scholarly journals Introduction to Special Issue Titled Digital Twin Driven Design and Manufacturing

2021 ◽  
pp. 1-6
Author(s):  
Bin He ◽  
Yu Song ◽  
Yan Wang
Keyword(s):  
Real Time ◽  
Intelligent Design ◽  
Physical World ◽  
Intelligent Manufacturing ◽  
Sensor Data ◽  
Seamless Integration ◽  
Physical Systems ◽  
Digital Twin ◽  
Design And Manufacturing ◽  
Whole Life Cycle

Abstract A digital twin (DT) is the real-time digital replica of a physical entity and system. It enables the seamless integration between digital models and physical devices so that the operation, monitoring, control, and upgrade of the system, as well as personnel training, can be performed in a cyber-physical mixture mode. DTs integrate technologies such as multiphysics multiscale modeling, Internet of Things, smart sensing, machine learning, and model-based control, and act as a bridge between the physical world and the virtual world by mapping the whole life cycle of physical systems with real-time sensor data, and maintaining the complete digital trace. In the era of Industry 4.0, DT is becoming a powerful engine in the intelligent design of products and intelligent manufacturing.

Building a Trustworthy Product-level Shape-performance Integrated Digital Twin with Multi-fidelity Surrogate Model

2021 ◽  
pp. 1-28
Author(s):  
Shuo Wang ◽  
Xiaonan Lai ◽  
Xiwang He ◽  
Yiming Qiu ◽  
Xueguan Song
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Test Case ◽  
Digital Twin ◽  
Digital Twins ◽  
Training Samples ◽  
Practical Engineering ◽  
Simulation Results ◽  
Visualization Techniques ◽  
Quantities Of Interest

Abstract Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This paper presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multi-fidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares, named MFS-MLS. The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real-time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.

scholarly journals Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4410 ◽  
Author(s):  
Seunghwan Jeong ◽  
Gwangpyo Yoo ◽  
Minjong Yoo ◽  
Ikjun Yeom ◽  
Honguk Woo
Keyword(s):  
Reinforcement Learning ◽  
Data Quality ◽  
Data Management ◽  
Autonomous Systems ◽  
Sensor Data ◽  
Data Uncertainty ◽  
Seamless Integration ◽  
Digital Twin ◽  
Physical Attributes ◽  
Sensor Data Management

Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision “digital twin”, in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and digital assets at several different system layers. However, the inherent limitations of sampling and observing physical attributes often pose issues related to data uncertainty in practice. In this paper, we propose a learning-based data management scheme where the implementation is layered between sensors attached to physical attributes and domain-specific applications, thereby mitigating the data uncertainty between them. To do so, we present a sensor data management framework, namely D2WIN, which adopts reinforcement learning (RL) techniques to manage the data quality for CPS applications and autonomous systems. To deal with the scale issue incurred by many physical attributes and sensor streams when adopting RL, we propose an action embedding strategy that exploits their distance-based similarity in the physical space coordination. We introduce two embedding methods, i.e., a user-defined function and a generative model, for different conditions. Through experiments, we demonstrate that the D2WIN framework with the action embedding outperforms several known heuristics in terms of achievable data quality under certain resource restrictions. We also test the framework with an autonomous driving simulator, clearly showing its benefit. For example, with only 30% of updates selectively applied by the learned policy, the driving agent maintains its performance about 96.2%, as compared to the ideal condition with full updates.

scholarly journals Data driven discovery of cyber physical systems

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ye Yuan ◽  
Xiuchuan Tang ◽  
Wei Zhou ◽  
Wei Pan ◽  
Xiuting Li ◽  
...  
Keyword(s):  
Smart Grids ◽  
Underlying Mechanism ◽  
Physical World ◽  
Intelligent Manufacturing ◽  
Cyber Physical Systems ◽  
The Novel ◽  
Physical Systems ◽  
Embed Software ◽  
Wide Range ◽  
Intrinsic Complexity

Abstract Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.

