This repository houses an educational platform designed to bridge the domains of Additive Manufacturing (AM) and Generative Artificial Intelligence (GenAI). The platform serves as a comprehensive resource for AM professionals, researchers, engineers, and educators looking to leverage the power of generative AI technologies in their manufacturing workflows.

GenAI in AM empowers additive manufacturing professionals to harness cutting-edge artificial intelligence capabilities for applications including:

• Design optimization and generative design
• Process parameter prediction and optimization
• Defect detection and quality control
• Material property simulation
• Workflow automation through AI agents
• Knowledge extraction from technical documentation

Whether you're new to AI concepts or an experienced practitioner seeking AM-specific applications, this platform provides structured learning paths, technical tutorials, case studies, and resources tailored to the intersection of these transformative technologies.

• Executive Summary
• Educational Content Structure
  • Introduction to GenAI in AM
  • Develop GenAI model
  • GenAI agents
  • Resources
  • Miscellaneous

• Beginner Path
• Intermediate Path
• Advanced Path
• Technical Documentation
  • Repository Structure
  • Environment Setup
  • Local Development
  • Building and Deployment
• Contribution Guidelines
  • Content Contribution
  • Technical Contribution
  • Review Process
• Website Features
  • Navigation
  • Search Functionality
  • Content Depth Indicators
  • Responsive Design
• User Personas
  • Beginners
  • Experienced Engineers
  • Researchers
• Maintenance
  • Content Updates
  • Technical Maintenance
  • Version Compatibility
• Troubleshooting
  • Common Issues
  • Support Resources
• Licensing
• Acknowledgments
• Contact

The educational content is organized into five comprehensive sections, each addressing different aspects of GenAI in AM:

Foundational content for beginners to understand the basic concepts, terminology, and evolving landscape of GenAI applications in manufacturing. Also Technical deep dives into the foundation models and frameworks that power GenAI in AM:

• What is GenAI? - Fundamentals of generative artificial intelligence, its capabilities, and core principles.Understanding large language models, diffusion models, and their adaptation for AM.
• Benefits in AM - How generative AI enhances additive manufacturing through improved design, optimization, and production
• Current Landscape - Overview of the present state of GenAI applications in the additive manufacturing industry

Step-by-step guides for practical implementation of GenAI in AM workflows:

• Task Selection - Understanding and defining the problem scope and recognizing challenges for AM
• Model Selection - Technical details of transformer architectures, attention mechanisms, and other relevant AI structures
• Benchmarking Metrics - Methods for evaluating GenAI model performance in AM contexts
• Prompt Engineering - Techniques for crafting effective prompts for AM applications
• Fine-Tuning Approaches - Methods for adapting general models to AM-specific tasks and domains

GenAI agent application and implementation in various aspects of additive manufacturing:

• Agent Foundations - Fundamental concepts of GenAI agents and their potential in AM
• Process Optimization - Case studies on using GenAI for improving AM process parameters
• Defect Detection - Applications in quality control and anomaly detection
• Generative Design - Examples of AI-driven design creation for AM constraints

Comprehensive collection of training, bencmarking, evelaution datasets connections:

• Datasets - AM-specific datasets for training and fine-tuning GenAI models

Comprehensive collection of training resources, research papers, and community connections:

• Research Publications - Key papers and academic resources at the intersection of GenAI and AM
• Community Projects - Open-source initiatives and collaborative efforts
• Learning Resources - Additional educational materials and courses

The platform offers structured learning paths for users with different levels of expertise:

For those new to either GenAI or AM:

1. Introduction to GenAI (What is GenAI?)
2. Benefits in Additive Manufacturing
3. Current Landscape
4. Basic Resources

For users with foundational knowledge seeking implementation guidance:

1. Foundation Models
2. Prompt Engineering
3. Benchmarking Metrics and Tools
4. Basic Case Studies

For experienced practitioners looking to push boundaries:

