PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing

• URL: http://arxiv.org/abs/2112.04552v1
• Date: Wed, 8 Dec 2021 19:52:24 GMT
• Title: PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing
• Authors: Naresh S. Iyer, Amir M. Mirzendehdel, Sathyanarayanan Raghavan, Yang Jiao, Erva Ulu, Morad Behandish, Saigopal Nelaturi, Dean M. Robinson
• Abstract summary: 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) 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 maximum shear strain index (MSSI) We demonstrate the effectiveness of the proposed method through benchmark studies in 3D as well as experimental validation.
• Score: 2.57172274875712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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