Directed Energy Deposition (DED) is a free-form metal additive manufacturing process characterized as toolless, flexible, and energy-efficient compared to traditional processes. However, it is a complex system with a highly dynamic nature that presents challenges for modeling and optimization due to its multiphysics and multiscale characteristics. Additionally, multiple factors such as different machine setups and materials require extensive testing through single-track depositions, which can be time and resource-intensive. Single-track experiments are the foundation for establishing optimal initial parameters and comprehensively characterizing bead geometry, ensuring the accuracy and efficiency of computer-aided design and process quality validation. We digitized a DED setup using the Robot Operating System (ROS 2) and employed a thermal camera for real-time monitoring and evaluation to streamline the experimentation process. With the laser power and velocity as inputs, we optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM). The three-objective approach achieved better rewards in all iterations and, when implemented in a real setup, allowed to reduce the number of experiments and shorten setup time. Our approach can minimize waste, increase the quality and reliability of DED, and enhance and simplify human-process interaction by leveraging the collaboration between human knowledge and model predictions.

中文翻译：

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基于原位监测的定向能量沉积人在回路多目标贝叶斯优化


定向能量沉积 （DED） 是一种自由形态的金属增材制造工艺，与传统工艺相比，具有免工具、灵活且节能的特点。然而，它是一个具有高动态性的复杂系统，由于其多物理场和多尺度特性，给建模和优化带来了挑战。此外，多种因素（例如不同的机器设置和材料）需要通过单轨沉积进行大量测试，这可能是时间和资源密集型的。单轨实验是建立最佳初始参数和全面表征胶条几何形状的基础，可确保计算机辅助设计和工艺质量验证的准确性和效率。我们使用机器人操作系统 （ROS 2） 将 DED 设置数字化，并使用热像仪进行实时监控和评估，以简化实验过程。以激光功率和速度为输入，我们优化了熔池的尺寸和稳定性，并使用响应面模型 （RSM） 评估了不同的目标函数和方法。三目标方法在所有迭代中都获得了更好的回报，并且在实际设置中实施时，可以减少实验数量并缩短设置时间。我们的方法可以利用人类知识和模型预测之间的协作，最大限度地减少浪费，提高 DED 的质量和可靠性，并增强和简化人机交互。