufacturing Systems

Cognitive Decision-Making Algorithms, Real-Time Sensor Networks, and Internet of Things Smart Devices in Cyber-Physical Manufacturing Systems

Artificial Intelligence-based Decision-Making Algorithms, Industrial Big Data, and Smart Connected Sensors in Cloud-based Cyber-Physical Manufacturing Systems

Artificial Intelligence-based Decision-Making Algorithms, Industrial Big Data, and Smart Connected Sensors in Cloud-based Cyber-Physical Manufacturing Systems

Context-Sensitive Modeling and Analysis of Cyber-Physical Manufacturing Systems for Anomaly Detection and Diagnosis

Cyber-physical manufacturing systems (CPMS) can be defined by the integration of control, network communication, and computing with a physical manufacturing process. In this work, we present a hybrid model of CPMS combining sensor data, context information, and expert knowledge. We used the identification of global operational states and a multimodel framework to improve anomaly detection and diagnosis. The anomaly detection is based on context-sensitive adaptive threshold limits. Root cause diagnosis is based on classification models and expert knowledge. The proposed approach was implemented using the Internet of Things (IoT) to extract data from a computer numerical control machine. Results showed that using a context-sensitive modeling strategy allowed to combine physics-based and data-driven models for residual analysis to detect an anomaly in the part, machine, or process. The identification of root cause was improved by adding context information in classification models to identify worn or broken tools and wrong material. Note to Practitioners—Anomaly detection and diagnosis of manufacturing equipment is a complex problem. Some of the challenges are complex machine dynamics and nonstationary operating conditions. This paper describes a framework for modeling manufacturing equipment using a combination of sensor data, context information, and system knowledge. The proposed modeling framework is used to improve anomaly detection for diagnostics using a context-sensitive strategy. This work aims to support more effective maintenance actions by identifying problems in the machine, part, or process. The modeling and anomaly detection strategy was used to identify anomalies in computer numerical control machines and can be extended to other equipment on the plant floor.

Two - Echelon Vehicle Routing Problem with Recharge Stations

Abstract The efficient operation of logistic processes requires a wide range of design tasks to ensure efficient, flexible and reliable operation of connected production and service processes. Autonomous electric vehicles support the flexible in-plant supply of cyber-physical manufacturing systems. Within the frame of this article, the extension of the Two-Echelon Vehicle Routing Problem with recharge stations is analyzed. The objective function of the optimization problem is the minimization of operation costs. The extension of 2E-VRP means that the second level vehicles (electric vehicles, must be recharged) come from one recharge station, then pick up the products from the satellite, visit the customers and return to the recharge station from where it started. We solved the route planning problem with the application of construction heuristics and improvement heuristics. The test results indicate that the combination of this approach provides a superior efficiency.

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.

SmaRTI—A cyber-physical intelligent container for industry 4.0 manufacturing

Cyber-Physical Systems (CPSs) are becoming a significant research focus resulting from advancements in technologies such as the Internet of Things (IoT), Cloud Manufacturing and Intelligent Products. Successful deployment of CPSs has the potential to provide a step change in manufacturing efficiency, flexibility and production yield as envisaged by the fourth industrial revolution or Industry 4.0 paradigm. The realisation of intelligent products and services is in the provisioning of predictive, risk preventative and high-performance manufacturing systems. As part of these manufacturing systems, Returnable Transit Items (RTIs) play a critical role in provisioning robust and efficient means of component (e.g. Work in Progress and finished items) protection and logistics. The research outlined in this paper details how a Returnable Transit Item (RTI) can become an integral part of the Industrie 4.0 vision as an intelligent container that can interact with components, machines and other cyber-physical manufacturing services. This paper discusses a CPS reference architecture for the integration of intelligent containers and presents a hardware and software proof of concept solution suitable for industrial deployments. The paper concludes with feasibility studies utilising the intelligent container for context determination services including the identification of intelligent components and monitoring of logistical handling process (i.e. the detection of collisions, lifting and turns).

