An interactive materials selection and analysis platform that enables users to compare engineering materials based on their mechanical and physical properties. The project combines Materials Engineering concepts with Python-based data analysis and interactive web visualization to support material selection and performance evaluation.
Alloy Atlas helps users evaluate engineering materials using important engineering properties such as:
- Yield Strength
- Density
- Young's Modulus
- Cost
- Maximum Service Temperature
- Corrosion Resistance
The project consists of:
- 🌐 Interactive web application for material comparison
- 📊 Python-based engineering analysis and visualization
- 📈 Material ranking using performance metrics
- Compare up to 4 engineering materials
- Interactive material property comparison
- Filter materials by category
- Dynamic radar chart visualization
- Specific Strength ranking
- Specific Stiffness calculation
- Category-wise material statistics
- Export engineering analysis charts
- HTML5
- CSS3
- JavaScript
- Chart.js
- Python
- Pandas
- Matplotlib
The application compares engineering materials using key mechanical and physical properties stored in a structured dataset.
Python scripts calculate engineering performance metrics such as Specific Strength and Specific Stiffness, while the interactive web interface visualizes property comparisons using dynamic charts and graphical analysis.
The project demonstrates how computational tools can assist engineering material selection for structural and mechanical design applications.
- Material Selection
- Specific Strength
- Specific Stiffness
- Density-Based Comparison
- Mechanical Property Analysis
- Weight-Critical Design
- Performance-Based Material Ranking
These concepts are widely used in aerospace, automotive, manufacturing, and structural engineering applications.
git clone https://github.com/Arundhathi2425/Alloys-Atlas.git
cd AlloyAtlaspip install -r requirements.txtpython materials_analysis.pyOpen
index.html
in your browser.
- Expand the engineering materials database
- AI-based material recommendation system
- Machine Learning prediction of material properties
- Flask/FastAPI backend integration
- Stress–strain curve visualization
- Material cost-performance optimization
- Multi-objective material selection algorithms
Arundhathi
B.Tech – Metallurgical & Materials Engineering
Indian Institute of Technology Kharagpur