Quality4.0 -- Transparent product quality supervision in the age of Industry 4.0

Brandenburger, Jens; Schirm, Christoph; Melcher, Josef; Hancke, E.; Vannucci, Marco; Colla, Valentina; Cateni, Silvia; Sellami, Rami; Dupont, Sébastien; Majchrowski, Annick; Arteaga, Asier

doi:10.48550/arxiv.2011.06502

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Transparency in product quality can be supervised using ML approaches to detect the outliers in the data for ensuring its quality [159]. • Data Auditing: Data auditing is one of the key processes to be carried out in data analytics, where data is analyzed for a particular application.…”

Section: Prescriptive Analyticsmentioning

confidence: 99%

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

Jagatheesaperumal¹,

Rahouti²,

Ahmad³

et al. 2021

Preprint

View full text Add to dashboard Cite

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers, has paved the way for Artificial Intelligence (AI) and Big Data in support of smart manufacturing. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, Big data, Blockchain, 5G communications, in support of smart manufacturing and the dynamical processes in modern industries. In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and Big Data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and Big Data methods in smart industries with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. In a nutshell, we have explored the significance of AI and Big data towards Industry 4.0 applications through panoramic reviews and discussions. We believe, this work will provide a baseline for future research in the domain.

show abstract

Section: Prescriptive Analyticsmentioning

confidence: 99%