Cyber-physical Manufacturing Research Articles

Smart In-Process Inspection in Human–Cyber–Physical Manufacturing Systems: A Research Proposal on Human–Automation Symbiosis and Its Prospects

This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely endorsed as the future of human–automation research, emphasizes closer partnership and mutually beneficial collaboration between human and automation agents. In addition, to maintain high product quality and enable the in-time feedback of process issues for advanced manufacturing, in-process inspection is an efficient strategy that manufacturers adopt. In this regard, this paper outlines a research framework combining smart in-process inspection and human–automation symbiosis, enabling real-time defect identification and process optimization with cognitive intelligence. Smart in-process inspection studies the effective automation of real-time inspection and defect mitigation using data-driven technologies and intelligent agents to foster adaptability in complex production environments. Concurrently, human–automation symbiosis focuses on achieving a symbiotic human–automation relationship through cognitive task allocation and behavioral nudges to enhance human–automation collaboration. It promotes a human-centered manufacturing paradigm by integrating the studies in advanced manufacturing systems, cognitive engineering, and human–automation interaction. This paper examines critical technical challenges, including defect inspection and mitigation, human cognition modeling for adaptive task allocation, and manufacturing nudging design and personalization. A research roadmap detailing the technical solutions to these challenges is proposed.

Cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative artificial intelligence and cyber–physical manufacturing systems in the immersive industrial metaverse

Cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative artificial intelligence and cyber–physical manufacturing systems in the immersive industrial metaverse

Microservices architecture to enable an open platform for realizing zero defects in cyber-physical manufacturing

Abstract The market’s demand for high-quality products necessitates innovative manufacturing approaches that emphasize flexibility, adaptability, and the reduction of defects. Traditional systems are currently evolving towards embracing I4.0 technologies, including data collection, processing, analytics and digital twin, aiming for zero-defect manufacturing quality. This paper introduces an open platform, compliant with RAMI4.0 standards, designed to improve manufacturing quality. The platform integrates data using Asset Administration Shells with microservices adaptation for data ingestion and advanced analytics. Additionally, it incorporates Non-Destructive Inspection tools, demonstrating a seamless integration of measurement and quality assurance. This study details the architectural specification and validation of the openZDM platform, employing microservices to ensure flexibility, modularity and scalability, aligning with the RAMI4.0 model. The platform was validated through deployment in an automotive assembly line, highlighting its effectiveness in integrating inspection scenarios and early defect detection tools. The architecture employs a choreographed approach to manage loosely coupled microservices, enabling efficient data lifecycle management from collection through analytics to visualization.

A Framework for Effective Virtual Commissioning: Guiding Principles for Seamless System Integration

Virtual commissioning (VC), defined as the simulation and testing of systems in a virtual environment before physical implementation, plays a key role in addressing the challenges of integrating and validating complex systems efficiently and effectively. This paper focuses on the topic of virtual commissioning, summarizing and organizing existing research in the field. The paper provides a comprehensive overview of various design methods and technologies currently in use. A case study of virtual commissioning is also presented within the area of the Cyber-Physical Manufacturing Systems Laboratory of the Széchenyi István University, detailing the solution steps taken. Drawing on both research and practical experience, the paper proposes a novel framework to support virtual commissioning design, referred to as the “Virtual Commissioning House” (VCH). The methodology is evaluated through comparisons with existing virtual commissioning solutions, demonstrating its effectiveness.

