  In this paper, we propose PATO-a producibility-aware topology optimization
(TO) framework to help efficiently explore the design space of components
fabricated using metal additive manufacturing (AM), while ensuring
manufacturability with respect to cracking. Specifically, parts fabricated
through Laser Powder Bed Fusion are prone to defects such as warpage or
cracking due to high residual stress values generated from the steep thermal
gradients produced during the build process. Maturing the design for such parts
and planning their fabrication can span months to years, often involving
multiple handoffs between design and manufacturing engineers. PATO is based on
the a priori discovery of crack-free designs, so that the optimized part can be
built defect-free at the outset. To ensure that the design is crack free during
optimization, producibility is explicitly encoded within the standard
formulation of TO, using a crack index. Multiple crack indices are explored and
using experimental validation, maximum shear strain index (MSSI) is shown to be
an accurate crack index. Simulating the build process is a coupled,
multi-physics computation and incorporating it in the TO loop can be
computationally prohibitive. We leverage the current advances in deep
convolutional neural networks and present a high-fidelity surrogate model based
on an Attention-based U-Net architecture to predict the MSSI values as a
spatially varying field over the part's domain. Further, we employ automatic
differentiation to directly compute the gradient of maximum MSSI with respect
to the input design variables and augment it with the performance-based
sensitivity field to optimize the design while considering the trade-off
between weight, manufacturability, and functionality. We demonstrate the
effectiveness of the proposed method through benchmark studies in 3D as well as
experimental validation.
