ISSN 2594-5300
52º Seminário de Aciaria, Fundição e Metalurgia de Não-Ferrosos — vol. 52, num.52 (2023)
Título
DOI
Downloads
Resumo
Multiple defects, such as slivers, laminations or surface inclusions, can occur during steel production causing losses from reduced quality and rejected production. The Cracs Preventer analyses data from the entire process chain with the goal of reducing losses due to those defects. It does so by combining metallurgical expertise with artificial intelligence. The defects are predicted before they occur and the necessary countermeasures to avoid the defects are suggested. The Cracs preventer can currently be applied to flat products and long products production.
Multiple defects, such as slivers, laminations or surface inclusions, can occur during steel production causing losses from reduced quality and rejected production. The Cracs Preventer analyses data from the entire process chain with the goal of reducing losses due to those defects. It does so by combining metallurgical expertise with artificial intelligence. The defects are predicted before they occur and the necessary countermeasures to avoid the defects are suggested. The Cracs preventer can currently be applied to flat products and long products production.
Palavras-chave
continuous casting; quality and performance improvement; surface defects; machine learning
continuous casting; quality and performance improvement; surface defects; machine learning
Como citar
Kempken, Jens.
IMPROVEMENT OF CASTER PERFORMANCE BY REALTIME DEFECT PREVENTION UTILIZING AI AND ML
,
p. 1097-1110.
In: 52º Seminário de Aciaria, Fundição e Metalurgia de Não-Ferrosos,
São Paulo,
2023.
ISSN: 2594-5300
, DOI 10.5151/2594-5300-40239
