Relevant bibliographies by topics / Digital twin of machine tool

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Digital twin of machine tool'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Digital twin of machine tool"

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Rajendran, Ajith, and Gautham Asokan. "Real Time Monitoring of Machining Process and Data Gathering for Digital Twin Optimization." Thesis, KTH, Industriell produktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301594.

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In the development stages of a Digital twin of production assets, especially machine tools, real time process monitoring and data gathering proves to be vital. Having a monitoring system that monitors and updates the operators or managers in real time, helps improve productivity in terms of reducing downtime through predictive/preventive analytics and by incorporating in process quality assessment capabilities. When it comes to Real time monitoring of machine tools andprocesses, sensor technologies have proven to be the most effective and widely researched. Years of research and development have paved the way for many smart sensor technologies that come both in built with the machine tools as well as external applications. However, these technologies prove to be expensive and complicated to implement especially for Small and Medium Enterprises. This thesis focuses on evaluating and testing a simple, cost-efficient monitoring system using inexpensive sensor technologies that would help optimize an existing Digital twin setup for machine tools for Small and Medium Enterprises. Experiments with a 5 axis CNC machine tool using different tools and varying operating parameters, materials were performed,and the relevant sensor data were collected, mapped, analysed for accuracy and benchmarking. The thesis also evaluates the integration of this data with the information already collected from other sources, improve existing data reliability, and provides guidelines on this could be transformed usefully to create more value to SME’s.
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Ferdousi, Rahatara. "Digital Twin Disease Diagnosis Using Machine Learning." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42773.

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COVID-19 has led to a surge in the adoption of digital transformation in almost every sector. Digital health and well-being are no exception. For instance, now people get checkupsvia apps or websites instead of visiting a physician. The pandemic has pushed the health-care sector worldwide to advance the adoption of artificial intelligence (AI) capabilities.Considering the demand for AI in supporting the well-being of an individual, we presentthe real-life diagnosis as a digital twin(DT) diagnosis using machine learning. The MachineLearning (ML) technology enables DT to offer a prediction. Although several attemptsexist for predicting disease using ML and a few attempts through ML of DT frameworks,those do not deal with disease risk prediction. In addition, most of them deal with singledisease prediction after the occurrence and rely only on clinical test data like- ECG report,MRI scan, etc.To predict multiple disease/disease risks, we propose a dynamic machine learning algo-rithm (MLA) selection framework and a dynamic testing method. The proposed frameworkaccepts heterogeneous electronic health records (EHRs) or digital health status as datasetsand selects suitable MLA upon the highest similarity. Then it trains specific classifiers forpredicting a specific disease/disease risk. The dynamic testing method for prediction isused for predicting several diseases.We described three use cases: non-communicable disease(NCD) risk prediction, mentalwell-being prediction, and COVID-19 prediction. We selected diabetes, risk of diabetes,liver disease, thyroid, risk of stroke as NCDs, mental stress as a mental health issue, andCOVID-19. We employed seven datasets, including public and private datasets, with adiverse range of attributes, sizes, types, and formats to evaluate whether the proposedframework is suitable to data heterogeneity. Our experiment found that the proposed methods of dynamic MLA selection could select MLA for each dataset at cosine similarityscores ranging between 0.82-0.89. In addition, we predicted target disease/disease risks atan accuracy ranging from 94.5% to 98%.To verify the performance of the framework-selected predictor, we compared the accuracy measures individually for each of the three cases. We compared them with traditionalML disease prediction work in the literature. We found that the framework-selected algorithms performed with good accuracy compared to existing literature.
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Staněk, Václav. "Návrh digitálního dvojčete CNC obráběcího stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-402643.

