Journal articles: 'Digital twin of machine tool'

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Kubota, Tsubasa, Reza Hamzeh, and Xun Xu. "STEP-NC Enabled Machine Tool Digital Twin." Procedia CIRP 93 (2020): 1460–65. http://dx.doi.org/10.1016/j.procir.2020.06.004.

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Luo, Weichao, Tianliang Hu, Chengrui Zhang, and Yongli Wei. "Digital twin for CNC machine tool: modeling and using strategy." Journal of Ambient Intelligence and Humanized Computing 10, no. 3 (July 28, 2018): 1129–40. http://dx.doi.org/10.1007/s12652-018-0946-5.

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Olalere, Isaac O., and Oludolapo A. Olanrewaju. "Optimising Production through Intelligent Manufacturing." E3S Web of Conferences 152 (2020): 03012. http://dx.doi.org/10.1051/e3sconf/202015203012.

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Intelligent manufacturing system (IMS) has been the focus of most industries since Industry 4.0 revolution. IMS is being implemented through the integration of Internet of Things, (IoT), Cyber-Physical Systems (CPS), digital twin and big data analytics to optimize production through smart manufacturing. This research presents a conceptual approach of an adaptive clustering algorithm (ACA) for advanced manufacturing decision-making for smart machining manufacturing. The work considers product monitoring and assessment, machine health and operating parameters monitoring, as an important factor for intelligent decision making on a machining production line through the developed cyber twin of the machine tool for production optimisation. Cyber twin of the machine tool is developed which runs on a realtime sequence with the physical asset fussed with smart sensors and controllers enabled with cloud computing, IoT and data analytics. The ACA enables resources monitoring, production monitoring, machine condition monitoring, cloud feedback notification, product monitoring, and assessment, for intelligent decision-making from a cluster of similar machines using ANN clustering tool for self-aware, self-predict and self-reconfiguration in a smart machining production line to detect a cutting tool chipping of less than 0.25mm size. The method is proposed to optimise production by increasing productivity through intelligent decision and prediction for tool change, tool failure, maintenance, adjustment of operating parameters.

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Heo, Eunyoung, and Namhyun Yoo. "Numerical Control Machine Optimization Technologies through Analysis of Machining History Data Using Digital Twin." Applied Sciences 11, no. 7 (April 5, 2021): 3259. http://dx.doi.org/10.3390/app11073259.

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In numerical control (NC)-based machining, NC data-based tool paths affect both quality and productivity. NC data are generated according to cutting conditions. However, NC data causing excessive cutting load can accelerate tool wear and even result in tool damage. In the opposite case, increasing machining time can affect productivity. NC data can influence surface quality from the perspective of cutting dynamics according to machine tool–material–tool combination. There have been a lot of studies on tool-path optimization. However, it is impossible to perfectly predict cutting dynamics such as tool wear, material non-uniformity, chatter, and spindle deformation. In fact, such prediction-based tool-path optimization can cause errors. Therefore, this study attempts to synchronize spindle load and NC data and uniformize the machining load through the analysis of stored data using digital-twin technology, which stores and manages machining history. Uniformizing machining load can reduce rapid traverse in the event of no load, feed rate in an overload area, and shock on a tool when the tool and material are met by adding approach feed. Analyzing results of the attempts proposed in this paper, the chatter was completely removed in the machining with D100 and D16, although some chatter remained in the machining with D25 and D16R3 tools. In addition, the processing time could be reduced from a minimum of 7% to a maximum of 50% after optimization.

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Montavon, Benjamin, Philipp Dahlem, Martin Peterek, and Robert H. Schmitt. "A Digital Perspective on Machine Tool Calibration." International Journal of Automation Technology 14, no. 3 (May 5, 2020): 360–68. http://dx.doi.org/10.20965/ijat.2020.p0360.

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Machine tool calibration and subsequent controller-based compensation are industrially established and research-intensive techniques used to monitor and increase the volumetric performance in high-precision manufacturing. Moreover, a variety of interim performance checks and integrated sensor approaches have been developed to predict volumetric performance degradation and avoid an economically undesirable downtime. However, the fragmentation of data acquisition and management limits the potential for additional insights with respect to the value creation based on existing methods in the field of machine tool calibration. The authors reviewed the former from the perspective of data sources according to the frequency of their contribution to the digital twin of a machine tool, adopting a digital view regarding machine tool calibration within the Internet of Production concept. The latter proposes a semantic separation of cyber physical production systems into four layers: data sources, data access and provisioning, storage and analytics, and user respective agent feedback. To achieve a common representation across different layers, devices, and industrial Internet protocols, a model-based abstraction layer is required, which must be compatible with existing standards within the field, e.g., the ISO 230 series. Utilizing different Internet of Production architectures and platforms, a multitude of parallel analytic applications and an evaluation of complex models are enabled owing to the availability of ample computing resources, among which the machine tool’s numerical controller takes the role of an edge-device injecting the feedback into the production process. A proof-of-concept of a digital approach to machine tool calibration data storage and processing was established based on the software prototype VoluSoft, which implements an ISO 230-1:2012 based abstraction layer in JavaScript Object Notation format, and an evaluation of the kinematic models to estimate the volumetric performance at the functional point. Apart from generating compensation tables, the results are used to project the expected deviation at the tool tip to the computer-aided design-model of a work piece, correlate the error motions using the temperature data acquired by integrated sensors, and estimate the contribution of the volumetric performance limitation to the uncertainty budget of on-machine measurements.

