Lesson 117 of Industrial Engineering ONLINE Course
Part of Computer Aided Industrial Engineering (CAIE) - Proposal by Prof. Narayana Rao K.V.S.S.
https://nraoiekc.blogspot.com/2021/09/computer-aided-industrial-engineering.html
Industrial engineers can use digital twins to observe operations in processes. The work station in action will be available on their table to observe in real time as well as offline number of times and even in slow motion to understand the process and come with alternatives. Industrial engineers need not use video filming of the process anymore.
As alternatives are generated by industrial engineers to improve the process, they can use digital twin to simulate the new operation. They need not any more ask for time to experiment with the modified operation on a physical work station. They have the cyber version of the work station at their disposal to modify the operation and observe the performance for effectiveness and efficiency.
Digital twin technology provides real-time, interactive simulations of equipment and process at manufacturing plants. Digital twins can help manufacturers improve innovation, efficiencies, quality, and yield.
Digital twin technologies can intelligently simulate product, equipment and process during the development lifecycle of the product and process.
What is a Digital Twin?
A digital twin is a virtual representation of a physical entity or system. It is a dynamic, simulated view of a physical product that is continuously updated throughout the design, build and operation lifecycle. The digital twin evolves as the physical product progresses and matures.
The digital twin is informed by sensors embedded in twin’s physical counterpart. The data is fed into an IoT platform and enriched by artificial intelligence. The virtual replication of the object is presented on high-definition, immersive displays that engineers and operators can use to visualize the object’s status and interact with it in real time without disrupting production.
Teams can use a digital twin to modify product designs; and examine them through what-if simulations without building physical prototypes. Similarly the manufacturing processes can be modified in digital twin and can be assessed for benefits. Different views of a digital twin can be created for different individual departments. Hence a digital twin can be created for industrial engineering departments.
The digital twins and their use in simulation based on modifications in product and process designs are made possible by cognitive manufacturing or artificial intelligent manufacturing, which leverages cognitive computing, the Industrial Internet of Things (IIoT), data science and advanced analytics to help organizations improve manufacturing processes.
Digital Modeling and Digital Thread: Key Enablers for Digital Twin Solutions
Data-enriched simulations—can be used to model and remodel the performance of plant equipment under a variety of what-if scenarios. The technique can help identify the best approaches for improving key performance indicators (KPIs) for the manufacturing process and product quality.
Asset maintenance is also improve through digital twin based data-driven modeling. Machine learning, deep learning and artificial intelligence can be applied to dynamic process monitoring and machine health data to better detect anomalies and predict failures. The approaches can transform maintenance into a proactive activity and even enable feedback loops that automate procedures to resolve maintenance issues.
Digital Thread
The digital thread is the traceable flow of data that interconnects all relevant systems and functional processes involved in a product’s lifecycle and informs the digital twin and digital modeling activities.
The digital thread facilitates the exchange of real-time data between sensors that are monitoring a physical object and the object’s digital twin. The digital thread yields an end-to-end perspective of issues and problems that might emerge during the manufacturing life cycle.
PLM becomes more responsive and agile, enabling a company to produce high-quality products while increasing manufacturing efficiencies. The digital thread uses ISA-95 standards to automate communications between control and enterprise systems. The standards facilitates integration with companies that are partners in the digital twin ecosystem.
Digital twin technologies inform and guide continuous engineering practices. The tools help industrial engineers, other engineers and operators create and refine products at all stages of the product’s lifecycle: design, build, and operate. Industrial systems engineers design and build products, processes and processing facilities. Industrial engineers improve products and processes during operations.
Manufacturers are always striving to optimize quality, efficiency and yield through industrial engineering. Industrial engineers can now use digital twin technologies to understand how potential changes in the manufacturing process might impact production outcomes and modify the manufacturing process elements accordingly to achieve targeted improvements.
