Date of Award

12-1-2021

Degree Name

Master of Science

Department

Mechanical Engineering

First Advisor

Chu, Tsuchin

Abstract

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.

Available for download on Saturday, October 08, 2022

Share

COinS
 

Access

This thesis is only available for download to the SIUC community. Current SIUC affiliates may also access this paper off campus by searching Dissertations & Theses @ Southern Illinois University Carbondale from ProQuest. Others should contact the interlibrary loan department of your local library or contact ProQuest's Dissertation Express service.