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The Master’s thesis deals with design of digital twins of machine tools and application of designed procedure on a case study – digital twin of the machine tool MCV 754 Quick. In the first part are described current roles of digital twins in the field of machine tools and also hardware and software options for creating them. Software and hardware tools by Siemens (Mechatronics Concept Designer and SIMIT) are used for the case study. The complex procedure of creating the digital twin is designed in the second part, starting with preparation of a model, ending with the virtual commissioning of the machine tool MCV 754 Quick. The procedure is described in detail, including preparation of 3D model of the machine tool, assignment of physical properties and joints, preparation of PLC, connection all the elements of the whole system: Sinumerik – SIMIT – Mechatronics Concept Designer and controlling the twin via CNC automation system. Output of the thesis is the virtually commissioned machine tool, capable of being controlled by Sinumerik 840D sl. This is the first step in development of the full-fledged digital twin of machine tool, which can be used for testing the functionality and capability of this new technology in industry.
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Ravi, Rahul. "Digital Twin of an Automatic Machine Using Unity and Prespective Software." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Digital Twin is the modern way of prototyping. It is a new layer on top of an existing model, extending the possibilities. This thesis work was carried out and developed at the research laboratory “LIAM LAB”. The main focus was to study the efficacy in developing a Digital Twin of an automatic machine in detail. It is a packaging machine (Technocoil 2200) from “SICA” using PRESPECTIVE, a Digital Twining software based on a game engine UNITY3D. Digital Twin tools and solutions are offered by many major companies, but these have the potential disadvantage of vendor lock-in. Although these brands claim to be open, their tools do not operate very well with third-party software. This makes many users to opt for openness which will help in integrating many features as per their requirement. Another main factor is providing high resolution simulations with an ability to solve complex physical problems which could be handled by Unity. The process of modelling a Digital Twin is studied extensively in this thesis work. But, in particular the tube guide part of the machine was considered, and its Digital Twin was modelled using Prespective and subsequently the interfacing of the model in Prespective with a PLC was implemented. Finally, the interfacing is validated by forcing values from controller and visualizing the outputs in Unity.
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Smirnova, Tatiana. "Dynamic Analysis and Modeling of Machine Tool Parts." Licentiate thesis, Karlskrona : Department of Signal Processing, School of Engineering, Blekinge Institute of Technology, 2008. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/8628d01b6722765cc12573e70044bfd2?OpenDocument.

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McCoy, Mason Eugene. "A Twitter-Based Prediction Tool for Digital Currency." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2302.

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Digital currencies (cryptocurrencies) are rapidly becoming commonplace in the global market. Trading is performed similarly to the stock market or commodities, but stock market prediction algorithms are not necessarily well-suited for predicting digital currency prices. In this work, we analyzed tweets with both an existing sentiment analysis package and a manually tailored "objective analysis," resulting in one impact value for each analysis per 15-minute period. We then used evolutionary techniques to select the most appropriate training method and the best subset of the generated features to include, as well as other parameters. This resulted in implementation of predictors which yielded much more profit in four-week simulations than simply holding a digital currency for the same time period--the results ranged from 28% to 122% profit. Unlike stock exchanges, which shut down for several hours or days at a time, digital currency prediction and trading seems to be of a more consistent and predictable nature.
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Viking, Jakob. "Identifying Machine States and Sensor Properties for a Digital Machine Template : Automatically recognize states in a machine using multivariate time series cluster analysis." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42295.

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Digital twins have become a large part of new cyber-physical systems as they allow for the simulation of a physical object in the digital world. In addition to the new approaches of digital twins, machines have become more intelligent, allowing them to produce more data than ever before. Within the area of digital twins, there is a need for a less complex approach than a fully optimised digital twin. This approach is more like a digital shadow of the physical object. Therefore, the focus of this thesis is to study machine states and statistical distributions for all sensors in a machine. Where as majority of studies in the literature focuses on generating data from a digital twin, this study focuses on what characteristics a digital twin have. The solution is by defining a term named digital machine template that contains the states and statistical properties of each sensor in a given machine. The primary approach is to create a proof of work application that uses traditional data mining technologies and clustering to analyze how many states there are in a machine and how the sensor data is structured. It all results in a digital machine template with all of the information mentioned above. The results contain all the states a machine might have and the possible statistical distributions of each senor in each state. The digital machine template opens the possibility of using it as a basis for creating a digital twins. It allows the time of development to be shorter than that of a regular digital twin. More research still needs to be done as the less complex approach may lead to missing information or information not being interpreted correctly. It still shows promises as a less complex way of looking at digital twins since it may become necessary due to digital twins becoming even more complex by the day.
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Shi, Xun Xun. "Intelligent Differential Ion Mobility Spectrometry (iDMS): A Machine Learning Algorithm that Simplifies Optimization of Lipidomic Differential Ion Mobility Spectrometry Parameters." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42794.

