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Record W4417273119 · doi:10.31399/asm.hb.v25a.a0007124

Introduction to Residual Stress Measurement

2025· book-chapter· en· W4417273119 on OpenAlexaff
James Pineault, Iuliana Cernatescu, Philippe Bocher, M. Belassel

Bibliographic record

VenueASM International eBooks · 2025
Typebook-chapter
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsResidual stressResidualStress (linguistics)Measure (data warehouse)Nondestructive testing

Abstract

fetched live from OpenAlex

Abstract This article focuses on various methods that are used to measure residual stress in materials and presents the principles by which each method works. These include nondestructive and destructive methods; portable and nonportable methods; and diffraction and mechanical methods. The article also considers residual stress gradients and discusses surface residual stress gradients (i.e., gradients across the surface) and subsurface residual stress gradients (i.e., gradients into the depth of the material). The article also presents a checklist to verify and validate experimentally obtained residual stress measurement results.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.071
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0710.040

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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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