Over the next few decades, we are going to transition to a new economy where highly complex, customizable products are manufactured on demand by flexible robotic systems. This change is already underway in a number of fields. 3D printers are revolutionizing production of metal parts in consumer, aerospace, automotive, and medical industries. Manufacturing electronics on flexible substrates opens the door to a whole new range of products for consumer electronics and medical diagnostics. Overall, these new machines enable batch-one manufacturing of products that have unprecedented complexity.
This course gives an introduction to developing intelligent design and manufacturing workflows. Participants will be given an overview of the advanced manufacturing hardware, methods for creating customizable design templates, and virtual product testing. The course will cover performance-driven and generative design workflows that automatically translate functional specifications of objects to manufacturable designs. The course will introduce participants to AI/machine learning methods that allow design automation and customization. The course will showcase how AI methods can be used in a manufacturing workflow for automated inspections and improved performance.
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.
- 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.
Laptops or tablets with Windows and with which you have administrator privileges are required for this course.
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
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
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
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
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
This course runs 9:00 am - 5:00 pm Monday through Friday.
Professor Wojciech Matusik is an Associate Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory at MIT, where he leads the Computational Fabrication Group. Before coming to MIT, he worked at Mitsubishi Electric Research Laboratories, Adobe Systems, and Disney Research Zurich. He studied computer graphics at MIT and received his PhD in 2003. He also received a BS in EECS from the University of California at Berkeley in 1997 and MS in EECS from MIT in 2001. His research interests are in direct digital manufacturing and computer graphics. He holds more than 40 US patents. In 2004, he was named one of the world's top 100 young innovators by MIT's Technology Review Magazine. In 2009, he received the Significant New Researcher Award from ACM SIGGRAPH. In 2012, Matusik received the DARPA Young Faculty Award and was named a Sloan Research Fellow. He currently serves on the DARPA ISAT Study Group.
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|
|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|