Cyber-physical Production Systems Research Articles

Cooperating and Competing Digital Twins for Industrie 4.0 in Urban Planning Contexts

Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs). Today’s growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating digital twins. Moreover, societal trends such as worldwide urbanization and growing emphasis on sustainability highlight competing goals that must be reflected not just in cooperating but also competing digital twins, often even interacting in “coopetition”. This paper argues for multi-agent systems (MASs) to address this challenge, using the example of embedding industrial digital twins into an urban planning context. We provide a technical discussion of suitable MAS frameworks and interaction protocols; data architecture options for efficient data supply from heterogeneous sensor streams and sovereignty in data sharing; and strategic analysis for scoping a digital twin systems design among domain experts and decision makers. To illustrate the way still in front of research and practice, the paper reviews some success stories of MASs in Industrie/Logistics 4.0 settings and sketches a comprehensive vision for digital twin-based holistic urban planning.

Development of intelligent tools for logistics: practice and prospects of use

The article examines SMART products (SMARTtechnologies, SMART- systems, SMART-objects) used by logisticians in the process of carrying out logistics activities for business structures and others. It is noted that today, without SMART-products, logistics will lose its position as an effective tool that helps market players effectively conduct business and work effectively in the conditions of the development of globalization and internationalization of the market space. The purpose of the article is a thorough review and analysis of modern SMART-products, which are advisable to use in the field of logistics in order to rationally organize the logistics activities of enterprises, based on the formation of competitive supply chains. It was established that digitalization is currently one of the key factors of increasing value not only for individual enterprises and industries, but also for states. It is argued that the digital transformation marked the beginning of a new stage in the development of logistics - digital logistics based on the development of SMART-products: SMART-technologies, SMART-systems, SMART-objects. The main intelligent products are characterized, namely: Internet of Things, robotics and cyber systems, artificial intelligence, big data, paperless technologies, additive technologies (3D printing), cloud and fog computing, unmanned and mobile technologies, biometric technologies, quantum technologies, identification technologies, blockchain. It is noted that the listed products are constantly supplemented, in accordance with the development of IT technologies and means of communication between digital devices. It is substantiated that SMART -products are an important factor in the growth of sales volumes and obtaining significant profits from logistics activities. A thorough analysis of the suitability for use by logisticians in the practical activities of software products, which became the basis for the digitalization of transport, blockchain, big data (BIG DATA), uberization, was carried out; SMART technologies – 3D printing, sensor technologies (ST), GRID - technologies; SMART-objects - augmented reality (ar - augmented reality), smart products, drones, unmanned transport, robotics, as well as SMART-systems - cyber-physical systems (CPS) used in production (CYBER-PHYSICAL PRODUCTION SYSTEM (CPPS), intelligent systems and the Internet of Things (INTERNET OF THINGS). It has been proven that SMART -products today are able to ensure a high level of competitiveness of business entities in various sectors of the economy: wholesale and retail trade, agriculture, industry, health care, postal services, etc. Key words: logistics, intelligent logistics tools, logistics activity, digital transformation, logistics efficiency, logistics effectiveness, competitiveness.

Seamless Industry 4.0 Integration: A Multilayered Cyber-Security Framework for Resilient SCADA Deployments in CPPS

The increased connectivity and automation capabilities of Industry 4.0 cyber-physical production systems (CPPS) create significant cyber-security vulnerabilities in supervisory control and data acquisition (SCADA) environments if robust protections are not properly implemented. Legacy industrial control systems and new IP-enabled sensors, instruments, controllers, and appliances often lack basic safeguards like encryption, rigorous access controls, and endpoint security. This exposes manufacturers to substantial risks of cyberattacks that could manipulate, disrupt, or disable critical physical assets and processes related to their production lines and facilities. This study presents a multilayered cybersecurity framework to address these challenges and harden SCADA environments by implementing granular access controls, network micro-segmentation, anomaly detection, encrypted communications, and legacy system upgrades. The multilayered defense-in-depth (DID) approach combines policies, processes, and technologies to counter emerging vulnerabilities. The methodology was implemented in an electronics manufacturing facility across access control, zoning, monitoring, and encryption scenarios. Results show security improvements, including 57.4% fewer unauthorized access events, 41.2% faster threat containment, and 79.2% fewer hacking attempts. The quantified metrics highlight the CPPS resilience and threat mitigation capabilities enabled by the securely designed SCADA architecture, which allows manufacturers to confidently pursue Industry 4.0 integration and digital transformation with minimized disruption.

