Prototyping is understood as the road from an intention to solve a problem, to a first solution. It is analysis, approximation, process, production and critique at the same time. We have the intention to explain and show not finished prototypes, but processes and the keys that allow to handle them as conscious design choices. Once defined, the systems will allow for a group of solutions, a solution space, that the student will need to learn to navigate with a scientific approach. The design process finishes with the selection of a solution and its further development as a parametric prototype. The chosen system is then connected to a fabrication-oriented experiment, testing how its logic responds to material, machine and process constraints.
AI Prototyping & Fabrication
From file to physical matter
The module
Gallery
Objectives
- Analyse a problem and identify the opportunities to adapt a rule-based system.
- Explore the solution space and alternatives with comparative metrics, not by taste.
- Take a design decision based on criteria and prepare it for production
- Test how the selected parametric system meets fabrication constraints before committing material, using a production workflow appropriate to the project.
What you will be able to do
Learning outcomes
The student will: Learn to identify opportunities to frame a design problem as a system of inputs, relationships, constraints and outputs. Learn about different data systems and solutions that allow a rich solution space.
Professional outcomes
Builds the critical ability to translate problems into rule-based workflows and solutions. Connects design and production, opening prototyping and fabrication as a billable line of work.
Methodology
Learn by doing, on the student's own real project. No software tutorials: a pre-built starting package is provided and base resources are given apart. Sessions combine a short input, guided practice and critique.
Concepts
Session by session
- Identifying a problem and describing it as a system: inputs, relationships, constraints, states, outputs and feedback. Understanding behaviour before building geometry.
- Code-generated parametric systems grouped into applied. Understanding what each family can and cannot do.
- Selecting the pre-built system closest to each student's project. Mapping project data and intentions onto its logic, replacing inputs and defining what should remain fixed or variable.
- Advanced algorithms and computational techniques in the industry.
- Testing meaningful variations, valid ranges and failure cases. Refining the system through critique and project feedback.
- From digital model to digital fabrication.
- (Invited professor: Nik Eftekhar- Phd @ETH)
Deliverable
Faculty and resources
Module Faculty
Learn this in MIAWS
This module is part of the 18-week postgraduate program. Work with your own real project from week 1. First cohort February 2027.
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