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Armendia, Mikel, Frédéric Cugnon, Luke Berglind, Erdem Ozturk, Guillermo Gil, and Jaouher Selmi. "Evaluation of Machine Tool Digital Twin for machining operations in industrial environment." Procedia CIRP 82 (2019): 231–36. http://dx.doi.org/10.1016/j.procir.2019.04.040.

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TANAKA, Fumiki. "A Digital Twin of machining function for individual multi-axis machine tool." Proceedings of The Manufacturing & Machine Tool Conference 2019.13 (2019): C25. http://dx.doi.org/10.1299/jsmemmt.2019.13.c25.

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Autiosalo, Juuso, Riku Ala-Laurinaho, Joel Mattila, Miika Valtonen, Valtteri Peltoranta, and Kari Tammi. "Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane." Applied Sciences 11, no. 2 (January 12, 2021): 683. http://dx.doi.org/10.3390/app11020683.

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Industrial Internet of Things practitioners are adopting the concept of digital twins at an accelerating pace. The features of digital twins range from simulation and analysis to real-time sensor data and system integration. Implementation examples of modeling-oriented twins are becoming commonplace in academic literature, but information management-focused twins that combine multiple systems are scarce. This study presents, analyzes, and draws recommendations from building a multi-component digital twin as an industry-university collaboration project and related smaller works. The objective of the studied project was to create a prototype implementation of an industrial digital twin for an overhead crane called “Ilmatar”, serving machine designers and maintainers in their daily tasks. Additionally, related cases focus on enhancing operation. This paper describes two tools, three frameworks, and eight proof-of-concept prototypes related to digital twin development. The experiences show that good-quality Application Programming Interfaces (APIs) are significant enablers for the development of digital twins. Hence, we recommend that traditional industrial companies start building their API portfolios. The experiences in digital twin application development led to the discovery of a novel API-based business network framework that helps organize digital twin data supply chains.

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Fischer, Andreas, Thomas Semm, Michael F. Zäh, Daniel Spescha, and Nino Ceresa. "Der digitale Zwilling der Werkzeugmaschine/The digital twin of machine tools." wt Werkstattstechnik online 111, no. 03 (2021): 179–83. http://dx.doi.org/10.37544/1436-4980-2021-03-87.

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Der digitale Zwilling als Modell gewinnt sowohl für die Entwicklung neuer Maschinengenerationen als auch für Simulationen parallel zum Betrieb stark an Bedeutung. Zur Erstellung entsprechender Modelle sind moderne flexible Mehrkörpersimulationsprogramme besonders geeignet. Im Rahmen dieses Beitrags wird die Simulationsumgebung MORe präsentiert, die sich unter anderem durch ihre Benutzerfreundlichkeit und ihre Recheneffizienz auszeichnet. Zudem ist die Berücksichtigung von Effekten möglich, die bisher im industriellen Umfeld kaum betrachtet wurden, wie beispielsweise Dämpfung.   The digital twin is becoming increasingly important for the development of new machine generations and for process parallel simulations. Modern flexible multi-body simulation programs are particularly suitable for creating the relevant models. In this paper, the simulation environment MORe is presented, which is characterized by its user-friendliness and its computational efficiency. Furthermore, it is possible to study effects such as damping, which have hardly been considered in industrial environments so far.

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Vorkapić, Nikola, Saša Živanović, and Zoran Dimić. "Development of an educational 3-axis CNC machine tool for rapid prototyping with two translational and one rotary axis." Tehnika 75, no. 6 (2020): 725–32. http://dx.doi.org/10.5937/tehnika2006725v.

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The paper shows the development of a 3-axis CNC machine tool for rapid prototyping by subtracting materials, with two translational and one rotating axis, whose control is based on the LinuxCNC system, with an integrated virtual machine as a digital twin. The concepts of machines with one rotary axis were analyzed and the concept of a machine for realization was adopted, which is completely configured in a CAD/CAM environment. For successful verification of the programming system, a virtual machine has been configured in the CAD/CAM system, as well as in Vericut. The final validation of the machine design was achieved by its trial work on characteristic examples.

