Predictive modeling for quality prediction in multi-stage manufacturing systems using artificial intelligence
Bibliographic record
Abstract
Predicting quality characteristics in multi-stage manufacturing systems (MMSs) poses challenges due to the propagation of variation across stages. In MMSs, any variation introduced at an earlier stage can be amplified in subsequent stages. Many industries rely on in-process quality inspections to monitor and adjust manufacturing processes. Based on inspection outcomes, workers often make process adjustments to maintain product specifications. These adjustments are frequently guided by individual experience rather than systematic methods. This reliance on subjective judgment introduces variability in quality outcomes, as worker evaluations may differ. Moreover, unnecessary adjustments can inadvertently increase variation, further destabilizing the process. This study examines the literature of machine learning algorithms used for quality prediction in MMSs. Selected methods include partial least squares regression, principal component regression, support vector machines with linear and radial basis functions, random forest, k-nearest neighbors XGboost and Feed Forward Neural Network. We applied these techniques to an MMS that produces aircraft engine parts. The process involves intermediate inspections using coordinate measuring machines (CMM). Our predictions rely solely on in-process inspection data, without incorporating process parameters or sensor readings. Historical quality characteristic (QC) data guides the predictions for subsequent stages, including final inspections. This enables proactive quality control and production flow optimization. The results demonstrate that the chosen models can predict the QCs’ values for both consecutive and advanced stages in the MMS. Limitations and future directions are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".