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Glycosphingolipids such as α- and β-glucosylceramides (GlcCers) and α- and β- galactosylceramides (GalCers) are stereoisomers differentially synthesized by gut bacteria and their mammalian hosts in response to environmental insult. Thus, lipidomic assessment of α- and β-GlcCers and α- and β-GalCers is crucial for inferring biological functions and biomarker discovery. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Differential mobility mass spectrometry (DMS), as an orthogonal separation to high performance liquid chromatography used in electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS), can be used to separate stereoisomeric lipids. Generating LC-ESI-DMS-MS/MS methods for lipidomic analyses is exceedingly difficult demanding intensive manual optimization of DMS parameters that depend on the availability of synthetic lipid standards. Where synthetic standards do not exist, method development is not possible. To address this challenge, I developed a supervised in silico machine learning approach to accelerate method development for ion mobility-based quantification of lipid stereoisomers. I hypothesized that supervised neural network models could be used to learn the relationships between lipid structural characteristics and optimal DMS machine parameter values thereby reducing the total number of empirical experiments required to develop a DMS method and enabling users to “predict” DMS parameters for analytes that lack synthetic standards. Specifically, this thesis describes a supervised learning approach that learns the relationship between two DMS machine parameter values (separation voltage and compensation voltage) and two lipid structural features (N-Acyl chain length and degree of unsaturation). I describe here, iDMS, an algorithm that was trained on 17 lipid species, and can further simulate results of DMS manual method development and suggest optimal parameter values for 47 lipid species. This approach promises to greatly accelerate the development of assays for the detection of lipid stereoisomers in biological samples.
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Amato, Beatrice. "Virtual comissioning and software developement for an automatic machine prototype." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22466/.

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The objective of this work is to realize a prototype of an automatic machine for the pharmaceutical industry. This model aims to produce a new generation of drugs. The process, that needs to be followed to produce a new type of drug, starts from the medicine in liquid form, then through a drastic lowering of the temperature, it passes to the solid state, and then is freeze-dried, in a sterile environment. The final result is a medicine in beads form. The machine is mainly composed of three robots and a dosing arm unit. Starting from a reservoir where the drug is in liquid form, the dosing arm unit sucks the liquid through ninety-six pipettes. In the initialization phase, the content of each pipette is emptied inside the weighted tubes by a dosing arm unit, then the first robot proceeds to the gross weighing of the test pieces. If all the doses are equal, then the dosing arm unit can proceed with the production of the beads, starting by sucking again the fluid from the reservoir. For nominal dosing, the dosing arm unit moves above a nitrogen-cooled plate, at a temperature of -110°C and let each pipette discharges a drop of liquid on to the freezing plate. Trays are handled by a pair of robotic arms. Since this is a completely new type of machine, the different phases are being studied and under development, so most of them have not yet being manufactured. For this reason, it has been created a simulation model that forms the basis of the Digital Twin, since it can already be used at a very early stage of the development process to test and compare various concepts. A 3D graphic model of the machine has been devised and thanks to the virtual commissioning, the physical behavior of the machine has been reproduced in a computer-generated way, through a software simulation applied to the Digital Twin. This safe virtual environment allowed to test the logic programmed into the PLC, for the single components and the synchronization of the whole machine movements.
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Iftikhar, Rana Faheem, and Sabor Amini. "Digitalising The Smith Machine." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44815.

