Smart Factory Environment Research Articles

Transmission Time Scheduling for Stations with Different Packet Generation Periods in Smart Factory Environment: System Implementation and Experimental Evaluation

Transmission Time Scheduling for Stations with Different Packet Generation Periods in Smart Factory Environment: System Implementation and Experimental Evaluation

Framework for Designing Quality-Centered Assistance Systems for the Smart Factory

Employee assistance systems are essential for companies to manage the growing complexity of information and processes. However, current systems mainly provide process-oriented support through documentation options, leaving a gap in real-time quality assessment. Real-time or in-process quality assessment becomes increasingly attractive for large and complex components where automated inspection is not feasible. To fill this gap, this paper proposes a new modeling framework for quality-centric assistance systems that are tailored to the Smart Factory environment. The framework integrates digital design principles, standard manufacturing processes, and quality parameters, facilitating seamless integration and standardized data exchange. An expert evaluation of the framework yielded positive ratings across various dimensions, underscoring its potential impact. Feedback from experts indicated the need for further refinement, including enhancing credibility through implementation and field tests, providing more detailed procedure models, and addressing challenges in sensor integration. Overall, this framework represents a significant advancement towards process-oriented assistance in various application areas, with implications for optimizing production and quality processes.

Advanced 3D Navigation System for AGV in Complex Smart Factory Environments

The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot’s inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications.

Quantum Multiagent Actor–Critic Neural Networks for Internet-Connected Multirobot Coordination in Smart Factory Management

As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQC) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This paper verifies the potential of QRL, which will be further realized by implementing quantum multi-agent reinforcement learning (QMARL) from QRL, especially for Internet-connected autonomous multi-robot control and coordination in smart factory applications. However, the extension is not straightforward due to the non-stationarity of classical MARL. To cope with this, the centralized training and decentralized execution (CTDE) QMARL framework is proposed under the Internet connection. A smart factory environment with the Internet of Things (IoT)-based multiple agents is used to show the efficacy of the proposed algorithm. The simulation corroborates that the proposed QMARL-based autonomous multi-robot control and coordination performs better than the other frameworks.

Fuzzy Controller-empowered Autoencoder Framework for anomaly detection in Cyber Physical Systems

In recent times, Cyber Physical Systems (CPSs) have been extensively deployed in vital infrastructures to provide crucial services to society. In the smart factory scenario, the Industrial Control Systems (ICSs) are CPSs that autonomously regulate the manufacturing process through sensors and actuators. Protection of these industrial control systems against cyber threats is essential to prevent any malfunction in the production procedure. In this article, a Fuzzy Controller-enabled Autoencoder Framework (FCAF) is proposed. The proposed framework can detect anomalies that may arise due to cyberattacks in the smart factory environment. The results show evidence that the model can sense any rapid deviation from normal behaviour once the attack commences and also detects unforeseen anomalies. The model further reduces the bias arising from the machine learning algorithm by introducing a fuzzy controller. The highest specificity and sensitivity reported by the proposed technique are 93% and 49.9%, respectively.

Communication Safety of Cybernetic Systems in a Smart Factory Environment

The aim of this contribution is to propose the architecture for a layered design of the production system. This proposal uses the IEC 62443 norm, including the Defense-in-Depth strategy and proven technical principles applicable in a Smart Factory with a focus on communication security. Firstly, the identification of communication forms and trends in the Smart Factory environment was identified considering the spectrum of communication protocols used within various types of automation structures used in modern production facilities. The next part of the work deals with the definition of wired and wireless forms of data transfers in production systems including their advantages and disadvantages from the view of cybernetic safety and threads in communication systems, together with the description of norms from the field of security of communication systems applicable in the industrial environment. The core of this work is the proposal of the methodology to secure the Smart Factory production system in the Industry 4.0 environment. The proposal defines important implementation steps together with a summarization of the generally applicable basic principles suitable for the process of securing a Cyber production system or Smart Factory in an industrial environment, including the example of an Iptables firewall configuration within the OPC UA communication protocol and the real example of a Smart Factory production system segmentation.

Agnostic learning for packing machine stoppage prediction in smart factories

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as "agnostic". In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.