Automated Monitoring and Visualization System in Production

In the modern world cyber-physical production systems are increasingly used. They allow you to control the flow of the technological process in production in real time. But the use of such an approach is greatly complicated by the fact that the equipment of many enterprises is old and cannot support the necessary functions. This is primarily due to the lack of the necessary sensors, as well as the corresponding software. Since the complete replacement of production equipment is very expensive, the task is to create separate monitoring systems. They must be able to integrate into the necessary parts of the production process. And they should also be cheap. In this work, we propose to build a model of such a monitoring and visualization system. The main attention in the work is focused on the hardware implementation of the proposed system and the relationship of its individual elements.

The MAS4AI framework for human-centered agile and smart manufacturing.

Volatility and uncertainty of today's value chains along with the market's demands for low-batch customized products mandate production systems to become smarter and more resilient, dynamically and even autonomously adapting to both external and internal disturbances. Such resilient behavior can be partially enabled by highly interconnected Cyber-Physical Production Systems (CPPS) incorporating advanced Artificial Intelligence (AI) technologies. Multi-agent solutions can provide better planning and control, improving flexibility and responsiveness in production systems. Small modular parts can autonomously take intelligent decisions and react to local events. The main goal of decentralization and interconnectivity is to enable autonomous and cooperative decision-making. Nevertheless, a more efficient orchestration of various AI components and deeper human integration are required. In addition, global behaviors of coalitions of autonomous agents are not easily comprehensible by workers. Furthermore, it is challenging to implement an Industry 4.0 paradigm where a human should be in charge of decision-making and execution. This paper discusses a Multi-Agent System (MAS) where several software agents cooperate with smart workers to enable a dynamic and reconfigurable production paradigm. Asset Administration Shell (AAS) submodels hold smart workers' descriptions in machine-readable format, serving as an integration layer between various system's components. The self-description capability of the AAS supports the system's adaptability and self-configuration. The proposed concept supports the plug-and-produce functionality of the production modules and improves human-machine integration in the shared assembly tasks.

IBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

To meet the demand of the world’s largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts.

DES and IIoT fusion approach towards real-time synchronization of physical and digital components in manufacturing processes

Today's manufacturing systems offer more products to meet specific needs. Complex production systems in rapidly changing environments result from product variation, shorter product life cycles, and supply chain expansion. A cyber-physical production system (CPPS) can use manufacturing and logistics data to plan, monitor, and control production. Discrete event simulation (DES) and digital twin (DT) technology can model and evaluate manufacturing and logistics processes using high-level decision support and process monitoring. The cost of collecting input data from different enterprise data sources and mapping it into models and the lack of qualified experts prevent the widespread use of these methods in industry, especially in small and medium-sized enterprises and larger multinational companies. This research aims to create a modular digital twin framework for manufacturing process optimization and real-time monitoring in an industrial environment with few components. The system can identify and track the product through the manufacturing cycle while updating the DT in real-time and can be used independently to collect input parameters for discrete event-driven simulations and even for automatic simulation building in the future. The framework's operation will be shown through an example. With the proposed IIoT (industrial internet of things) system integration, it can detect faults and warn of deviations from normal operation, and DT can drastically reduce data collection and model building and support model reusability, increasing sustainability.

USE OF NEW INFORMATION TECHNOLOGIES IN THE OIL-REFINING INDUSTRY

The application of new information technologies in the field of automation of oil refining and petrochemistry has not yet been carried out at the proper level. At modern oil refineries, big data technologies are being introduced. Big Data is one of the key areas of digital production and can serve as a tool for integrating technological installations and equipment that have a wide range of information about the technological process and the main production as a whole. The fourth industrial revolution, which is based on the global "internetization" of technological installations and equipment, is closely related to the use of the Internet of things and cloud storage. The transition to Industry 4.0 in the refinery implies the ability of the refinery to integrate digital and physical technologies to improve operation and increase productivity. But, unfortunately, the oil refining industry has a low activity in the implementation of digital technologies. The emergence of new cyber-physical production systems will radically change the traditional logic of oil production. The work is devoted to the use of information technologies of the oil refining industry in the field of integrated automation of the refinery as a whole, and individual technological processes, including for the production of polypropylene, by leaching, washing and drying, which is part of the refinery, these issues are almost not considered. Keywords: big data, industry 4.0, security, information technologies.