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Rastorguev, D. A., and A. A. Sevastyanov. "Development of turning process digital twin based on machine learning." Vektor nauki Tol'yattinskogo gosudarstvennogo universiteta, no. 1 (2021): 32–41. http://dx.doi.org/10.18323/2073-5073-2021-1-32-41.

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Today, manufacturing technologies are developing within the Industry 4.0 concept, which is the information technologies introduction in manufacturing. One of the most promising digital technologies finding more and more application in manufacturing is a digital twin. A digital twin is an ensemble of mathematical models of technological process, which exchanges information with its physical prototype in real-time. The paper considers an example of the formation of several interconnected predictive modules, which are a part of the structure of the turning process digital twin and designed to predict the quality of processing, the chip formation nature, and the cutting force. The authors carried out a three-factor experiment on the hard turning of 105WCr6 steel hardened to 55 HRC. Used an example of the conducted experiment, the authors described the process of development of the digital twin diagnostic module based on artificial neural networks. When developing a mathematical model for predicting and diagnosing the cutting process, the authors revealed higher accuracy, adaptability, and versatility of artificial neural networks. The developed mathematical model of online diagnostics of the cutting process for determining the surface quality and chip type during processing uses the actual value of the cutting depth determined indirectly by the force load on the drive. In this case, the model uses only the signals of the sensors included in the diagnostic subsystem on the CNC machine. As an informative feature reflecting the force load on the machine’s main motion drive, the authors selected the value of the energy of the current signal of the spindle drive motor. The study identified that the development of a digital twin is possible due to the development of additional modules predicting the accuracy of dimensions, geometric profile, tool wear.

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Luo, Weichao, Tianliang Hu, Yingxin Ye, Chengrui Zhang, and Yongli Wei. "A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin." Robotics and Computer-Integrated Manufacturing 65 (October 2020): 101974. http://dx.doi.org/10.1016/j.rcim.2020.101974.

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Hänel, Albrecht, André Seidel, Uwe Frieß, Uwe Teicher, Hajo Wiemer, Dongqian Wang, Eric Wenkler, Lars Penter, Arvid Hellmich, and Steffen Ihlenfeldt. "Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach." Journal of Manufacturing and Materials Processing 5, no. 3 (July 27, 2021): 80. http://dx.doi.org/10.3390/jmmp5030080.

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This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

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Sundby, Tiril, Julia Maria Graham, Adil Rasheed, Mandar Tabib, and Omer San. "Geometric Change Detection in Digital Twins." Digital 1, no. 2 (April 15, 2021): 111–29. http://dx.doi.org/10.3390/digital1020009.

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Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica. Digital twin applications are required to rapidly update internal parameters with the evolution of their physical counterpart. Due to an essential need for having high-quality geometric models for accurate physical representations, the storage and bandwidth requirements for storing 3D model information can quickly exceed the available storage and bandwidth capacity. In this work, we demonstrate a novel approach to geometric change detection in a digital twin context. We address the issue through a combined solution of dynamic mode decomposition (DMD) for motion detection, YOLOv5 for object detection, and 3D machine learning for pose estimation. DMD is applied for background subtraction, enabling detection of moving foreground objects in real-time. The video frames containing detected motion are extracted and used as input to the change detection network. The object detection algorithm YOLOv5 is applied to extract the bounding boxes of detected objects in the video frames. Furthermore, we estimate the rotational pose of each object in a 3D pose estimation network. A series of convolutional neural networks (CNNs) conducts feature extraction from images and 3D model shapes. Then, the network outputs the camera orientation’s estimated Euler angles concerning the object in the input image. By only storing data associated with a detected change in pose, we minimize necessary storage and bandwidth requirements while still recreating the 3D scene on demand. Our assessment of the new geometric detection framework shows that the proposed methodology could represent a viable tool in emerging digital twin applications.

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Pan, Lida, Xiangkun Guo, Yan Luan, and Hongliang Wang. "Design and realization of cutting simulation function of digital twin system of CNC machine tool." Procedia Computer Science 183 (2021): 261–66. http://dx.doi.org/10.1016/j.procs.2021.02.057.

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Jo, Ok Hyun, Wonkyun Lee, and Kang Jae Lee. "Machining Condition Optimization based on a Digital Twin of Machine Tools." Transactions of the Korean Society of Mechanical Engineers - A 44, no. 2 (February 29, 2020): 117–25. http://dx.doi.org/10.3795/ksme-a.2020.44.2.117.

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Skýpala, Richard, and Roman Ružarovský. "Virtual commissioning of automated manufacturing systems — Quality-handling station case study." MATEC Web of Conferences 343 (2021): 04002. http://dx.doi.org/10.1051/matecconf/202134304002.

