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Neural Network Modeling as a Method for Creating Digital Twins: From Industry 4.0 to Industry 4.1

EasyChair Preprint no. 4011

5 pagesDate: August 9, 2020


Digital twins are one of the key technologies behind the Fourth Industrial Revolution. In the coming years they will be introduced on a large scale in the industry and in other spheres. A wide range of digital twins will be in demand: from separate components to complex technical facilities, such as automobiles, airplanes, manufacturing lines, factories, corporations, etc. To provide their successful interaction, it is important to create digital twins on the uniform principles. Currently, creating a digital twin is a complex scientific issue. It presents difficulties because it is necessary not only to describe physical (or chemical, biological, etc.) processes going on in the object, but also to envisage significant changes of its properties in the course of its operation. In this case the digital twin is supposed to adapt to the changes in the original object in accordance with the data received from the sensors. When the real object is in operation, its properties and specifics of the physical processes going on in it can change. The model is supposed to adapt in accordance with these changes, which is rather difficult if a model is generated by applying computer-aided engineering software packages (CAE) based on the finite element method (FEM). We think that another approach is more promising. It involves building an adaptive model at the second stage. This model can be specified and redesigned in accordance with the observations on the object. Since neural networks have proved to be efficient in solving complicated problems related to data processing, we recommend using them as the basic class of mathematical models for creating digital twins.

Keyphrases: data set, Digital Twins, Industry 4.0, mathematical model, Neural network modelling

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Alexandra Dashkina and Ludmila Khalyapina and Aleksandra Kobicheva and Tatiana Lazovskaya and Galina Malykhina and Dmitriy Tarkhov},
  title = {Neural Network Modeling as a Method for Creating Digital Twins: From Industry 4.0 to Industry 4.1},
  howpublished = {EasyChair Preprint no. 4011},

  year = {EasyChair, 2020}}
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