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Health and fitness sector is becoming increasingly popular, and technology is being integrated into the fitness industry. This project proposed to digitalise the smith machine by creating a system to generate force through an electric motor to replace free weights. Because the exercise machine no longer needs to be loaded and unloaded with free weights, digitalisation of the smith machine will allow several users to utilise the same training machine. It will also be possible to track users' training information, such as weight lifted and the number of sets and reps performed during a workout. In this thesis, an Electrical speed controller (ESC) called VESC was used to control the BLDC motor. Experiments were conducted to find a method to control the torque. The result was producing a constant torque with a margin of error of 1-1.5 kilograms. Furthermore, two algorithms were developed where the resistance varies during the path of motion, which indicates that it is possible to develop the system to individualise resistance to improve training performance and rehabilitation. The conclusion that could be drawn is that this system can be used to replace free weights with an electric motor.
Hälso- och fitness sektorn blir allt populärare ochtekniken integreras alltmer ifitnessbranschen. Detta projekt syftar på att digitaliserasmithmaskin genom att skapa ettsystem för att generera kraft genom en elmotor föratt ersätta fria vikter. Genom att ersättalösa vikter med en elmotor så slipper användaren hanteralösa vikter vilket också underlättarför flera användaren att använda samma träningsmaskin.Det kommer också vara möjligt attspåra användarnas träningsinformation, såsom vikt,antalet set och repetitioner som utförtsunder ett träningspass.I detta projekt användes en motorstyrenhet som heterVESC för att styra en BLDC-motor.Flertalet experiment utfördes för att hitta en metodför att kontrollera vridmomentet. Dettaresulterade till att ett konstant vridmoment med enfelmarginal på 1-1.5 kilogram uppnåddes.Dessutom utvecklades två algoritmer där motståndetvarierar dynamiskt, vilket indikerar attdet är möjligt att i framtiden utveckla systemet föratt individualisera motstånd och därmedförbättra resultatet i träning och rehabilitering.Slutsatsen som kan dras är att detta system kananvändas för att ersätta fria vikter med en elmotor.
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Books on the topic "Digital twin of machine tool"

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Armendia, Mikel. Twin-Control: A Digital Twin Approach to Improve Machine Tools Lifecycle. Cham: Springer Nature, 2019.

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Book chapters on the topic "Digital twin of machine tool"

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Armendia, Mikel, Aitor Alzaga, Flavien Peysson, Tobias Fuertjes, Frédéric Cugnon, Erdem Ozturk, and Dominik Flum. "Machine Tool: From the Digital Twin to the Cyber-Physical Systems." In Twin-Control, 3–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02203-7_1.

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Gönnheimer, Philipp, Jonas Hillenbrand, Thomas Betz-Mors, Philip Bischof, Lorenz Mohr, and Jürgen Fleischer. "Auto-configuration of a digital twin for machine tools by intelligent crawling." In Production at the leading edge of technology, 543–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-60417-5_54.

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Gupta, Meenu, Rakesh Kumar, and Divya Singh. "Crop Diseases Detection and Prevention Using AI and Machine Learning Techniques." In Digital Twin Technology, 61–82. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003132868-4.

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Sossenheimer, Johannes, Thomas Weber, Dominik Flum, Niklas Panten, Eberhard Abele, and Tobias Fuertjes. "Non-intrusive Load Monitoring on Component Level of a Machine Tool Using a Kalman Filter-Based Disaggregation Approach." In Twin-Control, 155–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02203-7_9.

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Wilhelm, Jasper, Thies Beinke, and Michael Freitag. "Improving Human-Machine Interaction with a Digital Twin." In Dynamics in Logistics, 527–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44783-0_49.

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Raineri, Ivan, Francesco La Mura, and Hermes Giberti. "Digital Twin Development of HexaFloat, a 6DoF PKM for HIL Tests." In Mechanisms and Machine Science, 258–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03320-0_28.

