Space Technology & ML

NASA Outgassing Analysis

Comprehensive machine learning application for NASA outgassing materials database analysis. Develops predictive models for material performance evaluation using TML, CVCM, and WVR metrics to optimize space-grade material selection and quality control processes.

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NASA Outgassing Materials Analysis

Project Overview

This project develops machine learning models to evaluate and predict material performance for space applications using NASA's outgassing materials database. The analysis focuses on critical material quality metrics that determine suitability for high-vacuum space environments.

The project encompasses comprehensive data processing, exploratory data analysis, feature engineering, and implementation of both regression and classification models to support material selection and quality control decisions for space missions.


Key Objectives

  • Performance Prediction – Develop linear regression models to evaluate material performance scores
  • Quality Classification – Implement classification models to assess material reliability categories
  • Material Optimization – Enable data-driven material selection for specific space applications
  • Quality Control – Support proactive quality assessment to prevent mission failures

Data Processing Pipeline

Comprehensive data cleaning and preprocessing workflow

Data Collection

Systematic extraction and compilation of NASA outgassing materials data, organizing critical performance metrics including TML, CVCM, WVR, and material specifications for comprehensive analysis.

Outlier Management

Applied quantile-based and IQR methods to identify and handle extreme outliers in TML, CVCM, WVR, and Space Code metrics, ensuring data quality while preserving meaningful variance.

Missing Value Treatment

Systematic handling of missing values with strategic column and row removal for 'Cure', 'Material Usage', and 'Space Code' fields, optimizing dataset completeness for machine learning applications.

Data Validation

Comprehensive duplicate detection and removal processes, final dataset validation, and preparation of clean data for exploratory analysis and machine learning model development.

Machine Learning Models

Advanced predictive modeling for material performance evaluation

Regression Models

Predictive models for continuous performance score evaluation based on material quality metrics.

  • Linear Regression for supplier performance prediction
  • Random Forest Regressor as alternative approach
  • Performance score calculation using TML, CVCM, WVR
  • Model evaluation and selection based on accuracy metrics

Classification Models

Classification systems for categorical material reliability assessment and quality control.

  • Logistic Regression for performance categorization
  • Random Forest Classifier for robust classification
  • Support Vector Machine (SVM) implementation
  • Multi-class performance category prediction

Feature Engineering

Advanced feature creation and transformation for enhanced model performance and realistic scenario simulation.

  • Supplier performance score calculation
  • Noise addition for realistic scenario simulation
  • Quality metric normalization and scaling
  • Categorical feature encoding and transformation

Project Presentation

Comprehensive analysis and findings from the NASA outgassing materials study

Key Insights & Applications

Material Quality Metrics

  • TML (Total Mass Loss) - Percentage of material mass lost during vacuum testing
  • CVCM (Collected Volatile Condensable Materials) - Volatile material condensation measurement
  • WVR (Water Vapor Regained) - Water vapor absorption after testing
  • Performance Optimization - Lower values indicate better space environment suitability

Model Performance

  • Linear Regression selected for performance prediction
  • Logistic Regression chosen for classification tasks
  • Comprehensive model evaluation and comparison
  • Robust validation using multiple metrics

Practical Applications

  • Material Selection - Optimized selection for specific space applications
  • Quality Control - Proactive assessment to prevent mission failures
  • R&D Support - Data-driven insights for material improvement research
  • Risk Mitigation - Early identification of material performance issues

Future Enhancements

  • Dataset expansion for improved model performance
  • Additional classification model experimentation
  • Categorical feature integration for deeper analysis
  • Real-time prediction pipeline development

Technology Stack

Advanced tools and frameworks for space-grade material analysis

Data Processing

  • Pandas - Data manipulation and cleaning
  • NumPy - Numerical computations and array operations
  • Matplotlib - Statistical visualization and plotting
  • Seaborn - Advanced statistical visualizations

Machine Learning

  • Scikit-learn - ML algorithms and preprocessing
  • Linear Regression - Performance prediction modeling
  • Logistic Regression - Classification tasks
  • Random Forest - Ensemble methods

Development Environment

  • Jupyter Notebook - Interactive development
  • Python 3.x - Core programming language
  • Git - Version control and collaboration
  • GitHub - Repository management

Analysis & Validation

  • Cross-validation - Model performance assessment
  • Statistical testing - Hypothesis validation
  • Performance metrics - Accuracy evaluation
  • Feature importance - Model interpretability