Enterprise AI Analysis
Predicting porosity in composite high-pressure hydrogen vessels using augmented fuzzy cognitive AI and manufacturing process parameters
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Executive Impact: At a Glance
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7.94%
XTS IVE RMSE on External Test
0.824
XTS IVE Correlation on External Test
3.52/5
XTS IVE Intelligibility Score (out of 5)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
XTRACTIS Modeling Workflow
XTS vs. Boosted Tree: Porosity Number Prediction
XTS Model Intelligibility Achieved
Porosity Rate Classification: A Case Study in Data Limitations
Enterprise Process Flow
Training Dataset (TrD) & Validation Dataset (VD): 153
→
20x5-fold Cross-validation
→
Exploration of 2,000 Induction Strategies → 6,000 CVE2 (3 ao)
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Selection of Best CVE2
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Transparent Engineering → Reverse Synthetic Dataset (~20,000 samples)
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Selection of Top IVE
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Final Evaluation on ETD
| Feature | XTRACTIS IVE | Boosted Tree IVE |
|---|---|---|
| RMSE on External Test | 7.94% | 7.02% |
| Correlation on External Test | 0.824 | 0.872 |
| Intelligibility Score | 3.52 / 5 | 0.00 / 5 (Opaque) |
| Predictors Retained | 15 out of 58 | 58 out of 58 (All) |
| Model Complexity | 54 conjunctive rules (26 disjunctive) | 116 trees (2,289 rules) |
3.52
Intelligibility Score (out of 5)
The XTS IVE achieved an intelligibility score of 3.52/5, retaining only 15 predictors and comprising 54 conjunctive rules aggregated into 26 disjunctive rules, with an average of 4.8 predictors per rule. This contrasts sharply with opaque models, ensuring full compliance with AI Act requirements for high-risk critical decision systems.
Porosity Rate Classification: A Case Study in Data Limitations
In the complementary study to predict porosity rate (low vs. medium/high), both XTRACTIS and Boosted Tree models failed to discover robust models, achieving F1-Scores of 57.14% and 50% respectively on the external test set. This poor performance was attributed to erroneous values and insufficient information within the variable to be predicted, arising from simplified geometric allocation of porosities during data collection. This highlights the critical importance of accurate and well-defined target variables for successful AI model induction and deployment.
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