Estimating Pore Location of PBF-LB/M Processes with Segmentation Models

TL;DR

This paper introduces a novel pore localization method for PBF-LB/M processes using segmentation models and Gaussian kernel density estimation, enabling precise in-situ pore detection with minimal preprocessing.

Contribution

It presents a new approach that combines segmentation models with density estimation for accurate pore localization in additive manufacturing.

Findings

The method achieves high localization accuracy across different machine parameters.

Segmentation models effectively learn the correlation between monitoring data and pore positions.

The approach requires minimal data preprocessing for pore detection.

Abstract

Reliably manufacturing defect free products is still an open challenge for Laser Powder Bed Fusion processes. Particularly, pores that occur frequently have a negative impact on mechanical properties like fatigue performance. Therefore, an accurate localisation of pores is mandatory for quality assurance, but requires time-consuming post-processing steps like computer tomography scans. Although existing solutions using in-situ monitoring data can detect pore occurrence within a layer, they are limited in their localisation precision. Therefore, we propose a pore localisation approach that estimates their position within a single layer using a Gaussian kernel density estimation. This allows segmentation models to learn the correlation between in-situ monitoring data and the derived probability distribution of pore occurrence. Within our experiments, we compare the prediction performance of different segmentation models depending on machine parameter configuration and geometry features. From our results, we conclude that our approach allows a precise localisation of pores that requires minimal data preprocessing. Our research extends the literature by providing a foundation for more precise pore detection systems.