[论文解读] PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing

Naresh Iyer, Amir M. Mirzendehdel+6 more|arXiv (Cornell University)|Dec 8, 2021

Manufacturing Process and Optimization被引用 6

一句话总结

PATO 是一种增强深度学习的拓扑优化框架，通过基于注意力机制的 U-Net 替代模型集成物理信息裂纹指数（MSSI），以预测金属增材制造中残余应力引起的开裂。通过使用自动微分计算 MSSI 梯度，PATO 实现了实时优化，生成无裂纹、高性能的零件，实验验证了无缺陷打印部件的可行性。

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.

研究动机与目标

• 解决因残余应力导致的后处理开裂所引起的金属增材制造中高设计迭代周期问题。
• 开发一种将可制造性——特别是抗裂性——直接嵌入拓扑优化的框架。
• 识别一种可靠的裂纹指数（MSSI），其与镍基高温合金 LPBF-AM 中实际缺陷形成的关联性强。
• 使用基于3D注意力机制的 U-Net 构建高保真、快速的替代模型，以预测无需运行完整热力仿真即可获得的空间 MSSI 场。
• 通过自动微分将 MSSI 敏感度整合到标准拓扑优化中，以平衡性能与可制造性。

提出的方法

• 定义裂纹指数 MSSI（最大剪切应变指数）作为 LPBF-AM 中裂纹萌生的预测指标，基于高保真多物理场仿真。
• 在模拟的 MSSI 场上训练基于3D注意力机制的 U-Net 深度神经网络，作为完整构建过程仿真的快速、精确替代模型。
• 使用自动微分计算从替代模型中得到的最大 MSSI 对设计变量（伪密度）的梯度。
• 将 MSSI 敏感度场与标准性能导向的敏感度场相结合，引导设计向无裂纹构型优化。
• 将增强的敏感度场集成到标准拓扑优化求解器（移动渐近线法）中，以同时优化性能与可制造性。
• 通过3D基准研究和优化试件的实验打印验证该框架。

实验结果

研究问题

• RQ1 深度学习替代模型能否在不运行完整热力仿真的情况下，准确预测金属 AM 零件中空间变化的 MSSI 场？
• RQ2 MSSI 是否是镍基高温合金 LPBF-AM 中残余应力诱发开裂的可靠且具有预测性的裂纹指数？
• RQ3 对 MSSI 替代模型进行自动微分能否提供用于拓扑优化的准确梯度？
• RQ4 将 MSSI 敏感度整合到拓扑优化中，如何影响零件性能与无裂纹可制造性之间的权衡？
• RQ5 所提出的 PATO 框架能否在真实制造环境中生成实验验证的无裂纹打印零件？

主要发现

• MSSI 指数经实验验证为裂纹的可靠预测指标，在预测实际缺陷形成方面优于其他裂纹指数。
• 基于注意力机制的3D U-Net 替代模型在显著降低计算成本的前提下，实现了对 MSSI 场的高保真预测。
• PATO 生成的设计在所有体积分数下均表现出更低的最大 MSSI 值，表明裂纹风险显著降低。
• PATO 优化的设计热柔度高于未考虑可制造性的拓扑优化设计，证实了性能与无裂纹可制造性之间存在可测量的权衡。
• 实验验证表明，PATO 优化的试件在打印后完全无裂纹，验证了该框架从初始阶段即能生成无缺陷零件的能力。