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Record W4281783946 · doi:10.4028/p-295y1h

Study of Durable Strength of Steel Mining and Metallurgical Equipment

2022· article· en· W4281783946 on OpenAlex
В. Д. Макаренко, S.Yu. Maksimov, Yu. V. Маkarenko, Olena S. Panchenko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceMetallurgyService lifeCorrosionFatigue limitCorrosion fatigueBendingForensic engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Erosion of the metal of mining and metallurgical equipment due to the impact of solid particles is one of the forms of wear that can significantly limit the service life of a working machine or technological equipment, for example, a pipeline conveyor, loading and unloading bodies of mine workings and metallurgical machines. At present, this problem has been little studied and there is not enough information in the literature to form a systematic picture of impact erosion of equipment elements of mining and processing plants. The purpose of the research was to study the fatigue strength and corrosion-mechanical crack resistance of some structural elements of mining and metallurgical equipment with a long service life in chemically aggressive environments. Experimental tests for corrosion fatigue (long-term strength) were performed under bending load. The tests were performed on a bend with zero average voltage and a cycle frequency of 30 Hz. The tests were performed in salt solutions with a concentration of NaCl 5%. To compare the results, tests were sometimes performed in the air. The given data analysis shows that the long-term fatigue of the metal of mining and metallurgical equipment is significantly reduced when reaching 20 years of operation, especially in an aggressive environment containing chlorine ions, which causes severe corrosion damage to steel equipment. In addition, samples cut from metal with a long service life in mining and metallurgical conditions (more than 20 years) are characterized by low long-term strength. It has been found that fatigue resistance decreases with an increase in the number of cycles. Steel samples tested based on N = 10 6 and especially on the basis of N = 10 7 cycles have low resistance, which inevitably leads to breakdown with the subsequent destruction of equipment. It has been established that with an increase in the service life of the experimental mining and metallurgical equipment, the fracture toughness of the metal decreases significantly, which causes further failure and destruction of technological equipment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.006
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.288
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it