Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing
Abstract
:1. Introduction
2. Methodology
2.1. Hybrid Data Acquisition
2.2. Computational Fluid Dynamics
2.2.1. Conservation Equations
2.2.2. Boundary Conditions
2.2.3. Model Results and Validation
2.3. Feature Engineering
2.3.1. Machine Setting Features
2.3.2. Physics-Aware Features
2.3.3. Feature Distribution
- Laser Power and Laser Scanning Speed exhibit strong alignment between the two datasets, ensuring that both subsets comprehensively cover a similar range of energy input and processing speeds.
- Linear Mass Density and Volumetric Energy Density distributions similarly show significant overlap, confirming that the key physical parameters influencing the DED process are consistently represented in both data sources.
2.4. Data Preprocessing and Metrics
2.5. Benchmark of Models
2.6. Hyperparameter Optimization
3. Results
3.1. Clad Geometry Prediction
- -
- Traditional Models: Support Vector Machine, Gaussian Process, K-nearest Neighbors, Polynomial Regression, Ridge Regression, and Lasso Regression
- -
- Ensemble Models: Gradient Boosting, Random Forest, Decision Tree, and AdaBoost
- -
- Deep Learning: Neural Network
3.2. Process Map Prediction
- -
- Traditional Models: K-Nearest Neighbors Classifier, Gaussian Process Classifier, Logistic Regression, Gaussian Naïve Bayes, Support Vector Classifier
- -
- Ensemble Models: Gradient Boosting Classifier, AdaBoost Classifier, Random Forest Classifier, Decision Tree Classifier
- -
- Deep Learning: Neural Network Classifier (Multilayer Perceptron)
4. Discussion
4.1. Feature Importance Analysis
- Volumetric energy density and linear mass density consistently emerged as the most influential features across all regression targets.
- The strong contributions of these physics-aware features highlight their critical role in governing clad geometry, reaffirming their significance in the DED process.
- The continued importance of volumetric energy density and linear mass density, alongside laser power and scanning speed, in determining clad quality.
- These features collectively provided robust predictive capability, enabling accurate classification of clad quality under varying process conditions.
4.2. Computational and Runtime Complexities
- Gradient boosting and Random Forest
- Neural network
- K-nearest neighbor
- Logistic regression
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
NN | Neural Network |
KNN | K-nearest Neighbor |
GBR | Gradient Boosting Regression |
GBC | Gradient Boosting Classification |
RF | Random Forest |
DT | Decision Tree |
AB | AdaBoost |
GPR | Gaussian Process Regression |
SVR | Support Vector Machine |
SVC | Support Vector Classification |
Poly | Polynomial Regression |
Lasso | Lasso Regression |
Ridge | Ridge Regression |
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Machine Features | Data Range | Physics Aware Features | Data Range |
---|---|---|---|
Laser power | 500–1250 | Volumetric energy density | 6.40–48.15 |
Laser scanning speed | 4.23–12.69 | Linear mass density | 0.0059–0.0118 |
Category | Tasks | Split | Metric |
---|---|---|---|
Width of the clad | Regression | Random | —MAE |
Height of the clad | Regression | Random | —MAE |
Depth of the clad | Regression | Random | —MAE |
Clad Classification | Classification | Random | Classification accuracy—AUC-ROC |
Category | Best Performance | Metric | Value |
---|---|---|---|
Width of clad | GBR | 0.985 | |
MAE | 24.41 | ||
Big-O | |||
RF | 0.981 | ||
MAE | 32.28 | ||
Big-O | |||
NN | 0.975 | ||
MAE | 29.85 | ||
Big-O | |||
Height of clad | GBR | 0.964 | |
MAE | 8.73 | ||
Big-O | |||
NN | 0.955 | ||
MAE | 8.98 | ||
Big-O | |||
RF | 0.952 | ||
MAE | 11.41 | ||
Big-O | |||
Depth of clad | GBR | 0.981 | |
MAE | 19.96 | ||
Big-O | |||
RF | 0.955 | ||
MAE | 8.98 | ||
Big-O | |||
Poly | 0.964 | ||
MAE | 23.41 | ||
Big-O | |||
Clad quality classification | NN | Classification accuracy | 92.90 |
AUC-ROC | 94.59 | ||
Big-O | |||
KNN | Classification accuracy | 92.28 | |
AUC-ROC | 93.78 | ||
Big-O | |||
LR | Classification accuracy | 91.97 | |
AUC-ROC | 94.48 | ||
Big-O |
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Tayebati, S.; Cho, K.T. Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing. J. Manuf. Mater. Process. 2025, 9, 49. https://doi.org/10.3390/jmmp9020049
Tayebati S, Cho KT. Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing. Journal of Manufacturing and Materials Processing. 2025; 9(2):49. https://doi.org/10.3390/jmmp9020049
Chicago/Turabian StyleTayebati, Sina, and Kyu Taek Cho. 2025. "Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing" Journal of Manufacturing and Materials Processing 9, no. 2: 49. https://doi.org/10.3390/jmmp9020049
APA StyleTayebati, S., & Cho, K. T. (2025). Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing. Journal of Manufacturing and Materials Processing, 9(2), 49. https://doi.org/10.3390/jmmp9020049