Origin of the Inclusions in Production-Scale Electrodes, ESR Ingots, and PESR Ingots in a Martensitic Stainless Steel

Persson, Ewa Sjöqvist; Karasev, Andrey; Mitchell, A.; Jönsson, Pär G.

doi:10.3390/met10121620

Cited by 11 publications

(25 citation statements)

References 27 publications

(41 reference statements)

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“…Recently, Sjöqvist Persson et al [26,27] stated that the inclusions in a remelted ingot can be primary, semi-secondary, or secondary inclusions. The primary inclusions are suggested to have been trapped in a fallen steel drop.…”

Section: Yearmentioning

confidence: 99%

“…The primary inclusions are suggested to have been trapped in a fallen steel drop. Furthermore, the semi-secondary have a core of an un-melted Mg-spinel (Al-Mg oxide) that has survived from the electrode together with an outer layer with a composition corresponding to the ESR process slag [25][26][27]. Specifically, this could, e.g., be MgAl 2 O 4 inclusions (with a low content of MgO) and Al 2 O 3 inclusions formed in the liquid metal pool as a result of the reactions between alloying elements and dissolved oxygen.…”

Section: Yearmentioning

confidence: 99%

“…This finding is further supported by the presence of a large number of small inclusions that presumably were precipitated during solidification. However, Sjöqvist Persson et al [26,27] stated that the inclusions in a remelted ingot can both be primary, semi-secondary, or secondary inclusions. The result also corresponds to the new theory, since even the semi-secondary inclusions will have a longer time to grow.…”

Section: Number Of Oxide Inclusions Per Size Position and Ingot Typementioning

confidence: 99%

“…For the ESR ingot, Al 2 O 3 and Al 2 O 3 -CaO oxides followed by Al 2 O 3 -CaO-MgO oxides are the most common inclusions. For the PESR-ingots, Al 2 O 3 -CaO-MgO oxides followed by Al 2 O 3 and Al 2 O 3 -CaO oxides, are the most common inclusions.The difference between ESR and PESR remelted ingots are probably mostly due to the continuous Al de-oxidation during ESR remelting in air[27].…”

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confidence: 98%

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Impact of Solidification on Inclusion Morphology in ESR and PESR Remelted Martensitic Stainless Steel Ingots

Persson¹,

Brorson²,

Mitchell

et al. 2021

Metals

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This study focuses on the impact of solidification on the inclusion morphologies in different sizes of production-scale electro-slag remelting (ESR) and electro-slag remelting under a protected pressure-controlled atmosphere, (PESR), ingots, in a common martensitic stainless steel grade. The investigation has been carried out to increase the knowledge of the solidification and change in inclusion morphologies during ESR and PESR remelting. In order to optimize process routes for different steel grades, it is important to define the advantages of different processes. A comparison is made between an electrode, ESR, and PESR ingots with different production-scale ingot sizes, from 400 mm square to 1050 mm in diameter. The electrode and two of the smallest ingots are from the same electrode charge. The samples are taken from both the electrode, ingots, and rolled/forged material. The solidification structure, dendrite arm spacing, chemical analyzes, and inclusion number on ingots and/or forged/rolled material are studied. The results show that the larger the ingot and the further towards the center of the ingot, the larger inclusions are found. As long as an ingot solidifies with a columnar dendritic structure (DS), the increase in inclusion number and size with ingot diameter is approximately linear. However, at the ingot size (1050 mm in diameter in this study) when the center of the ingot converts to solidification in the equiaxial mode (EQ), the increase in number and size of the inclusions is much higher. The transition between a dendritic and an equiaxial solidification in the center of the ingots in this steel grade takes place in the region between the ingot diameters of 800 and 1050 mm.

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Section: Yearmentioning

confidence: 99%

Section: Yearmentioning

confidence: 99%

Section: Number Of Oxide Inclusions Per Size Position and Ingot Typementioning

confidence: 99%

mentioning

confidence: 98%

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Impact of Solidification on Inclusion Morphology in ESR and PESR Remelted Martensitic Stainless Steel Ingots

Persson¹,

Brorson²,

Mitchell

et al. 2021

Metals

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“…An instance where there are insufficient data available is in the refined steel industry, specifically vacuum pumping in the Electroslag Remelting (ESR) process [2,15,23]. The process includes furnace cleaning [18], which requires vacuum pumping or extracting oxygen from the furnace.…”

Section: Introductionmentioning

confidence: 99%

Testing of Machine Learning Models with Limited Samples: An Industrial Vacuum Pumping Application

