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Record W2108875588 · doi:10.1504/ijpd.2008.016369

Design-for-six-sigma for multiple response systems

2007· article· en· W2108875588 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Product Development · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicOptimal Experimental Design Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRobustness (evolution)Six SigmaReliability engineeringEngineeringSigmaDesign for Six SigmaMathematical optimizationControl theory (sociology)Computer scienceControl engineeringControl (management)Manufacturing engineeringMathematics

Abstract

fetched live from OpenAlex

An important upstream activity in the overall design of a system is the allocation of the means and tolerances. This is a daunting task because of the need to satisfy multiple competing demands that arise from performance, cost and quality policies. Herein, the so-called design-for-six-sigma is adopted for the allocation process whereby the philosophy of zero defects in Six Sigma is now applied to the system performances. Probability-constrained optimisation is invoked. Robustness and cost measures are required. The production cost provides the objective function to be minimised and a maximum allowable system probability of non-conformance (e.g. a system defect rate) provides the primary design constraint. The design of an electro-mechanical servo system with three responses, three control variables and two noise variables is given. Means and tolerances for the three control variables plus system costs for progressively lower product defect rates show the practicality and potential of the approach.

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.

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)0.041
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.358
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.244
GPT teacher head0.482
Teacher spread0.238 · 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