On the Use of Machine Learning Algorithms to Predict the Corrosion Behavior of Stainless Steels in Lactic Acid
Bibliographic record
Abstract
Predicting the corrosion behavior of materials in specific environmental conditions is important for establishing a sustainable manufacturing system while reducing the need for time-consuming experimental investigations. Recent studies started to explore the application of supervised Machine Learning (ML) techniques to forecast corrosion behavior in various conditions. However, there is currently a research gap in utilizing classification ML techniques specifically for predicting the corrosion behavior of stainless steel (SS) material in lactic acid-based environments, which are extensively used in the pharmaceutical and food industry. This study presents a ML-based prediction model for corrosion behavior of SSs in different lactic acid environmental conditions, using a database that described the corrosion behavior by qualitative labels. Decision tree (DT), random forest (RF) and support vector machine (SVM) algorithms were applied for classification. Training and testing accuracies of, respectively 97.5% and 92.5% were achieved using the DT classifier. Four SS alloy composition elements (C, Cr, Ni, Mo), acid concentration, and temperature were found sufficient to consider as input data for corrosion prediction. The developed models are reliable for predicting corrosion degradation and, as such, contribute to avoiding failures and catastrophes in industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".