Application of Taguchi Methodology on the Manufacturing Quality Evaluation of Subcontract Factories in Tapping Screws
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".