Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Assisted Composite Materials Design shows how machine learning is accelerating the notoriously large design space of composites. You learn the essentials of composite behaviour — constituents, layups and anisotropy — then how data-driven models predict mechanical properties from material and process parameters, and how optimisation navigates trade-offs between strength, weight and cost. The course covers building and using property-prediction models and integrating them into the design loop, reducing reliance on slow physical testing. You finish able to reason about applying AI to a composite-design problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to composite materials design — predicting properties, optimising layups and accelerating the design of fibre-reinforced composites.
📋 Course Objectives
1. Explain composite constituents, layups and behaviour.
2. Build models to predict mechanical properties.
3. Optimise layup and material choices with AI.
4. Balance strength, weight and cost trade-offs.
5. Integrate models into the design loop.
👥 Who Should Enroll?
• Materials and mechanical engineers
• Composites R&D professionals
• Data scientists in manufacturing
• Students specialising in materials informatics
🚀 Key Learning Outcomes
• The ability to apply AI to composite design.
• A property-prediction or optimisation project.
• Skills bridging materials and data science.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and AI-Assisted Composite Materials Design Foundations
Develop a comprehensive understanding of AI fundamentals, including machine learning, deep learning, and neural networks, and their applications in composite materials design • Analyze mathematical concepts, such as linear algebra, calculus, and probability, and their role in AI-assisted composite materials design • Design and implement AI-assisted composite materials design workflows, integrating AI fundamentals and mathematical concepts
Data Engineering, Preprocessing, and Feature Pipelines
Configure data pipelines to ingest, process, and store large datasets related to composite materials, using tools such as Apache Beam and Apache Spark • Evaluate data quality and implement data preprocessing techniques, including data cleaning, feature scaling, and feature engineering, to prepare data for AI model training • Develop and deploy feature pipelines to extract relevant features from composite materials data, using techniques such as PCA, t-SNE, and autoencoders
Model Architecture, Algorithm Design, and AI-Assisted Composite Materials Design Methods
Design and implement AI model architectures, including CNNs, RNNs, and GANs, for composite materials design applications, such as material property prediction and optimization • Analyze and compare different algorithm design approaches, including supervised, unsupervised, and reinforcement learning, for AI-assisted composite materials design • Develop and evaluate AI-assisted composite materials design methods, including generative models and surrogate-based optimization, to accelerate materials design and discovery
Training, Hyperparameter Optimization, and Evaluation
Train AI models on large datasets related to composite materials, using techniques such as transfer learning, fine-tuning, and online learning • Implement hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization, to improve AI model performance • Evaluate AI model performance using metrics such as accuracy, precision, recall, and F1-score, and compare results to baseline models and experimental data
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments, using containerization tools such as Docker and Kubernetes, and orchestration tools such as Apache Airflow • Develop and implement MLOps workflows to monitor, maintain, and update AI models in production, including data drift detection and model retraining • Configure and manage production workflows to integrate AI models with existing composite materials design workflows, using APIs and data exchange protocols
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate bias in AI models and datasets related to composite materials design, using techniques such as data augmentation and fairness metrics • Develop and implement responsible AI practices, including transparency, explainability, and accountability, to ensure trustworthy AI-assisted composite materials design • Evaluate and address ethical concerns related to AI-assisted composite materials design, including environmental impact, social responsibility, and human safety
Industry Integration, Business Applications, and Case Studies
Integrate AI-assisted composite materials design with industry workflows and business applications, including CAD software, finite element analysis, and supply chain management • Develop and evaluate case studies of AI-assisted composite materials design in various industries, including aerospace, automotive, and energy • Analyze and compare the economic and environmental benefits of AI-assisted composite materials design, including cost savings, reduced material waste, and improved product performance
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Apache Spark |
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