Publication | Structural and Multidisciplinary Optimization 2020
A subtractive manufacturing constraint for level set topology optimization
This research shows our commitment to providing our customers with practical tools that will enable efficient structural designs that importantly can be manufactured with traditional manufacturing methods such as CNC milling.
Download publicationAbstract
A subtractive manufacturing constraint for level set topology optimization
Nigel Morris, Adrian Butscher, Francesco Iorio
Structural and Multidisciplinary Optimization 2020
We present a method for enforcing manufacturability constraints in generated parts such that they will be automatically ready for fabrication using a subtractive approach. We primarily target multi-axis CNC milling approaches but the method should generalize to other subtractive methods as well. To this end we take as user input: the radius of curvature of the tool bit, a coarse model of the tool head and optionally a set of milling directions. This allows us to enforce the following manufacturability conditions: (1) surface smoothness such that the radius of curvature of the part does not exceed the milling bit radius, (2) orientation such that every part of the surface to be milled is visible from at least one milling direction, (3) accessibility such that every surface patch can be reached by the tool bit without interference with the tool or head mount. We will show how to efficiently enforce the constraint during level set–based topology optimization modifying the advection velocity such that at each iteration the topology optimization maintains a descent optimization direction and does not violate any of the manufacturability conditions. This approach models the actual subtractive process by carving away material accessible to the machine at each iteration until a local optimum is achieved.
Associated Autodesk Researchers
Related Resources
2025
Bridging Design, Human Sciences, and Technology with AI ArtCreating an interactive experience for guests at a series of events…
2024
HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided DesignThis work presents a new Machine Learning architecture to support…
2021
Think-Aloud Computing: Supporting Rich and Low-Effort Knowledge CaptureWhen users complete tasks on the computer, the knowledge they leverage…
2006
Performing Incremental Bayesian Inference by Dynamic Model CountingThe ability to update the structure of a Bayesian network when new…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us