Cyber-physical Production Systems Research Articles

Enhancing and Explaining Energy Management in Smart Manufacturing Production via ML Within the Context of Industry 4.0

Objectives: This study aims to explore the integration of big data and machine learning to enhance energy efficiency in manufacturing, focusing on the opportunities presented by Industry 4.0 and cyber-physical production systems. Theoretical Framework: The research leverages supervised learning techniques to analyze and predict machine-specific energy consumption patterns, utilizing energy disaggregation as a foundational concept. Method: Various machine learning algorithms, including Lasso regression, Linear regression, and Decision Tree, will be employed to develop predictive models for energy usage. Additionally, Explainable Machine Learning (XML) techniques will be utilized to ensure interpretability and clarity in the prediction outcomes. Results and Discussion: The proposed framework aims to elucidate the relationship between equipment utility and energy consumption, providing comprehensible explanations that enhance decision-making processes within intelligent industrial environments. Research Implications: This work highlights the significant potential of XML in transforming machine learning applications in manufacturing, paving the way for improved energy efficiency and operational effectiveness. Originality/Value: The introduction of an innovative framework that combines machine learning with explainability in the context of energy consumption marks a valuable contribution to the fields of manufacturing and sustainable industry practices.

Security Emergence Framework for Cyber-Physical Production Systems in the Age of Industry 4.0

Security Emergence Framework for Cyber-Physical Production Systems in the Age of Industry 4.0

A Universal Framework for Skill-Based Cyber-Physical Production Systems

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing.

An adaptable Digital Twin model for manufacturing

In a smart manufacturing system, physical assets are often connected to their Digital Twins in the virtual world that composes a cyber-physical production system (CPPS). Digital Twin is synchronized with the physical asset. Digital Twin is therefore considered an enabling technology of smart manufacturing. In the process of developing Digital Twins, the Digital Twin model is of great significance. Many Digital Twin models have been proposed in the past decades, including geometry models, information models, and ontology models. However, there has not been an adaptable and generic Digital Twin model that can be used for various scenarios in a manufacturing system. This paper proposes an adaptable Digital Twin model based on the object-oriented concept. The proposed Digital Twin model can be applied to different situations using object-oriented concept features. Firstly, the base Digital Twin model is developed according to ISO 23247, including the essential attributes of a Digital Twin. The aggregation and composition mechanism of the proposed Digital Twin model is also described based on the object-oriented concept. After that, according to the categories of the physical assets on the shop floor, Digital Twin models of different physical assets are developed based on the base Digital Twin model, inheriting the features of the base Digital Twin model. In the end, a case study is carried out for verification on a machine tool in the Laboratory for Industry 4.0 Smart Manufacturing Systems (LISMS).

Industry 5.0 Research in the Sustainable Information Systems Sector: A Scoping Review Analysis

Industry 4.0, centered on cyber-physical production systems, has been criticized for prioritizing profit over social and environmental concerns. In contrast, Industry 5.0 emphasizes AI efficiency while promoting human-centric, resilient, and sustainable approaches, integrating economic, social, and environmental systems. Previous research has often focused solely on conceptual frameworks and technologies, overlooking Industry 5.0's sector-specific impacts. This study addresses that gap by conducting a scoping review to map research findings, identify trends, and highlight knowledge gaps and future research opportunities. By systematically analyzing literature from the Scopus database (2016-present), the study refined a large dataset to focus on Industry 5.0's relevance. The analysis revealed significant attention to sectors like Industry and Producer Services, while Agriculture and Retail, particularly natural resource-based sectors like agriculture and fisheries, are often neglected. Key findings indicate that Industry 5.0 is likely to be driven by the industrial sector, followed by product services and financial industries. The study also highlights the strong connection between IoT and AI in optimizing operations with real-time data and automation and identifies blockchain as a promising technology for enhancing transparency and security, despite existing implementation challenges. This research not only serves as a foundational record of Industry 5.0's implications across various sectors but also provides valuable insights into its role in Information Systems (IS). It lays the groundwork for future exploration of Industry 5.0 in diverse sectors and industries.

A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters.

Digital Twin-Driven Dynamic Scheduling of Flexible Manufacturing System in the Context of Smart Factory Producing Brass Accessories

This study aims to deal with a dynamic scheduling problem of a real Flow Shop follow-up of an assembly process considering the specific constraints and requirements of a brass accessories manufacturing (BAM) company. We basically set up a Digital Twin-driven dynamic scheduling approach for Industry 4.0 by addressing uncertainties of machine availability. In fact, the setup of this Digital Twin (DT) is the outcome of the combination of both optimization and simulation. For the first step, we have elaborated a Mixed Integer Linear Programming (MILP) scheduling model by taking into account the dedicated requisites of our case study. Concerning simulation, a 3D simulation platform of a workshop producing brass accessories controlled by a Cyber-Physical Production System (CPPS) has been developed, in our previous work, including constraints and stochastic aspects. The simulation constraints are difficult or impossible to be modeled in the MILP model. These designs are integrated with the real workshop to construct the Digital Twin. The proposed tool enables the rescheduling of production orders based on machine disturbances and unpredictability. Validation scenarios have been designed and conducted to highlight the efficacy of the Digital Twin approach utilizing the brass accessories case study. We are confident that this represents the initial endeavor addressing the optimization of production rescheduling in a flow shop follow-up of a mixed assembly system.