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Baldo, Crhistian R., Thiago L. Fernandes, Gustavo D. Donatelli, and Wim Dewulf. "Digital Twin as a Tool to Select CT Scan Parameters." In Proceedings of the 6th Brazilian Technology Symposium (BTSym’20), 561–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75680-2_62.

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Greis, Noel P., Monica L. Nogueira, and Wolfgang Rohde. "Digital Twin Framework for Machine Learning-Enabled Integrated Production and Logistics Processes." In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, 218–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85874-2_23.

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Berisha-Gawlowski, Angelina, Carina Caruso, and Christian Harteis. "The Concept of a Digital Twin and Its Potential for Learning Organizations." In Digital Transformation of Learning Organizations, 95–114. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-55878-9_6.

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AbstractThe digital transformation of organizations in the industrial sector is primarily driven by the opportunity to increase productivity while simultaneously reducing costs through integration into a cyber-physical system. One way to fully tap the potential of a cyber-physical system is the concept of the digital twin, i.e., the real-time digital representation of machines and resources involved – including human resources. The vision of representing humans by digital twins primarily aims at increasing economic benefits. The digital twin of a human, however, cannot be designed in a similar way to that of a machine. The human digital twin shall rather enable humans to act within the cyber-physical system. It therefore offers humans a power of control and the opportunity to provide feedback. The concept of the digital twin is still in its infancy and raises many questions in particular from an educational perspective. The contribution aims at answering the following questions and refers to the example of team learning: Which and how much data should and may the digital twin contain in order to support humans in their learning? To what extent will humans be able to control and design their own learning? How may skills, experiences, and social interactions of humans be represented in the digital twin; their growth and further development, respectively? With cyber-physical systems transcending corporate, national, and legal boundaries, what learning culture will be the frame of reference for the involved organizations?
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Kaur, Maninder Jeet, Ved P. Mishra, and Piyush Maheshwari. "The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action." In Internet of Things, 3–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18732-3_1.

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Conference papers on the topic "Digital twin of machine tool"

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Luo, Weichao, Tianliang Hu, Wendan Zhu, and Fei Tao. "Digital twin modeling method for CNC machine tool." In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2018. http://dx.doi.org/10.1109/icnsc.2018.8361285.

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Liu, Jinsong, Dong Yu, Xiaoxue Bi, Yi Hu, Haoyu Yu, and Beibei Li. "The Research of Ontology-based Digital Twin Machine Tool Modeling." In 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc51575.2020.9344997.

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Tanaka, Fumiki. "A Digital Twin of Multi-Axis Machine Tool for Micro Process Planning." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9652.

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Abstract Achieving high performance of machining production systems requires the use of multi-axis machine tools. In order to maximize the performance of multi-axis machine tools, micro process planning for creating machining data is important. Many researches on micro process planning mainly focused on 3-axis machining. As promising approaches among them, a micro process planning system was proposed that reuses actual machining cases and analyzes case data to derive the necessary rules. However, it is not always effective for multi-axis machining, because enough case data are not collected for micro process planning of a specific multi-axis machine tool. In this study, a digital twin of multi-axis machine tool in cyberspace is proposed to collect real and virtual machining case data for micro process planning.
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Cugnon, Frédéric, Luke Berglind, Denys Plakhotnik, and Mikel Armendia. "Simulation of Machining Operations Using the Virtual Machine Tool Concept." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85217.

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This paper focusses on the dynamic modeling of the machine tool including its Computer Numeric Control (CNC), and its interaction with the machining process. To properly simulate modern machine tools in machining condition, which show close interaction between the dynamic behavior of the mechanical structure, drives, and the CNC, we use an integrated methodology that combines control and MBS capabilities in a nonlinear FEA solver called SAMCEF Mecano. To fully capture the dynamic behavior of the machine, force interactions between the cutting tool and the workpiece are also considered. A strong coupling between the mechatronic model of the machine tool and a machining simulation tool is implemented. A specialized cutting force element has been developed. It considers the dynamics of the tool tip combined with the tool workpiece engagement to generate cutting forces. The use of such digital twin model is demonstrated considering some industrial machining operations.
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Kharazmi, Ehsan, Zhicheng Wang, Dixia Fan, Samuel Rudy, Themis Sapsis, Michael S. Triantafyllou, and George E. Karniadakis. "From Data to Assessment Models, Demonstrated through a Digital Twin of Marine Risers." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30985-ms.

