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Record W2016819987 · doi:10.1080/10426910903163223

Understanding Strength-Toughness Combination in the Processing of Engineering Steels: A Perspective

2010· article· en· W2016819987 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMaterials and Manufacturing Processes · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsMaterials scienceMetallurgyToughnessAusteniteThermomechanical processingFormabilityFracture toughnessMartensiteDuctility (Earth science)AlloyLathMicrostructure

Abstract

fetched live from OpenAlex

Abstract High strength-high toughness combination and formability has been the primary focus of the author's research over the last two decades, where the attempt was to either develop newer steels or maximize the fracture resistance of engineering steels at specified levels of strength. In this regard, significant success was achieved based on an extended program of basic research at the author's current and former institutions to understand the part played by crystal structure, solute additions, grain size, grain boundary chemistry, texture, and substructural features such as retained austenite, martensite lath, and packet size, and characteristics of other microstructural constituents. Each of these features influences the fracture mode, the degree of plasticity, and the rate of growth of nucleated voids. Important instances include maraging steels, precipitation hardened stainless steels, low alloy steels, interstitial-free steels, microalloyed steels, pipeline steels, and silicon-containing medium carbon steels. Underlying the attempt to maximize toughness through the study of determining role of microstructure were the development of concept of grain boundary segregation maps, application of stereological approach, new alloy design with lean chemistry, and streamlining of processing-related variables. The aforementioned instances of engineering steels provided a means of comprehensively analyzing the relationship of toughness to microstructural features and facilitate the development of high performance steels. Keywords: FormabilityHigh strength steelMicroalloyingMicrostructureToughness ACKNOWLEDGMENTS The author gratefully acknowledges a number of friends, colleagues, and their respective institutions in India, United States, Brazil, Canada, United Kingdom, Germany, Netherlands, Finland, and Korea with whom the author interacted and collaborated during the past two decades. Many of them have directly or indirectly contributed to the work described here. In view of a long list of contributors and participants, the author preferred to defer the listing. Every attempt was made to cite relevant references to the described work and any omission is unintentional. It is, however, relevant to acknowledge the most recent support of 2008, received from National Science Foundation (CMMI: 0757799), USA.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.214
Teacher spread0.192 · 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