Robot material processing and hardware-in-the-loop-based real-time simulations

Abstract This paper presents a cyber-physical production system that consists of a simulation, an industrial robot cell, and sensors. The industrial robot hardware, used for welding and additive manufacturing applications, is connected or “in-the-loop” with a real-time target machine on which simulations are running. These simulations are updated in real-time by the data provided by process sensors. Particular focus is given to wire-arc additive manufacturing (WAAM). Still, the cyber-physical system allows use in other robot-based material processes, such as sheet forming, (dis)assembly and material handling applications. It is also argued that the proposed cyber-physical system can be used so that it competes against the concept of using machine learning to optimize manufacturing processes. The proposed cyber-physical system enables the transition from traditional robot automation to autonomous robot systems.

Deep Reinforcement Learning and Discrete Simulation-Based Digital Twin for Cyber–Physical Production Systems

One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. Achieving such goals requires the comprehensive integration of digital technologies with real production processes towards the creation of the so-called Cyber–Physical Production Systems (CPPSs). Their architecture is based on physical and cybernetic elements, with a digital twin as the central element of the “cyber” layer. However, for the responses obtained from the cyber layer, to allow for a quick response to changes in the environment of the production system, its virtual counterpart must be supplemented with advanced analytical modules. This paper proposes the method of creating a digital twin production system based on discrete simulation models integrated with deep reinforcement learning (DRL) techniques for CPPSs. Here, the digital twin is the environment with which the reinforcement learning agent communicates to find a strategy for allocating processes to production resources. Asynchronous Advantage Actor–Critic and Proximal Policy Optimization algorithms were selected for this research.

A Cyber-Physical Production System for the Integrated Operation and Monitoring of a Continuous Manufacturing Train for the Production of Monoclonal Antibodies.

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber-physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.

An industrial edge computing architecture for Local Digital Twin

Cyber–Physical Production Systems usher in a new era of smart manufacturing, with Digital Twin being a pivotal concept driving their adoption. Most literature concentrates on global Digital Twin deployment, typically enterprise servers or the cloud. However, manufacturing relies on real-time control and decision-making, demanding swift system response. To address this, we introduce a novel Local Digital Twin architecture, bringing processing to the edge to meet strict timing constraints. We also introduce a Five-dimension GDT/LDTs model aligned with RAMI4.0 for practical application and integration. The LDT was implemented on a multinational company’s Brazilian subsidiary assembly line, predicting optimal assembly components. Machine learning techniques were employed, improving the First Pass Yield by 1.3 to 2.5% in a test machine, affirming the feasibility of the LDT approach.

A cyber-physical production system for autonomous part quality control in polymer additive manufacturing material extrusion process

A cyber-physical production system for autonomous part quality control in polymer additive manufacturing material extrusion process

Deterministic Network–Computation–Manufacturing Interaction Mechanism for AI-Driven Cyber–Physical Production Systems

Deterministic Network–Computation–Manufacturing Interaction Mechanism for AI-Driven Cyber–Physical Production Systems

An Internet of Things-Based Production Scheduling for Distributed Two-Stage Assembly Manufacturing with Mold Sharing

In digital product and ion scheduling centers, order–factory allocation, factory–mold allocation, and mold routing can be performed centrally and efficiently to maximize the utilization of manufacturing resources (molds). Therefore, in this paper, a manufacturing resource (molds)-sharing mechanism based on the Internet of Things (IoT) and a cyber-physical production system (CPPS) is designed to realize the coordinated allocation of molds and production scheduling. A mixed-integer mathematical model is developed to optimize the cost structure and obtain a reasonable profit solution. A heuristic algorithm based on evolutionary reversal is used to solve the problem. The numerical results show that based on the digital coordinated production scheduling method, distributed two-stage assembly manufacturing with shared molds can effectively reduce the order delay time and increase potential benefits for distributed production enterprises.