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Abstract Assessing the fatigue damage in marine risers due to vortex-induced vibrations (VIV) serves as a comprehensive example of using machine learning methods to derive assessment models of complex systems. A complete characterization of response of such complex systems is usually unavailable despite massive experimental data and computation results. These algorithms can use multi-fidelity data sets from multiple sources, including real-time sensor data from the field, systematic experimental data, and simulation data. Here we develop a three-pronged approach to demonstrate how tools in machine learning are employed to develop data-driven models that can be used for accurate and efficient fatigue damage predictions for marine risers subject to VIV.
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Patel, Harshkumar, Jianlin Cai, Gautier Noiray, and Subrata Bhowmik. "Digital Transformation and Automation of Flow Assurance Engineering Workflows Using Digital Field Twin." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31017-ms.

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Abstract Flow assurance is central to the design of a subsea production system and requires frequent interfacing with engineers from multiple disciplines. The objective of this paper is to demonstrate how cloud based digital field twin can be leveraged to automate subsea flow assurance engineering workflows and consequently, achieve efficient collaborations, faster and reliable designs, and reduced costs. In this proposed workflow, engineers use a web application built on top of a cloud-based digital twin platform to perform flow assurance calculations and design analysis. The web based platform integrates multiphase flow simulators and other relevant engineering tools through python scripts. A user is only required to input design constraints and necessary basic information. The application acquires inter-disciplinary data (e.g. pipeline, layout, equipment, etc.) and automatically performs pre-processing, model setup, simulation, and results processing in the background and make results available to all the users at the front-end. The digital flow assurance platform replaces traditional workflows requiring use of different standalone engineering software, and frequent exchange of information with other engineering teams in form of documents and spreadsheets. The proposed cloud-based workflow allows engineers to focus on technical analysis by eliminating several manual and repeating processes such as accessing different software, creating models, results extraction and formatting, etc. The ability to share results in form of auto-generated reports and formatted spreadsheets; minimizes human errors and promotes information exchange and transparency among project team members from different disciplines. The cloud based platform enables engineers to work on a same project from different geophysical locations and devices. Overall, this digital flow assurance workflow significantly improves engineering efficiency, save costs, and allows faster and reliable concept design and FEED (Front End Engineering Design). The ideas widely discussed for flow assurance digitalization typically include use of data analytics and machine learning, virtual flow metering, real time data monitoring, predictive analytics, etc. This paper, however, presents novel practical idea to bring digital transformation to the way flow assurance engineers work and collaborate.
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Guo, Jiangzhen, Liangbao Liu, Yunfeng Zhang, Xiaofei Wang, and Rui Fan. "Multi-axis load-while-track device based on parallel robot for simulating feeding resistance in machine tool test." In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2021. http://dx.doi.org/10.1109/dtpi52967.2021.9540191.

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Kuts, Vladimir, Tauno Otto, Toivo Tähemaa, Khuldoon Bukhari, and Tengiz Pataraia. "Adaptive Industrial Robots Using Machine Vision." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86720.