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The aim of the contribution is to create an event-controlled simulation of an automated production system using selected simulation software according to analyses and creation PLC control program compatible with human-machine control interface (HMI) visualization. The programmer can test the control program without the need for additional hardware on a virtual model of the machine. This method is called as a Virtual Commissioning method. The question is whether these new digital technologies are beneficial in the development process of new machines. The theoretical part of the contribution deals with the evaluation of the current state of knowledge in the field of virtual commissioning systems from the perspective of the Industry 4.0 concept and the use of principles taken from the technology of digital twins for analysing the possibilities of productive support of simulation, programming and visualization of control. For the Virtual Commissioning of the Quality-handling station from a flexible production system. It was chosen Tecnomatix Process simulating software for the developing of the Digital Twin of the station that is suitable for the connection of the logical behaviour inputs and outputs of the Digital Twin to the PLCSIM emulator. The main goal is to create a functional PLC program, so that in the second phase the process of virtual commissioning can be tested by the Software-the-Loop method. The preparation of the simulation model consists in a sample of the control station model of the production system, the process of creating kinematic bonds, behaviour model, creating control signals in the Tecnomatix Process Simulate program. Programming procedure and acquaintance with control means in the Simatic Manager program, selection of communication interface and creation of control visualization. In the final work, validation is performed and evaluation supports the objectives and the results confirmed the benefits of the Virtual Commissioning tool.

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Liu, Jinsong, Dong Yu, Yi Hu, Haoyu Yu, Wuwei He, and Lipeng Zhang. "CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework." IEEE Access 9 (2021): 118801–14. http://dx.doi.org/10.1109/access.2021.3106797.

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Vorkapić, Nikola, Saša Živanović, Nikola Slavković, Zoran Dimić, and Branko Kokotović. "Configuring of 3-axis vertical CNC Machine for Rapid Prototyping with two Translatory and one Rotary Axes." Advanced Technologies & Materials 45, no. 1 (October 1, 2020): 1–9. http://dx.doi.org/10.24867/atm-2020-1-003.

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This paper describes the configuration of a 3-axis vertical CNC machine tool for rapid prototyping with one rotary and two translational axes. The machine works in a polar-cylindrical coordinate system. The structure of the machine is C’OXZ. The virtual machine model is configured in the PTC Creo software environment. After configuring the virtual machine, the simulation of the CLFbased was performed in the mentioned software environments, and then the verification according to the G-code program in the Vericut software environment was performed. Programming and control of the configured prototype machine are realized in the LinuxCNC software environment, which is based on the PC platform. Also, in this paper, digital twin of machine realized in a python software environment is shown. The presented results show the proper functioning of the whole system.

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Et. al., Mathew Chacko,. "Cyber-Physical Quality Systems in Manufacturing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2006–18. http://dx.doi.org/10.17762/turcomat.v12i2.1805.

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Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. The proposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of the tools we can detect quality issues and develop an automated quality system using machine learning techniques.

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Kabaldin, Y. G., D. A. Shatagin, M. S. Anosov, and A. M. Kuzmishina. "Intelligent Control of Technological Systems in Digital Manufacturing." Proceedings of Higher Educational Institutions. Маchine Building, no. 01 (718) (January 2020): 3–12. http://dx.doi.org/10.18698/0536-1044-2020-1-3-12.

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This paper presents an analysis of the development of adaptive control systems for CNC machines. It is shown that the construction of systems for optimal control of machining processes is based on such approaches as artificial intelligence, genetic algorithms for optimizing processing modes, expert systems for knowledge accumulation, cloud technologies and the development of digital twins of the equipment. An adaptive system of intelligent control of a CNC machine is developed based on training of a neural network model, which can improve the quality of machining parts and reduce the wear of the cutting tool.

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Scaglioni, Bruno, and Gianni Ferretti. "Towards digital twins through object-oriented modelling: a machine tool case study." IFAC-PapersOnLine 51, no. 2 (2018): 613–18. http://dx.doi.org/10.1016/j.ifacol.2018.03.104.

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Ilyushkin, Maxim, Kirill Savelev, Oleg Krupennikov, and Evgeniy S. Kiselev. "Use of Digital Twins for Mathematical Modeling of Ultrasonic Cutting of Titanium Blanks." Materials Science Forum 1037 (July 6, 2021): 369–76. http://dx.doi.org/10.4028/www.scientific.net/msf.1037.369.

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The paper presents the results of numerical experimental studies of cutting titanium blanks using mathematical modeling programs, which make it possible to completely repeat technological processes in a computer (digital twin). The LS-DYNA product was used as a program to simulate the process of stock removal from titanium blank. It has been established that the use of this method adequately describes the cutting processes, including with the introduction of the energy of an ultrasonic field into the processing zone, can significantly reduce the duration of experimental research and evaluate the influence of the elements of the cutting mode and design parameters of the tool on the thermal power aspects of the formation of new surfaces of machine parts.

