Acerinox (Spain)

A growing need to reduce the environmental impact and cost of manufacturing stainless steels has led to the development of ferritic stainless steel as an alternative to austenitic and duplex steels. The development of new stainless steels involves the optimization of their hot rolling processes, with the aim of minimizing the occurrence of defects and improving productivity. In this context, numerical simulation using the finite element method (FEM) is presented as a key tool to reduce the time and cost associated with traditional trial-and-error optimization methods. Previous work oriented towards the simulation of stainless steels has been focused on the study of small samples, on the performance of laboratory-scale tests, and on the use of 2D FEM models. In this study, a three-dimensional FEM model is proposed to simulate the hot rolling process of large-scale AISI 430 ferritic stainless-steel slabs using an industrial rolling schedule employed in the actual manufacturing process of flat products. Model optimization is performed in order to reduce the computational cost of the simulations, based on the simulation of the first pass of the hot rolling process of AISI 430 stainless steel. The results show that model optimization reduces the computational time by 90.2% without compromising the accuracy of the results. Thus, it was found that the results for thickness and rolling load showed a good correlation with the experimental values. In addition, the simulations performed allowed for the analysis of the distribution of temperature and effective plastic strain.

The introduction of Self-X capabilities into industrial control offers a tremendous potential in the development of resilient, adaptive production systems that enable circular economy. The Self-X capabilities, powered by Artificial Intelligence (AI), can monitor the production performance and enable timely reactions to problems or suboptimal operation. This paper presents a concept and prototype for Self-X AI in the process industry, particularly electric steelmaking with the EAF (Electric Arc Furnace). Due to complexity, EAF operation should be optimized with computational models, but these suffer from the fluctuating composition of the input materials, i.e., steel scrap. The fluctuation can be encountered with the Self-X method that monitors the performance, detecting anomalies and suggesting the re-training and re-initialization of models. These suggestions support the Human-in-the-Loop (HITL) in managing the AI models and in operating the production processes. The included Self-X capabilities are self-detection, self-evaluation, and self-repair. The prototype proves the concept, showing how the optimizing AI pipeline receives alarms from the external AI services if the performance degrades. The results of this work are encouraging and can be generalized, especially to processes that encounter drift related to the conditions, such as input materials for circular economy.

QP steels and other third generation AHSS possess outstanding combinations of strength and ductility, making them very attractive for the automotive sector. However, an Achilles heel of these materials is their rather limited weldability. Despite the obvious importance of this problem, very few works have been published characterising 3rd generation AHSS welded joints. For the current contribution, 3 novel QP stainless steels were investigated. Resistance spot welded joints were prepared following QP treatment of sheets. Results following paint baking revealed a high nugget hardness and a microstructure of martensite, retained austenite and delta ferrite. Cross tension strength was highest for the alloy with an optimum dispersion of retained austenite which improved weld metal toughness despite the high hardness. A further improvement in cross tension strength was realised on tempering at high temperatures, leading to an 60% improvement in strength, thanks to improved toughness of the martensite constituent.

In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control.

The “gold dust defect” (GDD) appears at the surface of ferritic stainless steels (FSS) and degrades their appearance. Previous research showed that this defect might be related to intergranular corrosion and that the addition of aluminium improves surface quality. However, the nature and origin of this defect are not properly understood yet. In this study, we performed detailed electron backscatter diffraction analyses and advanced monochromated electron energy-loss spectroscopy experiments combined with machine-learning analyses in order to extract a wealth of information on the GDD. Our results show that the GDD leads to strong textural, chemical, and microstructural heterogeneities. In particular, the surface of affected samples presents an α-fibre texture which is characteristic of poorly recrystallised FSS. It is associated with a specific microstructure in which elongated grains are separated from the matrix by cracks. The edges of the cracks are rich in chromium oxides and MnCr2O4 spinel. In addition, the surface of the affected samples presents a heterogeneous passive layer, in contrast with the surface of unaffected samples, which shows a thicker and continuous passive layer. The quality of the passive layer is improved with the addition of aluminium, explaining the better resistance to the GDD.

A pandemia da COVID-19 impulsionou o ensino remoto como uma forma de manter o ensino continuado, o que trouxe a necessidade de capacitação dos professores para se adaptarem a essa nova modalidade de ensino. OBJETIVOS: A capacitação de professores no ensino remoto tem como objetivo aprimorar as habilidades e competências dos docentes para atuarem com mais eficácia e qualidade no ambiente virtual de aprendizagem, além de possibilitar que os alunos obtenham um ensino de qualidade mesmo a distância. METODOLOGIA: A pesquisa do presente resumo foi elaborada seguindo alguns pontos, inicialmente foi feito uma revisão bibliográfica a cerca do tema e posteriormente uma analise criteriosa dos estudos já publicados, tendo em vista abordar a relevância que o tema apresenta na atualidade e a necessidade de explanar tal assunto. RESULTADOS: A capacitação de professores em todas as áreas do ensino na modalidade remota embora seja algo que apresente resistência por parte de alguns profissionais tem apresentado resultados positivos e promissores na melhoria da qualidade do ensino, na utilização adequada de tecnologias educacionais, na criação de conteúdos pedagógicos adequados e na formação de uma comunidade de professores que compartilham conhecimentos e experiências. CONCLUSÃO: Em resumo, a capacitação de professores no ensino remoto é fundamental para que eles possam desempenhar seu papel com mais eficiência e qualidade no ambiente virtual de aprendizagem, garantindo uma educação de qualidade para os alunos, mesmo à distância. É preciso que as instituições de ensino invistam nessa formação para que os professores estejam preparados para enfrentar os desafios dessa nova modalidade de ensino.

Article Influence of Cu and Sn addition on the corrosion resistance of AISI 304 stainless steel in acid media was published on December 1, 2003 in the journal International Journal of Materials Research (volume 94, issue 12).