OPC UA Research Articles

Micro-milling digital twin for real-time tool condition monitoring

Micro-milling is a promising manufacturing process due to its wide material compatibility with work materials and ability to fabricate complex micro features. However, the monitoring of tool condition is challenging because of the stochastic wear behavior these micro tools exhibit, which can drastically change due to instances of tool chipping. This paper presents the development of a high-fidelity digital twin of a micro-milling machine. High throughput data communication over OPC UA was used to communicate instantaneous sensor data to a high-performance computer. Using a trained deep learning model, we demonstrate real-time tool condition prediction over OPC UA. The deep learning model used was found to be in good agreement with post-machining tool wear measurements. The presented work highlights the importance of developing sound machine communication protocols and lays the foundation for integrating machine learning with micro-milling in real-time.

A federated information system framework for vertical integration

The convergence of operational and information technology in the era of Industry 4.0 has significantly increased the volume of data being generated on the manufacturing shop floors. Current research trends have focused on integrating this data with information present throughout the different systems in manufacturing facilities from Enterprise Resource Planning (ERP) to Manufacturing Execution System (MES) software, otherwise known as vertical integration. More specifically, previous works have looked at aggregating this data being read from Open Platform Communication Unified Architecture (OPC UA) servers by utilizing centralized aggregation servers to create information systems. This article, however, proposes a framework that adopts a federated information system designed to fulfill the manufacturing industry’s requirements and is capable of connecting to different OPC UA servers and storing the data within knowledge graph-based information models. GraphQL servers are also utilized in this system for easy accessibility of information by users. This system provides modularity, scalability, and interoperability to supplement the capabilities of information systems previously implemented. A prototype was also developed in this article for a federated information system for a boiler monitoring system.

A digital twin model of urban utility tunnels and its application

Background Multiple pipelines in utility tunnels may lead to various accidents and serious social impact. In the era of digitalization, how to better model the operation of a utility tunnel, dynamically predict the accident evolutions, and support corresponding decision-makings are essential issues. Methods In this study, a CFD-based digital twin framework for accidents in utility tunnels is proposed. First, Kalman filtering is applied to correct the parameter drift of sensors used for long-term monitoring. A data interaction system is then developed based on Internet of Things (IOT) and OPC Unified Architecture (OPC UA) to comprehensively manage data transmission within the utility tunnel. Subsequently, a natural gas leakage prediction model is developed to enable the efficient prediction of the spatial and temporal distribution in the case of leakage. Finally, these components are integrated for visualization in a digital twin platform for natural gas leakage in utility tunnels. Additionally, numerical simulations are employed to validate of the proposed method. Results The utility tunnel data transmission system based on IoT and OPC UA proposed in this paper is case-validated. By comparing the simulation results at 10 s, 20 s, 30 s, and 40 s, the model accurately predicts the methane concentration at the leak position after 10 seconds and maintains acceptable accuracy thereafter. The simulation results of different cases are introduced to verify the reliability of the risk indicator proposed in this paper, which increases with the leakage rate. Finally, A process for visualizing numerical simulation is proposed into a digital twin. Conclusions The proposed predictive digital twin technology facilitates the rapid risk assessment of and emergency management of natural gas accidents in utility tunnels. Based on the results of predictive model, a risk indicator is introduced to evaluate the natural gas accidents.

Enhancing Manufacturing Excellence with Digital-Twin-Enabled Operational Monitoring and Intelligent Scheduling

This research examines the potential of digital twin (DT) technology for reformation within China’s traditional solid-wood-panel processing industry, which currently suffers from production inefficiencies and the slow adoption of digital technology. The research centers around developing a digital twin system, elucidating improvements in manufacturing efficiency, waste management, process simulation, and real-time monitoring. These capabilities facilitate immediate problem solving and offer transparency in the process. The digital twin system is comprised of physical, transport, virtual, and application layers, employing a MySQL database and using the Open Platform Communications Unified Architecture (OPC UA) protocol for communication. The application of this system has led to heightened production efficiency and better material use in the solid-wood-panel manufacturing line. Integrating the dynamic selection adaptive genetic algorithm (DSAGA) into the virtual layer drives the system’s efficiency forward. This evolved approach has allowed for an enhancement of 8.93% in the scheduling efficiency of DSAGA compared to traditional genetic algorithms (GAs), thereby contributing to increased system productivity. Real-time mapping and an advanced simulation interface have strengthened the system’s monitoring aspect. These additions enrich data visualization, leading to better comprehension and a holistic process view. This research has ignited improvements in solid-wood-panel production, illustrating the tangible benefits and representing progress in incorporating digital technology into traditional industries. This research sets a path for transforming these industries into smart manufacturing by effectively bridging the gap between physical production and digital monitoring. Furthermore, the adjustability of this approach extends beyond solid-wood-panel production, indicating the capability to expedite movement towards intelligent production in various other manufacturing sectors.

