Predicting the hardness of carbon nanotube reinforced copper matrix nanocomposites using two adaptive fuzzy inference system identifiers
Bibliographic record
Abstract
The paper deals with devising two different fuzzy inference systems to predict the hardness of copper/carbon nanotube nanocomposite. These composites are outstanding candidates for thermal management applications in electronic packaging due to the high conductivity of copper. Knowing the extraordinary properties of carbon nanotubes, it seems that copper-based composites reinforced with small amount of carbon nanotubes, resulted in improved mechanical properties. Hence, carbon nanotube reinforced copper matrix nanocomposites are fabricated by hot-press sintering of high energy ball milled copper/carbon nanotube powders. Different milling factors are investigated. Finally the Vickers hardness of sintered nanocomposites is reported. To simulate a predictive framework for current case study, two different machine learning algorithms are engaged. The first learning algorithm is the classic least square optimization method, which provides the requirements for fast adaption of the consequent parts of fuzzy inference system. The second method learning algorithm uses the rudiments of neural computing through layering the fuzzy inference system and using back-propagation optimization algorithm. Based on the experiments, the authors realize that the adopted fuzzy systems can effectively extract the knowledge required for predicting the hardness of copper/carbon nanotube nanocomposite.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".