Development of a cyber physical production system framework for smart tool health management

Development of a cyber physical production system framework for smart tool health management

Development of a cyber physical production system framework for 3D printing analytics

3D printing technology is considered one of the emerging areas to deal with global sustainability challenges and to facilitate the Industry 4.0 adoption. However, 3D printing technology is still immature due to several limitations and negative perceptions about its quality and performance. The goal of this paper is to propose a cyber physical production system (CPPS) framework for a 3D printer to (i) monitor the process, parameters, and carbon footprint, (ii) predict the nozzle’s remaining useful life (RUL), and (iii) prescribe optimum 3D printing parameters for minimizing carbon footprint and printing time, simultaneously at the targeted surface quality. Experiments were designed based on Taguchi L-27 orthogonal array to investigate the relationship between printing parameters and performance characteristics. The usefulness of the proposed framework has been demonstrated for a 3D printer to predict the remaining useful life of the printer nozzle (prognostic model), and to find an optimal combination of printing parameters for the simultaneous optimization of sustainability and productivity at the targeted surface quality (prescriptive model). Layer height was found to have a statically significant impact on the specific carbon footprint followed by scale and bed temperature. Layer height is the only statically significant contributor to the surface roughness of 3D printed parts. The scale and layer height followed by infill have significant effect on the printing time. The significance of the present work lies in enhancing the performance of a conventional 3D printer using low-cost smart sensors, devices, and open-source software. The usefulness of the proposed CPPS framework is demonstrated as a decision support tool for a 3D printer real-time monitoring, visualization, and control. The proposed CPPS framework and its application for prognostic and prescriptive analytics is generic in nature, and is transferable and applicable to other FDM 3D printers, irrespective of brand and size.

A Vision-Based Human Digital Twin Modeling Approach for Adaptive Human–Robot Collaboration

Abstract Human–robot collaboration (HRC) has been identified as a highly promising paradigm for human-centric smart manufacturing in the context of Industry 5.0. In order to enhance both human well-being and robotic flexibility within HRC, numerous research efforts have been dedicated to the exploration of human body perception, but many of these studies have focused only on specific facets of human recognition, lacking a holistic perspective of the human operator. A novel approach to addressing this challenge is the construction of a human digital twin (HDT), which serves as a centralized digital representation of various human data for seamless integration into the cyber-physical production system. By leveraging HDT, performance and efficiency optimization can be further achieved in an HRC system. However, the implementation of visual perception-based HDT remains underreported, particularly within the HRC realm. To this end, this study proposes an exemplary vision-based HDT model for highly dynamic HRC applications. The model mainly consists of a convolutional neural network that can simultaneously model the hierarchical human status including 3D human posture, action intention, and ergonomic risk. Then, on the basis of the constructed HDT, a robotic motion planning strategy is further introduced with the aim of adaptively optimizing the robotic motion trajectory. Further experiments and case studies are conducted in an HRC scenario to demonstrate the effectiveness of our approach.

Cyber Physical Production Systems for Deep Drawing

Abstract Deep Drawing is an essential manufacturing technology for car body parts. High process stability is a key to reducing scrap and therefore the ecological footprint during production. To deal with an unknown fluctuation of the incoming material properties and uncertainties considering the friction, an adaptive process needs to be implemented. Various approaches have been pursued in the past, but not all of them are suited for industrial series production with high demands for equipment durability, cost efficiency, and flexibility. For this reason, a new concept for cyber-physical production systems (CPPS) in deep drawing is presented, linking the data from the simulation, tool, press, material, and finished part quality. Two common application scenarios are distinguished. First, these are large outer parts with a complex geometry and high value, typically produced with tandem presses. Second, smaller structural parts from high-strength steel for the body in white (BIW) are usually produced through a transfer or progressive die. Non-destructive material testing, supplier material certificates, and data measured directly in the forming tool are considered regarding the input. The servo curve and blank holder force (BHF) operate as control instances. Within the two application scenarios, a reactive and a preventive solution are characterized. As a first step toward the implementation of the CPPS, material inflow, and force sensors are installed and tested in an industrially relevant deep drawing tool.