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Szuster, Marcin, and Bartłomiej Kozioł. "Hidden Security Breaches in Automatic Control of Technological Processes." Pomiary Automatyka Robotyka 25, no. 2 (June 30, 2021): 31–39. http://dx.doi.org/10.14313/par_240/31.

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The progressing automation and robotization in the industrial plants as well as the increasing complexity of the control systems of integrated machines make it necessary to constantly improve the functional safety of machines through the correct validation of safety systems. Despite the validation process carried out, the potential software errors may reveal during the usage of the machine as hidden security breaches. The article presents examples of security breaches of real machine tools and attempts to implement solutions of automated mechanisms for detecting security problems. Another aspect of the article is the new approach for detecting hidden security breaches. Using the „digital twin” model of the machine, a program that generates a sequence of events for testing control systems, and the use of a virtual reality (visual verification of the safety programs), it is possible to maximize the functional safety functions of the machine.

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López-Estrada, Luis, Marcelo Fajardo-Pruna, Santos Gualoto-Condor, José Ríos, and Antonio Vizán. "Creation of a micro cutting machine tool digital-twin using a cloud-based model-based PLM Platform: first results." Procedia Manufacturing 41 (2019): 137–44. http://dx.doi.org/10.1016/j.promfg.2019.07.039.

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Yang, Xin, Yan Ran, Genbao Zhang, Hongwei Wang, Zongyi Mu, and Shengguang Zhi. "A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool." Robotics and Computer-Integrated Manufacturing 73 (February 2022): 102230. http://dx.doi.org/10.1016/j.rcim.2021.102230.

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Wang, Chia-Pei, Kaan Erkorkmaz, John McPhee, and Serafettin Engin. "In-process digital twin estimation for high-performance machine tools with coupled multibody dynamics." CIRP Annals 69, no. 1 (2020): 321–24. http://dx.doi.org/10.1016/j.cirp.2020.04.047.

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Akyazi, Tugce, Aitor Goti, Aitor Oyarbide-Zubillaga, Elisabete Alberdi, Roberto Carballedo, Rafael Ibeas, and Pablo Garcia-Bringas. "Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization." Metals 10, no. 12 (December 13, 2020): 1665. http://dx.doi.org/10.3390/met10121665.

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The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor force.

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Villalba-Diez, Javier, Miguel Gutierrez, Mercedes Grijalvo Martín, Tomas Sterkenburgh, Juan Carlos Losada, and Rosa María Benito. "Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization." Sensors 21, no. 15 (July 24, 2021): 5031. http://dx.doi.org/10.3390/s21155031.

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With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

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Chemnitz, M., O. Heimann, and A. Vick. "Reale Daten für Simulationen im digitalen Zwilling*/Real data for simulation in the digital twin -Study on how to record Profinet data and reproduce them in complex simulation environments." wt Werkstattstechnik online 109, no. 09 (2019): 662–66. http://dx.doi.org/10.37544/1436-4980-2019-09-48.

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Die hohen Anforderungen an moderne Fertigungssysteme erfordern leistungsfähige Engineering-Lösungen. Wie man die Identifikation von Fehlerursachen in komplexen Anlagen erleichtert, wurde in einer Machbarkeitsstudie des Fraunhofer IPK im Auftrag von Siemens DI FA untersucht. In der vorgestellten Lösung werden die Daten der Anlage auf Feldbusebene erfasst und in den digitalen Zwilling eingespeist. So kann das Verhalten der Komponenten taktgenau nachvollzogen werden. Dies elaubt einen tiefen Einblick in das System und unterstützt so bei der Fehlerbehebung.   Powerful engineering tools are required to keep modern production systems manageable. Siemens DI FA and the Fraunhofer IPK present a novel tool for root cause analysis within complex manufacturing systems. The solution combines a CAx plant model with control data recorded from the field bus. This creates a comprehensive digital twin, allowing to analyse past machine behavior with bus clock resolution.

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Gonzalez-Abril, Luis, Cecilio Angulo, Juan-Antonio Ortega, and José-Luis Lopez-Guerra. "Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients." Electronics 10, no. 18 (September 10, 2021): 2220. http://dx.doi.org/10.3390/electronics10182220.

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The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.

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Lovas, Róbert, Attila Farkas, Attila Csaba Marosi, Sándor Ács, József Kovács, Ádám Szalóki, and Botond Kádár. "Orchestrated Platform for Cyber-Physical Systems." Complexity 2018 (July 5, 2018): 1–16. http://dx.doi.org/10.1155/2018/8281079.

