Information Package / Course Catalogue
Machine Learning Applications in Additive Manufacturing Design
Course Code: MME550
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

This course aims to introduce the integration of machine learning techniques into additive manufacturing design processes. Students will learn how to combine design for additive manufacturing (DfAM), topology optimization, and simulation-based approaches with machine learning methods to develop data-driven and optimized engineering designs.

Course Content

Design for additive manufacturing (DfAM), additive manufacturing processes, topology and shape optimization, generative design methods, structural analysis, dataset generation from CAD and finite element simulations, feature extraction, supervised machine learning methods (regression models), model training and validation, and data-driven design optimization.

Name of Lecturer(s)
Learning Outcomes
1.1. Understand design principles for additive manufacturing systems
2.2. Apply topology and generative design techniques
3.3. Perform structural analysis for design validation
4.4. Generate datasets from CAD and simulation environments
5.5. Apply machine learning methods for engineering prediction
6.6. Integrate machine learning into design optimization workflows
Recommended or Required Reading
1.1. Fusion 360 AI script tutorials
2.2. Ansys Parametric Design and Simulation Tutorials
3.3. Martins, Joaquim RRA, and Andrew Ning. Engineering design optimization. Cambridge University Press, 2021.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to additive manufacturing and DfAM
Week 2 - Theoretical
Additive manufacturing processes and materials
Week 3 - Theoretical
CAD-based design for additive manufacturing
Week 4 - Theoretical
Topology optimization fundamentals
Week 5 - Theoretical
Topology optimization applications
Week 6 - Theoretical
Generative design methods
Week 7 - Theoretical
Structural analysis fundamentals (FEA)
Week 8 - Intermediate Exam
Midterm exam
Week 9 - Theoretical
Dataset generation from CAD and FEA
Week 10 - Theoretical
Feature extraction for engineering data
Week 11 - Theoretical
Introduction to machine learning in engineering
Week 12 - Theoretical
Regression models (XGBoost, Random Forest)
Week 13 - Theoretical
Model training and validation
Week 14 - Theoretical
Design Optimization Project
Week 15 - Theoretical
Design Optimization Project
Week 16 - Final Exam
Final Project
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment5%10
Project1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory1453112
Assignment57560
Project110515
Midterm Examination1426
Final Examination1426
TOTAL WORKLOAD (hours)199
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
OÇ-1
3
3
3
3
4
3
4
3
3
4
3
3
OÇ-2
4
4
4
4
5
4
3
4
4
5
4
4
OÇ-3
3
3
5
4
5
4
4
4
5
5
4
3
OÇ-4
4
5
5
5
4
5
5
5
4
4
5
4
OÇ-5
3
5
5
3
4
4
4
3
3
4
5
4
OÇ-6
Adnan Menderes University - Information Package / Course Catalogue
2026