PERANCANGAN BERBASIS KOMPUTER UNTUK REKAYASA PRODUK DAN PROSES KOMPLEKS
Bibliographic record
Abstract
We enhanced DFSS Characterize and Optimize Phases to deal with multiple and functional response optimization. The enhancement basically starts with a development of functional meta-models of CAE outputs that are fast-to-compute and accurate enough within a certain design space. Having developed the multiple and functional meta-models, the influence of design variables to the functional responses are then obtained via visualization and sensitivity analysis based on Sobol's index. Multi-objective optimization is finally applied to search for design variable settings that give optimal functional responses.
 
 
 Abstract in Bahasa Indonesia : 
 
 Di artikel ini kita memperkaya fase-fase Characterize and Optimize dari design for six sigma (DFSS) methodology untuk bisa menyelesaikan rekayasa produk dan proses kompleks yang mengandung banyak respons fungsional. Pemerkayaan dimulai dengan pembangunan meta-model fungsional dari keluaran computer aided engineering (CAE) yang diperoleh lewat rancangan eksperimental. Kemudian, dilanjutkan dengan pembangunan sebuah algoritma untuk mengidentifikasi tingkat pengaruh dari variabel-variable rancangan ke respons fungsional secara visual maupun analitis. Akhirnya, optimasi multi obyektif diterapkan untuk mencari nilai-nilai variabel design yang mampu memberikan respons fungsional secara optimal dan tangguh.
 
 Kata-kunci: perancangan berbasis komputer, response fungsional.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".