Daniele Grandi

As a Sr. Research Engineer at Autodesk Research, I'm working to further the machine understanding of mechanical design problems. I'm interested in leveraging knowledge representation and reasoning, ontologies, knowledge graphs, and semantic technologies to create the next generation of design tools. In collaboration with UC Berkeley, Oregon State University, and MIT, we are researching methods to automatically learn design best practices from CAD databases.

At Autodesk, I've also worked as a Design Engineer on generative design research prototypes. Previously, I worked as an engineering consultant for a metal AM startup, focusing on design, simulation, and optimization of assemblies for AM.

I graduated from UC Berkeley with a Mechanical Engineering degree. I started working with 3D printers at Berkeley, where I founded the 3D Modeling Club. While additive manufacturing had been my main field of focus, I also enjoyed traditional manufacturing methods, applied to mechatronics projects. I enjoy getting my hands on all parts of a project, whether it involves design, coding, circuits, or fabrication.

The first 3D printing startup that I joined was eucl3D, a Berkeley startup working with game developers to provide custom high-quality 3D printed collectibles.

Through Project BAM, the second 3D printing startup I worked at, I learned more about metal additive manufacturing and became interested in design optimization.

Other projects at Autodesk, GE, and Bay Area IP, are sampled below.

Publications

Wang, Y., Grandi, D., Cui, D., Rao, V., & Goucher-Lambert, K. (2021, August). Understanding professional designers’ knowledge organization behavior: a case study in product teardowns. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers.

Ferrero, V., Hassani, K., Grandi, D., & DuPont, B. (2021). Classifying Component Function in Product Assemblies with Graph Neural Networks. arXiv preprint arXiv:2107.07042.

Nourbakhsh, M., Morris, N., Bergin, M., Iorio, F., & Grandi, D. (2016, August). Embedded sensors and feedback loops for iterative improvement in design synthesis for additive manufacturing. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 50077, p. V01AT02A031). American Society of Mechanical Engineers.

Recent Projects

Concept Interplanetary Lander

Research collaboration with NASA JPL leveraging generative design for space design

Generative Quadcopter

Quadcopter chassis designed using generative design research prototype software


Hackrod

Applied generative design research prototypes to the generation of a manufacturable car chassis

Other Work

autodeskinternship

Modular Chair

Leveraging Fusion 360 to automate the design customization of a chair

GE Oil & Gas

GE Turbine Sensor

Rapid prototype of a new turbine sensor casing

Olsryd 9 Cylinder

Olsryd 9 Cylinder Engine

Complete assembly of an airplane engine composed of more 1600 parts

Previous Projects

3D Modeling Club

Founder of the additive club at UC Berkeley

3D Printing Designathon

Organized the first additive hackathon at UC Berkeley

Project Azimuth

LabVIEW architecture of a robotic swarm to map environments

Amplify

Amplify

Winner of the GrabCAD rapid prototype and product development competition

Canoe rod-holder

Canoe Rod Holder

Product design competition

Fly High

Fly High

Design and manufacturing project