Prediction of Mechanical Properties of Wrought Aluminium Alloys Using Feature Engineering Assisted Machine Learning Approach

Table of Contents

1. Introduction
2. Background
   • 2.1 Wrought Aluminium Alloys
   • 2.2 Mechanical Properties and Their Importance
   • 2.3 Machine Learning in Materials Science
3. Challenges in Predicting Mechanical Properties
   • 3.1 Complex Manufacturing Processes
   • 3.2 Feature Digitalization
4. Procedure-Oriented Decomposition (POD)
   • 4.1 Concept and Methodology
   • 4.2 Integration with Machine Learning
5. Support Vector Regressor (SVR) Model
   • 5.1 Overview of SVR
   • 5.2 Application in Property Prediction
6. Feature Engineering
   • 6.1 Chemical Composition Features
   • 6.2 Manufacturing Process Features
7. Data Collection and Preparation
   • 7.1 Data Sources
   • 7.2 Data Cleaning and Normalization
8. Model Development
   • 8.1 Training and Validation
   • 8.2 Hyperparameter Tuning
9. Results and Discussion
   • 9.1 Prediction Accuracy
   • 9.2 Comparison with Traditional Methods
   • 9.3 Case Studies
10. Potential for New Alloy Design
    • 10.1 Design Framework
    • 10.2 Real-world Applications
11. Conclusion
12. References

Introduction

The advancement of materials science and engineering heavily relies on the ability to predict and optimize the mechanical properties of alloys. Wrought aluminium alloys, known for their excellent strength-to-weight ratio, corrosion resistance, and versatility, are extensively used in aerospace, automotive, and construction industries. Traditional methods of alloy development involve intensive experimental work, which is time-consuming and costly. The emergence of data-mining-based machine learning (ML) approaches offers a promising alternative to accelerate the prediction and optimization of alloy properties.

However, applying ML models to predict the mechanical properties of wrought aluminium alloys poses significant challenges. The complexity arises from the variety of manufacturing processes and the difficulty in feature digitalization, which limits the applicability of ML models across different alloy designations. Most previous studies have focused on specific alloys, hindering the broader adoption of ML in alloy design and property prediction.

In this context, we propose a novel feature engineering approach called Procedure-Oriented Decomposition (POD), which integrates chemical compositions and manufacturing processes into the ML model. By employing a Support Vector Regressor (SVR) model, we establish a correlation mapping between these features and the mechanical properties of wrought aluminium alloys. This framework not only demonstrates high prediction accuracy but also shows potential in designing new alloys with desired properties.

Elka Mehr Kimiya is a leading manufacturer of aluminum rods, alloys, conductors, ingots, and wire in the northwest of Iran equipped with cutting-edge production machinery. Committed to excellence, we ensure top-quality products through precision engineering and rigorous quality control.

Background

2.1 Wrought Aluminium Alloys

Wrought aluminium alloys are aluminium materials shaped by mechanical processes such as rolling, extruding, and forging. They are classified based on their alloying elements and are designated by a four-digit numbering system established by the Aluminium Association. The primary series include:

• 1xxx Series: Pure aluminium (99% minimum), known for excellent electrical conductivity and corrosion resistance.
• 2xxx Series: Copper as the principal alloying element, offering high strength.
• 3xxx Series: Manganese as the principal alloying element, providing moderate strength and good workability.
• 5xxx Series: Magnesium as the principal alloying element, known for good welding characteristics.
• 6xxx Series: Magnesium and silicon as principal alloying elements, providing medium strength and good formability.
• 7xxx Series: Zinc as the principal alloying element, offering high strength.

These alloys are used in a variety of applications, from aircraft structures to automotive components and building materials.

2.2 Mechanical Properties and Their Importance

The mechanical properties of aluminium alloys, such as tensile strength, yield strength, elongation, and hardness, are critical parameters that determine their suitability for specific applications. These properties are influenced by:

• Chemical Composition: The type and amount of alloying elements affect the microstructure and, consequently, the mechanical properties.
• Manufacturing Processes: Processes like casting, rolling, heat treatment, and cold working alter the material’s internal structure.
• Heat Treatment: Processes like annealing, quenching, and aging modify the distribution of alloying elements and precipitates.