Characteristic Analyzation of Cyber Physical Systems

2014 ◽  
Vol 484-485 ◽  
pp. 427-430
Author(s):  
Zhe Jun Kuang ◽  
Liang Hu ◽  
Chen Zhang
Keyword(s):  
Real Time ◽  
Heterogeneous Systems ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Physical Processes ◽  
Physical Systems ◽  
Time Performance ◽  
Research Challenges ◽  
Computer Mediated ◽  
And Control

Cyber-physical systems (CPS) are complex distributed heterogeneous systems which integrating cyber and physical processes by computation, communication and control. During interaction between cyber and physical world, the traditional theories and applications has been difficult to satisfy real-time performance and efficient. Cyber-physical systems clearly have a role to play in developing a new theory of computer-mediated physical systems. The aim of this work is to analysis the features and relation technology of CPS that get better understanding for this new field. We summarized the research progresses from different perspectives such as modeling, classical tools and applications. Finally, the research challenges for CPS are in brief outlined.

Will This Be the Decade of Full Digital Twins for Well Construction?

2021 ◽  
Vol 73 (03) ◽  
pp. 34-37
Author(s):  
Judy Feder
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Real World ◽  
Data Analytics ◽  
Oil And Gas ◽  
Real Life ◽  
Physical World ◽  
Sensor Technology ◽  
Digital Twin ◽  
Digital Twins

The time needed to eliminate complications and accidents accounts for 20–25% of total well construction time, according to a 2020 SPE paper (SPE 200740). The same paper notes that digital twins have proven to be a key enabler in improving sustainability during well construction, shrinking the carbon footprint by reducing overall drilling time and encouraging and bringing confidence to contactless advisory and collaboration. The paper also points out the potential application of digital twins to activities such as geothermal drilling. Advanced data analytics and machine learning (ML) potentially can reduce engineering hours up to 70% during field development, according to Boston Consulting Group. Increased field automation, remote operations, sensor costs, digital twins, machine learning, and improved computational speed are responsible. It is no surprise, then, that digital twins are taking on a greater sense of urgency for operators, service companies, and drilling contractors working to improve asset and enterprise safety, productivity, and performance management. For 2021, digital twins appear among the oil and gas industry’s top 10 digital spending priorities. DNV GL said in its Technology Outlook 2030 that this could be the decade when cloud computing and advanced simulation see virtual system testing, virtual/augmented reality, and machine learning progressively merge into full digital twins that combine data analytics, real-time, and near-real-time data for installations, subsurface geology, and reservoirs to bring about significant advancements in upstream asset performance, safety, and profitability. The biggest challenges to these advancements, according to the firm, will be establishing confidence in the data and computational models that a digital twin uses and user organizations’ readiness to work with and evolve alongside the digital twin. JPT looked at publications from inside and outside the upstream industry and at several recent SPE papers to get a snapshot of where the industry stands regarding uptake of digital twins in well construction and how the technology is affecting operations and outcomes. Why Digital Twins Gartner Information defines a digital twin as a digital representation of a real-world entity or system. “The implementation of a digital twin,” Gartner writes, “is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.” Data from multiple digital twins can be aggregated for a composite view across several real-world entities and their related processes. In upstream oil and gas, digital twins focus on the well—and, ultimately, the field—and its lifecycle. Unlike a digital simulation, which produces scenarios based on what could happen in the physical world but whose scenarios may not be actionable, a digital twin represents actual events from the physical world, making it possible to visualize and understand real-life scenarios to make better decisions. Digital well construction twins can pertain to single assets or processes and to the reservoir/subsurface or the surface. Ultimately, when process and asset sub-twins are connected, the result is an integrated digital twin of the entire asset or well. Massive sensor technology and the ability to store and handle huge amounts of data from the asset will enable the full digital twin to age throughout the life-cycle of the asset, along with the asset itself (Fig. 1).