A Blockchain-Enabled Secure Digital Twin Framework for Early Botnet Detection in IIoT Environment

Resource constraints in the Industrial Internet of Things (IIoT) result in brute-force attacks, transforming them into a botnet to launch Distributed Denial of Service Attacks. The delayed detection of botnet formation presents challenges in controlling the spread of malicious scripts in other devices and increases the probability of a high-volume cyberattack. In this paper, we propose a secure Blockchain-enabled Digital Framework for the early detection of Bot formation in a Smart Factory environment. A Digital Twin (DT) is designed for a group of devices on the edge layer to collect device data and inspect packet headers using Deep Learning for connections with external unique IP addresses with open connections. Data are synchronized between the DT and a Packet Auditor (PA) for detecting corrupt device data transmission. Smart Contracts authenticate the DT and PA, ensuring malicious nodes do not participate in data synchronization. Botnet spread is prevented using DT certificate revocation. A comparative analysis of the proposed framework with existing studies demonstrates that the synchronization of data between the DT and PA ensures data integrity for the Botnet detection model training. Data privacy is maintained by inspecting only Packet headers, thereby not requiring the decryption of encrypted data.

IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories

The smart factory environment has been transformed into an Industrial Internet of Things (IIoT) environment, which is an interconnected and open approach. This has made smart manufacturing plants vulnerable to cyberattacks that can directly lead to physical damage. Most cyberattacks targeting smart factories are carried out using malware. Thus, a solution that efficiently detects malware by monitoring and analyzing network traffic for malware attacks in smart factory IIoT environments is critical. However, achieving accurate real-time malware detection in such environments is difficult. To solve this problem, this study proposes an edge computing-based malware detection system that efficiently detects various cyberattacks (malware) by distributing vast amounts of smart factory IIoT traffic information to edge servers for deep learning processing. The proposed malware detection system consists of three layers (edge device, edge, and cloud layers) and utilizes four meaningful functions (model training and testing, model deployment, model inference, and training data transmission) for edge-based deep learning. In experiments conducted on the Malimg dataset, the proposed malware detection system incorporating a convolutional neural network with image visualization technology achieved an overall classification accuracy of 98.93%, precision of 98.93%, recall of 98.93%, and F1-score of 98.92%.

A Resource Recommendation Model for Heterogeneous Workloads in Fog-Based Smart Factory Environment

The wide deployment of advanced robots with industrial IoT (IIoT) technologies in smart factories generates a large volume of data during production and a wide variety of data processing workloads are launched to maintain productivity and safety of smart manufacture. The emerging fog computing paradigm offers a promising solution to enhancing data processing performance in a smart factory environment while on the other hand brings in new challenges to resource management, which call for a more effective approach for recommending resource configurations to heterogeneous workloads. In this paper, we propose an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that employs machine learning techniques to provide resource configuration recommendations for the heterogeneous workloads in a fog computing-based smart factory environment. ORHRC learns a recommendation model by leveraging the operating characteristics and execution time of workloads on fog servers with different configurations. We also design a decision model in ORHRC to further improve prediction accuracy and reduce operational overheads. Experiment results show that ORHRC outperforms the state of art configuration recommendation methods in terms of average prediction accuracy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The various data processing workloads in a smart factory environment need to be processed by the computational resources with optimal configurations for meeting their performance requirements. In this paper, we employ machine learning technologies for enabling automatic recommendation of resource configurations to heterogeneous workloads. Specifically, we develop an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that can identify the optimal resource configurations for various workloads. We also conducted extensive experiments that verify the effectiveness of the proposed ORHRC model.

Robust RFID tag design for reliable communication within the Internet of Things

Radio frequency identification (RFID) is a cost-effective method to support the Industrial Internet of Things (IIoT) and is an enabling technology for Industry 5.0. The use of RFID is particularly suited to IIoT as it does not require a line of sight for communication and can be retroactively affixed to non-smart items. However, RFID communication is affected by the properties of the material the tag is affixed to, specifically the material permittivity. Metal is commonly present in smart factory environments and supply chains and impedes RFID communication. A suitable tag design is required to accommodate these challenges and ensure resilience for Industry 5.0 applications. The research presented in this paper has assessed the communication performance of RFID antenna designs with and without metal present beneath the tag. The RFID tag designs that performed reliably in the simulation were manufactured, and their read range was tested on materials of varying relative permittivity and thickness to represent MP and NMP scenarios. The results have verified the robustness of “Cyber” to “Physical” designs and provide recommendations to support practitioner IIoT tag selection.