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One of the main driving forces in the era of cyber-physical systems (CPSs) is the introduction of massive sensor networks (or nowadays various Internet of things solutions as well) into manufacturing processes, connected cars, precision agriculture, and so on. Therefore, large amounts of sensor data have to be ingested at the server side in order to generate and make the “twin digital model” or virtual factory of the existing physical processes for (among others) predictive simulation and scheduling purposes usable. In this paper, we focus on our ultimate goal, a novel software container-based approach with cloud agnostic orchestration facilities that enable the system operators in the industry to create and manage scalable, virtual IT platforms on-demand for these two typical major pillars of CPS: (1) server-side (i.e., back-end) framework for sensor networks and (2) configurable simulation tool for predicting the behavior of manufacturing systems. The paper discusses the scalability of the applied discrete-event simulation tool and the layered back-end framework starting from simple virtual machine-level to sophisticated multilevel autoscaling use case scenario. The presented achievements and evaluations leverage on (among others) the synergy of the existing EasySim simulator, our new CQueue software container manager, the continuously developed Occopus cloud orchestrator tool, and the latest version of the evolving MiCADO framework for integrating such tools into a unified platform.

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Sepasgozar, Samad M. E. "Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment." Buildings 11, no. 4 (April 2, 2021): 151. http://dx.doi.org/10.3390/buildings11040151.

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Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

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Zhao, Peng, and Beibei Sun. "Adaptive Modification of Digital Twin Model of CNC Machine Tools Coordinately Driven by Mechanism Model and Data Model." Journal of Physics: Conference Series 1875, no. 1 (April 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/1875/1/012003.

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Helfert, Mark, Adrian von Hayn, Jonas Wendt, Leon Dungs, and Matthias Weigold. "Phasenwechselmaterialspeicher zur Lastflexibilisierung von Werkzeugmaschinen/Phase change material accumulator for energy flexibility of machine tools." wt Werkstattstechnik online 110, no. 05 (2020): 346–53. http://dx.doi.org/10.37544/1436-4980-2020-05-78.

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Im Rahmen des vom BMWi geförderten Projekts „Phi-Factory“ am Institut für Produktionsmanagement, Technologie und Werkzeugmaschinen (PTW) der Technischen Universität Darmstadt wurden, angesichts des steigenden Anteils fluktuierend einspeisender Stromerzeuger, technische Potenziale zur energetischen Flexibilisierung von Produktionsanlagen und Industrieprozessen untersucht. Im Bereich der Kühlung von Werkzeugmaschinen bietet der Einsatz von innovativen Phasenwechselmaterialspeichern die Möglichkeit, Lastflexibilisierung auf Maschinenebene umzusetzen, ohne den Fertigungsprozess zu beeinflussen. Zur experimentellen Untersuchung dieser Speicher wurde ein Demonstrator aufgebaut an dem ein parallel entwickelter „digitaler Zwilling“ validiert wurde.   Within the framework of the BMWi-funded project „Phi-Factory“ at the Institute for Production Management, Technology and Machine Tools (PTW) of the Technical University of Darmstadt, technical potentials for the energetic flexibilization of production plants and industrial processes were investigated in view of the increasing share of renewable energy sources. In the field of cooling of machine tools, the use of innovative phase-change material storage systems offers the possibility of implementing energy flexibility at machine level without influencing the production process. For the experimental investigation of these storages, a demonstrator was built on which a parallel developed „digital twin“ was validated.

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Dli, Maksim, Andrei Puchkov, Valery Meshalkin, Ildar Abdeev, Rail Saitov, and Rinat Abdeev. "Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach." Energies 13, no. 21 (November 8, 2020): 5829. http://dx.doi.org/10.3390/en13215829.

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The paper presents a structure of the digital environment as an integral part of the “digital twin” technology, and stipulates the research to be carried out towards an energy and recourse efficiency technology assessment of phosphorus production from apatite-nepheline ore waste. The problem with their processing is acute in the regions of the Russian Arctic shelf, where a large number of mining and processing plants are concentrated; therefore, the study and creation of energy-efficient systems for ore waste disposal is an urgent scientific problem. The subject of the study is the infoware for monitoring phosphorus production. The applied study methods are based on systems theory and system analysis, technical cybernetics, machine learning technologies as well as numerical experiments. The usage of “digital twin” elements to increase the energy and resource efficiency of phosphorus production is determined by the desire to minimize the costs of production modernization by introducing advanced algorithms and computer architectures. The algorithmic part of the proposed tools for energy and resource efficiency optimization is based on the deep neural network apparatus and a previously developed mathematical description of the thermophysical, thermodynamic, chemical, and hydrodynamic processes occurring in the phosphorus production system. The ensemble application of deep neural networks allows for multichannel control over the phosphorus technology process and the implementation of continuous additional training for the networks during the technological system operation, creating a high-precision digital copy, which is used to determine control actions and optimize energy and resource consumption. Algorithmic and software elements are developed for the digital environment, and the results of simulation experiments are presented. The main contribution of the conducted research consists of the proposed structure for technological information processing to optimize the phosphorus production system according to the criteria of energy and resource efficiency, as well as the developed software that implements the optimization parameters of this system.