Autonomous Agent-Based Adaptation of Energy-Optimized Production Schedules Using Extensive-Form Games

Modular cyber-physical production systems are an important paradigm of Industry 4.0 to react flexibly to changes. The flexibility of those systems is further increased with skill-based engineering and can be used to adapt to customer requirements or to adapt manufacturing to disturbances in supply chains. Further potential for application of these systems can be found in the topic of electrical energy supply, which is also characterized by fluctuations. The relevance of energy-optimized production schedules for manufacturing systems in general becomes more important with the increased use of renewable energies. Nevertheless, it is often difficult to adapt when short-term energy price updates or unforeseen events occur. To address these challenges with an autonomous approach, this contribution focuses on extensive-form games to adapt energy-optimized production schedules in an agent-based manner. The paper presents agent-based modeling to transform and monitor energy-optimized production schedules into game trees to respond to changing energy prices and disturbances in production. The game is setup with a scheduler agent and energy agents who are considered players. The implementation of the mechanism is presented in two use cases, realizing decision making for an energy price update in a simulation example and for unforeseen events in a real-world demonstrator.

Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber–Physical Production Systems

Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber–Physical Production Systems

Exploring hidden pathways to sustainable manufacturing for cyber-physical production systems

Future manufacturing scenarios will likely be built around cyber-physical production systems. To succeed, this new manufacturing paradigm will also have to comply with the golden rule of sustainability. However, the concept of sustainability as defined in a number of high-level policy documents and recommendations requires disambiguation. The paper introduces HITECS, a novel, context-independent text analytics methodology for hidden correlation analysis in documents. HITECS is based on the assumption that there is a strong link between a concept and the words implicitly chosen to explain it. The analysis is based on the combination of bare words frequency and cosine similarity, excluding trivial, first-level terms (titles, keywords, and definitions). Processing a corpus of generally accepted documents related to various definitions and requirements of sustainability unfolded their hidden correlations and some common key concepts. These results indicate that terms like access, inclusion, global, change, together with others like resource, share, and integration, are among leading concepts in the high-level documents discussing the requirements of sustainability. A similar analysis in the domain of cyber-physical production systems shows strong conceptual overlaps but also gaps indicating pathways for future research and actions.

Monitoring manufacturing systems using AI: A method based on a digital factory twin to train CNNs on synthetic data

Modern cyber–physical production systems provide advanced solutions to enhance factory throughput and efficiency. However, monitoring its behaviour and performance becomes challenging as the complexity of a manufacturing system increases. Artificial Intelligence (AI) provides techniques to manage not only decision-making tasks but also to support monitoring. The integration of AI into a factory can be facilitated by a reliable Digital Twin (DT) that enables knowledge-based and data-driven approaches. While computer vision and convolutional neural networks (CNNs) are crucial for monitoring production systems, the need for extensive training data hinders their adoption in real factories. The proposed methodology leverages the Digital Twin of a factory to generate labelled synthetic data for training CNN-based object detection models. Regarding their position and state, the focus is on monitoring entities in manufacturing systems, such as parts, components, fixtures, and tools. This approach reduces the need for large training datasets and enables training when the actual system is unavailable. The trained CNN model is evaluated in various scenarios, with a real case study involving an industrial pilot plant for repairing and recycling Printed Circuit Boards (PCBs).

Variability modeling of products, processes, and resources in cyber–physical production systems engineering

Cyber-Physical Production Systems (CPPSs), such as automated car manufacturing plants, execute a configurable sequence of production steps to manufacture products from a product portfolio. In CPPS engineering, domain experts start with manually determining feasible production step sequences and resources based on implicit knowledge. This process is hard to reproduce and highly inefficient. In this paper, we present the Extended Iterative Process Sequence Exploration (eIPSE) approach to derive variability models for products, processes, and resources from a domain-specific description. To automate the integrated exploration and configuration process for a CPPS, we provide a toolchain which automatically reduces the configuration space and allows to generate CPPS artifacts, such as control code for resources. We evaluate the approach with four real-world use cases, including the generation of control code artifacts, and an observational user study to collect feedback from engineers with different backgrounds. The results confirm the usefulness of the eIPSE approach and accompanying prototype to straightforwardly configure a desired CPPS.

Model-based Trustworthiness Evaluation of Autonomous Cyber-Physical Production Systems: A Systematic Mapping Study

The fourth industrial revolution, i.e., Industry 4.0, is associated with Cyber-Physical Systems (CPS), which are entities integrating hardware (e.g., smart sensors and actuators connected through the Industrial Internet of Things) together with control and analytics software used to drive and support decisions at several levels. The latest developments in Artificial Intelligence (AI) and Machine Learning (ML) have enabled increased autonomy and closer human-robot cooperation in the production and manufacturing industry, thus leading to Autonomous Cyber-Physical Production Systems (ACPPS) and paving the way to the fifth industrial revolution (i.e., Industry 5.0). ACPPS are increasingly critical due to the possible consequences of their malfunctions on human co-workers, and therefore, evaluating their trustworthiness is essential. This article reviews research trends, relevant attributes, modeling languages, and tools related to the model-based trustworthiness evaluation of ACPPS. As in many other engineering disciplines and domains, model-based approaches, including stochastic and formal analysis tools, are essential to master the increasing complexity and criticality of ACPPS and to prove relevant attributes such as system safety in the presence of intelligent behaviors and uncertainties.