Date of Award


Degree Name

Master of Science


Mechanical Engineering

First Advisor

Chu, Tsuchin


Selective laser melting (SLM) is a method of additive manufacturing that has become increasingly popular in recent years for fabricating complex components, especially in the medical and aerospace industries. By fabricating components in a layerwise fashion, SLM provides users the freedom to design components based on their desired functionality rather than their manufacturability. The current state-of-the-art for SLM is limited though, as defects induced by the SLM process have proven to greatly alter the material properties of fabricated parts. In addition, traditional post-process nondestructive inspection methods have experienced significant difficulty in accurately detecting these process-induced defects. Therefore, the objective of this study is to investigate methods of processing and analysis for optical in-situ monitoring data recorded during SLM fabrication of six test samples. Four of the samples were designed with seeded (i.e., intentional) defects located at their center to serve as a reference defect signatures in the resulting in-situ data. An off-axis optical tomography (OT) sensor was used to capture near-infrared (NIR) melt pool emissions during the fabrication of each layer. Image analysis was subsequently performed using a custom squared difference (SD) operator to enhance defect signatures in the OT data. Results from the SD operator were then used to perform k-means clustering to partition the data into k relevant clusters, where the optimal number of k clusters for each image is employed as metric for detecting the onset of defects in the samples. By employing OT image data from samples containing seeded intentional defects, the k-means clustering approach was investigated as a method of defect detection for the in-situ OT images. Results showed that the SD operator is capable of elucidating anomalous signatures in the in-situ data. However, variations within the SD distributions ultimately limited detection capabilities as the output from k-means clustering was unable to accurately distinguish the seeded defects from the fused regions of material.

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