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The use of industrial robots in modern manufacturing scenarios is a rising trend in the engineering industry. Currently, industrial robots are able to perform pre-programmed tasks very efficiently irrespective of time and complexity. However, often robots encounter unknown scenarios and to solve those, they need to cooperate with humans, leading to unnecessary downtime of the machine and the need for human intervention. The main aim of this study is to propose a method to develop adaptive industrial robots using Machine Learning (ML)/Machine Vision (MV) tools. The proposed method aims to reduce the effort of re-programming and enable self-learning in industrial robots. The elaborated online programming method can lead to fully automated industrial robotic cells in accordance with the human-robot collaboration standard and provide multiple usage options of this approach in the manufacturing industry. Machine Vision (MV) tools used for online programming allow industrial robots to make autonomous decisions during sorting or assembling operations based on the color and/or shape of the test object. The test setup consisted of an industrial robot cell, cameras and LIDAR connected to MATLAB through a Robot Operation System (ROS). The online programming tests and simulations were performed using Virtual/Augmented Reality (VR/AR) toolkits together with a Digital Twin (DT) concept, to test the industrial robot program on a digital object before executing it on the real object, thus creating a safe and secure test environment.
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Guerra-Zubiaga, David A., Kathy S. Schwaig, Sabih Nasir, and Alex Bondar. "Digital Tools to Capture Manufacturing Tacit Knowledge to Support Next Generation Automation Systems." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10889.

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Abstract In today’s complex environment, it is not only important to handle/control digital manufacturing tools, but also essential to capture tacit knowledge from people. Creating a digital twin, it is an extensive effort including different fields and subjects. For example, creating a physical prototype and connecting it with a virtual prototype. From this, two questions arise. What will be the framework used to create the digital twin and what method will be used to capture the experiences to develop Next Generation Automation System (NGAS). This research explores a new method capturing tacit knowledge creating a digital twin for a NGAS, worked at station level connecting machines and humans implementing knowledge modelling and providing guidance in design for manufacturability at NGAS. The motivation of this research is that capturing tacit knowledge is an important aspect in Industry 4.0. According to literature review, different researchers have been exploring digital twins using digital tools. This research proposal explores the effects of automation in the workplace using Digital Manufacturing Tools (DMT). The proposed approach demonstrates how to capture valuable experiences we can transfer or communicate between the digital twins, increasing productivity to fulfill the need to adopt new and emerging technologies in the workplace. The research will talk about capturing Tacit Knowledge in different forms like experiences, analysis, and intuitions etc and how this type of knowledge is processed by DMT and communicated to the other digital twin. Tacit knowledge modeling and sharing is used by implementing the Internet of Things (IoT) to understand the interaction among humans, instruments, controls, and robots. Understanding tacit manufacturing knowledge types is required to create better digital twins.
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Lahlou, Kenza, Sven Inge Oedegaard, Morten Svendsen, Tore Weltzin, Knut Steinar Bjørkevoll, and Bjørn Rudshaug. "Drilling Advisory for Automatic Drilling Control." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204074-ms.

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Abstract This paper describes a system being developed for providing an optimized real-time decision support with automatic forward-looking and what-if simulations. It will address the challenge of achieving automation, better performance, and avoidance of non-productive time (NPT) in drilling operations. It will additionally address the demanding human support currently required in the entire decision support workflow. The approach includes utilization of Model based reasoning in Artificial Intelligence (AI) with a Digital Twin combined with Machine Learning (ML) and advanced 3D visualization which is a key enabler for operation alerts and optimization. Multiple forward-looking and what-if simulations will also be run in real-time to find optimal parameters for flow, rotation and running speed. A Diagnostic module will detect abnormalities and trigger safeguards. Auto-configuration and auto-calibration will be the key elements for Drilling Advisory system and deployment without the need for back-office support. The personnel involved in the operation (drilling contractor, service provider and operator) will be able to quickly provide the necessary operational input and then the system will be auto-calibrated during the operation. Results will be an Advisory Tool providing the operation with an optimal flow, rotation speed and running speed during Drilling, Tripping, Casing/liner/screen running and cement operations in two applications areas: In front of the driller as an Advisory tool for rigs with legacy drilling control systems not capable of receiving automated instructions. Base for providing direct commands and safeguards to rigs with control systems capable of receiving automated commands of optimal flow, rotation speed and running speed.
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Reports on the topic "Digital twin of machine tool"

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Herrera, Allen, Eugene Moore, and Alexander Heifetz. Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1735365.

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