Quality prediction in manufacturing system design
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.
Bibliographic record
Abstract
Expected product quality is affected by manufacturing system design decisions. Product quality prediction would allow a manufacturer to make better choices of system parameters at the early design stage and, hence, enhance competitiveness through achieving higher quality levels. A Configuration Capability Indicator (CCI) that maps the manufacturing system configuration parameters into an expected product quality level has been developed. A hierarchical fuzzy inference system was developed for modeling the relationship between manufacturing system design parameters and the resulting product quality level. A configuration capability zone is proposed to graphically represent the configuration capability, for a system configuration that produces more than one product, and compare it to the benchmark six-sigma capability. The developed model has been applied to two case studies (Test Parts ANC-90 and ANC-101, and Rack Bar Machining) with different system configurations for illustration and verification. The results demonstrate the ability of the CCI to compare different system configurations from a quality point of view and to support the decision-making during the early stages of manufacturing system planning and design.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it