Cyber-Physical Hybrid Processing System Digital Twin

Dmitrii A. Shatagin; Andrei Galkin; Alexander N. Osmehin; Natalia Klochkova

doi:10.4028/www.scientific.net/MSF.1037.119

Paper Titles

Kinematic Error Modeling of Delta 3D Printer
p.77

Towards the Possibility of Additive Manufacturing of XNA-Based Devices Using Molecular Engineering Principles
p.84

Sample Printing Device for Additive Manufacturing of Electronic Housings
p.105

Technology for Producing Fine Tungsten Carbide Powders
p.111

Cyber-Physical Hybrid Processing System Digital Twin
p.119

The Structure and Properties of Dispersed Aluminium Composite Compounds Made by Welding Friction with Mixing
p.127

Influence of Additive’s Concentration on the Temperature Conditions for the Formation of the Solid Solutions of the System MgAl₂O₄-Ga₂O₃
p.135

Studies of Magnetically Active Silicone Elastomers on a Vibrostend
p.141

Comparative High-Field Magnetization Study of (Sm,Er)₂Fe₁₇ and Er₂Fe₁₇ Compounds and their Nitrides
p.148

HomeMaterials Science ForumMaterials Science Forum Vol. 1037Cyber-Physical Hybrid Processing System Digital...

Cyber-Physical Hybrid Processing System Digital Twin

Abstract:

The article proposes a method for obtaining a digital twin of the process of 3D printing by electric arc surfacing using an ensemble of machine learning methods. On the basis of the structural-parametric approach, a set of diagnostic parameters for the signals of current strength, voltage and acoustic emission was determined. Using exploratory analysis, the significance of each diagnostic parameter was assessed. A complex of statistical models has been developed to assess the stability of 3D printing processes using decision trees. Their optimal parameters and efficiency have been determined.

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Periodical:

Materials Science Forum (Volume 1037)

Pages:

119-124

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.1037.119

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Online since:

July 2021

Authors:

Dmitrii A. Shatagin*, Andrei Galkin, Alexander N. Osmehin, Natalia Klochkova

Keywords:

3D Printing, Cyber-Physical System, Digital Twin, Hybrid System, Machine Learning, WAAM

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References

[1] Pentegov IV Comparative analysis of models of dynamic welding arc, I. V Pentegov, VN Sidorets, Automatic welding. - 1989. -№ 2. - S. 33-36.

DOI: 10.1080/09507119009447722

Google Scholar

[2] Vereshchago E.N., Kostyuchenko V.I., model of the electric arc in matlab / Simulink, Electrical engineering and power engineering. 2013. No. 2. pp.40-46.

Google Scholar

[3] Lisienko V.G., Lavrov N.G., Bliznik M.G. Simulation of welding and furnace electric arc, In the collection: Modern scientific achievements of metallurgical heat engineering and their implementation in industry. Collection of reports of the international scientific and practical conference dedicated to the 95th anniversary of the founding of the Department of TIM, UrFU and the 85th anniversary of the founding of JSC VNIIMT,. Ural Federal University the first President of Russia B.N. Yeltsin. 2015.P. 378-386.

DOI: 10.21046/spacedebris2019-84-89

Google Scholar

[4] Savitsky A., Haltof M., Electricity .Problems of determining the parameters of mathematical models of electric arcs in circuits with current sources, Electricity, 2016. No. 1. pp.25-34.

Google Scholar

[5] Na Lv, Jiyong Zhong, Huabin Chen, Shanben Chen, Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length, The International Journal of Advanced Manufacturing Technology 74(1-4):235-249.

DOI: 10.1007/s00170-014-5875-7

Google Scholar

[6] Chao Chen, Runquan Xiao, Huabin Chen, Na Lv, Arc sound model for pulsed GTAW and recognition of different penetration states, The International Journal of Advanced Manufacturing Technology 108(1-4).

DOI: 10.1007/s00170-020-05462-z

Google Scholar

[7] Jesus Gonzalez, Wen Yu, Non-linear system modeling using LSTM neural networks, IFAC-PapersOnLine 51(13):485-489.

DOI: 10.1016/j.ifacol.2018.07.326

Google Scholar

[8] Shitikov V. K., Mastitskiy S. E. Classification, regression and other algorithms of Data Mining using R. 2017.351 p. (EBook).

Google Scholar

[9] https://ru.wikipedia.org/wiki/Random_forest.

Google Scholar