Online Prediction of Automotive Tempered Glass Quality using Machine Learning
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
Abstract This study introduce the application of machine learning algorithms for supporting the manufacturing quality control of a complex process as an alternative for the destructive testing methodologies. The choice of this application field was motivated by the lack of a robust engineering technique to assess the production quality in real time, this arise the need of using advanced smart manufacturing solution as AI in order to save the extremely high cost of destructive tests. In concrete, this paper investigates the performance of machine learning techniques including Ridge regression, Linear Regression, Light Gradient Boosting Machine, Lasso Regression and more, for predicting the flat glass tempering quality within the building glass industry. In the first part, we applied the selected machine learning models to a dataset collected manually and made up by the more relevant process parameters of the heating and the quenching process. Evaluating the results of the applied models, based on several performance indicators such as Mean Absolute Error, Mean Squared Error, R Squared, declared that Ridge Regression was the most accurate model. The second part consist of developing a digitalized device connected with the manufacturing process in order to provide predictions in real time. This device operates as an error-proofing system that send a reverse signal to the machine in case the prediction shows a non-compliant quality of the current processed product. This study can be expanded to predict the optimal process parameters to use when the predicted values does not meet the desired quality, and can advantageously replace the trial and error approach that is generally adopted for defining those parameters. The contribution of our work relies on the introduction of a clear methodology (from idea to industrialization) for the design and deployment of an industrial-grad predictive solution within a new field which is the glass manufacturing.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 it