Geometrical Deep Learning on 3D Models: Classification for Additive Manufacturing

This project took place in summer term 2021, you CAN NOT apply to this project anymore!

Results of this project are explained in detail in the final report and presentation.

About VW Data:Lab Munich

We are part of Volkswagen AG and we collaborate successfully across all Group brands, working not only with a number of internal departments but also with external customers and partners. Throughout our projects we work closely with the brand-specific AI teams. We increase efficiency and facilitate enhanced predictive capacity in almost all areas, and our products also help to optimize the customer experience for businesses and brands.

About the project:

Follow the link to have more information about the project.

3D printing has a lot of potential to the classical manufacturing process: high geometric freedom of design, functional integration, production on demand, strong and lightweight parts, etc. In automotive, additive manufacturing is mainly used to produce customizable details, prototypes and pre-series vehicles, operating equipment and motorsport parts.

This project mainly focuses on the initial step of 3D manufacturing process – estimation if certain part could be produced on the specific 3D printer. Currently this involves manual work of engineers, that check if certain part meets requirements that are specified by the 3D printer manufacturer. This process is usually very time consuming and requires expert knowledge. We would like to develop a machine learning approach that would be able to differentiate between printable and non- printable parts based on their geometrical form.

The project is organized in the following steps. First, 3D model data is normalized and converted to the voxel representation. Second step is synthetic data generation: defects, that lead to the non-printable forms, are inserted into the 3D shapes objects. Finally, we will develop and test a neural-network-based solution for classification between printable and non-printable shapes.

The project pipeline will include elements of differential geometry, geometrical deep learning, transfer learning and basics behind additive manufacturing process.

Accepted students to this project should attend (unless they have proven knowledge) online workshops at the LRZ from 06.04.2021 - 09.04.2021 (9:00 AM to 5:00 PM). More information will be provided to students accepted to this project.