Laser metal deposition (LMD) is an additive manufacturing method for metal parts by using focused thermal energy to fuse materials as they are deposited. During LMD, transient thermal signatures such as the in-situ thermal images of melt pool, contain rich information about process performance. Early prediction of such transient thermal signatures provides opportunities for process monitoring and defect prevention. While physics-based models of LMD have been conventionally used for thermal signature prediction, they have limitations and are computationally expensive for real-time prediction. A scalable, efficient data-science-based model is therefore needed. This paper develops a deep-learning-based surrogate model, called LMD-cGAN, to predict and emulate the transient thermal signatures in LMD. The model generates images for the thermal dynamics of melt pool conditionally on the deposition layer. It enables early prediction of future-layer thermal signatures for an in-process part based on its early-layer thermal signatures. To respect the physics in LMD, a physics-guided image selection (PGIS) mechanism is integrated with LMD-cGAN to calibrate the predictions against physical benchmarks of transient melt pool for the process. The effectiveness and efficiency of the proposed method are demonstrated in a case study on the LMD of Ti-4Al-6V thin-walled structures. Note to Practitioners—With online sensing, many LMD applications have real-time process data that convey valuable information about the process status and part quality. The proposed method leverages these data for thermal signature prediction. LMD-cGAN is a deep-learning-based surrogate model that learns the population profile of real thermal signatures and generates thermal signatures from there. The proposed PGIS mechanism in LMD-cGAN ensures the physical validity of these predictions by benchmarking them against physical insights about the process. LMD-cGAN can be applied to predict thermal signatures in future layers based on early-layer thermal signatures of an in-process part (an implicit assumption here is that the in-process part to be predicted for is the same type). LMD-cGAN can also be applied to emulate thermal signatures in specific layers. To generate thermal signatures for generic, non-defect parts, the training data should be selected with caution – the part where the data were collected should have no obvious defects, so the thermal signatures generated by LMD-cGAN show the regular thermal dynamics. Compared with pure physical models, the proposed method incorporates process uncertainties captured from the early-layer data, hence “on-the-fly” emulation of the melt pool, while characterizing the inherent relationship between the LMD process and thermal signatures.

中文翻译：

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基于深度学习的激光金属沉积热特征预测替代模型

激光金属沉积 (LMD) 是一种增材制造金属部件的方法，在沉积材料时使用聚焦热能熔化材料。在 LMD 期间，瞬态热特征（例如熔池的原位热图像）包含有关工艺性能的丰富信息。这种瞬态热特征的早期预测为过程监控和缺陷预防提供了机会。虽然基于物理的 LMD 模型通常用于热特征预测，但它们具有局限性并且实时预测的计算成本很高。因此，需要一种可扩展、高效的基于数据科学的模型。本文开发了一种基于深度学习的替代模型，称为 LMD-cGAN，以预测和模拟 LMD 中的瞬态热特征。该模型有条件地在沉积层上生成熔池热动力学图像。它可以根据早期层的热特征早期预测过程中部件的未来层热特征。为了尊重 LMD 中的物理学，物理引导图像选择 (PGIS) 机制与 LMD-cGAN 集成，以根据过程的瞬态熔池物理基准校准预测。在 Ti-4Al-6V 薄壁结构的 LMD 案例研究中证明了所提出方法的有效性和效率。从业者须知——通过在线传感，许多 LMD 应用程序都拥有实时过程数据，这些数据传达有关过程状态和零件质量的宝贵信息。所提出的方法利用这些数据进行热特征预测。LMD-cGAN 是一种基于深度学习的替代模型，可学习真实热特征的总体概况并从中生成热特征。LMD-cGAN 中提议的 PGIS 机制通过将这些预测与过程的物理见解进行基准测试来确保这些预测的物理有效性。LMD-cGAN 可用于根据过程中部件的早期层热特征预测未来层中的热特征（此处隐含的假设是要预测的过程中部件是同一类型）。LMD-cGAN 也可用于模拟特定层中的热特征。要为通用的、无缺陷的零件生成热特征，应谨慎选择训练数据——收集数据的零件应该没有明显的缺陷，因此 LMD-cGAN 生成的热特征显示了常规的热动力学。与纯物理模型相比，所提出的方法结合了从早期数据中捕获的过程不确定性，因此对熔池进行了“即时”仿真，同时表征了 LMD 过程与热特征之间的内在关系。