“Operate” refers to operation, servicing and maintenance activities. Companies can apply digital twin solutions in these contexts to increase uptime and improve operating efficiencies while making sure equipment and products function at optimum levels.
Digital twin solutions introduce unprecedented conveniences in this use case because the technology enables technicians to “see” inside the virtual representation of a device to identify potential problems. The digital twin can also incorporate information from enterprise asset management (EAM) software and automation programs so technicians have up-to-the-minute information about a machine’s operating status, recent alarms or maintenance activities. The solution can also advise technicians on how to perform maintenance procedures for the problems they’re addressing.
Manufacturers can enable these capabilities
An Integrated Framework for Digital Twin Implementations
Enterprise applications: The framework makes it possible to use real-time data from enterprise applications to support decision-making on the shop floor and in corporate sales, strategy and operations offices.
Typical applications include predictive maintenance tools, enterprise resource planning (ERP) and enterprise asset management (EAM) programs, supply chain management software, manufacturing execution systems (MES), and customer relationship management (CRM) solutions.
Implementing digital twin:
• Obtain the CAD/CAM versions of the machine or product from the team or engineering partner that produced the original design.
• Create a new digital model of the machine that considers the equipment’s mechanics, the machine’s interactions with other equipment in the facility, the product being produced, and relevant operational or enterprise software applications.
• The physical machine has to be monitored by sensors and connected to a gateway that integrates with an IoT platform.
• Give special attention to data quality at every stage of design, build and operate.
Apply cognitive analytics and machine learning to the sensor data to bring real-time context and characteristics to the digital twin.
• Implement a digital thread capability to facilitate information flow between data
sources and applications.
• The solution has to generate analytics at every stage of the lifecycle so that improvements at each stage of a project and overall can be made
• Provide displays that enable teams to view and interact with the digital model on the shop floor or from their corporate offices.
• Use an open approach that avoids centralizing data in a proprietary system so your digital twin solution can be used by all stakeholders.
• Conduct a proof of concept project, using one machine or one operation. Once that is in place and working, expand it to an entire manufacturing line.
Digital Twin Technologies help companies transform their operations through business and operating models that are enabled by the IoT and led by analytics to optimize efficiency, customer-centric strategies, economic growth and maximum asset productivity.
New - 2022
Analyzing the Implementation of a Digital Twin Manufacturing
by JH Loaiza · 2022 ·
https://www.mdpi.com/2079-8954/10/2/22/pdf
Digital Twins - Google Books
Hands-On Azure Digital Twins: A practical guide to building distributed IoT solutions
Alexander Meijers
Packt Publishing Ltd, 03-Mar-2022 - Computers - 446 pages
In today's world, clients are using more and more IoT sensors to monitor their business processes and assets. Think about collecting information such as pressure in an engine, the temperature, or a light switch being turned on or off in a room. The data collected can be used to create smart solutions for predicting future trends, creating simulations, and drawing insights using visualizations. This makes it beneficial for organizations to make digital twins, which are digital replicas of the real environment, to support these smart solutions.
This book will help you understand the concept of digital twins and how it can be implemented using an Azure service called Azure Digital Twins. Starting with the requirements and installation of the Azure Digital Twins service, the book will explain the definition language used for modeling digital twins. From there, you'll go through each step of building digital twins using Azure Digital Twins and learn about the different SDKs and APIs and how to use them with several Azure services. Finally, you'll learn how digital twins can be used in practice with the help of several real-world scenarios.
By the end of this book, you'll be confident in building and designing digital twins and integrating them with various Azure services.
https://books.google.co.in/books?id=QGpEEAAAQBAJ
DigiTwin: An Approach for Production Process Optimization in a Built Environment
Josip Stjepandić, Markus Sommer, Berend Denkena
Springer Nature, 23-Aug-2021 - Technology & Engineering - 259 pages
The focus of this book is an application of Digital Twin as a concept and an approach, based on the most accurate view on a physical production system and its digital representation of complex engineering products and systems. It describes a methodology to create and use Digital Twin in a built environment for the improvement and optimization of factory processes such as factory planning, investment planning, bottleneck analysis, and in-house material transport. The book provides a practical response based on achievements of engineering informatics in solving challenges related to the optimization of factory layout and corresponding processes.
