A digital twin (DT) is a virtual representation of physical process, products and/or systems that requires a high-fidelity computational model for continuous update through the integration of sensor data and user input. In the context of laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the manufacturing process can offer predictions for the produced parts, diagnostics for manufacturing defects, as well as control capabilities. This paper introduces a parameterized physics-based digital twin (PPB-DT) for the statistical predictions of LPBF metal additive manufacturing process. We accomplish this by creating a high-fidelity computational model that accurately represents the melt pool phenomena and subsequently calibrating and validating it through controlled experiments. In PPB-DT, a mechanistic reduced-order method-driven stochastic calibration process is introduced, which enables the statistical predictions of the melt pool geometries and the identification of defects such as lack-of-fusion porosity and surface roughness, specifically for diagnostic applications. Leveraging data derived from this physics-based model and experiments, we have trained a machine learning-based digital twin (PPB-ML-DT) model for predicting, monitoring, and controlling melt pool geometries. These proposed digital twin models can be employed for predictions, control, optimization, and quality assurance within the LPBF process, ultimately expediting product development and certification in LPBF-based metal additive manufacturing.

翻译：数字孪生（DT）是物理过程、产品和/或系统的虚拟表征，需通过集成传感器数据和用户输入实现持续更新的高保真计算模型。在激光粉末床熔融（LPBF）增材制造领域，制造过程的数字孪生可提供对零部件的预测、制造缺陷的诊断以及控制能力。本文提出了一种参数化物理驱动数字孪生（PPB-DT）模型，用于LPBF金属增材制造过程的统计预测。我们通过构建精确表征熔池现象的高保真计算模型，并借助受控实验对其进行校准与验证来实现这一目标。在PPB-DT中，引入了一种基于机理降阶模型的随机校准过程，可实现熔池几何形状的统计预测以及缺陷（如欠熔合孔隙和表面粗糙度）的识别，尤其适用于诊断应用。利用该物理模型及实验所得数据，我们训练了基于机器学习的数字孪生（PPB-ML-DT）模型，用于熔池几何形状的预测、监测与控制。所提出的数字孪生模型可应用于LPBF工艺的预测、控制、优化及质量保证，最终加速基于LPBF的金属增材制造中的产品开发与认证进程。