Nested Lifecycles-Improving the Visibility of Product Lifespans in Smart Factories

Within the smart factory environment, we have a complex system of systems composed of Industry 4.0/digital technologies and assets with varying lifespans. Different degrees of innovation maturity and different lifespans of integrated assets within the industrial internet of things (IIOT) are considered problematic and lead to different perspectives on asset lifecycles as they impact significantly on the asset lifespans. This leads to the loss of a clear end-of-life phase defined in existing Product Lifecycle Management (PLM) models. Through an integrative literature review, this study introduces the concept of nested lifecycles that takes a systems perspective to asset management, and considers subsystems with different lifespans that must be managed holistically by different actors with different perspectives. Additionally, this study provides a framework to derive strategies for lifecycle management by allowing the nested lifecycles to be clearly identified and then addressed. From the theoretical perspective, the notion of nested lifecycles provides a novel viewpoint for the asset management and PLM research community. From the managerial perspective, the proposed framework will help managers identify why and where collaboration between different actors may create difficulties due to their varying outlooks and training.

Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches.

Data distribution is a cornerstone of efficient automation for intelligent machines in Industry 4.0. Although in the recent literature there have been several comparisons of relevant methods, we identify that most of those comparisons are either theoretical or based on abstract simulation tools, unable to uncover the specific, detailed impacts of the methods to the underlying networking infrastructure. In this respect, as a first contribution of this paper, we develop more detailed and fine-tuned solutions for robust data distribution in smart factories on stationary and mobile scenarios of wireless industrial networking. Using the technological enablers of WirelessHART, RPL and the methodological enabler of proxy selection as building blocks, we compose the protocol stacks of four different methods (both centralized and decentralized) for data distribution in wireless industrial networks over the IEEE 802.15.4 physical layer. We implement the presented methods in the highly detailed OMNeT++ simulation environment and we evaluate their performance via an extensive simulation analysis. Interestingly enough, we demonstrate that the careful selection of a limited set of proxies for data caching in the network can lead to an increased data delivery success rate and low data access latency. Next, we describe two test cases demonstrated in an industrial smart factory environment. First, we show the collaboration between robotic elements and wireless data services. Second, we show the integration with an industrial fog node which controls the shop-floor devices. We report selected results in much larger scales, obtained via simulations.

SCADA System for Next-Generation Smart Factory Environments

Today, in factories, production is spread into a wide area and became complicated. Complicated production requires a better monitoring and control system to improve efficiency. For this purpose, SCADA systems are used. SCADA systems allow industries to monitor, control and access to real-time data and data logging. Nowadays, all large-scale industries have SCADA systems. The cost of the SCADA system makes it unreachable for small and medium-size industries but a SCADA system that is designed by using C# programming language would be cheaper and could perform the same. In this study, a SCADA system has been developed to monitor and control the smart model factory system in AFSUAM. SCADA interface is created by using C# programming language and the communication between PLC and the SCADA is set by using Mitsubishi MX Component tool. With the developed SCADA system, the process of a product entering the factory as raw material, going through several processes and becoming a product from the supply chain to the distributor has been modeled on a sample marble factory model, and a graphical user interface has been created considering the purposes such as real-time monitoring, controlling, data logging and preventing errors in the context of this model.

An Augmented Reality Framework for Visualization of Internet of Things Data for Process Supervision in Factory Shop-Floor