scholarly journals Digital Triplet Approach for Real-Time Monitoring and Control of an Elevator Security System

Designs ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 9 ◽  
Author(s):  
Michael M. Gichane ◽  
Jean B. Byiringiro ◽  
Andrew K. Chesang ◽  
Peterson M. Nyaga ◽  
Rogers K. Langat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Object Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Performance Testing ◽  
Sensor Data ◽  
Security Measures ◽  
Monitoring And Control ◽  
Digital Twin ◽  
The Impact

As Digital Twins gain more traction and their adoption in industry increases, there is a need to integrate such technology with machine learning features to enhance functionality and enable decision making tasks. This has lead to the emergence of a concept known as Digital Triplet; an enhancement of Digital Twin technology through the addition of an ’intelligent activity layer’. This is a relatively new technology in Industrie 4.0 and research efforts are geared towards exploring its applicability, development and testing of means for implementation and quick adoption. This paper presents the design and implementation of a Digital Triplet for a three-floor elevator system. It demonstrates the integration of a machine learning (ML) object detection model and the system Digital Twin. This was done to introduce an additional security feature that enabled the system to make a decision, based on objects detected and take preliminary security measures. The virtual model was designed in Siemens NX and programmed via Total Integrated Automation (TIA) portal software. The corresponding physical model was fabricated and controlled using a Programmable Logic Controller (PLC) S7 1200. A control program was developed to mimic the general operations of a typical elevator system used in a commercial building setting. Communication, between the physical and virtual models, was enabled using the OPC-Unified Architecture (OPC-UA) protocol. Object recognition using “You only look once” (YOLOV3) based machine learning algorithm was incorporated. The Digital Triplet’s functionality was tested, ensuring the virtual system duplicated actual operations of the physical counterpart through the use of sensor data. Performance testing was done to determine the impact of the ML module on the real-time functionality aspect of the system. Experiment results showed the object recognition contributed an average of 1.083 s to an overall signal travel time of 1.338 s.

scholarly journals Field-synchronized Digital Twin framework for production scheduling with uncertainty

2020 ◽  
Author(s):  
Elisa Negri ◽  
Vibhor Pandhare ◽  
Laura Cattaneo ◽  
Jaskaran Singh ◽  
Marco Macchi ◽  
...  
Keyword(s):  
Real Time ◽  
Production Scheduling ◽  
Flow Shop ◽  
Health Management ◽  
Discrete Event ◽  
Operating Conditions ◽  
Sensor Data ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Digital Twin

Abstract Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

Quality Deviation Control for Aircraft Using Digital Twin

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Hongxia Cai ◽  
Jiamin Zhu ◽  
Wei Zhang
Keyword(s):  
Asset Management ◽  
Aircraft Design ◽  
Physical World ◽  
Quality Data ◽  
Digital Twin ◽  
Long Time ◽  
Heterogeneous Quality ◽  
Design Defects ◽  
Whole Life Cycle ◽  
Quality Deviation

Abstract During the manufacturing process of aircraft, quality deviation problems inevitably occur due to the high complexity of aircraft design, manufacturing errors, tooling mistakes, human factors, environmental influences, design defects, and other factors. The current quality deviation control system of civil aircraft suffers from two problems: (1) quality deviation control data are scattered in more than 100 management systems, and it is difficult to extract quality data-related information from the whole life cycle of the aircraft involving the main manufacturer and each supplier and (2) there is a lack of quality data analysis and a closed-loop information-physics fusion system for quality deviation control. Thus, it is difficult to locate the quality deviation problems and it takes a long time to deal with these problems as well. In this paper, a digital twin-based quality deviation control model is proposed. Through the digital twin modeling based on asset management shell technology, the multi-source and heterogeneous quality deviation data can be extracted and integrated. Furthermore, to deal with the second problem, a quality deviation system has been built based on digital twin. In this system, the aircraft quality deviation data can be analyzed by the FP-growth association rule algorithm and the results are provided through the system to guide the assembly site, improving the efficiency and accuracy of quality problem-solving in the physical world. In addition, a case study is stated, where the proposed approach is applied to deal with the aircraft quality deviation problems.

