A machine learning digital twin approach for critical process parameter prediction in a catalyst manufacturing line-Reference-Cited by-同舟云学术

A machine learning digital twin approach for critical process parameter prediction in a catalyst manufacturing line

Published:2023-10 Issue: Volume:151 Page:103987
ISSN:0166-3615
Container-title:Computers in Industry
language:en
Short-container-title:Computers in Industry

Author:

Perno Matteo^ORCID,Hvam Lars^ORCID,Haug Anders^ORCID

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference62 articles.

1. Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing;Alexopoulos;Int. J. Comput. Integr. Manuf.,2020

2. Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing;Alexopoulos;Int J. Comput. Integr. Manuf.,2020

3. Modular and Platform-based Product Development in the Process Industry - Enabling Efficient Product Variety Through Complexity Management;Andersen,2022

4. A survey on digital twin: Definitions, characteristics, applications, and design implications;Barricelli;IEEE Access,2019

5. Industry use of virtual reality in product design and manufacturing: a survey;Berg;Virtual Real.,2017

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