Review of simulation software for cyber-physical production systems with intelligent distributed production control

ABSTRACT Many intelligent distributed production control architectures have been developed for cyber-physical production systems (CPPSs), but the difficulty in predicting performance has hindered acceptance by industry. Performance predictions for systems with conventional control can be made by simulating product flow using discrete-event simulation (DES) software. However, DES is inadequate for capturing the intricacies of intelligent distributed production control architectures. Alternatively, agent-based simulation (ABS) software is more effective for capturing distributed intelligence. A hybrid discrete-event and agent-based simulation tool combines the strengths of both approaches, making it effective for capturing the intertwined physical and cyber layers of a CPPS. In this paper, a review is carried out to determine which off-the-shelf simulation tools are capable of using hybrid discrete-event and agent-based simulation for performance predictions in the conceptual design phase of a CPPS. This review is carried out using the following structured steps. First, the scope and evaluation criteria are identified. Next, a selection of simulation tools is collected. The selected tools are then evaluated and classified. Finally, the most promising simulation tool according to this evaluation – Anylogic – is subjected to a case study to assess if hybrid simulation can be used to predict the performance of a CPPS.

Meta-inventory

In an Industry 4.0 Factory, physical entities such as humans, machines and materials are digitized into digital twins (DT) with smart IoT (Internet of Things) devices resulting in Cyber-Physical Production Systems (CPPS). Real-time data analytics builds up traceability and visibility, not only in the physical domain but also cyber space. This paper adds a new concept of cyber-physical inventory or simply meta-inventory to Industry 4.0 CPPS. In addition to physical items, their digital twins are considered as part of production inventory. Traceability and visibility enabled by digital twin can significantly reduce complexity and uncertainties (e.g. lead times and variability) while achieving resilience in case of major disturbances. The CPPS factory hedges the risks through meta-inventory without incurring cost for holding inventory digitally. After reflecting upon the developments of production inventory management corresponding to the evolutionary history of manufacturing systems to Industry 4.0, the paper presents the meta-inventory paradigm within a simple Industry 4.0 compliant supply chain. The factory, the supplier, and the transport implement a VMI (vendor-managed inventory) strategy. Two well-known basic EOQ (Economic Order Quantity) and EPQ (Economic Production Quantity or production Lot Sizing) problems are extended to demonstrate and quantify the impacts of using meta-inventory on the supply chain and the member enterprise. The analyses allow us to unfold key perspectives in more complex production and supply chain systems for further research.

Transitioning additive manufacturing from rapid prototyping to high‐volume production: A case study of complex final products

AbstractThis paper seeks answers to the question: what are the key factors that enable the scaling of additive manufacturing (AM) from rapid prototyping to high‐volume production? Using a longitudinal case study, we collected primary and secondary data to trace the AM scaling journey of AeroCo, a highly innovative aerospace firm. Based on the case findings, we position AM as a whole system technology because it can print components for a wide range of subsystems in a complex final product. Scaling AM requires a significant realignment of existing, and often deeply entrenched, new technology, and product development processes. To achieve this alignment, AeroCo formed institutional alliances with the UK government and universities to establish university technology centers, which facilitated early stage ideation and “catapult” centers, which enabled high‐volume testing in factory‐like facilities. The case reveals how multiple functions needed to integrate, including research and development, product design, and future programs, to ensure that design changes cascaded from one subsystem to another, and that new technologies were linked to a future product to create a final product pull. These findings inform a managerial framework for additive manufacturing scaling that is generalizable to other digital technologies used in the design and production of complex final products, including artificial intelligence, machine learning, smart factories, and cyber physical production systems. Our framework contributes to innovation thought and practice by explaining how new product development processes and organizational structures change under the effect of digital technologies.

Digital Twin Based SUDIHA Architecture to Smart Shopfloor Scheduling

Standing on the brink of the fourth industrial revolution, Cyber Physical Systems (CPS) are considered the basic components of the Smart Factory. One important challenge in cyber physical production systems is dynamic scheduling that can handle random disruptions such as failures, raw material shortages and quality defects. To achieve dynamic scheduling, we have proposed a Supervised and Distributed Holonic architecture we called SUDIHA. This architecture incorporates three Holons: Product Holon, Resource Holon and Order Holon and combines global supervision, achieved by Product Holon, with dynamic local control, achieved by Resource Holon. The Digital Twin (DT) concept is generally used to design CPS; it is virtual copies of the system that can interact with the physical counterparts in a bi-directional way. It seems to be promising to tackle the complexity and increase manufacturing system flexibility. In this paper, we use a DT Model to improve the SUDIHA architecture. We propose a Digital Twin based SUDIHA architecture (DT-SUDIHA). The paper will describe Digital Twins’ configuration of each Holon of the SUDIHA Architecture, and the intelligent and real time data driven operation control of this architecture. A case study is carried out at the ENSAM-Meknes flexible workshop to prove the effectiveness of the proposed approach.