Modern industrial environments provide a high level of digitalization, enabling great volumes of information to be used for shopfloor monitoring and control purposes. In cases where human operators participate in the production process, it is important to support operator’s intervention with Internet of Things (IoT) data and empower process supervision with constant information over Key Performance Indicators (KPIs). Towards this end, the proposed framework exploits Augmented Reality (AR) for the provision of an IoT data visualization solution to support KPIs based process supervision in the smart factory environment. The solution utilizes IoT-enabled components, in order to receive dynamic information over the status of sensors and resources spread across the factory shop-floor. The AR application facilitates the need for visualization of information available on the network, on the actual shopfloor components, which offer transparency and ease the supervision of production processes. The development of the AR and cloud API has been based on CSharp and Java, in a reusable framework that can be reused for similar industrial cases. The reconfigurability and reuse of the AR solution is supported by a template configuration, allowing industry practitioners to configure application features and external connections without any additional software development. The solution was validated on a thermoforming process supervision case of a white goods production system. The main goal was to use the AR framework to support operators during the process parameters design phase, and dynamic supervision of the shopfloor environment. It is demonstrated that the proposed system can be used for KPIs monitoring of the production cycles and for empowering process supervision and optimization.

Smart Additive Manufacturing: The Path to the Digital Value Chain

The aim of this article is to characterize the impacts of Smart Additive Manufacturing (SAM) on industrial production, digital supply chains (DSCs) and corresponding digital value chains (DVCs), logistics and inventory management. The method used consists of a critical review of the literature, enriched by the authors’ field experience. The results show that digital transformation of manufacturing is affecting business models, from resource acquisition to the end user. Smart manufacturing is considered a successful improvement introduced by Industry 4.0. Additive Manufacturing (AM) plays a crucial role in this digital transformation, changing the way manufacturers think about the entire lifecycle of a product. SAM combines AM in a smart factory environment. SAM reduces the complexity of DSCs and contributes to a more flexible approach to logistics and inventory management. It has also spurred the growth and popularization of customized mass production as well as decentralized manufacturing, rapid prototyping, unprecedented flexibility in product design, production and delivery, and resource efficiency and sustainability. SAM technology impacts all five Fletcher’s stages in DVCs. However, the need for clear definitions and regulations on 3D printing of digital files and their reproduction, as well as product health, safety, and integrity issues, cannot be ignored. Furthermore, investment in this technology is still expensive and can be prohibitive for many companies, namely SMEs.

스마트공장용 로컬 5G의 효과적 도입을 위한 공동사용정책방안

In this study, interference simulations are performed between the local 5G service and ultra-wideband sensors in a smart factory environment. Moreover, the necessity of introducing various interference determination criteria, such as the duty cycle and application of interference avoidance technology, in addition to the existing interference probability is evaluated. This study also suggests improvements in both the technology and policy for an effective introduction of local 5G after carefully reviewing cognitive ratio, automatic frequency coordination, and spectrum-sharing policy. As a result, the proposed scheme will greatly benefit the policy makers.

Cellular Connectivity and Wearable Technology Enablers for Industrial Mid-End Applications

Industrial digital transformation through efficient automation hinges largely on the deployment of communication infrastructures that meet the requirements of smart factory use cases. These infrastructures involve multiple devices that utilize different communication technologies to increase the overall operational efficiency. Rooting from the key implementation requirements of a smart factory environment, this article focuses on the role of cellular connectivity and wearable technology in enabling new industrial applications. Specifically, we shed light on a novel category of services - industrial mid-end wearable applications - by positioning their requirements among the 5G service classes. We then identify features that can complement cellular connectivity to further support the given requirements. More precisely, we review cellu-lar-network-aided device-to-device communications and reduced-capability devices. Our performance evaluation results justify the choice of these features and show that they can work in concert with cellular connectivity to enhance spectral efficiency and reliability in industrial mid-end wearable applications.

A Study on the Automated Guided Vehicle Platform for a Logistics Robot

The need for automated material handling inside the factory has been steadily increasing, especially due to implementation of intelligent manufacturing for better productivity and product quality. Automated material handling devices include logistics robots, automated guided vehicles, industrial robots, collaborative robots, and pick-and-place devices. This study focuses on the development of a low-cost logistics robot that works effectively within a simulated smart factory environment. A nominal PID controller is implemented to guide the robot to follow the line painted on the factory floor. The tracking error information is generated by four down-facing infrared sensors and is fed into the controller. The line-following performance is significantly improved with augmentation of a model-based friction compensator. Optimization of battery power depending on the remaining charge status enhances the reliability. All hardware/software development is supported by the Arduino platform. The step-by-step movement and performance of the logistics robot is verified inside the simulated smart factory environment that includes a robot arm, three conveyors, and two processing stations.

Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning.

The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.