Reading between the Lines: Process Mining on OPC UA Network Data.

The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs.

OPC UA-based Force Control for Deep Rolling with Mechanical Tools

Deep rolling can increase the service life of components under cyclic loads. This is achieved by plastic deformation of the component surface using a roller. The degree of plastic deformation depends on the rolling force. For a maximum increase in service life, the rolling force applied to the roller must stay within defined limits. When performing deep rolling using mechanical deep rolling tools, the rolling force is applied via an elastic element such as a leaf spring. The deep rolling force therefore depends on the deflection of leaf spring. It varies when the workpiece bends or has a varying diameter. These variations in the deep rolling force are not desired. They must be reduced to achieve optimum process results. To achieve this, a novel deep rolling force control for mechanical deep rolling was introduced in this paper. To control the rolling force, the force was measured with a sensory tool and machine tool axes were adjusted accordingly. The adjustment of the axes was implemented using the OPC UA standard. It is shown that this can reduce the force error during mechanical deep rolling by 90%. This ensures a constant process result and improves the mechanical deep rolling process.

A quick response data collection and management system for digital twin production line based on cloud-fog-edge computing collaboration

Background A production line is the basic unit of smart factories and smart manufacturing. However, owing to the development of the industrial Internet of Things, sensors, and other technologies, more data are being collected, leading to a data explosion, and the heterogeneous nature of multiple sources makes it difficult to manage data in a unified manner. Methods A production line data collection, storage, and management system based on cloud-fog-edge computing collaboration and a digital twin was designed. Multi-source heterogeneous data were collected and transmitted based on the OPC UA, and an information model of the production line was established. Modules for data mapping, publishing, and receiving were developed to achieve unified data collection and transmission. The data storage and management platform was constructed by front-end and back-end separation technologies. Results The developed data collection and management system was verified for functionality and performance on a digital twin production line. Functional tests show that the system has the functions of data acquisition and transmission, device addition and viewing, device data querying and downloading, data and model visualization, and user rights setting. The average time for edge data collection and transmission is 183.6ms. The average response time of the cloud for fog requests is less than 1s. This shows that the system can satisfy the real-time requirements of a digital twin production line. Conclusions The proposed system is real-time and stable, providing support for big data and virtual-reality interaction in digital twins.

Edge architecture for the automation and control of flexible manufacturing lines

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system.

Design and implementation of a machine log for PBF-LB/M on basis of IoT communication architectures and an ETL pipeline

Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M) has gained more industrial relevance and already demonstrated applications at a small series scale. However, its widespread adoption in various use cases faces challenges due to the absence of interfaces to established Manufacturing Execution Systems (MES) that support customers in the predominantly data-driven quality assurance. Current state-of-the-art PBF-LB/M machines utilize communication architectures, such as OPC Unified Architecture (OPC UA), Message Queuing Telemetry Transport (MQTT) and Representational State Transfer Application Programming Interface (REST API). In the context of the Reference Architecture Model Industry 4.0 (RAMI 4.0) and the Internet of Things (IoT), the assets, particularly the physical PBF-LB/M machines, already have an integration layer implemented to communicate data such as process states or sensor values. Missing is an MES component acting as a communication and information layer. To address this gap, the proposed Extract Transform Load (ETL) pipeline aims to extract relevant data from the fabrication of each build cycle down to the level of scan vectors and additionally to register process signals. The suggested data schema for archiving each build cycle adheres to all terms defined by ISO/TC 261—Additive Manufacturing (AM). In relation to the measurement frequency, all data are reorganized into entities, such as the AM machine, build cycle, part, layer, and scan vector. These scan vectors are stored in a runtime-independent format, including all metadata, to be valid and traceable. The resulting machine log represents a comprehensive documentation of each build cycle, enabling data-driven quality assurance at process level.

Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production

Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.