Cyber-Physical Manufacturing Systems

Cyber-physical systems, in which computation and networking technologies interact with physical systems, have made great strides into manufacturing systems. From the early days, when electromechanical relays were used to automate conveyors and machines, through the introduction of programmable logic controllers and computer numerical control, computing and networking have become pervasive in manufacturing systems. By increasing the amount of automation at multiple levels within a factory and across the enterprise, cyber-physical manufacturing systems enable higher productivity and higher quality as well as lower costs.

Resilience of cyber-physical manufacturing control systems

Cyber-physical production systems (CPPS) introduce a paradigm shift towards distributed manufacturing control architectures. We investigate the question: How does the transition to semi-heterarchical control structures in CPPS affect the resilience of manufacturing control structures against cascade and non-cascade disruptions in the control process? While the transition to CPPS improves resilience against non-cascade disruptions (e.g., demand variability), it also increases the vulnerability against cascade disruptions (e.g., cyber-attack). Nevertheless, our study reveals that the magnitude of improvement in the resilience of a control structure as it shifts towards heterarchy is 2.5–8 times more than the magnitude of decline in its cascade resilience.

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

In the context of Industry 4.0, the matrix production concept represents revolutionary solutions from a technological and logistics point of view. In a matrix production system, flexible, configurable production and assembly cells are arranged in a grid layout, and the in-plant supply is based on autonomous vehicles. Adaptable and flexible material handling solutions are required to perform the dynamically changing supply-demands of standardized and categorized manufacturing and assembly cells. Within the frame of this paper, the authors describe the in-plant supply process of matrix production and the optimization potential in these processes. After a systematic literature review, this paper introduces the structure of matrix production as a cyber-physical system focusing on logistics aspects. A mathematical model of this in-plant supply process is described including extended and real-time optimization from routing, assignment, and scheduling points of view. The optimization problem described in the model is an NP-hard problem. There are no known efficient analytical methods to find the best solution for this kind of problem; therefore, we use heuristics to find a suitable solution for the above-described problem. Next, a sequential black hole–floral pollination heuristic algorithm is described. The scenario analysis, which focuses on the clustering and routing aspects of supply demands in a matrix production system, validates the model and evaluates its performance to increase cost-efficiency and warrants environmental awareness of the in-plant supply in matrix production.

Networked Information-driven Technologies for Cyber-Physical System-based Smart Manufacturing

Networked Information-driven Technologies for Cyber-Physical System-based Smart Manufacturing

Development of an education program for digital manufacturing system engineers based on ‘Digital Triplet’ concept

Abstract In the Industry 4.0 era, cyber physical manufacturing systems (CPPS) has started to change activities of manufacturing system engineers into CPS based ones. In typical Japanese factories, manufacturing system engineers are always stationed at the shop floor and continuously improve manufacturing systems with workers. For supporting such engineers’ activities, we are developing the concept of ‘Digital Triplet’ as an extension of Digital Twin. Digital Triplet consists of intelligent activity world in addition to the cyber world and the physical world and supports manufacturing system engineers in developing engineering processes with the cyber and physical worlds. Based on this, we are developing an education program. In this paper, we first describe the concept of Digital Triplet. Next, after explaining the overview of the education program, we introduce a course in which trainees (mainly novice engineers) execute ‘Kaizen’ with a prototype CPS system of a learning factory.

