Unveiling Trends and Predictions in Digital Factories

Καραγιώργου, Σοφία; Vafeiadis, Georgios; Ntalaperas, Dimitris; Lykousas, Nikolaos; Vergeti, Danai; Αλεξάνδρου, Δ.

doi:10.1109/dcoss.2019.00073

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…Steel sector: there are several examples of ML used for different processes as for example for fatigue property analysis [6] and to predict the tensile strength of steel rods manufactured in an electric arc furnace [7]. ML is also used to enable prediction of systems failure [8] and neural network-based solutions are used for crack prediction to improve the steel-casting process. ES have been developed to analyse the quality of the steel products [9] and for the analysis of metallurgical processes related to continuous casting with a modular system architecture while the DM system is based on the physical and chemical features of steel [10].…”

Section: Resultsmentioning

confidence: 99%

AI and BD in Process Industry: A Literature Review with an Operational Perspective

Fornasiero¹,

Nettleton²,

Kiebler³

et al. 2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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Among digital technologies, Artificial Intelligence (AI) and Big Data (BD) have proven capability to support different processes, mainly in discrete manufacturing. Despite the fact that a number of AI and BD literature reviews exist, no comprehensive review is available for the Process Industry (i.e. cements, chemical, steel, and mining). This paper aims to provide a comprehensive review of AI and BD literature to gain insights into their evolution supporting operational phases of the Process Industry. Results allow to define the areas where AI/BD are proven to have greater impact and areas with gaps like for example the process control (predictive models) area, machine learning and cyber-physical systems technologies. The sectors lagging behind are Ceramics, Cement and nonferrous metals. Areas to be studied in the future include the interaction between intelligent systems. humans and the external environment, the implementation of AI for the monitoring and optimization of parameters of different operations, ethical and social impact.

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