Computational Design for AI in Manufacturing: Creating a Pipeline for the Future of Production

Advances in CAD

Over the next few decades, industries around the world will transition to a new economy in which highly complex, customizable products will be manufactured by on-demand flexible robotic systems. In a number of fields, this change is already underway—for example, 3D printers are revolutionizing the production of consumer, aerospace, automotive, and medical parts.

This course provides an introduction to developing intelligent design and manufacturing workflows. Over the course of five days, you will explore advanced manufacturing hardware and learn about methods for creating customizable design templates, and virtual product testing. In this course, you will also:

  • Explore performance-driven and generative design workflows that automatically translate functional specifications of objects to manufacturable designs
  • Discover AI/machine learning methods that enable design automation and customization
  • Learn how AI methods can be used in a manufacturing workflow for automated inspections and improved performance

Earn a Professional Certificate

This course may be taken individually or as part of the Professional Certificate Program in Design & Manufacturing or the Professional Certificate Program in Machine Learning & Artificial Intelligence.

Lead Instructor(s): 

Wojciech Matusik


Jul 13, 2020 - Jul 17, 2020

Course Length: 

5 Days

Course Fee: 





  • Open

It is highly recommended that you apply for a course at least 6-8 weeks before the start date to guarantee there will be space available. After that date you may be placed on a waitlist. Courses with low enrollment may be cancelled up to 4 weeks before start date if sufficient enrollments are not met. If you are able to access the online application form, then registration for that particular course is still open.

Registration for this program will close by June 29.

Participant Takeaways: 

  • Learn how to develop an intelligent design and manufacturing workflow.
  • Understand capabilities and limitations of current advanced manufacturing hardware.
  • Understand geometric representations for digital manufacturing.
  • Design and manufacture objects using advanced manufacturing.
  • Learn how to mass customize designs using parametric models.
  • Learn how to predict design performance using virtual testing and numerical simulation.
  • Understand performance-driven design workflow.
  • Understand principles of generative design.
  • Design objects using topology optimization methods.
  • Experience designing and optimizing objects across multiple domains.
  • Learn how AI methods can aid design workflows.
  • Design and build a data-driven (machine learning) model.
  • Learn how to design training datasets from image collections.
  • Learn how data-driven models can be used for product customization.
  • Understand AI tools for manufacturing.

Who Should Attend: 

This course is designed for engineers, designers, product managers, marketing directors, scientists and educators in industries that are involved in translating concepts to physical objects/products. Relevant areas include consumer products, medical devices, textile, packaging, electronics, automotive, aerospace, and defense.


Computer Requirements:

Laptops or tablets with Windows and with which you have administrator privileges are required for this course.

Program Outline: 

Day 1:

Computational design and manufacturing workflow

Basics of advanced manufacturing

  • Overview of advanced manufacturing processes
  • Lab tour: examples of advanced manufacturing
  • Solid modeling and design representations
  • From geometry to hardware abstraction languages

Lab 1:  Designing and printing models using a virtualized 3D printer

Day 2

Geometric modeling methods

  • Introduction to geometric modeling and CAD
  • Parametric modeling
  • Procedural modeling

Lab 2: Designing parametric models for additive manufacturing

Predicting design performance

  • Computer simulation and virtual testing
  • Data-driven methods

Lab 3: Predicting design performance for additive manufacturing

Day 3

Performance-driven design workflow

  • Introduction to performance-driven design
  • Performance space representation
  • Optimizing design for multiple-objectives

Generative design workflows

  • Introduction to generative design
  • Topology optimization

Lab 4: Design for 3D printing using topology optimization

Day 4

Automating design across multiple-domains

  • Introduction to concurrent design
  • Integrated design tools for domain-specific applications

AI tools for computational design

  • Introduction to AI methods
  • Expert systems for computational design
  • Data-driven/machine learning methods for computational design

Lab 5: Designing and building a data-driven model

Day 5

Advanced AI tools for computational design

  • Creating custom image datasets   
  • Designing machine learning models for image datasets
  • Data-driven models for design customization

Lab 6: Data-driven models for design customization

AI tools for manufacturing

  • Inspection methods
  • Intelligent manufacturing systems

Developing an end-to-end computational design and manufacturing workflow

Course Schedule: 

View 2020 schedule (pdf)

This course runs 9:00 am - 5:00 pm Monday through Friday.



This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please complete the Custom Programs request form for further details.


Fundamentals: Core concepts, understandings, and tools (30%) 30
Latest Developments: Recent advances and future trends (40%) 40
Industry Applications: Linking theory and real-world (30%) 30

Delivery Methods: 

Lecture: Delivery of material in a lecture format (45%) 45
Discusson or Groupwork: Participatory learning (25%) 25
Labs: Demonstrations, experiments, simulations (30%) 30


Introductory: Appropriate for a general audience (40%) 40
Specialized: Assumes experience in practice area or field (40%) 40
Advanced: In-depth explorations at the graduate level (20%) 20