Research on a Real-Time Control System for Discrete Factories Based on Digital Twin Technology

Gear factories are most typical discrete manufacturing factories. Many gear factories are striving to explore how to achieve intelligent manufacturing in order to improve efficiency and reduce costs. Digital twin technology is currently one of the most reliable ways to achieve intelligent manufacturing. This article aims to establish a real-time control system in order to promote intelligent manufacturing for discrete manufacturing factories. Firstly, a model for a digital twin gear factory is put forward based on the characteristics of gear factories, and the composition of a real-time control system for gear factories is clarified. Then, a human–computer interaction architecture for the real-time control system is proposed. The real-time control system consists of three parts as follows: a monitoring module, a virtualizing module, and a controlling module. At work, it appears as a kind of human–machine interaction form with the three following interfaces: a monitoring window, a virtualizing window, and a controlling window. Finally, a gear factory, which is specialized in manufacturing the intermediate shaft dual gear of a new energy vehicle gearbox, develops a set of software for the real-time control system. The prototype software is obtained through some development activities such as 3D MAX and WebGL virtualization modeling and OPC UA and REST communication design.

Deriving semantic validation rules from industrial standards: An OPC UA study

Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards in terms of their compliance with the modeling constraints prescribed by the guidelines. This raises the risk of costly interoperability errors induced by the incorrect use of the standards. There is, therefore, an increased interest in automatic semantic validation of information objects based on industrial standards. In this paper we focus on an approach to semantic validation by formally representing the modeling constraints from unstructured documents as explicit, machine-actionable rules (to be then used for semantic validation) and (semi-)automatically extracting such rules from pdf documents. While our approach aims to be generically applicable, we exemplify an adaptation of the approach in the concrete context of the OPC UA industrial standard, given its large-scale adoption among important industrial stakeholders and the OPC UA internal efforts towards semantic validation. We conclude that (i) it is feasible to represent modeling constraints from the standard specifications as rules, which can be organized in a taxonomy and represented using Semantic Web technologies such as OWL and SPARQL; (ii) we could automatically identify modeling constraints in the specification documents by inspecting the tables ( P = 87 %) and text of these documents (F1 up to 94%); (iii) the translation of the modeling constraints into formal rules could be fully automated when constraints were extracted from tables and required a Human-in-the-loop approach for constraints extracted from text.

Application of Computer Virtual Reality Technology in Welding Simulation Training of Complex Structures Based on Unity3D

A virtual reality welding robot simulation system for practical application is developed based on the training and application of welding robot for practical application, which has important theoretical and practical application value for promoting the development of industrial robots in China. The simulation system is synchronized with the welding simulation training platform to achieve the purpose of VR simulation of the welding robot. By modeling the welding process, the process parameters can be inferred automatically, and the digital twin knowledge base for practical application is built. The OPC UA protocol is used to realize real-time communication with mobile devices. The digital twinning technology of CNC machine tool is studied through the simulation experiment of weld motion. This system lays a foundation for realizing the intelligentization of CNC machine tools.

Industrial Internet of Things Gateway with OPC UA Based on Sitara AM335X with ModbusE Acquisition Cycle Performance Analysis.

This article presents the hardware and software architectures used to implement the Modbus Extension (ModbusE) IIoT gateway, the performance of the acquisition cycle at the PRU real-time programmable core level, the acquisition cycle communication flow-dispatcher-OPC UA server (Linux)-OPC UA client (Windows) as well as the performance analysis of data communications between the IIoT ModbusE gateway and the OPC UA client (Windows). In order to be able to implement both the ModbusE acquisition cycle and the OPC UA server, the BeagleBone Black Board was chosen as the hardware platform. This board uses the Sitara AM335x processor (Texas Instruments (TI), Dallas, TX, USA) from Texas Instruments. Thus, the acquisition cycle was implemented on the PRU0 real-time core, and the OPC UA server, running under the Linux operating system, was implemented on the ARM Cortex A8 processor. From the analysis of the communication speed of the messages between the OPC UA client and the ModbusE servers, it was found that the ModbusE acquisition cycle speed was higher than the acquisition speed of the OPC UA client.

OPC UA Application Study in Oil and Gas Pipeline Network Monitoring Data Forwarding

