Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition

Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computational costs, conventional data-driven ML models require large, labeled training datasets to achieve accurate predictions. Unfortunately, generating large datasets for ML model training through time-consuming experiments or high-fidelity simulations is highly expensive in metal AM. To address these challenges, this study introduces a physics-informed neural network (PINN) framework that incorporates governing physical laws into deep neural networks (NNs) to predict temperature and thermal stress evolution during the laser metal deposition (LMD) process. The study also discusses the enhanced accuracy and efficiency of the PINN model when supplemented with small simulation data. Furthermore, it highlights the PINN transferability, enabling fast predictions with a set of new process parameters using a pre-trained PINN model as an online soft sensor, significantly reducing computation time compared to physics-based numerical models while maintaining accuracy.

Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics

Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation including computational fluid dynamics (CFD) is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning (PIML) method by integrating neural networks with the governing physical laws to predict the melt pool dynamics such as temperature, velocity, and pressure without using any training data on velocity. This approach avoids solving the highly non-linear Navier-Stokes equation numerically, which significantly reduces the computational cost. The difficult-to-determine model constants of the governing equations of the melt pool can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the soft penalty by incorporating governing partial differential equations (PDEs), initial conditions, and boundary conditions in the PINN model.

Toward Natural Gesture/Speech Control of a Large Display

In recent years because of the advances in computer vision research, free hand gestures have been explored as means of human-computer interaction (HCI). Together with improved speech processing technology it is an important step toward natural multimodal HCI. However, inclusion of non-predefined continuous gestures into a multimodal framework is a challenging problem. In this paper, we propose a structured approach for studying patterns of multimodal language in the context of a 2D-display control. We consider systematic analysis of gestures from observable kinematical primitives to their semantics as pertinent to a linguistic structure. Proposed semantic classification of co-verbal gestures distinguishes six categories based on their spatio-temporal deixis. We discuss evolution of a computational framework for gesture and speech integration which was used to develop an interactive testbed (iMAP). The testbed enabled elicitation of adequate, non-sequential, multimodal patterns in a narrative mode of HCI. Conducted user studies illustrate significance of accounting for the temporal alignment of gesture and speech parts in semantic mapping. Furthermore, co-occurrence analysis of gesture/speech production suggests syntactic organization of gestures at the lexical level.