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Gregor Lammer

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Papers by Gregor Lammer

$Research paper thumbnail of Digital Optimization of Refractory Maintenance$

AISTech 2021 Proceedings of the Iron and Steel Technology Conference, 2021

$Research paper thumbnail of Refractory Condition Monitoring and Lifetime Prognosis for RH Degasser$

AISTech2019 Proceedings of the Iron and Steel Technology Conference, 2019

$Research paper thumbnail of Refractories 4.0$

BHM Berg- und Hüttenmännische Monatshefte, 2017

The full digitalization of industry—also known as “Industry 4.0”—promises significant efficiency ... more The full digitalization of industry—also known as “Industry 4.0”—promises significant efficiency gains and will also trigger changes. This development has already started to have an impact on refractory operations in steel plants: smart automation, digitalized processes, and big data analysis are changing the working environment. Such systems can reduce manpower in harsh or dangerous working environments and provide more traceable and reliable data for decision making. Data lakes consisting of process, application, consumption, and production data enable smart systems to calculate process influences on refractory performance and consequently to manage a consumption triggered supply chain.New manufacturing processes like additive manufacturing have the potential to increase customizing and localizing of refractory production. A fully digital value chain requires new and closely integrated business approach with bidirectional data flow to deliver its benefits to the full extent.ZusammenfassungDie vollständige Digitalisierung der Industrie – auch als „Industrie 4.0“ bezeichnet – verspricht erhebliche Effizienzgewinne, wird jedoch auch Änderungen bewirken. Diese Entwicklung zeichnet sich auch bereits in Stahlwerken bei der Anwendung von feuerfesten Produkten ab: Intelligente Automatisierung, digitalisierte Prozesse und Big Data-Analyse verändern die Arbeitswelt. Solche Systeme können den Einsatz von Menschen in rauen oder gefährlichen Arbeitsumgebungen minimieren und nachvollziehbare und zuverlässige Daten für die Entscheidungsfindung liefern. Sogenannte Data Lakes, die aus Prozess‑, Anwendungs‑, Verbrauchs- und Produktionsdaten bestehen, ermöglichen es intelligenten Systemen, die Prozesseinflüsse auf die Feuerfestperformance zu berechnen und auf diese Weise eine verbrauchsgesteuerte Lieferkette zu verwalten.Neue Herstellungsverfahren wie die additive Fertigung haben das Potenzial, die Feuerfestproduktion zu individualisieren und zu lokalisieren. Die digitale Wertschöpfungskette erfordert einen neuen und tief integrierten Geschäftsansatz mit bidirektionalem Datenfluss, um ihre Vorteile voll ausschöpfen zu können.

$Research paper thumbnail of Advanced Data Mining and Machine Learning for Smart Refractory Control$

ABM Proceedings, 2018

The full digitization of the industry promises significant efficiency gains. This development beg... more The full digitization of the industry promises significant efficiency gains. This development begins to have an impact on the operation in steel plants, when decisions are made based on traceable data.This paper presents an approach to discover patterns in big data sets and applying methods of artificial intelligence for interpretation. As example, the identification of the main refractory wear mechanism in the hot spots and improvements applying this approach will be presented. Further, we applied this intelligent system for process optimization to calculated to optimal campaign length considering production, maintenance and refractory parameters.The paper also examines and discusses the operational impact and future applications.

$Research paper thumbnail of Refractory Lifetime Prognosis for RH-Degassers$

$Research paper thumbnail of Refractory Lifetime Prediction in Industrial Processes with Artificial Intelligence$

REWAS 2022: Developing Tomorrow’s Technical Cycles (Volume I), 2022

$Research paper thumbnail of Digital Refractory Age$

$Research paper thumbnail of Advanced Data Mining for Process Optimizations and Use of A.I. to Predict Refractory Wear and to Analyse Refractory Behavior$