PATO：基于深度学习的金属增材制造生产率感知拓扑优化
PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing
摘要：
在本文中，我们提出了PATO-一个可生产性感知的拓扑优化（TO）框架，以帮助有效地探索使用金属增材制造（AM）制造的组件的设计空间，同时确保开裂的可制造性。具体而言，通过激光粉末床熔合制造的部件容易出现翘曲或开裂等缺陷，这是由于在构建过程中产生的陡峭热梯度产生的高残余应力值。这些零件的成熟设计和制造计划可能需要数月至数年的时间，通常涉及设计和制造工程师之间的多次协商。PATO基于无裂纹设计的先验发现，因此可以在一开始就构建无缺陷的优化部件。为了确保设计在优化过程中没有裂纹，在TO的标准公式中使用裂纹指数明确编码了生产率。多裂纹指数进行了探讨，并使用实验验证，最大剪切应变指数（MSSI）被证明是一个准确的裂纹指数。模拟构建过程是一个耦合的多物理场计算，并且将其并入TO循环中可能在计算上是禁止的。我们利用当前深度卷积神经网络的进展，并提出了一个高保真的代理模型的基础上，基于注意力的U-网络架构来预测MSSI值作为一个空间变化的字段在部分的域。此外，我们采用自动微分直接计算相对于输入设计变量的最大MSSI的梯度，并增加它与基于性能的灵敏度字段，以优化设计，同时考虑重量，可制造性和功能之间的权衡。我们证明了所提出的方法的有效性，通过基准研究在3D以及实验验证。
英文摘要：
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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