About Workshop
Aim
What Participants Will Learn
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Define smart polymer composites and key functional properties
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Contrast traditional workflows with AI-enabled design benefits
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Acquire and clean materials datasets from open sources
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Engineer descriptors (composition/process/microstructure)
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Train and benchmark ML models for property prediction
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Validate with CV and metrics (MAE, R²) + basic uncertainty checks
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Apply inverse design to meet target property specs
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Feed AI results into simulations (ANSYS/COMSOL) and outline Industry 4.0 integration
Structure
📅 Day 1 – Understanding Smart Polymer Composites and AI Fundamentals
- Introduction to Smart Polymer Composites
- Definition and characteristics of smart polymers
- Types and functional properties of polymer composites
- Use-cases in aerospace, biomedical, automotive, and other sectors
- Limitations of Traditional Design Approaches
- Challenges with conventional testing and prototyping
- Data scarcity and inefficiencies in trial-and-error methods
- Role of Artificial Intelligence in Material Innovation
- How AI accelerates material discovery and design
- Industry examples of AI integration in materials science
- Hands-on: Data collection and preparation using open-source material science databases
📅 Day 2 – Machine Learning for Property Prediction and Design Optimization
- Fundamentals of Machine Learning in Materials Science
- Key algorithms: Random Forest, Neural Networks, Support Vector Machines
- Data types and preprocessing techniques
- Predictive Modeling of Material Properties
- Estimating strength, elasticity, thermal resistance, etc.
- Identifying critical features influencing performance
- AI-Driven Design Optimization
- Material selection using optimization algorithms
- Introduction to inverse design: deriving structure from property requirements
- Hands-on: Building and evaluating a basic ML model to predict polymer composite properties
📅 Day 3 – Simulation, Smart Manufacturing & Industry 4.0 Integration
- AI-Assisted Simulation of Composite Behavior
- Use of simulation tools (e.g., ANSYS, COMSOL) for stress and performance modeling
- Incorporating AI outputs into simulation workflows
- Smart Manufacturing and Digital Integration
- Role of AI in additive manufacturing (3D printing)
- Real-time process monitoring using IoT and edge AI
- Quality control, defect prediction, and process optimization
- Industry Case Studies and Global Trends
- How leaders like Boeing, BASF, and NASA are applying AI in composite development
- Future of sustainable, recyclable smart materials
- Hands-on: Running simulations using AI-optimized parameters; final group challenge: design and present an AI-driven composite solution
Important Dates
Registration Ends
Workshop Dates
What You Will Gain
Outcomes
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Curate and clean composite datasets from open sources
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Engineer features (composition, process, microstructure)
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Train/evaluate ML models (RF/NN/SVM) for key properties
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Validate with CV and metrics (MAE, R²); assess uncertainty
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Perform basic inverse design for target properties
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Feed AI outputs into ANSYS/COMSOL simulations
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Outline Industry 4.0 workflows (3D printing, IoT QC, defect prediction)
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Deliver a capstone: data → model → simulation pipeline + slides
Who Should Attend
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Background: UG/PG students, researchers, professionals in materials/polymer/mechanical/chemical engineering or applied physics
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Roles: R&D engineers, data scientists/ML engineers entering materials informatics, faculty, industry practitioners
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Sectors: Aerospace, automotive, biomedical, energy, advanced manufacturing
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Skill level: Beginner–intermediate (no prior AI-in-materials required)
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Recommended prep: Basic materials/mechanics and intro Python (starter notebooks provided)
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Logistics: Laptop for Jupyter notebooks; readiness to use provided datasets and simulation demos (ANSYS/COMSOL)