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Matthys, Mario, Laure De Cock, John Vermaut, Nico Van de Weghe, and Philippe De Maeyer. "An “Animated Spatial Time Machine” in Co-Creation: Reconstructing History Using Gamification Integrated into 3D City Modelling, 4D Web and Transmedia Storytelling." ISPRS International Journal of Geo-Information 10, no. 7 (July 6, 2021): 460. http://dx.doi.org/10.3390/ijgi10070460.

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More and more digital 3D city models might evolve into spatiotemporal instruments with time as the 4th dimension. For digitizing the current situation, 3D scanning and photography are suitable tools. The spatial future could be integrated using 3D drawings by public space designers and architects. The digital spatial reconstruction of lost historical environments is more complex, expensive and rarely done. Three-dimensional co-creative digital drawing with citizens’ collaboration could be a solution. In 2016, the City of Ghent (Belgium) launched the “3D city game Ghent” project with time as one of the topics, focusing on the reconstruction of disappeared environments. Ghent inhabitants modelled in open-source 3D software and added animated 3D gamification and Transmedia Storytelling, resulting in a 4D web environment and VR/AR/XR applications. This study analyses this low-cost interdisciplinary 3D co-creative process and offers a framework to enable other cities and municipalities to realise a parallel virtual universe (an animated digital twin bringing the past to life). The result of this co-creation is the start of an “Animated Spatial Time Machine” (AniSTMa), a term that was, to the best of our knowledge, never used before. This research ultimately introduces a conceptual 4D space–time diagram with a relation between the current physical situation and a growing number of 3D animated models over time.

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Savelyev, K. S., M. V. Ilyushkin, and E. S. Kiselev. "Use of digital twins for mathematical modeling of ultrasonic drilling of titanium blanks." Vektor nauki Tol'yattinskogo gosudarstvennogo universiteta, no. 1 (2021): 42–54. http://dx.doi.org/10.18323/2073-5073-2021-1-42-54.

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The paper considers the creation and research of a virtual prototype of titanium blanks drilling using the Lagrange and Galerkin method. The developed finite-element models are designed to study the process of mechanical treatment and optimize technological cutting parameters. The paper presents the results of computational investigation of titanium blanks drilling using mathematical modeling programs, which allow complete simulating operating procedures in a computer (digital twin). As a program to simulate the process of removing the allowance from a titanium workpiece, the authors used a multipurpose software product of finite-element modeling and analysis of highly-linear dynamic processes using various Ls-DYNA time integration schemes. The application of the Galerkin method allows adequately describing the drilling process with the introduction of the ultrasonic field energy into a treatment zone, can significantly reduce the duration of experimental research and evaluates the influence of the cutting mode elements and the tool design parameters on the power and energy aspects of the formation of new machine parts surfaces. Both methods are applicable to create various processes of mechanical treatment, however, the Lagrange method is less sensitive to the ultrasonic field energy. The introduction of the ultrasonic field energy into the drilling zone of workpieces made of hard-processing titanium alloys can significantly reduce energy costs. As a result of the simulation, the authors obtained a calculation file containing the simulation process, the solution of which visually reflects the drilling process of a titanium workpiece in a real-life setting with the removal of chips. However, for complete verification of numerical study results, it is necessary to carry out an experimental check and make adjustments to the calculated data.

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B. A. Alaasam, Ameer. "The Challenges and Prerequisites of Data Stream Processing in Fog Environment for Digital Twin in Smart Industry." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 15 (August 11, 2021): 126. http://dx.doi.org/10.3991/ijim.v15i15.24181.

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<p class="0abstract">Smart industry systems are based on integrating historical and current data from sensors with physical and digital systems to control product states. For example, Digital Twin (DT) system predicts the future state of physical assets using live simulation and controls the current state through real-time feedback. These systems rely on the ability to process big data stream to provide real-time responses. For, example it is estimated that one autonomous vehicle (AV) could produce 30 terabytes of data per day. AV will not be on the road before using an effective way to managing its big data and solve latency challenges. Cloud computing failed in the latency challenge, while Fog computing addresses it by moving parts of the computations from the Cloud to the edge of the network near the asset to reduce the latency. This work studies the challenges in data stream processing for DT in a fog environment. The challenges include fog architecture, the necessity of loosely-coupling design, the used virtual machine versus container, the stateful versus stateless operations, the stream processing tools, and live migration between fog nodes. The work also proposes a fog computing architecture and provides a vision of the prerequisites to meet the challenges.</p>

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Kabaldin, Yu G., D. A. Shatagin, M. S. Anosov, P. V. Kolchin, and A. M. Kuz’mishina. "CNC Machine Tools and Digital Twins." Russian Engineering Research 39, no. 8 (August 2019): 637–44. http://dx.doi.org/10.3103/s1068798x19080070.

