Automated melt pool characterization for metal additive manufacturing

Abstract

Additive manufacturing (AM) is rapidly expanding as a result of its ability to efficiently produce complex parts that are difficult or impossible to fabricate with traditional manufacturing processes. However, a key factor currently limiting wide- scale implementation of this technology is the lack of confidence in the reliability of AM components. Laser powder bed fusion (LPBF) is an AM process where lasers are used to successively melt layers of metal powder to produce threedimensional parts. Selecting the appropriate processing parameters, such as laser power and scan speed, directly impacts the quality and performance of AM depositions. By studying the characteristics of single-track melt pools produced via LPBF, the appropriate processing parameters for a given build may be determined. Quantification of critical melt pool characteristics – such as width, depth, and surface morphology – is primarily performed through manual measurements on high resolution images; a process which can be immensely time consuming and is subject to user bias. In this study, computer vision techniques, such as edge detection and texture segmentation, are used to automatically detect and quantify these characteristics. Clustering methods were then applied to these measurements to classify the processing regime for the melt pool geometry and associated operating parameters used in its creation. Operators can then use the classification information to select build parameters which reduce the likelihood of defects in constructed parts. This characterization process has been demonstrated through automated classification of single-tracks experiments for 4340 steel produced by an EOS M290 LPBF system.