Gradient-Based Dovetail Joint Shape Optimization for Stiffness

Sun, X., Cai, C., Adams, R. P., & Rusinkiewicz, S. (2023). Gradient-Based Dovetail Joint Shape Optimization for Stiffness. Symposium on Computational Fabrication.
It is common to manufacture an object by decomposing it into parts that can be assembled. This decomposition is often required by size limits of the machine, the complex structure of the shape, etc. To make it possible to easily assemble the final object, it is often desirable to design geometry that enables robust connections between the subcomponents. In this project, we study the task of dovetail-joint shape optimization for stiffness using gradient-based optimization. This optimization requires a differentiable simulator that is capable of modeling the contact between the two parts of a joint, making it possible to reason about the gradient of the stiffness with respect to shape parameters. Our simulation approach uses a penalty method that alternates between optimizing each side of the joint, using the adjoint method to compute gradients. We test our method by optimizing the joint shapes in three different joint shape spaces, and evaluate optimized joint shapes in both simulation and real-world tests. The experiments show that optimized joint shapes achieve higher stiffness, both synthetically and in real-world tests.
  year = {2023},
  title = {Gradient-Based Dovetail Joint Shape Optimization for Stiffness},
  author = {Sun, Xingyuan and Cai, Chenyue and Adams, R. P. and Rusinkiewicz, Szymon},
  booktitle = {Symposium on Computational Fabrication}