Predictive Capability Evaluation of Micrograph-Driven Deep Learning for Ti6Al4V Alloy Tensile Strength Under Varied Preprocessing Strategies
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Image Standardization
2.2.1. Color Processing
2.2.2. Size Adjustment
- Center cropping
- Bilinear interpolation
2.2.3. Image Normalization
- Linear scaling
- 2.
- Min–max normalization
- 3.
- Z-score normalization
2.3. Image Enhancement
2.4. Models for Classification and Prediction
Method and Hyperparameter | Configuration |
---|---|
Optimizer | Adam |
Epoch | 200 |
Early stop patience [29] | 7 |
Learning rate | 0.0001 |
Batch size | 8 |
Initialization of weights in FC | Xavier uniform |
K-fold cross validation | K = 5, Shuffle = True, random_state = 42 |
Method and Hyperparameter | Configuration |
---|---|
Optimizer | Adam |
Epoch | 200 |
Early stop patience | 16 |
Learning rate | 0.008 |
Batch size | 8 |
Initialization of weights in FC | Xavier uniform |
K-fold cross validation | K = 5, Shuffle = True, random_state = 42 |
Random Forest | Configuration | |
n_estimators | 100 | |
Random state | 42 | |
GLCM | distance | [1] |
angles | [0] | |
Symmetric | True | |
normed | True | |
levels | 256 | |
Extracted features | contrast | |
energy | ||
homogeneity | ||
correlation |
3. Results
3.1. Test Results of Classification Model
3.1.1. Test Results of Image Standardization
3.1.2. Test Results of Image Enhancement
3.2. Test Results of Regression Models
3.2.1. Test Results of Image Standardization
3.2.2. Test Results of Image Enhancement
3.2.3. Test Results of Subsets with Less Heterogeneity
4. Discussion
4.1. Predictive Capability of the Model
4.2. Impact of Size Adjustment and Normalization Methods on Model Performance
4.3. Impact of Image Enhancement Methods on Model Performance
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Method | Role | Parameters |
---|---|---|
Mean Filtering (MF) | Remove noise and smooth image | Ksize = (3,3) (Based on OpenCV) |
Gaussian Filtering (GF) | Remove noise and smooth image | Ksize = (3,3), sigmaX = 0 (Based on OpenCV) |
Gray Linear Transformation-1 (GLT-1) | Reduce brightness and contrast | Y = kx + b, x represents pixel value, k = 0.75, b = 0 |
Gray Linear Transformation-2 (GLT-2) | Increase brightness and contrast | Y = kx + b, k = 1.15, b = 0 |
Gamma Correction-1 (GC-1) | Increase brightness and highlight dark details | Y = xγ, x is divided by 255, x ∈ [0, 1]. γ = 0.5 |
Gamma Correction-2 (GC-2) | Reduce brightness and highlight bright details | Y = xγ, x ∈ [0, 1]. γ = 1.7 |
Contrast Limited Adaptive Histogram Equalization (CLAHE) | Enhance local contrasts of the image | clipLimit = 2.0, titleGridsize = (4,4) (Based on OpenCV) |
Histogram Equalization (HE) | Enhance contrasts and details of the image | Default parameters (Based on OpenCV) |
Strength Grade | UTS Interval (MPa) | Proportion of Samples (%) |
---|---|---|
Low-strength | 28 | |
Medium-strength | 35 | |
High-strength | 37 |
Crop | Scale | Linear Scaling | Min–Max | Z-Score | Mean R2 | Standard Deviation |
---|---|---|---|---|---|---|
√ | √ | 0.061 | 0.029 | |||
√ | √ | 0.111 | 0.017 | |||
√ | √ | 0.074 | 0.018 | |||
√ | √ | 0.079 | 0.006 | |||
√ | √ | 0.061 | 0.031 | |||
√ | √ | 0.102 | 0.026 | |||
√ | 0.047 | 0.021 | ||||
√ | 0.026 | 0.020 | ||||
Mean baseline | −0.003 | 0 |
Linear Regression | Random Forest | Crop | Scale | Mean R2 |
---|---|---|---|---|
√ | √ | −0.024 | ||
√ | √ | 0.003 | ||
√ | √ | −0.028 | ||
√ | √ | 0.109 | ||
Mean baseline | −0.003 |
MF | GF | GLT-1 | GLT2-2 | GC-1 | GC-2 | CLAHE | HE | Mean R2 | Standard Deviation |
---|---|---|---|---|---|---|---|---|---|
√ | 0.117 | 0.029 | |||||||
√ | 0.126 | 0.017 | |||||||
√ | 0.080 | 0.018 | |||||||
√ | 0.058 | 0.006 | |||||||
√ | 0.102 | 0.031 | |||||||
√ | 0.089 | 0.026 | |||||||
√ | 0.071 | 0.021 | |||||||
√ | 0.163 | 0.020 | |||||||
Mean baseline | −0.003 | 0 |
MF | GF | GLT-1 | GLT2-2 | GC-1 | GC-2 | CLAHE | HE | Mean R2 |
---|---|---|---|---|---|---|---|---|
√ | 0.093 | |||||||
√ | 0.082 | |||||||
√ | 0.045 | |||||||
√ | 0.014 | |||||||
√ | 0.052 | |||||||
√ | 0.024 | |||||||
√ | −0.086 | |||||||
√ | −0.043 | |||||||
Mean baseline | −0.003 |
MF | GF | GLT-1 | GLT-2 | GC-1 | GC-2 | CLAHE | HE | Mean R2 |
---|---|---|---|---|---|---|---|---|
√ | 0.016 | |||||||
√ | 0.085 | |||||||
√ | 0.111 | |||||||
√ | 0.139 | |||||||
√ | −0.021 | |||||||
√ | 0.048 | |||||||
√ | −0.016 | |||||||
√ | −0.057 | |||||||
Mean baseline | −0.003 |
Subset | Sample Size | Mean R2 | Standard Deviation |
---|---|---|---|
SLM-based | 56 | 0.298 | 0.003 |
DED-based | 16 | 0.360 | 1.47 × 10−5 |
EBM-based | 18 | 0.329 | 6.66 × 10−5 |
Subset | Sample Size | Mean R2 |
---|---|---|
SLM-based | 56 | 0.137 |
DED-based | 16 | 0.148 |
EBM-based | 18 | -0.233 |
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Xiong, Y.; Duan, W. Predictive Capability Evaluation of Micrograph-Driven Deep Learning for Ti6Al4V Alloy Tensile Strength Under Varied Preprocessing Strategies. Metals 2025, 15, 586. https://doi.org/10.3390/met15060586
Xiong Y, Duan W. Predictive Capability Evaluation of Micrograph-Driven Deep Learning for Ti6Al4V Alloy Tensile Strength Under Varied Preprocessing Strategies. Metals. 2025; 15(6):586. https://doi.org/10.3390/met15060586
Chicago/Turabian StyleXiong, Yuqi, and Wei Duan. 2025. "Predictive Capability Evaluation of Micrograph-Driven Deep Learning for Ti6Al4V Alloy Tensile Strength Under Varied Preprocessing Strategies" Metals 15, no. 6: 586. https://doi.org/10.3390/met15060586
APA StyleXiong, Y., & Duan, W. (2025). Predictive Capability Evaluation of Micrograph-Driven Deep Learning for Ti6Al4V Alloy Tensile Strength Under Varied Preprocessing Strategies. Metals, 15(6), 586. https://doi.org/10.3390/met15060586