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

Development of Cyber Physical System Based Manufacturing System Design

Cyber Physical System (CPS) is Informatics and computer science. Cyber physical manufacturing system is a new research field. In the fields of computer science and manufacturing science and technology Promote the Fourth Industrial Revolution known as Industry 4.0. CPS is generally focused. About the integration of the physical world and cyberspace. It`s the integration of communication, Computational, control, and physical elements. Currently, CPS is Science, government and industry. Systematic literature review on cyber. The physical system of the manufacturing system is not available. The purpose of the chapters in this bookis to gain insight into manufacturing systems and develop cyber-physical systems. Physical systems for intelligent manufacturing. CPS is written using the concept of CPS. A 5-tier architecture system for manufacturing systems. Continuous Key Release. It also describes the technology of cyber physical manufacturing systems. Combined with manufacturing, CPS enables intelligent manufacturing and provides technical support for manufacturing upgrades and conversions. This can be summarized in three aspects: internet-based manufacturing, process intelligence, and product intelligence. Future manufacturing is said to be personalized manufacturing based on cyber physical systems, intelligent manufacturing, digital manufacturing, and network-based manufacturing. CPS continues to transform the manufacturing trends of the industry, creating even more amazing value for future global manufacturing scenarios

A component-based design approach for energy flexibility in cyber-physical manufacturing systems

AbstractEnergy flexibility of manufacturing systems helps to meet sustainable manufacturing requirements and is getting significant importance with rising energy prices and noticeable climate changes. Considering the need to proactively enable energy flexibility in modern manufacturing systems, this work presents a component-based design approach that aims to embed energy flexibility in the design of cyber-physical production systems. To this end, energy management using Industry 4.0 technologies (e.g., Internet of Things and Cyber-physical Systems) is compared to the literature on energy flexibility in order to evaluate to what extent the energy flexibility practice takes advantage of Industry 4.0 technologies. Another dimension is the coverage of the life cycle of the manufacturing system which guarantees its sustainable design and the ability to achieve energy flexibility by configuring the energy consumption behaviour. A data-based design approach of the energy-flexible components is proposed in the spirit of the Reference Architectural Model Industrie 4.0 (RAMI 4.0), and then it is exemplified using an electric drive (as a component) in order to show the practical applicability of the approach. The energy consumption behaviour of the component is modelled using machine learning techniques. The digital twin of the studied component is developed using Visual Components virtual engineering environment, then its energy consumption behaviour is included in the model allowing the system integrator/decision-maker to configure the component based on the energy availability/price. Finally, external services in terms of an optimisation module and a deep learning module are connected to the digital twin.

Flexibility management and decision making in cyber-physical systems utilizing digital lean principles with Brain-inspired computing pattern recognition in Industry 4.0

ABSTRACT Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the Brain-Inspired Computing of Structural and Syntactic (BIC-SS) pattern recognition method. The performance analysis of these findings is simulated in MATLAB software. Simulation can be done with the identification of ideal Kanban parameters like cycle time, lead time, delivery frequency and lot size. Furthermore, lean manufacturing improves company quality and productivity by decreasing waste and production costs, as well as adapting well to the many innovative systems that encourage the culture of change and quality inside organizations.

Architectural development of a cyber-physical manufacturing range

Cyber-Physical Systems (CPS) are one example of new systems created from the technological progress started by the fourth industrial revolution. CPS integrate cyber and physical components together leaving the system vulnerable to cyber-physical attacks. Attacks on CPS in manufacturing can be costly and dangerous, and manufacturers need resources to safely learn more about these attacks and how to prevent them. Researchers in cyber-security have been able to safely assess attacks using cyber ranges. In this paper, we present the idea of a Cyber-Physical Manufacturing Range that will allow for further research into cyber-physical attacks and defense mechanisms in manufacturing. We used an iterative process that emphasizes the importance of designing a system based on the needs and requirements of the various stakeholders. From this process, we determined that the range needs to have a physical and virtual component. These components will allow for the granularity needed to model the tool and workpiece, connected systems, and operator and process interfaces.

Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems

ABSTRACT Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework.

