Digital Twin-based Machine Control for Laser Powder Bed Fusion of Metallic Aerospace Components

Additive manufacturing (AM) processes such as laser powder bed fusion (LPBF) have received a great deal of attention due to their unique capability in producing parts with complex geometries. However, a major challenge limiting the application of LPBF today is the high variability in the quality of finished components. To address this challenge, HIDENN-AI, LLC and Northwestern University (NU) propose to establish a simulation-guided machine-learning (ML) based feedback control approach to additive manufacturing of metallic aerospace components based on Laser Powder Bed Fusion (LPBF). The project goal is accomplished by building a data-driven machine learning digital twin (ML-DT) framework that integrates real-time experimental data collection/process control with machine-learning-based process modeling and optimization. This integration is critical as it bridges the gap between high-fidelity physics-based models that are known to be computationally expensive and real-time process control and optimization. Seven technical tasks are proposed in this program for the Phase I base and optional period: Task 1: Calibration and validation of an existing physics-based computational model for LPBF process (Phase I Base for HIDENN-AI, LLC) Task 2: Development of machine learning based digital twin for online monitoring and process control (Phase I Base for HIDENN-AI, LLC) Task 3: Integration of feedback control model with the hardware platform of LPBF process (Phase I Base for NU) Task 4: Demonstration of the feasibility of the developed ML-DT for process optimization through LPBF experiment of simple AlSi10Mg Aluminum coupons (Phase I Base for NU) Task 5: Phase II demonstration plan for complex geometry with select materials and Phase II proposal preparation (Phase I Base for HIDENN-AI, LLC and NU) Task 6: Develop a framework of Convolution Hierarchical Deep Neural Network (C-HiDeNN) to significantly enhance the capabilities of the DT and demonstrate the capability for modeling part-scale LPBF process (Phase I option for HIDENN-AI, LLC) Task 7: Extension of ML-DT based control of additive manufacturing process to other process conditions (Phase I option for NU) The key deliverables of the Phase I project will be a software tool and its demonstration for real-time control of the laser power signal to control the Lack of Fusion (LOF) porosity and surface roughness in LPBF processing of AlSi10Mg material system. Future Phase II efforts will be directed towards the extension of the ML-DT based control system to manufacture complex geometry with varied AM materials and improving the predictive capability of the high-fidelity solver using the novel Convolution Hierarchical Deep Neural Network.