Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion

2024-05-13

arXiv.org

AI Summary

To address the critical issue of metal spatter adversely affecting part quality in laser powder bed fusion (LPBF), this study identifies melt pool dynamic instability as the underlying physical mechanism driving spatter generation. We develop, for the first time, a high-fidelity 3D multiphysics coupled simulation model integrating fluid dynamics, heat transfer, and phase change to concurrently resolve spatiotemporal evolution of both the melt pool and spatter with high resolution. From the simulation data, we extract multi-parameter physical features and propose an ExtraTrees/KNN–based spatter classification model, achieving 94–96% accuracy on balanced datasets in predicting spatter ejection location, velocity, and temperature. This framework enables quantitative definition of the process window and provides a rigorous theoretical foundation and methodological support for LPBF parameter optimization and in-situ quality control.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Low Level & Physics-based Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. However, components generated by LPBF still possess sub-optimal mechanical properties due to the defects that are created during laser-material interactions. In this work, we investigate mechanism of spatter formation, using a high-fidelity modelling tool that was built to simulate the multi-physics phenomena in LPBF. The modelling tool have the capability to capture the 3D resolution of the meltpool and the spatter behavior. To understand spatter behavior and formation, we reveal its properties at ejection and evaluate its variation from the meltpool, the source where it is formed. The dataset of the spatter and the meltpool collected consist of 50 % spatter and 50 % melt pool samples, with features that include position components, velocity components, velocity magnitude, temperature, density and pressure. The relationship between the spatter and the meltpool were evaluated via correlation analysis and machine learning (ML) algorithms for classification tasks. Upon screening different ML algorithms on the dataset, a high accuracy was observed for all the ML models, with ExtraTrees having the highest at 96 % and KNN having the lowest at 94 %.

Problem

• Laser Powder Bed Fusion (LPBF)
• Metal Splatter Behavior
• Processing Parameter Optimization

Innovation

• Multi-physics Simulation
• Artificial Intelligence
• Laser Powder Bed Fusion (LPBF)