Digital-Twin-Based Real-Time Optimization for a Fractional Order Controller for Industrial Robots

Digital twins are applied in smart manufacturing towards a smarter cyber-physical manufacturing system for effective analysis, fault diagnosis, and system optimization of a physical system. In this paper, a framework applying a digital twin to industrial robots is proposed and realizes the real-time monitoring and performance optimization of industrial robots. This framework includes multi-domain modeling, behavioral matching, control optimization, and parameter updating. The properties of the industrial robot are first modeled in a digital environment to realize the strong interactive and all-around 3D visual monitoring. Then, behavioral matching is performed to map the virtual system to the physical system in real time. Furthermore, the control performance of the system is improved by using a fractional order controller based on the improved particle swarm optimization algorithm. This framework is applied to the experimental verification of real-time control optimization on an industrial robot. The time-domain performance is improved in the simulation and experimental results, where the overshoot is promoted at least 42%, the peak time is promoted at least 32%, and the settling time is promoted at least 33%. The simulation and experimental results demonstrate the effectiveness of the proposed framework for a digital twin combined with fractional order control (FOC).

Development of machine tool communication method and its edge middleware for cyber-physical manufacturing systems

ABSTRACT By enabling the fusion of cyber and physical world, Cyber-physical systems (CPSs) have emerged as a key technology of the fourth industrial revolution – Industry 4.0. One of the major challenges for developing manufacturing CPSs is establishing communication between physical resources and Internet/cloud-hosted applications. Recently, MTConnect has become a widely popular communication method for monitoring machine tools in cyber manufacturing. However, it does not allow remote operations of machine tools over the Internet, which is essential in many manufacturing CPSs. This paper presents an Internet-scale service-oriented communication method named Machine Tool Communication (MTComm), which provides capabilities of both monitoring and operating heterogeneous machine tools over the Internet. MTComm allows machine tools to exchange manufacturing services and associated information in XML format through RESTful web services. This paper describes design and development of MTComm components, ontology, and methodology of data acquisition and direct operation through five types of services. To reduce communication latency and improve performance, three optimization strategies were developed and used in an edge-based middleware. Experiments were conducted in a testbed with five machines at different stages of MTComm development and results demonstrated excellent feasibility for efficiently monitoring and operating heterogeneous machine tools in a scalable cyber-physical manufacturing environment.

Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach

Within the frame of the Fourth Industrial Revolution, the application of Internet of Things technologies makes it possible to convert conventional manufacturing systems into cyber-physical systems, where the used new technologies enable the improvement of maintenance and operation processes. The aim of this work is to develop and validate a new real-time maintenance policy model and optimization algorithm based on digital twin simulation. The maintenance policy model is based on the conventional failure and operation data from ERP (enterprise resource planning) and the real-time and forecasted failure and operation data from digital twin simulation. The described maintenance policy model and its optimization algorithm represent an innovative way to manage predictive, preventive, corrective, and opportunistic maintenance strategies. The novelty of the presented method is that the real-time data generated by the digital twin solution allow the definition of a more accurate maintenance strategy. The optimization algorithm is characterized by a standard evolutionary algorithm. The impact of maintenance policy optimization on the energy efficiency and emission was analyzed in the case of both conventional and cyber-physical manufacturing systems with and without digital-twin-based simulation. The results showed that the energy consumption and the greenhouse gas emission in the real-time maintenance policy optimization scenario decreased by 21%, depending on the electricity generation source.

An analytic hierarchy process augmented with expert rules for product driven control in cyber-physical manufacturing systems

In Cyber-Physical Manufacturing Systems (CPMS), numerous distributed control architectures were suggested to make different production entities active with respect to decision-making and control processes, so that they can process information, interact, and make control decisions in an autonomous and adaptive way. Nevertheless, developing Product-Driven Control (PDC) mechanisms that enable Smart Products (SPs) to make control decisions to cope with disturbances is still a complex, open-ended, and challenging problem. This article suggests a PDC approach that enables SPs to learn how to make control decisions to react to disturbances and maintain continuity of operations. The control mechanism involves an Analytic Hierarchy Process (AHP) augmented with Expert rules to cope with the limitations of standard AHP in dealing with dynamic problems. The mechanism is applied to a dispatching problem in an industrial scale assembly process. A multi-agent discrete event simulation model is used to create a set of normal and disturbed production scenarios. SP agents use context indicators and performance assessment to activate Expert rules and update AHP preferences and scales before making dispatching control decisions to react to disturbances. Data analytics tools are developed to help manufacturing system Experts define and fine-tune rules, based on rule firing statistics and corresponding context indicators and performance assessment acquired from simulation. Experimentations and results show competitive performance and highlight interesting research directions.