Integrated Real-Time Simulation in an Earth Model – Automating Drilling and Driving Efficiency

2021 ◽  
Author(s):  
Pedro J. Arévalo ◽  
Olof Hummes ◽  
Matthew Forshaw
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Earth Model ◽  
Seamless Integration ◽  
The North ◽  
Time Simulation ◽  
Drilling Parameters ◽  
Related Risk ◽  
Drilling Engineering ◽  
The North Sea

Abstract Real-time while drilling simulations use an evergreen digital twin of the well, consisting of physics-based models in an earth model to constantly update boundary conditions and parameters while drilling. The approach actively contributes to prediction or early detection of specific drilling issues, thus reducing drilling-related risk, non-productive time (NPT), and invisible-lost time (ILT). The method also unlocks further drilling optimization opportunities, while staying within a safe operative envelope that protects the wellbore. In the planning phase, a run plan is prepared based on drilling engineering simulations – such as downhole hydraulics and Torque and Drag (T&D) – within the lithology and geomechanics of the earth model. While drilling, the run plan continuously evolves as automatic updates with actual drilling parameters refine the simulations. Smart triggering algorithms constantly monitor sensor data at surface and downhole, automatically updating the simulations. Drilling automation services consume the simulation results, shared across an aggregation layer, to predict drilling dysfunctions related to hole-cleaning, downhole pressure, tripping velocity (which might lead to fractured formations or formation fluids entering the wellbore), tight hole and pipe sticking. Drillers receive actionable information, and drilling automation applications are equipped to control specific drilling processes. Case studies from drilling runs in the North Sea and in Middle East confirm the effectiveness of the approach. Deployment on these runs used a modular and scalable system architecture to allow seamless integration of all components (surface data acquisition, drilling engineering simulations, and monitoring applications). As designed, the system allows the integration of new services, and different data providers and consumers.

Designing a Shape–Performance Integrated Digital Twin Based on Multiple Models and Dynamic Data: A Boom Crane Example

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Xiaonan Lai ◽  
Shuo Wang ◽  
Zhenggang Guo ◽  
Chao Zhang ◽  
Wei Sun ◽  
...  
Keyword(s):  
Structural Analysis ◽  
Numerical Model ◽  
Design Optimization ◽  
Real Time ◽  
Analytical Model ◽  
Sensor Data ◽  
Dynamic Data ◽  
Digital Twin ◽  
Heavy Equipment ◽  
Boom Crane

Abstract The digital twin, a concept that aims to establish a real-time mapping between physical space and virtual space, can be used for real-time analysis, reliability assessment, predictive maintenance, and design optimization of products. This article presents an enabling technology named shape–performance integrated digital twin (SPI-DT) and takes a boom crane as an example to illustrate how to design the SPI-DT step by step for the structural analysis of complex heavy equipment. The SPI-DT contains different types of models, such as an analytical model, a numerical model, and an artificial intelligence (AI) model. In addition, it leverages multisource dynamic data obtained by placing different sensors at multiple measurement positions. In the SPI-DT, the AI model plays a central role, invoking the numerical model and sensor data as the input to predict the structural performance of key components of heavy equipment, while the analytical model analyzes the structure of noncritical components with sensor data as input. This significantly improves the computational efficiency of the digital twin used for the structural analysis of complex heavy equipment, making the digital twin computationally affordable, and thus can be used for the safety assessment and damage protection of the equipment in the operation, as well as the design optimization of next-generation products. Moreover, to visually demonstrate the models and data in the SPI-DT, a three-dimensional application used to display and record the shape and performance information in real time during the operation of the boom crane is developed.

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