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Record W2783390982 · doi:10.11159/ijmem.2018.001

Application of Taguchi Methodology on the Manufacturing Quality Evaluation of Subcontract Factories in Tapping Screws

2018· article· en· W2783390982 on OpenAlexvenueno aff
Chih-Cheng Yang, Wen-Ching Chao

Bibliographic record

VenueInternational Journal of Mechanical Engineering and Mechatronics · 2018
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTappingTaguchi methodsQuality (philosophy)Manufacturing engineeringEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

The tapping screw is a well-developed, widely used fastener. The manufacturing processes of tapping screws are sequentially wire-manufacturing, forming (heading and threading), carburizing heat treatment, and phosphate coating, which can be conducted by subcontract factories. The performance qualities of tapping screws are affected by the manufacturing qualities of subcontract factories. In this study, to improve the quality of AISI 1018 low carbon steel tapping screws, Taguchi method is used to evaluate the manufacturing qualities of subcontract factories. The quality characteristics of tapping screws, such as case hardness, core hardness, torsional strength, and drilling time, are investigated, and so are their process capabilities. The most important quality characteristic of a tapping screw is the drilling performance. It is experimentally revealed that the heat treating process (H) and the coating process (C) are the significant processes; while the forming process (F) and the wire manufacturing (W) are relatively not significant since they are well-developed manufacturing processes for tapping screws. Subsequently, the determined subcontract factories: W2, F2, H3 together with C2, evidently improve the performance measures. The drilling performance of the AISI 1018 tapping screws could be improved based on the findings of this research.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.050
GPT teacher head0.318
Teacher spread0.268 · 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 designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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