This course is offered in a blended format, with in-person and live virtual cohorts attending simultaneously. When registering, select the appropriate registration button below. At this time the only method of payment accepted is online card payment.

Course is closed
Lead Instructor(s)
Date(s)
Jul 11 - 15, 2022
Registration Deadline
Location
On Campus or Live Virtual
Course Length
5 Days
Course Fee
$4,700
CEUs
3.3 CEUs
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Course is closed
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Fuel your organization’s ability to produce large volumes of highly integrated, complex, customized products by leveraging intelligent design and manufacturing strategies powered by the latest in artificial intelligence. In this highly interactive course, you’ll join a group of accomplished global peers to explore the latest smart manufacturing strategies and hardware, acquire skills to develop machine learning-based design templates, and participate in generative design sessions. 

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.

Course Overview

Over the next few decades, industries around the world will transition to a new economy in which highly complex, customizable products will be manufactured on-demand using intelligent manufacturing systems. In a number of fields, this change is already underway—for example, additive manufacturing is revolutionizing the production of consumer, aerospace, automotive, and medical parts. To stay ahead, professionals need a thorough understanding of the AI-powered strategies and tools that are enabling these rapid advancements—and a plan for implementing them in their own organizations.

This course provides an introduction to developing end-to-end AI-based design and manufacturing workflows. Over the course of five days, you will explore how AI methods are advancing digital manufacturing and the entire design ecosystem. In this course, you will also:

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

COVID-19 Updates

We fully expect to resume on-campus Short Programs courses during the Summer of 2022. However, the possibility remains of ongoing disruption and restrictions due to COVID-19 which may require that the course be delivered via live virtual format. Please read more here.

Learning Outcomes

  • Learn how to develop an intelligent design and manufacturing workflow using the latest AI/machine learning methods.

  • Recognize the capabilities and limitations of current advanced manufacturing hardware.

  • Enhance your ability to use AI tools for optimizing manufacturing processes and workflow designs. 

  • Increase your understanding of traditional and AI-based geometric representations for digital manufacturing.

  • Explore how to automatically mass-customize designs.

  • Learn how to predict design performance using virtual testing, numerical simulation, and AI methods.

  • Delve into performance-driven design workflow, as well as principles of generative and inverse design.

  • Design objects using generative design methods.

  • Acquire experience designing and optimizing objects for multiple objectives and across multiple domains.

  • Design and build data-driven (machine learning) models that drive design customization.

  • Master principles of numerical optimization techniques for machine learning.

Program Outline

Day 1
Morning: 9 am – 12 pm

  • Computational design and manufacturing workflow
  • Introduction to optimization

Afternoon: 1 pm – 5 pm

  • Introduction to AI and machine learning
  • Machine learning methods (including neural networks)
  • Lab 1: Designing and building a machine learning model 

Day 2
Morning: 9 am – 12 pm

  • Overview of advanced manufacturing processes
  • From geometry to hardware abstraction languages
  • Lab 2: Designing and fabricating models using a virtualized manufacturing system

Afternoon: 1 pm – 5 pm

  • Intelligent manufacturing systems
  • Advanced AI tools for manufacturing process optimization (Bayesian optimization)
  • Lab 3: Process optimization using Bayesian optimization

Day 3
Morning: 9 am – 12 pm

  • Digital design representations 
  • Customizable designs using parametric modeling
  • Advanced design customization: procedural modeling and geometric deformation methods

Afternoon: 1 pm – 5 pm

  • Lab 4: Designing customizable models for manufacturing
  • Advanced AI tools for design customization (deep neural networks, convolutional neural networks)
  • AI methods for representing design spaces (generative models, autoencoders, GANs)

Day 4
Morning: 9 am – 12 pm

  • Lab 5: Advanced AI methods for design customization
  • Predicting design performance using simulation methods

Afternoon: 1 pm – 5 pm

  • Lab 6: Predicting design performance for manufacturing
  • Predicting performance using ML methods
  • Inverse methods performance-driven design
  • AI methods for inverse methods

Day 5
Morning: 9 am – 12 pm

  • Topology optimization
  • Lab 7: Generative design

Afternoon: 1 pm – 5 pm

  • Symbolic and neurosymbolic AI methods for computational design
  • Optimizing design for multiple objectives and multiple domains
  • Lab 8: AI Clinic – using learned material to solve real-life challenges brought by course participants
  • Course review: developing an intelligent computational design and manufacturing workflow

Links & Resources

News & Articles

Course Outline/Schedule

All times are EDT

Day 1
Morning: 9 am – 12 pm

  • Computational design and manufacturing workflow (Matusik)
  • Overview of advanced manufacturing processes (Matusik)
  • Digital design representations (Matusik)

Afternoon: 1 pm – 5 pm

  • From geometry to hardware abstraction languages (Matusik)
  • Lab 1: Designing and printing models using a virtualized 3D printer (Spielberg)
  • Design space & customizable designs using parametric modelling (Matusik)

Day 2
Morning: 9 am – 12 pm

  • Advanced design customization: procedural modeling and geometric deformation methods (Matusik)
  • Lab 2: Designing customizable models for additive manufacturing (Spielberg)

Afternoon: 1 pm – 5 pm

  • Predicting design performance using simulation methods (Matusik)
  • Lab 3: Predicting design performance for additive manufacturing (Spielberg)
  • Inverse methods and performance-driven design (Matusik)

Day 3
Morning: 9 am – 12 pm

  • Introduction to optimization (Matusik)
  • Topology optimization (Matusik)
  • Lab 4: Design for AM using topology optimization (Spielberg)

Afternoon: 1 pm – 5 pm

  • Optimizing design for multiple objectives (Matusik)
  • Interactive design applications (Matusik)
  • Optimizing designs across multiple domains (Matusik) 

Day 4
Morning: 9 am – 12 pm 

  • Introduction to AI and machine learning (Matusik)
  • Symbolic AI methods for computational design (Matusik)
  • Machine learning methods (including neural networks) (Matusik)

Afternoon: 1 pm – 5 pm

  • Lab 5: Designing and building a machine learning model (Spielberg) 
  • Advanced AI tools for design customization (deep neural networks, convolutional neural networks) (Matusik)
  • Lab 6: Advanced AI methods for design customization (Spielberg)

Day 5
Morning: 9 am – 12 pm

  • AI methods for representing design spaces (generative models, autoencoders, GANs) (Matusik)
  • Automated discovery of optimal designs (Matusik)
  • Intelligent manufacturing systems (Matusik)

Afternoon: 1 pm – 5 pm

  • Advanced AI tools for manufacturing process optimization (Bayesian optimization) (Matusik)
  • Lab 7: Process optimization using Bayesian optimization (Matusik) 
  • Course review: developing an intelligent computational design and manufacturing workflow (Matusik)
     
Who Should Attend

This course is designed for engineers, designers, product managers, production managers, research and development managers,  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, chemical, architecture, aerospace, and defense.

Requirements

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

Brochure
Download the Course Brochure
AI for Computational Design and Manufacturing-2022-thumbnail

Content

The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

Fundamentals: Core Concepts, understandings, and tools - 30%|Latest Developments: Recent advances and future trends - 40%|Industry Applications: Linking theory and real-world - 30%
30|40|30
Delivery Methods

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

Lecture: Delivery of material in a lecture format - 45%|Discussion or Groupwork: Participatory learning - 25%|Labs: Demonstrations, experiments, simulations - 30%
45|25|30
Levels

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

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