Neural method designs manufacturable 3d acoustic structures for personalized ultrasound
AcoustiPrint: Physics-Guided Neural Design of Manufacturable Acoustic Structures
Machine LearningArtificial Intelligence
Summary
It is hard to design three-dimensional shapes that control sound waves accurately while also being possible to 3D print. The authors present AcoustiPrint, a new neural network method that makes sure designs can actually be printed by checking physical constraints during the design process. Their method improves sound focusing through structures optimized for individual patients, especially for ultrasound treatments through the head. The approach combines accurate physics simulation and printability checks to create better, realistic devices.
What this means in practice
- •For medical device developers: Design patient-specific ultrasound devices that better focus sound through the skull using manufacturable 3D printed acoustic structures.$Commercial implications: This enables customized medical ultrasound hardware that fits individual patients, improving therapy effectiveness and manufacturability.
- •For 3d printing engineers: Create complex acoustic structures that satisfy printing constraints throughout design rather than relying on post-processing fixes.
Authors
Shanmuka Sadhu, Harshvardhan C. Takawale, Yehia Zakaria, Ahmed Allam, Nirupam Roy
Abstract
Recent work in additive manufacturing and neural inverse design enables the creation of complex three-dimensional structures to manipulate physical fields, yet these designs must also adhere to discrete, non-differentiable fabrication constraints. Current methods limit the design space by imposing differentiable soft manufacturability loss penalties, or by fixing structures through post-processing, causing a discrepancy between the geometry optimized by the physics model and the final fabricated result. Thus, we present AcoustiPrint, a physics-informed neural architecture for constrained inverse design that represents three-dimensional geometries with an implicit neural representation (INR) and passes each candidate through a printability operator before evaluating its final response. The printability operator executes layer-wise self-support, discrete material selection, and whole-body connectivity, assuring fabrication feasibility. To allow for backpropagation through the discrete non-differentiable operations, we use surrogate gradients for the backward pass and a self-supervised printability objective that progressively drives the INR toward a valid design domain. We demonstrate AcoustiPrint for personalized transcranial focused ultrasound, where inter-patient differences in acoustic properties and head geometry deform wave propagation and requires patient-specific acoustic focusing. AcoustiPrint jointly optimizes three-dimensional geometry and material composition through differentiable wave physics while ensuring manufacturability. Experiments show manufacturable acoustic structures with enhanced focal accuracy and over density-based and phase-based baselines, demonstrating a generalized methodology for neural design through non-differentiable manufacturing constraints and differentiable physics.