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Record W1967813726 · doi:10.1115/1.2204952

Use of Inverse Solutions for Residual Stress Measurements

2006· article· en· W1967813726 on OpenAlexafffund
G. S. Schajer, Michael B. Prime

Bibliographic record

VenueJournal of Engineering Materials and Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of British Columbia
FundersLos Alamos National LaboratoryNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsTikhonov regularizationResidual stressResidualHole drilling methodIntegral equationInverse problemDeep hole drillingApplied mathematicsInverseVolterra integral equationRegularization (linguistics)MathematicsMathematical analysisComputer scienceMathematical optimizationAlgorithmMaterials scienceDrillingGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract For most of the destructive methods used for measuring residual stresses, the relationship between the measured deformations and the residual stresses are in the form of an integral equation, typically a Volterra equation of the first kind. Such equations require an inverse method to evaluate the residual stress solution. This paper demonstrates the mathematical commonality of physically different measurement types, and proposes a generic residual stress solution approach. The unit pulse solution method that is presented is conceptually straightforward and has direct physical interpretations. It uses the same basis functions as the hole-drilling integral method, and also permits enforcement of equilibrium constraints. In addition, Tikhonov regularization is shown to be an effective way to reduce the influences of measurement noise. The method is successfully demonstrated using data from slitting (crack compliance) measurements, and excellent correspondence with independently determined residual stresses is achieved.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.220
Teacher spread0.187 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations224
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Engineering Materials and TechnologySame topicWelding Techniques and Residual StressesFrench-language works237,207