Course syllabus
Course-PM
PPU191 Engineering design and optimization lp1 HT26 (7.5 hp).
The course is offered by the Department of Mechanical Engineering
Course purpose
The course aims at integrating traditional design methodologies with concepts and techniques of modern optimization theory and practice. With the approach and instilled knowledge, the students are expected to create design solutions that are creative and have better performance than traditional conservative methods. Furthermore, the course aims to:
- Demonstrate a selection of different tools and methods for optimization of mechanical products and structures
- Design for improvement of components in products and mechanical systems by bridging detailed design and creative side of the design process in a systematic way
- Demonstrate the iterative nature of the development chain including modeling-analysis-test
- Use and familiarize students with modern CAE tools
- Incorporate material selection as a part of the product development process
Learning objectives
- Master the complete development chain including modelling-analyses-test-evaluation
- Identify areas for improvement in a product design
- Identify and choose appropriate material alternatives for a product
- Apply previously-learned design methods and tools to practical problems
- Create appropriate simulation models of the design problem
- Use Computer Aided Engineering (CAE) tools to design and simulate product performance
- Apply previous knowledge in mathematics and mechanics to formulate and solve optimization problems.
- Formulate design optimization problems based on project or product requirements
- Apply numerical optimization techniques and computer tools to solve optimization problems
- Select an appropriate algorithm based on problem specification
- Calculate one iteration from different gradient based algorithms
- Handle multiple objectives and system in a single optimization
- Integrate robustness and reliability in the optimization formulation
- Interpret optimization results for design decision making (e.g., material selection, geometry, manufacturing, production)
- Create CAE drawings for use with three-dimensional printing tools
- Iterate on design solutions to continually improve a product's design and performance
- Communicate design solutions, including rationales for a given choice, advantages, and disadvantages over alternatives
Prerequisites
- CAD
- Machine Elements, Applied Mechanics, or similar
- FEM
- Programming in Python
- Material science
- Manufacturing technology
- Mechanics
- Solid mechanics
Course literature
The main literature in the course is:
- Engineering Design Optimization by Joaquim R. R. A. Martins and Andrew Ning (ISBN: 9781108833417). Available free online
Additional sources include:
- Christensen & Klarbring: An Introduction to Structural Optimization, 2009. Available as an e-book through Chalmers Library
- Bendsoe & Sigmund: Topology Optimization: Theory, Methods, and Applications, 2004. Available as an e-book through Chalmers Library
- Selected research articles - provided on Canvas
Schedule
The schedule is available in the file Schedule_2026.
Course design
The course is divided into 15 lectures over the course of 8 weeks. Project assignments will reinforce the lecture material through design tasks that reflect the content of the lectures leading up to them. The lecture topics are as follows:
| Lecture | Title | Lecturer |
| 1 | Introduction to optimization | GA |
| 2 | Applied mechanics Part 1 (of 3) | HJ |
| 3 | Applied Mechanics Part 2 (of 3) | HJ |
| 4 | Modelling for Optimization | GA |
| 5 | Material Selection in Design | GA |
| 6 | Optimization Algorithms and tools Part 1 (of 3) | GA |
| 7 | Optimization Algorithms and tools Part 2 (of 3) | GA |
| 8 | Optimization Algorithms and tools Part 3 (of 3) | GA |
| 9 | Multi-objective optimization & Trade-off Analysis | GA/KB |
| 10 | Applied Mechanics Part 3 (of 3) | HJ |
| 11 | Multi-System Optimization | KB |
| 12 | Reverse Engineering | KB |
| 13 | Quality and Uncertainty | GA |
| 14 | Concept and Embodiment Design | GA |
| 15 | Course recap | GA |
Guest Lectures:
Three guest lectures from the industry are scheduled. Mandatory to attend X1 and X2 and to do a reflection on the guest lectures.
X1 – Intro to ANSYS Topological Optimization, Magnus Gustafsson, EDR Medeso
X2 - Topology optimization in automotive, Harald Hasselblad, VCC
X2 – Engineering Optimization in Practice, Petter Andersson, GKN
Workshops
There will be scheduled workshops to reinforce concepts from the lectures and to support your work in the assignments.
- Topology Optimization - Ansys
- Optimization using Python
- Response Surface Modelling
- MOO using Python
- Robustness and Reliability
- Optimization Examples
- Examples and Exam question
Project Assignments
There will be three project assignments throughout the course focusing on the later stages of design. In the assignments, students will work in groups of two (pairs). To achieve positive cross-over effects, students in a group should be enrolled in different MSc programs.
PA 1 - The Cantilever Challenge
Design, build, and test a cantilever beam over two iterations ending with a live competition. Who will achieve the highest performance and win the challenge?
PA 2 – Materials selection and design optimization
Optimization of a structural lug considering material selection, failure modes, and sizing.
PA 3 – MOO - Multi-Objective Optimization
Continuation of the structural lug optimization using Multi-Objective Optimization
Changes made since the last occasion
The course has been updated to include additional time allocated to calculations and optimisation examples. General introduction to Optimisation lecture and Introduction to Engineering Optimization lecture has been merged
Location
Lectures and workshops will be held at different locations on the Johanneberg campus. Check the schedule before each lecture.
Examination form
The examination is based on four parts
Written final exam (Grade 5, 4, 3, Fail): One written examination, covering all the theory and material from the lectures, guest lectures, recommended readings, and workshops.
Midterm exam (Max 4 bonus points for the exam) - Online in Canvas
Three approved assignments (Pass/Fail)
Attend 2 compulsory guest lectures and write a short reflection (1 bonus point in total for the exam for both). A complementary assignment will be provided in case students are not able to attend for some unforeseen reason.
Each assignment is graded on a scale of (not passed/passed). The projects will be worth 3 credits (1,1,1) of the student’s individual course grade, and the exams will be worth 4.5 credits. Bonus points from the midterm exam and guest lectures (if such is given) are valid for the first regular examination and are included in the final score after passing the exam. If students are not able to attend and review two out of three compulsory guest lectures, a complimentary review will be assigned.
Contact details
The organization of the course is as follows:
|
Dr. Gauti Asbjörnsson |
Varun Gowda |
|
Alex Pradas |
Dr. Kanishk Bhadani |
|
Dr. Håkan Johansson |
Dr. Harald Hasselblad, VCC |
|
Petter Andersson, GKN |
Magnus Gustafsson, EDR Medeso |
Course evaluation committee
Course summary:
| Date | Details | Due |
|---|---|---|