Course syllabus


PPU191 Engineering design and optimization lp1 HT23 (7.5 hp). 

The course is offered by the Department of Industrial and Materials Science


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 modeling-analyses-test-evaluation
  • Identify areas for improvement in a product design
  • Identify and choose appropriate material alternatives based on product requirements
  • 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
  • Assessing the advantages, and disadvantages of different based algorithms based on the optimization problem
  • Interpret optimization results for design decision making (e.g., material selection, geometry, manufacturing, production)
  • Create CAE drawings for use with three-dimensional printing tools
  • Understand the possibilities and limitations of different manufacturing techniques when it comes to optimal design, such as obtained with topology optimization
  • Create and 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


  • CAD
  • Machine Elements, Applied Mechanics, or similar
  • FEM
  • Programming in Python (or Matlab)
  • 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:

  • Papalambros & Wilde: Principles of Optimal Design, 3nd Edition. 2000 ISBN 0-521-62727-3. Available as an e-book through Chalmers library
  • 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 is available in TimeEdit.

Course design 


The course is divided into 16 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:

Key Title Lecturer
L-01 Introduction and general engineering approach GA
L-02 Applied Mechanics, Part 1 of 3 HJ
L-03 Introduction to Optimization GA
L-04 Modelling for Optimization GA
L-05 Optimization Algorithms and tools Part 1 (of 3) GA
L-06 Design for Additive Manufacturing EH
L-07 Material Selection in Design CP
L-08 Applied Mechanics, Part 2 of 3 HJ
L-09 Optimization Algorithms and tools Part 2 (of 3) GA
L-10 Optimization Algorithms and tools Part 3 (of 3) GA
L-11 Multi-objective optimization & Trade-off Analysis GA or AP
L-12 Concept and Embodiment Design GA
L-13 Quality and Uncertainty GA
L-14 Multi-System Optimization KB
L-15 Applied Mechanics Part 3 (of 3) HJ
L-16 Course Recap GA

Guest Lectures:

Three guest lectures from the industry are scheduled and will be held online.  Mandatory to attend and reflect on two out of three guest lectures.

  • X1 – Topology optimization in automotive, Harald Hasselblad, VCC
  • X2 – Optimization in practice, Petter Andersson, GKN
  • X3 – COMSOL Multiphysics, Johan Potrus/Björn Bragée COMSOL


Workshops - Flipped classroom

There will be scheduled workshops to reinforce concepts and introduce software from the lectures under supervision. Some of the workshops will be used for supervision and are therefore intentionally left blank in the schedule.

  1. Applied Optimization - Ansys
  2. Optimization using Python
  3. Material Selection using Granta EDUPack
  4. Metamodeling –DoE – The Helicopter
  5. MOO using Python
  6. Robustness and Reliability using MATLAB
  7. Examples and Exam questions
  8. Optimization Examples 


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 changed the programming language from Matlab to Python. There is still limited support if the students wants to program in Matlab


Lectures and workshops will be held at different locations on the Johanneberg campus. Check the schedule or TimeEdit 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)
  • Attended compulsory guest lectures and write a short reflection (1 bonus point for the exam for all 3).

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 credits 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
Examiner, lecturer



Alex Pradas
Course administration & supervisor



Kanishk Bhadani
Course Supervisor



Varun Gowda
Course Supervisor


Håkan Johansson 2021 3_zoom.jpg

Dr. Håkan Johansson


Prof. Christer Persson



Prof. Eduard Hryha


Dr. Harald Hasselblad, VCC
Guest lecturer


Petter Andersson, GKN
Guest lecturer


Johan Potrus, Comsol
Guest lecturer

Course evaluation committee

• MPPDE - - Pratik Bhise
• MPPDE - - Vijaya Kumar Dasappa Ashoka
• MPPDE - - Aditya Deokar
• MPMOB - - Dennis Söderlund
• MPPDE - - Atchuta Mruthyan Jaya Sai Pranav Uppuluri


Course summary:

Date Details Due