Chatterjee¹,

Ahmed²,

Hallin³

et al. 2022

Preprint

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There is often a scarcity of training data for machine learning (ML) classification and regression models in industrial production, especially for time-consuming or sparsely run manufacturing processes. Traditionally, a majority of the limited ground-truth data is used for training, while a handful of samples are left for testing. In that case, the number of test samples is inadequate to properly evaluate the robustness of the ML models under test (i.e., the system under test) for classification and regression. Furthermore, the output of these ML models may be inaccurate or even fail if the input data differ from the expected. This is the case for ML models used in the Electroslag Remelting (ESR) process in the refined steel industry to predict the pressure in a vacuum chamber. A vacuum pumping event that occurs once a workday generates a few hundred samples in a year of pumping for training and testing. In the absence of adequate training and test samples, this paper first presents a method to generate a fresh set of augmented samples based on vacuum pumping principles. Based on the generated augmented samples, three test scenarios and one test oracle are presented to assess the robustness of an ML model used for production on an industrial scale. Experiments are conducted with real industrial production data obtained from Uddeholms AB steel company. The evaluations indicate that Ensemble and Neural Network are the most robust when trained on augmented data using the proposed testing strategy. The evaluation also demonstrates the proposed method's effectiveness in checking and improving ML algorithms' robustness in such situations. The work improves software testing's state-of-the-art robustness testing in similar settings. Finally, the paper presents an MLOps implementation of the proposed approach for real-time ML model prediction and action on the edge

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Effect of the Slag Composition on the Process Behavior, Energy Consumption, and Nonmetallic Inclusions during Electroslag Remelting

Schneider

Wiesinger

Gelder

et al. 2022

steel research int.

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Electroslag remelting (ESR) is a well-established secondary refining process for many steels and Ni-base alloys with highest requirements regarding material properties. The main purposes are a dense solidification of ingots with a low degree of segregation as well as the reduction of medium-sized and especially complete removal of large nonmetallic inclusions (NMI). The specific energy consumption of ESR is documented in the range from 880 to over 2000 kWh t À1 . [1][2][3][4][5][6][7] Rising requirements regarding sustainability, emission control, and environmental protection have triggered new awareness for this topic. [4,8,9] Besides plant geometry and design, the customarily CaF 2 -based slag plays the key role in the heat generation and energy consumption of ESR. Key properties are the melting point as well as the electrical and thermal conductivity. [2,10] Other factors such as fill ratio and the amount of slag, or the melt rate can also have a strong effect. [4,8,[11][12][13][14][15][16] According to Holzgruber, [16] rising fill ratios up to 0.4 lead to a reduction in specific energy consumption due to a better heat transfer into the electrode and less radiation losses at the free slag surface. A further increase in fill ratio surprisingly resulted in a reversed trend due to changing immersion depths. Results from Li et al., [14] both laboratory scale and industrial size, demonstrate the strong effects of fill ratio (0.24 and 0.6) and electrical conductivity on the specific energy consumption with values below 1000 kWh t À1 at higher fill ratios combined with low or no CaF 2 -containing slags. CaF 2 -free slags in Brückmann and Schwerdtfeger [17] confirmed their particular advantage in specific energy consumption with values below 1000 kWh t À1 .There are only few reports of systematic research on energy consumption in ESR on the industrial scale. A recent investigation with a wider variation of slags is documented in refs. [5][6][7], and confirms an almost linear increase with electrical conductivity. A similar but less pronounced increase is reported in Jäger and Kühnelt [18] for slag composition with a wide variety of CaF 2 contents and a significantly higher fill ratio. A recent summary on the different effects of the fill ratio and the electrical conductivity on the specific energy consumption can be found in Schneider et al., [4] indicating that the energy consumption data from laboratory scale

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Origin of the Inclusions in Production-Scale Electrodes, ESR Ingots, and PESR Ingots in a Martensitic Stainless Steel

Cited by 11 publications

References 27 publications

Impact of Solidification on Inclusion Morphology in ESR and PESR Remelted Martensitic Stainless Steel Ingots

Impact of Solidification on Inclusion Morphology in ESR and PESR Remelted Martensitic Stainless Steel Ingots

Testing of Machine Learning Models with Limited Samples: An Industrial Vacuum Pumping Application

Effect of the Slag Composition on the Process Behavior, Energy Consumption, and Nonmetallic Inclusions during Electroslag Remelting

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