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Record W2327492763 · doi:10.9773/sosei.52.469

Development of Long-Life Heading Tool with Stress Reduction Structure

2011· article· en· W2327492763 on OpenAlexaff
Yuji Mure, Kenji Nakanishi, Toshihiro Higashi, Shunichirou OZAKI, Kazuo Sugiyama, Hiroshi KONAKA

Bibliographic record

VenueJournal of the Japan Society for Technology of Plasticity · 2011
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsMD Precision (Canada)
Fundersnot available
KeywordsHeading (navigation)Reduction (mathematics)Stress (linguistics)Structural engineeringEngineeringGeologyMathematicsGeometryGeodesyPhilosophy

Abstract

fetched live from OpenAlex

In recent years, a decrease in tool life has been observed in the manufacturing of small screws for precision instruments by cold heading because of the complication of the head shape and hard workpiece material. Thus, remarkable cost saving and high productivity could be achieved by increasing the tool life used in the cold heading of screws such as an M2 pan head screw made of SWCH16A. The prevalent methods of improving the tool life are related to the wear or stiffness enhancement of the tool. In the present study, we propose a method of improving the tool life related to the elastic strain energy absorbed by the tool. First, we analyzed the heading tool for forming the M2 pan head screw, which had a structure configuration for reducing stress in the tool, using the finite element method (FEM) code. Second, the dimensions of the structure configuration of the tool with a hollow section were optimized by FEM analysis. Then, the tool life was increased successfully. The tool life of the developed heading tool was increased 3.7 times compared with that of a conventional heading tool.

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 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 score0.034
Threshold uncertainty score0.259

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 head0.014
GPT teacher head0.198
Teacher spread0.184 · 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 teacher head, 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

Citations0
Published2011
Admission routes1
Has abstractyes

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