Decentralized Real-Time Anomaly Detection in Cyber-Physical Production Systems under Industry Constraints.

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.

Evolution of the Human Role in Manufacturing Systems: On the Route from Digitalization and Cybernation to Cognitization

Modern society is living at a time of revolutionary changes in all areas of human life. For example, the field of industrial manufacturing has greatly influenced the role of human beings during the past 30 years. Modern manufacturing systems are in a phase of transition, in accordance with the concept of the fourth industrial revolution (Industry 4.0). A new manufacturing paradigm based on the principles of Industry 4.0 is presented by Smart Manufacturing Systems (SMS). A basic building block of SMS is cyber-physical production systems (CPPS), which together with innovative-management principles of emergence, self-organization, learning, open innovation, collaboration and the networking of people and organizations are the key principles of Industry 4.0. The three key enablers of Industry 4.0, i.e., the connectivity, the digitization and the cybernation of work processes in manufacturing systems, have paved the way for a new industrial revolution, i.e., Industry 5.0 concept that is bringing about a new paradigm in the field of manufacturing systems, the so-called Adaptive Cognitive Manufacturing Systems (ACMS). A fundamental building block of ACMS is the new generation of manufacturing systems called Cognitive Cyber-Physical Production Systems (C-CPPS), which are based on CPPS concepts and incorporate cognitive technologies and artificial intelligence. This paper presents the revolutionary development of manufacturing and manufacturing systems through the industrial revolutions and the evolution of the role of humans in manufacturing systems towards Industry 5.0.

QualSec: An Automated Quality-Driven Approach for Security Risk Identification in Cyber-Physical Production Systems

As the threat landscape in the industrial domain continually advances, security-by-design is an ever-growing concern in the engineering of cyber-physical production systems (CPPSs). Often, quality aspects are not considered when securing CPPSs, which creates attack vectors that could lead to malicious activity affecting the products' quality. Since quality control systems generally provide inadequate protection against intentionally introduced defects, and can be susceptible to attacks, quality considerations must be integrated into security-aware CPPS engineering. For this purpose, we propose the QualSec method that automatically identifies security risks pertaining to CPPSs, building on the quality characteristics associated with manufacturing operations to determine cascading effects. QualSec is based on a semantic representation of engineering knowledge, allowing to efficiently reuse engineering models from AutomationML artifacts. Moreover, QualSec utilizes Petri nets to facilitate the analysis of security risks and cascading effects. In this way, QualSec informs users about possible attack paths for compromising quality characteristics, how attackers may disguise their malicious actions, and the possible consequences of attacks with respect to product quality. We demonstrate the benefits of QualSec in a case study and analyze its scalability through a rigorous performance evaluation.

An Automatic Assembly SMT Production Line Operation Technological Process Simulation Model Development

This article describes an automatic assembly SMT production line operation technological process simulation model development within the framework of modern cyber-physical production systems. In the course of the research, the authors conduct an analysis of modern publications on the topic of research, and the need to find new approaches for modeling the work of a production line at the initial stages of design. This makes it possible to assess the load on each element of the system at the design stage, and identify disadvantages at the early stages of production planning. The authors give an example of the task solution for modeling a fragment of an automated production line of SMT assembly, based on the use of typical mathematical schemes of mass service systems (MSS). A description of the functioning of the technological link of the process is carried out, and a Q-diagram of the technological process of SMT assembly is developed. Using the simulation modeling language GPSS and the technological parameters of the selected equipment, a block diagram was developed, on the basis of which the program was written and the work of the SMT assembly production line was simulated. The obtained results further allow us to estimate the production capacity, the loading of each equipment, the functioning processes of both the selected fragment, and the developed model and they can be applied to the simulation of large production lines within the framework of PCB production.