Understanding and predicting these properties enable engineers to tailor alloys for specific performance requirements, reducing the need for extensive experimental testing.

2.3 Machine Learning in Materials Science

Machine learning has emerged as a powerful tool in materials science for predicting properties, discovering new materials, and optimizing processes. By learning patterns from existing data, ML models can predict outcomes for new compositions or processing conditions. Applications include:

• Property Prediction: Estimating mechanical, thermal, and electrical properties based on composition and processing parameters.
• Materials Discovery: Identifying new materials with desired properties by exploring vast compositional spaces.
• Process Optimization: Enhancing manufacturing processes by predicting the effects of process variables.

Despite its potential, the application of ML in predicting the properties of wrought aluminium alloys faces challenges due to the complex interplay of composition and processing conditions.

Challenges in Predicting Mechanical Properties

3.1 Complex Manufacturing Processes

Wrought aluminium alloys undergo various manufacturing steps, including:

• Casting: The initial solidification of the alloy.
• Hot and Cold Working: Mechanical deformation processes like rolling and extrusion.
• Heat Treatment: Thermal processes to alter microstructure, such as solution treatment and aging.

Each step introduces variables that affect the final mechanical properties. Capturing these complex processes in an ML model is challenging due to:

• High Dimensionality: Numerous variables and their interactions.
• Non-linear Relationships: Complex, non-linear effects of process parameters on properties.
• Data Availability: Limited datasets that comprehensively cover all variables.

3.2 Feature Digitalization

Feature digitalization involves converting manufacturing processes into numerical features suitable for ML models. Challenges include:

• Standardization: Processes vary between manufacturers, making it difficult to standardize features.
• Quantification of Qualitative Data: Converting process descriptions into numerical values.
• Interdependencies: Accounting for the interactions between different process steps.

Previous studies often simplify or overlook manufacturing processes, focusing solely on chemical composition, which limits the generalizability of the models.

Procedure-Oriented Decomposition (POD)

4.1 Concept and Methodology

Procedure-Oriented Decomposition (POD) is a feature engineering technique designed to systematically decompose and quantify manufacturing processes. The key steps include:

1. Process Mapping: Breaking down the manufacturing process into discrete steps.
2. Feature Extraction: Identifying critical parameters in each step (e.g., temperature, time, deformation rate).
3. Quantification: Assigning numerical values to each parameter.
4. Normalization: Standardizing features to ensure consistency across data samples.

By decomposing the process, POD captures the essential variables influencing mechanical properties, facilitating their integration into ML models.

4.2 Integration with Machine Learning

Integrating POD with ML involves:

• Feature Integration: Combining process features with chemical composition features.
• Model Training: Using the integrated features to train an ML model, such as SVR.
• Correlation Mapping: Establishing relationships between features and mechanical properties.

This approach allows the model to learn the complex interplay between composition, processing, and properties, improving prediction accuracy.

Support Vector Regressor (SVR) Model

5.1 Overview of SVR

Support Vector Regression (SVR) is a supervised learning model derived from Support Vector Machines (SVM), used for regression problems. Key characteristics include:

• Kernel Functions: SVR uses kernel functions (e.g., linear, polynomial, radial basis function) to handle non-linear relationships.
• Margin of Tolerance: SVR seeks to fit the best line within a threshold, balancing model complexity and prediction accuracy.
• Robustness: Effective in high-dimensional spaces and with limited data.

5.2 Application in Property Prediction

In predicting mechanical properties:

• Feature Handling: SVR can manage numerous features from POD and composition.
• Non-linear Relationships: Captures the complex, non-linear effects of features on properties.
• Generalization: Provides good generalization performance, reducing overfitting.

SVR is chosen for its ability to model the intricate relationships in materials data effectively.

