Header

Shop : Details

Shop
Details
45,80 €
ISBN 978-3-8440-9268-4
Softcover
152 pages
74 figures
225 g
21 x 14,8 cm
English
Thesis
November 2023
Seyed Ruhollah Dokhanchi
Towards Digital Shadow in Plasma Spraying
Atmospheric Plasma Spraying (APS) is a versatile coating technology with diverse functional features. Deposition efficiency (DE) is a major performance measure in APS, influenced by various factors. Due to intricate interdependencies of these factors, enhancing DE has always been a challenging task in the process development of APS. Hence, employing a variety of computer-aided methods is essential to understand and manage these correlations. The concept of the so-called Digital Shadow combines domain-specific models with data-driven techniques of Artificial Intelligence (AI), inferred by autonomous agents to create a sufficiently accurate image of the production process including all relevant data. This dissertation is devoted to the development of the primary steps towards a Digital Shadow in APS with the ultimate goal of improving the process efficiency.

Modern AI methods, namely Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), were used in this work to predict DE. To tackle the problem of insufficient data for training the aforementioned AI models two approaches were pursued: 1) A method was developed for in situ determination of spatially resolved deposition efficiencies on the substrate, namely Local Deposition Efficiency (LDE). By using LDE, sufficient amount of data for learning algorithms could be generated, while providing that much data for ex situ measurements of global DE and their corresponding particle properties would be impractical. 2) Simulation data for the in-flight particle properties were generated by using the simulation models of the plasma jet already developed at IOT. The combination of these two strategies provided the aggregated and purpose driven data sets required for a Digital Shadow in APS.
Keywords: thermal spraying; atmospheric plasma spraying (APS); deposition efficiency (DE); digital shadow; digital twin; expert system; artificial intelligence (AI); machine learning (ML); adaptive neuro-fuzzy inference system (ANFIS); support vector machine (SVM)
Schriftenreihe Oberflächentechnik
Edited by Prof. Dr.-Ing. K. Bobzin, Aachen
Volume 74
Available online documents for this title
You need Adobe Reader, to view these files. Here you will find a little help and information for downloading the PDF files.
Please note that the online documents cannot be printed or edited.
Please also see further information at: Help and Information.
 
 DocumentDocument 
 TypePDF 
 Costs34,35 € 
 ActionDownloadPurchase in obligation and download the file 
     
 
 DocumentTable of contents 
 TypePDF 
 Costsfree 
 ActionDownloadDownload the file 
     
User settings for registered online customers (online documents)
You can change your address details here and access documents you have already ordered.
User
Not logged in
Export of bibliographic data
Shaker Verlag GmbH
Am Langen Graben 15a
52353 Düren
Germany
  +49 2421 99011 9
Mon. - Thurs. 8:00 a.m. to 4:00 p.m.
Fri. 8:00 a.m. to 3:00 p.m.
Contact us. We will be happy to help you.
Captcha
Social Media