In-Situ Infrared Camera Monitoring for Defect and Anomaly Detection in Laser Powder Bed Fusion: Calibration, Data Mapping, and Feature Extraction

TL;DR

This paper introduces a novel in-situ infrared monitoring approach for laser powder bed fusion that maps data to 3D geometry and extracts new IR features for defect detection, reducing data storage needs and improving process monitoring.

Contribution

It proposes a new data mapping method to 3D geometry and introduces novel IR features for defect detection and process calibration in LPBF.

Findings

Effective defect detection using new IR features

Reduced data storage through 3D data mapping

Validated approach on various printed parts

Abstract

Laser powder bed fusion (LPBF) process can incur defects due to melt pool instabilities, spattering, temperature increase, and powder spread anomalies. Identifying defects through in-situ monitoring typically requires collecting, storing, and analyzing large amounts of data generated. The first goal of this work is to propose a new approach to accurately map in-situ data to a three-dimensional (3D) geometry, aiming to reduce the amount of storage. The second goal of this work is to introduce several new IR features for defect detection or process model calibration, which include laser scan order, local preheat temperature, maximum pre-laser scanning temperature, and number of spatters generated locally and their landing locations. For completeness, processing of other common IR features, such as interpass temperature, heat intensity, cooling rates, and melt pool area, are also presented with the underlying algorithm and Python implementation. A number of different parts are printed, monitored, and characterized to provide evidence of process defects and anomalies that different IR features are capable of detecting.