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Kutin, A. A., V. V. Bushuev, and V. V. Molodtsov. "Digital twins of mechatronic machine tools for modern manufacturing." IOP Conference Series: Materials Science and Engineering 568 (September 17, 2019): 012070. http://dx.doi.org/10.1088/1757-899x/568/1/012070.

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Botkina, Darya, Mikael Hedlind, Bengt Olsson, Jannik Henser, and Thomas Lundholm. "Digital Twin of a Cutting Tool." Procedia CIRP 72 (2018): 215–18. http://dx.doi.org/10.1016/j.procir.2018.03.178.

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Ghosh, Angkush Kumar, AMM Sharif Ullah, Roberto Teti, and Akihiko Kubo. "Developing sensor signal-based digital twins for intelligent machine tools." Journal of Industrial Information Integration 24 (December 2021): 100242. http://dx.doi.org/10.1016/j.jii.2021.100242.

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Ayani, M., M. Ganebäck, and Amos H. C. Ng. "Digital Twin: Applying emulation for machine reconditioning." Procedia CIRP 72 (2018): 243–48. http://dx.doi.org/10.1016/j.procir.2018.03.139.

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Shahabi, Hejar, Ben Jarihani, Sepideh Tavakkoli Piralilou, David Chittleborough, Mohammadtaghi Avand, and Omid Ghorbanzadeh. "A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia." Sensors 19, no. 22 (November 9, 2019): 4893. http://dx.doi.org/10.3390/s19224893.

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Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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Qiao, Qianzhe, Jinjiang Wang, Lunkuan Ye, and Robert X. Gao. "Digital Twin for Machining Tool Condition Prediction." Procedia CIRP 81 (2019): 1388–93. http://dx.doi.org/10.1016/j.procir.2019.04.049.

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Nemtinov, Kirill, Maria Eruslanova, Alexander Zazulya, Yulia Nemtinova, and Haider Sabah Salih. "Creating a digital twin of an agricultural machine." MATEC Web of Conferences 329 (2020): 05002. http://dx.doi.org/10.1051/matecconf/202032905002.

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In this paper, the authors developed an approach to creating a digital twin or an electronic model of complex agricultural machinery and proposed its structure as a set of frames. It is represented by a tuple that includes: a frame describing structural composition of the technical system under consideration; a frame describing the properties that characterize it as a whole; a set of ways to determine its properties; a set of attributive characteristics; a set of parametric graphic models of elements and a set of two-dimensional drawings. Examples of digital twins of agricultural machinery are given: a combined unit for soil preparation and sowing of grain crops and a unit for cleaning and calibration of grain seeds.

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Sundaram, Sarvesh, and Abe Zeid. "Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework." Sensors 21, no. 18 (September 7, 2021): 5994. http://dx.doi.org/10.3390/s21185994.

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Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

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Feofanov, Alexander, and Nikolay Baranov. "Risk analysis in digital twin creation of machine building production." MATEC Web of Conferences 298 (2019): 00081. http://dx.doi.org/10.1051/matecconf/201929800081.

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Digital twin is one of instruments of Industry 4.0 in order to effectively manage the enterprise and its activities. However, the digital twin of production application is exposed to various risks. Careful identification of these risks speeds up digital twin creation and implementation process.

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Liu, Yang, Yuhui Sun, Ang Yang, and Jing Gao. "Digital Twin-Based Ecogreen Building Design." Complexity 2021 (June 4, 2021): 1–10. http://dx.doi.org/10.1155/2021/1391184.

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The high operation cost of green building, insufficient informationization and automation management capability, and the lack of effective operation cost control seriously restrict the development of the industry and the realization of green goals. In order to solve the problem of insufficient capability of green building operation cost management, based on the digital twin technology in the manufacturing field, we analyze the characteristic requirements and theoretical basis of green building operation cost management for system, propose a green building operation cost management system based on digital twin, and refine the design of each structural layer of this system. It is necessary to set up a series of lines, although it takes a certain amount of time. There are four types of applications, namely, the number of types of applications, the completion of the effective number, the comparison of functions, and the implementation capabilities. The study shows that the proposed system framework can improve the efficiency and quality of green building operation cost management through technology upgrade and process optimization. The implementation of digital twin and human-machine collaboration is an advanced stage in the development of digital architecture because virtual things and real things, materials, and numbers are mutually promoting processes. The inspiration of this technological view for architecture is that digital twin and human-machine collaboration not only allow the interaction between virtual and reality and emphasize the feedback of actual construction to virtual simulation but also promote a kind of mutual promotion of human and machine thinking and construction ability.

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Journal articles on the topic 'Digital twin of machine tool'

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