Application of AI in Additive Manufacturing
- Week 1: Introduction to AI and ML in Additive Manufacturing
Lecture: Role of AI/ML in AM, challenges, and opportunities
Lab: Introduction to datasets and Jupyter environment setup
- Week 2: Machine Learning Fundamentals
Lecture: ML pipeline, supervised vs. unsupervised learning
Lab: Train/test split, simple regression on AM property datasets
- Week 3: Feature Engineering for AM
Lecture: Feature extraction, normalization, PCA
Lab: Preprocessing AM datasets, dimensionality reduction
- Week 4: Supervised Learning for Process Prediction
Lecture: Regression models (Linear, Decision Trees, Random Forests)
Lab: Predicting tensile strength using regression
- Week 5: Classification in AM
Lecture: Classification algorithms (SVM, Logistic Regression)
Lab: Defect classification using AM image/label data
- Week 6: Unsupervised Learning & Clustering
Lecture: K-Means, DBSCAN, Hierarchical Clustering
Lab: Process parameter analysis with clustering techniques
- Week 7: Deep Learning Fundamentals
Lecture: Neural networks, backpropagation, model structure
Lab: Implementing a basic feedforward NN on AM sensor data
- Week 8: CNNs in AM
Lecture: Convolutional Neural Networks for image analysis
Lab: Training CNNs on microstructure/SEM images
- Week 9: LSTMs and Time-Series Modeling
Lecture: Recurrent neural networks, sequence modeling
Lab: Predicting thermal/vibration signals using LSTM
- Week 10: Transfer Learning and Fine-Tuning
Lecture: Using pre-trained models (e.g., ResNet) for AM data
Lab: Fine-tuning CNNs for defect recognition in images
- Week 11: Generative Models (GANs, VAEs)
Lecture: Generating synthetic microstructures or features
Lab: Build and train a basic GAN for image/data generation
- Week 12: Optimization and Digital Twins
Lecture: Bayesian optimization, digital twins for process simulation
Lab: Using Optuna for hyperparameter tuning of AM models
- Week 13: Case Studies & Multiscale Design Optimization
Lecture: Topology optimization, functionally graded materials
Lab: Applying ML to lattice design and AM simulations
- Week 14: Final Project Presentations - Part I
Lecture: Presentations from Groups 1-3, feedback session
Lab: Peer review and evaluation
- Week 15: Final Project Presentations - Part II & Wrap-Up
Lecture: Presentations from Groups 4-6, course summary, open discussion
Lab: Reflection and discussion of future applications and directions