This book introduces the topic, providing a foundation of knowledge on process planning, before discussing the acquisition of objects in a factory and the methods for object recognition. It presents process simulation techniques, explores challenges in process planning, and concludes by looking at future areas of progression. By providing a holistic, trans-disciplinary perspective, this book will showcase Digital Twin technology as state-of-the-art both in research and practice.
Twin-Control: A Digital Twin Approach to Improve Machine Tools Lifecycle
Mikel Armendia, Mani Ghassempouri, Erdem Ozturk, Flavien Peysson
Springer, 05-Jan-2019 - Technology & Engineering - 296 pages
This open access book summarizes the results of the European research project “Twin-model based virtual manufacturing for machine tool-process simulation and control” (Twin-Control). The first part reviews the applications of ICTs in machine tools and manufacturing, from a scientific and industrial point of view, and introduces the Twin-Control approach, while Part 2 discusses the development of a digital twin of machine tools. The third part addresses the monitoring and data management infrastructure of machines and manufacturing processes and numerous applications of energy monitoring. Part 4 then highlights various features developed in the project by combining the developments covered in Parts 3 and 4 to control the manufacturing processes applying the so-called CPSs. Lastly, Part 5 presents a complete validation of Twin-Control features in two key industrial sectors: aerospace and automotive. The book offers a representative overview of the latest trends in the manufacturing industry, with a focus on machine tools.
Digital Twin Driven Smart Manufacturing
Fei Tao, Meng Zhang, A.Y.C. Nee
Academic Press, 07-Feb-2019 - Technology & Engineering - 282 pages
Digital Twin Driven Smart Manufacturing examines the background, latest research, and application models for digital twin technology, and shows how it can be central to a smart manufacturing process. The interest in digital twin in manufacturing is driven by a need for excellent product reliability, and an overall trend towards intelligent, and connected manufacturing systems. This book provides an ideal entry point to this subject for readers in industry and academia, as it answers the questions: (a) What is a digital twin? (b) How to construct a digital twin? (c) How to use a digital twin to improve manufacturing efficiency? (d) What are the essential activities in the implementation of a digital twin? (e) What are the most important obstacles to overcome for the successful deployment of a digital twin? (f) What are the relations between digital twin and New Technologies? (g) How to combine digital twin with the New Technologies to achieve high efficiency and smartness in manufacturing?
This book focuses on these problems as it aims to help readers make the best use of digital twin technology towards smart manufacturing.
Analyzes the differences, synergies and possibilities for integration between digital twin technology and other technologies, such as big data, service and Internet of Things
Discuss new requirements for a traditional three-dimension digital twin and proposes a methodology for a five-dimension version
Investigates new models for optimized manufacturing, prognostics and health management, and cyber-physical fusion based on the digital twin
Table 1.1 Theoretical concept of Digital Twin
https://books.google.co.in/books?id=PvKGDwAAQBAJ
Video: Continuous Engineering with Digital Twin
22 Jan 2018
Software Education
IBM has great support for the Digital Twin. Have a look at the Continuous Engineering story.
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Example of car reversing is interesting. Digital twin can capture every reversing event and in the case of any mishap, the cause can be analyzed using the digital twin information.
Video: Introduction to Digital Twin: Simple, but detailed - IBM
28 Jun 2017, IBM Internet of Things
What is the Digital Twin?