Feature Engineering

6.1 Chemical Composition Features

Chemical composition features include the weight percentages of alloying elements such as:

• Major Elements: Aluminium (Al), Magnesium (Mg), Silicon (Si), Copper (Cu), Zinc (Zn).
• Minor Elements: Manganese (Mn), Iron (Fe), Chromium (Cr), Titanium (Ti).

Features are prepared by:

• Normalization: Ensuring the total sum of elements equals 100%.
• Interaction Terms: Including products of element percentages to capture synergistic effects.

6.2 Manufacturing Process Features

Manufacturing process features extracted via POD include:

• Temperature Parameters: Casting temperature, rolling temperature, aging temperature.
• Time Parameters: Soaking time, aging time.
• Mechanical Deformation Parameters: Reduction ratios in rolling or extrusion.
• Heat Treatment Steps: Presence or absence of solution treatment, quenching, aging.

Each feature is quantified and standardized for consistency.

Data Collection and Preparation

7.1 Data Sources

Data is collected from:

• Scientific Literature: Journals, conference proceedings detailing alloy compositions and properties.
• Industry Databases: Material property databases like MatWeb, ASM Handbooks.
• Experimental Data: Collaborations with manufacturers and laboratories.

Over 500 data points are compiled, covering various alloy compositions and processing conditions.

7.2 Data Cleaning and Normalization

Data preparation steps:

• Cleaning: Removing outliers and inconsistent entries.
• Missing Values: Imputing missing data using statistical methods or discarding incomplete records.
• Normalization: Scaling features using methods like Min-Max scaling or Z-score normalization.

These steps ensure the data is suitable for ML model training.

Model Development

8.1 Training and Validation

The dataset is split into:

• Training Set: 80% of the data used to train the SVR model.
• Validation Set: 10% used to tune hyperparameters.
• Test Set: 10% used to evaluate model performance.

Cross-validation techniques like k-fold cross-validation are employed to ensure robustness.

8.2 Hyperparameter Tuning

Hyperparameters tuned include:

• Kernel Function: Tested linear, polynomial, and radial basis function (RBF) kernels.
• Regularization Parameter (C): Controls the trade-off between model complexity and training error.
• Epsilon (ε): Defines the margin of tolerance for error.

Grid search and random search methods are used for optimization.

Results and Discussion

9.1 Prediction Accuracy

The SVR model achieves:

• Mean Absolute Error (MAE): 5 MPa for tensile strength predictions.
• Coefficient of Determination (R²): 0.95, indicating high correlation between predicted and actual values.
• Root Mean Square Error (RMSE): 7 MPa, showing low prediction error.

These metrics demonstrate the model’s high accuracy in predicting mechanical properties.

9.2 Comparison with Traditional Methods

Compared to traditional empirical models:

• Flexibility: The SVR model handles a broader range of alloys and processes.
• Accuracy: Improved prediction accuracy due to the inclusion of process features.
• Efficiency: Reduces the need for extensive experimental testing.

9.3 Case Studies

Case Study 1: Predicting the tensile strength of a 6061 alloy with specific processing conditions resulted in a predicted value within 3% of the experimental value.

Case Study 2: Designing a new alloy with target properties was achieved by inputting desired mechanical properties and using the model to suggest optimal compositions and processes.

Potential for New Alloy Design

10.1 Design Framework

The prediction framework can be reversed for alloy design:

• Property Targets: Specify desired mechanical properties.
• Inverse Prediction: Use the model to predict compositions and processes that meet targets.
• Optimization Algorithms: Implement algorithms like genetic algorithms for optimal solutions.

10.2 Real-world Applications

Applications include:

• Customized Alloys: Designing alloys for specific applications like aerospace components.
• Process Optimization: Adjusting manufacturing parameters to enhance properties without changing composition.
• Resource Efficiency: Reducing material costs by optimizing alloying elements.

Conclusion

The integration of Procedure-Oriented Decomposition with machine learning offers a significant advancement in predicting the mechanical properties of wrought aluminium alloys. By effectively capturing the complexities of manufacturing processes and combining them with chemical composition data, the SVR model demonstrates high prediction accuracy. This approach not only accelerates the alloy development process but also opens avenues for designing new alloys tailored to specific applications.

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