1. Advanced Model Architectures and Fine-Tuning
2. Implementation of GenAI Agents
3. Complex Case Studies
4. Cutting-Edge Research

GenAI_in_AM.github.io/
├── index.html                  # Main landing page
├── css/                        # Stylesheets
│   ├── style.css               # Main styles
│   ├── components.css          # Component-specific styles
│   └── responsive.css          # Responsive design rules
├── js/                         # JavaScript files
│   ├── main.js                 # Core functionality
│   ├── navigation.js           # Navigation handling
│   └── search.js               # Search functionality
├── images/                     # Image assets
├── sections/                   # Content sections
│   ├── intro/                  # Introduction materials
│   ├── core-technologies/      # Technical foundations
│   ├── tutorials/              # Implementation guides
│   ├── case-studies/           # Real-world examples
│   └── resources/              # Additional resources
└── README.md                   # This documentation file

To work with this repository locally, you'll need:

1. Basic Requirements:

   • Git
   • A modern web browser
   • A text editor or IDE (VS Code recommended)

2. Optional Development Tools:

   • Node.js and npm (for running build scripts or linters)
   • Live Server extension or similar for local development

1. Clone the repository:

   git clone https://github.com/yourusername/GenAI_in_AM.github.io.git
   cd GenAI_in_AM.github.io

2. View the site locally:

   • Open index.html in your browser
   • Alternatively, use a local development server:

     # If you have VS Code with Live Server extension:
     # Right-click on index.html and select "Open with Live Server"
     
     # Or with Python (Python 3):
     python -m http.server
     # Then visit http://localhost:8000 in your browser

3. Making changes:

   • Edit HTML files for content changes
   • Modify CSS files in the /css directory for styling updates
   • Update JavaScript in the /js directory for functionality changes

This is a static website that doesn't require a build process. To deploy:

1. GitHub Pages (Recommended):

   • Push changes to the main branch
   • The site will automatically deploy via GitHub Pages

2. Alternative Hosting:

   • Upload all files to any web hosting service
   • Ensure the directory structure remains intact

We welcome contributions from the community to enhance this educational platform. Please follow these guidelines:

To contribute new educational content:

1. Content Types Accepted:

   • Tutorial articles
   • Case studies
   • Technical explanations
   • Code examples
   • Research summaries

2. Content Requirements:

   • Clear indication of difficulty level (beginner, intermediate, advanced)
   • Accurate technical information with references
   • Well-structured with appropriate headings
   • Inclusion of visual aids where applicable (diagrams, charts, examples)
   • Proper attribution for any external content

3. Content Format:

   • HTML files following the existing templates in the repository
   • Markdown files that can be converted to HTML
   • Code examples with proper syntax highlighting and comments

4. Submission Process:

   • Fork the repository
   • Create your content in the appropriate section directory
   • Submit a pull request with a clear description of the contribution

For contributing to the website's functionality or design:

1. Code Style:

   • Follow existing code formatting patterns
   • Include comments for complex functionality
   • Ensure cross-browser compatibility

2. Feature Development:

   • Open an issue describing the proposed feature before implementation
   • Focus on accessibility and performance
   • Include documentation for new features

3. Submission Process:

   • Fork the repository
   • Create a feature branch
   • Submit a pull request with clear documentation

All contributions undergo a review process:

1. Initial review by maintainers
2. Technical accuracy verification (for educational content)
3. Feedback and requested changes if necessary
4. Approval and integration into the main repository

The website features a comprehensive navigation system to help users find content:

• Main Navigation: Primary sections accessible from the top navigation bar
• Breadcrumbs: Path indicators showing the current location in the content hierarchy
• Table of Contents: Page-specific navigation for longer articles
• Related Content: Links to associated topics at the end of each article
• Pagination: Previous/next navigation between sequential content

The integrated search feature allows users to:

• Search across all content sections
• Find specific technologies, concepts, or applications
• Access results with direct links to relevant sections
• Filter search results by content type or difficulty level

The website implements a visual system to indicate content depth and complexity:

• Beginner content: Fundamental concepts and introductions
• Intermediate content: Implementation details and practical applications
• Advanced content: Technical deep dives and cutting-edge research

These indicators help users navigate to content appropriate for their knowledge level.

