Data-driven design & fabrication methods

Summary

There is an increasing need for data-driven methods for automated design and fabrication of complex mechanical systems. This course covers methods for encoding the design space, optimization and search approaches and digital fabrication methods.

Content

Keywords

Design, Manufacturing, Optimization, Data-driven methods, Fabrication, Robotics

Learning Prerequisites

Recommended courses

• Systèmes mécatroniques
• Mechanical product design and development

Important concepts to start the course

• Some working knowledge of python/Matlab
• Interest and awarness of mechanical or mechatronic design
• Experience of mechatronic systems and their fabrication, for example 3D printing and using microcontrollers and actuators would be benefical

Learning Outcomes

• Select appropriately methods of encoding for design spaces of different dimensionality and type of problem, and implement methods of extracting the key parameters
• Implement different optimization methods
• Compare and contrast the relative performance of different optimization approaches and select appropriate techniques for different applications
• Describe different simulation tools and approaches for computational design
• Design and design appropriate fabrication tools and ‘compilers’ for automated design
• Design a data-driven approach for a real world scenario and implement the approach
• Justify choice of fabrication methods for a given task

Transversal skills

• Continue to work through difficulties or initial failure to find optimal solutions.
• Plan and carry out activities in a way which makes optimal use of available time and other resources.
• Make an oral presentation.
• Summarize an article or a technical report.

Teaching methods

The course will use a mix of both tradiational teaching methods for more theory oriented lectures and also active learning including group debates, reviews and also in-class questions for more indepth exploration of topics.  For some lectures there will be some preparatory work (e.g. reading papers, watching videos) to allow for in lecture exploration.

Expected student activities

• Individual programming exercises and report on encoding design spaces and implementing and contrasting various optimization methods
• Participation in active learning aspects of the class icluding debates, discussions and other participary activities
• Group project on using the methods presented to perform some data-driven design of a product/system, with final presentation and report.  This will be more open ended and research focused.  There will be the opportunity to fabricate and physically test the designs developed.

Assessment methods

• 30% Individual Report. Students will be asked to implement a number of the data-driven design techniques to contract their effectivness and reflect on the useage of different approaches.
• 70% Group Project (20% Intial Presentation, 20% Final Presentation, 30% Final Report). Students will be be asked to select from a number of 'open' design challenges and asked to work in teams to develop a method to both optimize or select approiate methods of finding an appropiate design,

Supervision

Resources

Bibliography

Suggested books/reading will be added.

Notes/Handbook

Will be provided during the course.

Moodle Link

• https://go.epfl.ch/ME-428