PhD Student, MIT
Computational Design for the Next Manufacturing Revolution
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 the production of metal parts in aerospace, automotive, and medical industries. Whole garment knitting machines allow automated production of complex apparel and shoes. Manufacturing electronics on flexible substrates opens the door to a whole new range of products for consumer electronics and medical diagnostics. Collaborative robots, such as Baxter from Rethink Robotics, allow flexible and automated assembly of complex objects. Overall, these new machines enable batch-one manufacturing of products that have unprecedented complexity.
In my work, I argue that the field of computational design is essential for the next revolution in manufacturing. This new field has to embrace the following key concepts. First, new design methods have to become more intelligent by utilizing large data repositories and associated machine learning methods. Second, workflows need to support concurrent design of shape, materials, control, and software in order to simplify the process and fully utilize the design space. Third, design tools need to transition from declarative to functional, automatically translating functional specifications of an object to manufacturing instructions. I will showcase how these three concepts are applied by developing new systems for designing robots, drones, and furniture. I will conclude by discussing open problems and challenges for this new emerging research field.
Adriana Schulz is a Ph.D. student in computer science at MIT, where she works with Professor Wojciech Matusik. Before coming to MIT, she obtained a Master’s in mathematics from IMPA, Brazil and a Bachelors in electronics engineering from UFRJ, Brazil. Her current research focuses on fabrication oriented design and she develops methods that combine data driven techniques with interactive simulation and optimization to allow casual users to design complex functional mechanisms.