Digital twin is the ability to make a virtual representation of the physical elements and the dynamics of how an Internet of Things device operates and works. It's more than a blueprint, it's more than a schematic. It's not just a picture. It's a lot more than a pair of ‘virtual reality’ glasses. It's a virtual representation of both the elements and the dynamics of how an Internet of Things device responds throughout its lifecycle. It can be a jet engine, a building, process on factory floor, and much, much more.
https://www.youtube.com/watch?v=RaOejcczPas
See the presentation on Slideshare https://www.slideshare.net/IBMIoT/ibm-watson-internet-of-things-introducing-digital-twin
See the full session https://www.youtube.com/watch?v=gUCCnVXgYvw
Machine Tool Industrial Engineering Using Digital Twins
Machine Tool Digital Twin with Life Cycle features
Summary
A new approach to simulate machining processes has been developed based in SAMCEF Mecano FEM solver. A digital representation of machine tools can be developed in this environment by combining structural FEM analysis, specific elements for the feed drives and control loop models. The novelty of this approach consists in the integration of new machining process models that provides the chance to evaluate machine tool performance during manufacturing operations.
VERICUT Machine Tool Digital Twin
For most, CAM covers every step of the manufacturing process, including the engineering master model of the component, stage definitions associated to each operation, fixturing and tooling, cutting tools, the NC toolpath and set-up information. The ‘digital twins’ of each element allow the engineering teams within the companies to test and prove processes in a virtual environment before they are applied – error free – to the real world. With OTIF (On Time In Full) being a key performance indicator for many, it is not unusual for 90 per cent or more of the machine tools used to be fully simulated.
https://www.cgtech.com/component/k2/item/377-vericut-digital-twin-ready-for-lift-off.html
March 2018
The ESPRIT CAM system from DP Technology - Digital Twin Machining Simulation for Greater Productivity in the Smart Factory
ESPRIT allows users to create a digital twin of their machine tools for programming, optimization and simulation. This virtual machine ensures that whatever happens on screen will also occur on the shop floor. Workpieces and cutting tools are set up virtually, resulting in exacting simulations, greater productivity and better toolpaths for higher quality parts. A digital thread ties together each step of the workflow from CAD design to finished part.
Digital Twins for Cutting Tools
2017-07-17
Digital Twins for Cutting Tools
Digitalisation of tool- selection and assembly creation
The digitalisation of tooling item selection and tool assembly creation can help to significantly increase efficiency and machining security. Cutting tool data can be gathered more accurately and used to create precise digital twin representations.
Creating tool assemblies is a somewhat laborious task for the CAM programmer, where there exist several opportunities for error including failing to select the optimum tool items. Many typical tool assemblies can take up to 1 hour to create.
Creating a digital twin representation for a tool assembly simulation is still difficult. In order to make the most accurate possible representation of a tool assembly in a CAM system, the creator would first need to search various vendors’ catalogues, download the 3D model files, and assemble them in a CAD programme.
Digital database of tools can help in tool selection. An integrated tool database would allow CAM programmers to select from holders, tools and inserts for milling. Once data such as component, type of machining operation and material has been input, users can get tool recommendations and suggested cutting parameters.
CoroPlus® ToolGuide from Sandvik Coromant is a digital cutting tool database. It uses an open Application Programming Interface (API) to connect with the CAM software. CoroPlus ToolGuide enables users to find a suitable cutting tool for a given task. It provides an organised list of all the suitable tools, with the most economical choice at the top. It will further show the suggested machining process and cutting data.
The list is generated by an algorithm that matches the stated task and conditions with Sandvik Coromant tools. This algorithm combines information about the different machining processes that can be used for different tasks with the product data on the tool that has information on the machining processes to which the cutter is suited. The data of the selected tools can be sent to CoroPlus® ToolLibrary, where standard tool assemblies can be created ready for export to the CAM or simulation software.
Until recently there has been no industry standard for communicating tool data to tool libraries. CAM vendors, machine tool builders and tool suppliers have historically had their own way to denominate and structure tool information so far. Now ISO 13399 has been created so that tool information is available in a standard format from all vendors. Sandvik Coromant, the KTH Royal Institute of Technology and other players in the metal cutting sector are behind the development of ISO 13399, which is now a globally recognised way of describing tool data.
