Deep Learning-Guided Production Quality Estimation for Virtual Environment-Based Applications
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
In modern smart factories, quality estimation is vital for maximum productivity. However, quality estimation by definition relies on an imbalanced dataset, as most smart factories are highly efficient. In this research, we propose a guided quality estimation system that can recognize faulty data among a highly imbalanced production dataset. We also propose a customized LSTM model that is trained to ensure high accuracy in the quality estimation system. This is achieved by our proposed batch-wise balanced training method. Moreover, traditional means of evaluation for this type of method are not suitable, again due to the highly imbalanced nature of the dataset. Thus, a proper evaluation metric is also discussed. The proposed customized LSTM model with custom batch-wise SMOTE + ENN achieved 99.9% accuracy with an f1 score of 95%. This new proposed method for the imbalanced smart factory quality estimation will improve drastically and give pathway to more improved quality. Finally, we discuss practical implementation for the edge server consisting of the proposed guided production estimation system and real-time visualization. Feasibility analysis of this virtual environment-based application of the proposed framework ensured low computational overhead and faster processing.
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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) | 0.000 | 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