A framework for graph-base neural network using numerical simulation of metal powder bed fusion for correlating process parameters and defect generation

Abstract

Powder bed fusion (PBF) is the most common technique used for metal additive manufacturing. This process involves consolidation of metal powder using a heat source such as laser or electron beam. During the formation of three-dimensional(3D) objects by sintering metal powders layer by layer, many different thermal phenomena occur that can create defects or anomalies on the final printed part. Similar to other additive manufacturing techniques, PBF has been in practice for decades, yet it is still going through research and development endeavors which is required to understand the physics behind this process. Defects and deformations highly impact the product quality and reliability of the overall manufacturing process; hence, it is essential that we understand the reason and mechanism of defect generation in PBF process and take appropriate measures to rectify them. In this paper, we have attempted to study the effect of processing parameters (scanning speed, laser power) on the generation of defects in PBF process using a graph-based artificial neural network that uses numerical simulation results as input or training data. Use of graph-based machine learning is novel in the area of manufacturing let alone additive manufacturing or powder bed fusion. The outcome of this study provides an opportunity to design a feedback controlled in-situ online monitoring system in powder bed fusion to reduce printing defects and optimize the manufacturing process.