This international standard defines tool attributes – for example length, width and radius – in a standardised way. ISO 13399 simplifies the exchange of data for cutting tools. When all tools in the industry share the same parameters and definitions, communicating tool information between software systems becomes very easy.
CoroPlus ToolLibrary is built on the ISO 13399 structure and is open to all tooling suppliers, ensuring there is no longer any need to interpret data from paper catalogues and then manually enter it into the system.
CoroPlus ToolLibrary allows CAM programmers to work with any tool vendor catalogue compliant to ISO 13399 standards and to create assemblies safe in the knowledge that all suggested items will fit together. The results can be viewed instantly in 2D and 3D, while users can also digitally store all information about the tools. Once saved, programmers simply import the tool assembly into their CAM or simulation software. All of the tool data is pre-set and a 3D model included.
Users report that this efficient and easy process makes it possible to cut the time from tool assembly to simulation by at least 50%. There is a much better chance of making the right tool choice by using digital databases of tools. Having accurate tool data, real tool shape and a precise digital twin representation will help to detect and avoid collisions during simulation routines.
Through the latest digital solutions such as CoroPlus ToolGuide and CoroPlus ToolLibrary, it is possible to demonstrate how much easier and faster pre-machining tasks can be executed. Both are part of the wider CoroPlus® suite of connected solutions from Sandvik Coromant aimed at helping manufacturers prepare for Industry 4.0.
https://www.sandvik.coromant.com/en-gb/news/pages/how-to-create-the-perfect-digital-twin.aspx
Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing
Jay Lee,Moslem Azamfar,Jaskaran Singh,Shahin Siahpour
Volume2, Issue1, March 2020, Pages 34-36
Cyber-physical system (CPS) and digital twin (DT) are two essential elements of smart manufacturing systems. CPS enhances communication between smart manufacturing entities (sensors, actuators, control, etc.) and cyber computational resources to facilitate monitoring, data collection, perception, analysis, and real-time control of manufacturing resources. DT integrates historical and real-time data obtained from physical systems with physics-based models and advanced analytics to create digital counterparts with high integrity, awareness, and adaptability to provide predictive services to manufacturing entities. It enhances transparency and feasibility of functions in CPS and facilitates real-time monitoring, simulation, optimisation, and control of cyber-physical elements. A DT-based CPS (DT-CPS) constantly acquires, integrates, analyses, simulates, and synchronises data across multiple stages of the product life cycle to provide on-demand predictive services to different users in both physical and cyber spaces.
Deep learning (DL) is part of a broader family of machine learning (ML) methods that have the capability to use raw data and automatically provide the representations required for various applications such as classification, regression, clustering, and pattern recognition. DL is very powerful in discovering complex structures in high-dimensional data and therefore, it has enormous applications in the manufacturing domain. It allows higher levels of abstraction without manual feature engineering and its high performance has been validated in other domains such as speech recognition, image processing, inventory management, and fault detection and diagnosis.
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-cim.2020.0009
4/1/2019
DIGITAL TWIN-DRIVEN MANUFACTURING
Machining Demonstration Shows the Digital-Twin Concept in Action
A demonstration at IMTS 2018 showed that all of the pieces are now in place, making digital-twin manufacturing feasible for shops.
Mark Albert, Editor Emeritus, Modern Machine Shop
https://www.mmsonline.com/articles/machining-demonstration-shows-the-digital-twin-concept-in-action
Digital Twins in Chemical Plants for Productivity and Quality
https://web-material3.yokogawa.com/15/28517/overview/Digital%20Twin%20White%20Paper%20X09.pdf
Updated on 20.9.2022, 26.11.2021, 20.9.2021, 8 January 2021, 15 December 2020
First published on 15.11.2020
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