Artificial neural network-based modelling and prediction of white layer formation during hard turning of steels
Bibliographic record
Abstract
During hard machining, steels subjected to very high thermal and mechanical loads can result in microstructural/phase changes such as the formation of a white layer. This layer, which is often harder than the raw material, is considered detrimental to the fatigue performance and in-service life of machined parts. This paper proposes a comprehensive study of white layer formation during hard machining of steels using statistical analysis and artificial neural networks (ANN) modeling. To this end, two steals, named AISI 52100 and AISI 4340, commonly used in the manufacturing of structural machines’ components and extensively studied in the last decade, have been considered in this study. First, Taguchi method combined with response surface methodology (RSM) was applied to analyze and to optimize the machining parameters regarding the white layer thickness. Second, an ANN model is developed to predict the white layer thickness during hard machining of the studied steels using a large amount of machining data. Three training algorithms were tested to find the most robust configuration. The equivalent carbon parameter was introduced for the first time in machining modeling which make the proposed ANN-based model capable of predicting the white layer thickness for different hardened steels. The results show a significant agreement between predictions and experimental results, avoiding costly experimental machining tests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".