Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience

The COVID-19 pandemic and crises like the Ukraine-Russia war have led to numerous restrictions for industrial manufacturing due to interrupted supply chains, staff absences due to illness or quarantine measures, and order situations that changed significantly at short notice. These influences have e...

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Detalles Bibliográficos
Autores principales: Mukherjee, Avik, Glatt, Moritz, Mustafa, Waleed, Kloft, Marius, Aurich, Jan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637926/
https://www.ncbi.nlm.nih.gov/pubmed/36373025
http://dx.doi.org/10.1016/j.procir.2022.10.054
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author Mukherjee, Avik
Glatt, Moritz
Mustafa, Waleed
Kloft, Marius
Aurich, Jan C.
author_facet Mukherjee, Avik
Glatt, Moritz
Mustafa, Waleed
Kloft, Marius
Aurich, Jan C.
author_sort Mukherjee, Avik
collection PubMed
description The COVID-19 pandemic and crises like the Ukraine-Russia war have led to numerous restrictions for industrial manufacturing due to interrupted supply chains, staff absences due to illness or quarantine measures, and order situations that changed significantly at short notice. These influences have exposed that it is crucial to address the issue of manufacturing resilience in the context of current disruptions. This can be plausibly guaranteed by subjecting the ML model of a manufacturing system to attacks deliberately designed to fool its prediction. Such attacks can provide useful insights into properties that can increase resilience of manufacturing systems.
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spelling pubmed-96379262022-11-07 Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience Mukherjee, Avik Glatt, Moritz Mustafa, Waleed Kloft, Marius Aurich, Jan C. Procedia CIRP Article The COVID-19 pandemic and crises like the Ukraine-Russia war have led to numerous restrictions for industrial manufacturing due to interrupted supply chains, staff absences due to illness or quarantine measures, and order situations that changed significantly at short notice. These influences have exposed that it is crucial to address the issue of manufacturing resilience in the context of current disruptions. This can be plausibly guaranteed by subjecting the ML model of a manufacturing system to attacks deliberately designed to fool its prediction. Such attacks can provide useful insights into properties that can increase resilience of manufacturing systems. The Author(s). Published by Elsevier B.V. 2022 2022-11-07 /pmc/articles/PMC9637926/ /pubmed/36373025 http://dx.doi.org/10.1016/j.procir.2022.10.054 Text en © 2022 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Mukherjee, Avik
Glatt, Moritz
Mustafa, Waleed
Kloft, Marius
Aurich, Jan C.
Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience
title Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience
title_full Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience
title_fullStr Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience
title_full_unstemmed Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience
title_short Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience
title_sort designing resilient manufacturing systems using cross domain application of machine learning resilience
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637926/
https://www.ncbi.nlm.nih.gov/pubmed/36373025
http://dx.doi.org/10.1016/j.procir.2022.10.054
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