The website is fully responsive and optimized for various devices:

• Desktop computers and laptops
• Tablets
• Mobile phones
• Adjusts layout, navigation, and content presentation automatically

The platform is designed to serve different user types with tailored content:

For users new to GenAI and/or AM:

• Start with the Introduction section
• Follow the beginner learning path
• Utilize the glossary for terminology
• Engage with visual explanations and simplified examples

Example navigation path:

1. Home page
2. "What is GenAI?" article
3. "Benefits in AM" article
4. Basic tutorials

For AM professionals looking to implement GenAI:

• Focus on implementation tutorials
• Explore case studies relevant to specific applications
• Utilize code examples and integration guides
• Access benchmarking tools for evaluation

Example navigation path:

1. Home page
2. Core Technologies section
3. Implementation Tutorials
4. Specific application case studies

For academic or R&D professionals:

• Engage with advanced content sections
• Access research publications and references
• Explore cutting-edge applications
• Connect with datasets for experimentation

Example navigation path:

1. Home page
2. Advanced topics in Core Technologies
3. Research references in Resources section
4. Datasets and benchmarking tools

Regular content updates maintain the platform's relevance:

• Update Frequency:

  • Major content reviews quarterly
  • News and emerging technology updates monthly
  • Bug fixes and minor corrections as needed

• Update Process:

  1. Review of current content for accuracy
  2. Addition of new developments in the field
  3. Refresh of examples and case studies
  4. Update of references and external links

Ensuring the platform remains technically robust:

• Code Maintenance:

  • Quarterly review of JavaScript functionality
  • Testing across major browsers
  • Performance optimization
  • Accessibility compliance checks

• Infrastructure:

  • GitHub Pages configuration management
  • Domain and DNS maintenance
  • Analytics review and implementation

The website is designed to be compatible with:

• Browsers: Chrome, Firefox, Safari, Edge (latest two major versions)
• Devices: Desktop, tablet, and mobile
• Accessibility: WCAG 2.1 AA compliance target

Content Viewing Issues:

• Problem: Content not displaying properly
  • Solution: Clear browser cache or try a different browser
• Problem: Images not loading
  • Solution: Check internet connection or reload the page

Search Functionality:

• Problem: Search not returning expected results
  • Solution: Try more general keywords or check spelling
• Problem: Search appears unresponsive
  • Solution: Ensure JavaScript is enabled in your browser

Navigation Issues:

• Problem: Links not working
  • Solution: Report broken links via the contact form
• Problem: Mobile navigation menu not responding
  • Solution: Try refreshing the page or updating your browser

For additional help:

• Open an issue on the GitHub repository
• Contact the maintenance team via the contact form
• Check the FAQs section for common questions

This educational platform is licensed under the MIT License, which allows for:

• Free use, modification, and distribution
• Commercial and private use
• Required attribution and license inclusion when redistributed

Content and code contributions are accepted under this same license.

This platform is made possible through the contributions of researchers, educators, and practitioners in both the Generative AI and Additive Manufacturing fields:

• Academic Contributors:

  • Paul Witherell
  • Maja Vukovic
  • Soundar Kumara

• Referenced Research:

  • International Journal of Information Management (https://doi.org/10.1016/j.ijinfomgt.2023.102749)
  • Additional citations available in individual articles

• Open Source Communities:

  • Contributors to foundation models and AI frameworks
  • AM research communities and standards organizations

For questions, suggestions, or collaboration opportunities:

• GitHub Issues: For bug reports and feature requests
• Email: [email protected]
• Twitter: @GenAI_AM
• LinkedIn: GenAI in Additive Manufacturing Group