Towards end-to-end optimization in multimaterial 3D printing
For designers of functionally graded multimaterial components, this framework replaces laborious empirical prototyping with interpretable machine-learning models, enabling efficient optimization of both topology and material distribution.
This work introduces an end-to-end framework that integrates sparsified physics-augmented neural networks with finite-element topology optimization to concurrently optimize material distribution and structural topology in multimaterial 3D printing, demonstrated on soft robotic grippers with anisotropic contact responses.
Multimaterial 3D printing enables the fabrication of functionally graded components, but optimizing their spatial material distribution alongside structural topology remains a formidable challenge due to high-dimensional design spaces and complex constitutive modeling. This paper presents an end-to-end computational framework integrating sparsified physics-augmented neural networks with finite-element-based topology optimization. By extracting closed-form, composition-aware hyperelastic constitutive laws from experimental data, this approach facilitates exact symbolic differentiation via the adjoint state method implemented with FEniCSx, efficiently circumventing the bottlenecks of applying neural network constitutive models. This pipeline is deployed on soft robotic gripper applications, demonstrating continuous composition optimization for highly anisotropic contact responses, and the concurrent optimization of macroscopic topology and material distribution under non-failure stretch constraints. This methodology could replace laborious empirical prototyping, establishing interpretable machine-learning models as practical, robust design primitives for advanced multimaterial additive manufacturing.