INTRODUCTION： With the continuous development of oil and gas pipeline network monitoring and control technology, the need for data transmission and communication is becoming more and more prominent. In this context, OPC UA has attracted wide attention. This study aims to explore the application of OPC UA in data forwarding for oil and gas pipeline network monitoring in order to improve the efficiency, reliability and security of data transmission.PURPOSE: The purpose of this study is to evaluate the applicability of OPC UA in oil and gas pipeline network monitoring and to verify its performance in data forwarding through empirical studies. By gaining an in-depth understanding of the characteristics of OPC UA, it aims to provide a more advanced and efficient monitoring data transfer solution for the oil and gas industry.METHOD: The study adopts a combination of field monitoring and laboratory simulation. First, the essential characteristics and requirements of monitoring data in oil and gas pipeline networks were collected. Subsequently, a monitoring system with OPC UA as the communication protocol was established and field tested. In the laboratory environment, data transmission scenarios under different working conditions were simulated, and the performance of OPC UA under different conditions was analyzed.RESULT: The field monitoring results show that the data transmission efficiency is significantly improved by using OPC UA as the communication protocol for data forwarding in oil and gas pipeline network monitoring. Meanwhile, the system performs well in different environments with high reliability and security. The laboratory simulation results further verify the stability and adaptability of OPC UA under complex working conditions.CONCLUSION: OPC UA is an effective communication protocol that can meet the data transmission requirements for oil and gas pipeline network monitoring. Its efficient, reliable, and secure characteristics make it an ideal choice for improving the communication performance of monitoring systems in the oil and gas industry. The empirical results of this study provide reliable technical support for the oil and gas industry in the field of data transmission and a vital reference for the optimization and upgrading of monitoring systems in the future.

OPC UA DRIVEN AUTONOMOU DECISION MAKING IN INDUSTRIAL IOT

The creation of autonomous and intelligent devices for manufacturing firms has been made possible by the Industrial Internet of Things and the progress made in machine-to-machine communication. These autonomous machines are able to collect data from various sensing devices and other related machine systems by using communication protocols such as OPC UA. Here we perform machine to machine communication through an Arduino microcontroller. Any board or computer with a full-size USB A host connector can be connected to the Arduino with the help of a USB-A to USB-B cable. The communication takes place over the serial port (USART/UART). Machines can currently send and receive enough data to enable independent decision-making. Existing low-level machine-to-machine communication only involves data processing, and higher-level decision-making processes are limited to a single machine system rather than communications amongst networked machines. The goal of this effort is to employ OPC UA protocols to improve these limitations in machine-to-machine communication. This could mean deciding how to respond to receiving information and directing both horizontally (same level machines) and vertically (higher level machines) connected equipment. Proactive machine-to-machine communication improves factory intelligence in this way. Keywords: M2M,OPC UA,IIOT

Implementation of OPC UA for Machine-to-Machine Communications in a Smart Factory

The development of autonomous and intelligent robots in manufacturing plants is being made possible by advancements in communications technologies and the Industrial Internet of Things (IoT). These autonomous devices are capable of gathering data from several sensor devices and multiple linked device systems through the use of OPC UA communication protocols. These devices can transmit and receive enough data these days to allow them to make decisions. Organization of individual devices is the sole application where high-level decision-making powers are available; communication between devices only entails low- level processing. The purpose of these images is to increase the OPC UA protocol-enabled devices' communication capabilities. This involves choosing methods for data reception and processing between devices connected vertically (deeper devices) and horizontally (equal devices). Because of this, the energetic gadget device makes the manufacturing facility site visitors better. KEYWORDS: Internet of things, Machine, Communications, Enterprises, Protocols, OPC UA protocols

Enhancing Industrial Digitalisation through an Adaptable Component for Bridging Semantic Interoperability Gaps

Digital transformation has a great effect on industries but also in the perception of their products with high consequences on society as a whole, as driver for innovation. However, there are still many barriers to the adoption of digital technologies, especially in small and medium-sized enterprises (SMEs). In the present study we propose a new software component that facilitates the adoption of industrial reference architectures (RAs) based on the semantic interoperability concept. Among these RAs, FIWARE has become a key enabler for flexible and resource effective digitalisation of shop floor processes. The proposed software component aims to enable full interoperability between these more widely used technologies and industrial standards and protocols such as OPC UA. As a result, a flexible, open, and adaptable implementation assists the automatic translation of messages from different standards enabling semantic interoperability of shop floor processes. The component aims to enable industrial SMEs to bring current barriers to digitalisation towards a more human-centred, resilient, and sustainable industry and society.

Design of a Flexible Skill-Based Process Control System Considering Process-Relevant Properties

The Industry 4.0 research initiative strives to facilitate globally interconnected, flexible, and highly adaptable production systems. The use of skill-based control mechanisms such as OPC UA skills offers the prospect of a straightforward and flexible interchange, as well as the seamless integration of individual participants and processes through standardized interfaces. Furthermore, by enhancing these skills with evaluation parameters pertinent to the processes, such as CO2 equivalents or the duration of specific skill executions, a foundation is laid for creating a customizable and adaptable composition of processes based on specific production process needs. In this article, the OPC UA skill concept is expanded with process-relevant properties, and a structured procedure for the introduction of skill-based process control is presented. The developed concept was implemented and tested on an industrial use case of glass pane completion. The aim of this publication is to demonstrate the potential of skill-based process control that has an integrated assessment of skills.