A Realization of Cyber-Physical Manufacturing Control System Through Industrial Internet of Things

A Cyber-Physical System in Industry 4.0 is with interconnected elements of cyber and physical worlds. However, the interconnection relies on the success of communication among the equipment, sensors, controllers with different data and communication standards. (Protocols) This research takes an approach of applying Industrial Internet of Things (IIoT) to integrate the sensing data from various equipment and sources. Industrial Micro Control Unit (MCU) is applied to interface with the data sources, actuators, and equipment. The MCU transmits the sensing data/control commands back and forth with the cloud/fog computing platform. By deploying data analytics and reasoning for the manufacturing knowledge ontology in the cloud platform, the cyber world is able to recognize the situations and problems in the physical world. Thus, a decision of preventive or predicative actions can be made in the cyber world and then be implemented in the physical world. The results show that IIoT can eliminate the problems of heterogeneous protocols and databases in manufacturing data transmission. Based on IIoT, CPS is realized without the concerns or difficulties of data transmission

Sustainable Manufacturing With Cyber-Physical Discrete Manufacturing Networks: Overview and Modeling Framework

Cyber-physical systems (CPS) enable unprecedented communication between product designers and manufacturers. Effective use of these technologies both enables and requires a new paradigm of methods and models to identify the most profitable and environmentally friendly production plans for a manufacturing network. The operating system for cyber-physical manufacturing (OSCM) and the paired network operations administration and monitoring (NOAM) software are introduced. These technologies guide our development of a mixed integer bilevel programming (BP) model that models the hierarchy between designers and manufacturers as a Stackelberg game while considering multiple objectives for each of them. Designers select and pay manufacturers, while manufacturers decide how to execute the order with the payment provided by the designer. To solve the model, a tailored solution method combining a decomposition-based approach with approximation of the lower level Pareto-optimal solution set is proposed. The model is applied to a case study based on a network of manufacturers in Wisconsin and Illinois. With the proposed model, designers and manufacturers alike can take full advantage of CPS to increase profits and decrease environmental impacts.

Enhancing cyber-physical security in manufacturing through game-theoretic analysis

ABSTRACTModern manufacturing systems utilise cyber-physical systems that are interconnected through an Internet of Things (IoT) network. An IoT infrastructure enables cyber-physical systems to communicate with each other and coordinate the manufacturing process autonomously with minimal human assistance. A significant challenge for cyber-physical systems is cybersecurity. Adversaries can exploit weaknesses in the IoT security infrastructure to gain remote access to the system and modify or damage manufacturing processes and/or products. Due to the autonomous nature of cyber-physical systems, cyberattacks can go unnoticed by the users for a long time, further compounding the problem. Attacks on cyber-physical systems are on the rise, and manufacturers need to address this problem. In this article, we discuss how game-theoretic thinking and modelling can help to identify a manufacturer’s cyber vulnerabilities and enhance its security. We consider various attack and defence scenarios and analyse each through the corresponding game-theoretic model. Our approach and findings can be used to improve the effectiveness and efficiency of security strategies for cyber-physical manufacturing systems.

Industrial IoT in 5G environment towards smart manufacturing

Abstract Smart manufacturing based on cyber-physical manufacturing systems (CPMS) has become the development trend and been widely recognized all over the world. Throughout the development trend of CPMS, one of the key issues is industrial Internet-of-Things (IIoT) with the characteristics of automation, smart connected, real-time monitoring, and collaborative control. Along with the permeation and applications of advanced technologies in manufacturing, massive amounts of data have been generated in the manufacturing process. However, the current 3th generation mobile network (3G), 4G and other communication technologies cannot meet the demands of CPMS for high data rate, high reliability, high coverage, low latency, etc., which hinders the development and implementation of CPMS. As a future advanced wireless transmission technology, 5G has a significant potential to promote IIoT and CPMS. Based on the architecture and characteristics of 5G wireless communication technology, this paper proposes the architecture of 5G-based IIoT, and describes the implementation methods of different advanced manufacturing scenarios and manufacturing technologies under the circumstances of three typical application modes of 5G, respectively, i.e., enhance mobile broadband (eMBB), massive machine type communication (mMTC), ultra-reliable and low latency communication (URLLC). Besides, the characteristics, key technologies and challenges of the 5G based IIoT are also analyzed.