scholarly journals Analysis and application of manufacturing data driven by digital twins

2021 ◽  
Vol 1983 (1) ◽  
pp. 012104
Author(s):  
Xiyu Gao ◽  
Peng Liu ◽  
Qixun Zhang ◽  
Dawei Gao ◽  
Xin Huang
Keyword(s):  
Data Driven ◽  
Digital Twins ◽  
Manufacturing Data

scholarly journals Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho–Kalman Method for Order Estimation

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 431
Author(s):  
Alexios Papacharalampopoulos
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Data Driven ◽  
Driven Systems ◽  
Control Techniques ◽  
Digital Twin ◽  
Order Estimation ◽  
Digital Twins ◽  
Initial Stage ◽  
System Order

System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.

Development of a Pilot Manufacturing Cyberinfrastructure With an Information Rich Mechanical CAD 3D Model Repository

2019 ◽  
Author(s):  
Akshay Bharadwaj ◽  
Yang Xu ◽  
Atin Angrish ◽  
Yong Chen ◽  
Binil Starly
Keyword(s):  
Product Design ◽  
Academic Research ◽  
Digital Design ◽  
Data Driven ◽  
Concept Design ◽  
Design Data ◽  
Meta Data ◽  
Pilot Implementation ◽  
Project Data ◽  
Manufacturing Data

Abstract Data driven advanced manufacturing research is dependent on access to large datasets made available from across the product lifecycle — from the concept design phase all the way down to end use and disposal. Despite such data being generated at a rapid pace, most product design data is archived in inaccessible silos. This is particularly acute in academic research laboratories and with data generated during product design and manufacturing courses. This project seeks to create an infrastructure that allow users (academia and the general public) to easily upload project data and related meta-data. Current manufacturing research must shift from siloed repositories of product manufacturing data to a federated, decentralized, open and inter-operable approach. In this regard, we build ‘FabWave’ a cyber-infrastructure tool designed to capture manufacturing data. In its first pilot implementation, we focused our attention to gathering information rich 3D Mechanical CAD data and related meta-data associated with them, with the intent to make it easier for users to upload and access product design data. We describe workflows that we have initially tested out within the two academic universities and under two different course structures. We have also developed automated workflows to gather license appropriate CAD assemblies from commercial repositories. Our intent is to create the only known largest available CAD model set within academia for enabling research in data-driven computational research in digital design, fabrication and quality control.

Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

2020 ◽  
pp. 1-1
Author(s):  
Hossein Darvishi ◽  
Domenico Ciuonzo ◽  
Eivind Roson Eide ◽  
Pierluigi Salvo Rossi
Keyword(s):  
Fault Detection ◽  
Data Driven ◽  
Sensor Fault ◽  
Digital Twins ◽  
Modular Data ◽  
Sensor Fault Detection

scholarly journals A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3639
Author(s):  
Matevz Resman ◽  
Jernej Protner ◽  
Marko Simic ◽  
Niko Herakovic
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Multiple Scales ◽  
Building Blocks ◽  
Heterogeneous Data ◽  
Digital Model ◽  
Data Driven ◽  
Digital Models ◽  
Digital Twin ◽  
Digital Twins

A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven factories. The problem with developing digital models that form the digital twin is that they operate with large amounts of heterogeneous data. Since the models represent simplifications of the physical world, managing the heterogeneous data and linking the data with the digital twin represent a challenge. The paper proposes a five-step approach to planning data-driven digital twins of manufacturing systems and their processes. The approach guides the user from breaking down the system and the underlying building blocks of the processes into four groups. The development of a digital model includes predefined necessary parameters that allow a digital model connecting with a real manufacturing system. The connection enables the control of the real manufacturing system and allows the creation of the digital twin. Presentation and visualization of a system functioning based on the digital twin for different participants is presented in the last step. The suitability of the approach for the industrial environment is illustrated using the case study of planning the digital twin for material logistics of the manufacturing system.

scholarly journals On generative models as the basis for digital twins

2021 ◽  
Vol 2 ◽  
Author(s):  
George Tsialiamanis ◽  
David J. Wagg ◽  
Nikolaos Dervilis ◽  
Keith Worden
Keyword(s):  
Linear Structure ◽  
Generative Models ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Deterministic Models ◽  
Adversarial Networks ◽  
Digital Twins ◽  
Different Types ◽  
Potential Use ◽  
Model Approach

Abstract A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modeling applications. Two different types of generative models are considered here. The first is a physics-based model based on the stochastic finite element (SFE) method, which is widely used when modeling structures that have material and loading uncertainties imposed. Such models can be calibrated according to data from the structure and would be expected to outperform any other model if the modeling accurately captures the true underlying physics of the structure. The potential use of SFE models as digital mirrors is illustrated via application to a linear structure with stochastic material properties. For situations where the physical formulation of such models does not suffice, a data-driven framework is proposed, using machine learning and conditional generative adversarial networks (cGANs). The latter algorithm is used to learn the distribution of the quantity of interest in a structure with material nonlinearities and uncertainties. For the examples considered in this work, the data-driven cGANs model outperforms the physics-based approach. Finally, an example is shown where the two methods are coupled such that a hybrid model approach is demonstrated.

scholarly journals Manufacturing data-driven process adaptive design method

Procedia CIRP ◽  
2020 ◽  
Vol 91 ◽  
pp. 728-734
Author(s):  
Wei Wei ◽  
Jun Yuan ◽  
Ang Liu
Keyword(s):  
Adaptive Design ◽  
Design Method ◽  
Data Driven ◽  
Manufacturing Data
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