Smart Agent System for Cyber Nano-Manufacturing in Industry 4.0

The development of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) has influenced Cyber-Physical Manufacturing Systems (CPMS). Collaborative manufacturing among organizations with geographically distributed operations using Nanomanufacturing (NM) requires integrated networking for enhanced productivity. The present research provides a unique cyber nanomanufacturing framework by combining digital design with various artificial neural networks (ANN) approaches to predict the optimal nano/micro-manufacturing process. It enables the visualization tool for real-time allocation of nano/micro-manufacturing resources to simulate machine availability for five types of NM processes in real-time for a dynamic machine identification system. This research establishes a foundation for a smart agent system with predictive capabilities for cyber nanomanufacturing in real-time.

Analyzing Security Risks in Cyber-Physical Manufacturing Systems with Actor–Network Theory

Abstract This article suggests that actor–network theory (ANT) can reveal unique challenges and consequences of cyberattacks in manufacturing. As an approach, ANT rejects the dualism that often separates humans and nonhumans, recognizing the active role of both in affecting events. Our approach adds an important new perspective to an existing body of research that focuses on analyzing vulnerabilities in cyberspace instead of their ramifications in the material world. Drawing on the case study of a faulty airbag inflator in an automobile, we use concepts and vocabularies drawn from ANT to discuss the consequences of attacks in manufacturing, such as viewing altered products as actants with agency to alter subsequent networks (e.g., when a manufactured part is integrated into an automotive vehicle). By tracing the movement of specific materials and products through networks it is possible to elucidate how cyberattacks not only impact cyber-physical systems themselves, but also reverberate into a multitude of broader impacts, potentially endangering physical safety, shaping public opinion, and influencing economic markets. Our examination of one particular context draws on existing work that has brought ANT and cybersecurity in dialogue, but we extend this work by focusing on the role of “translation” and “depunctualization” across the lifecycle of a cyberattack in manufacturing. This analysis stresses the need for sector-specific examinations of cyberthreats, while also demonstrating the value of interdisciplinary methods like ANT that do not reify artificial dualisms in addressing for conceptualizing security risks in cyber-physical manufacturing systems.

Algebraic Synthesis of Safety Logical Filter on Manufacturing Systems

This work focuses on the management of safety constraints for the control of cyber-physical manufacturing systems. A methodology for constructing a set of constraints to ensure the safety of an existing control law is proposed. The resolution of this constraint set is performed using algebraic synthesis. This tool facilitates the implementation of a logic filter in a way that complies with IEC 61131-3.

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

Additive Manufacturing of Large Coreless Filament Wound Composite Elements for Building Construction.

Digitization and automation are essential tools to increase productivity and close significant added-value deficits in the building industry. Additive manufacturing (AM) is a process that promises to impact all aspects of building construction profoundly. Of special interest in AM is an in-depth understanding of material systems based on their isotropic or anisotropic properties. The presented research focuses on fiber-reinforced polymers, with anisotropic mechanical properties ideally suited for AM applications that include tailored structural reinforcement. This article presents a cyber-physical manufacturing process that enhances existing robotic coreless Filament Winding (FW) methods for glass and carbon fiber-reinforced polymers. Our main contribution is the complete characterization of a feedback-based, sensor-informed application for process monitoring and fabrication data acquisition and analysis. The proposed AM method is verified through the fabrication of a large-scale demonstrator. The main finding is that implementing AM in construction through cyber-physical robotic coreless FW leads to more autonomous prefabrication processes and unlocks upscaling potential. Overall, we conclude that material-system-aware communication and control are essential for the efficient automation and design of fiber-reinforced polymers in future construction.

Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.