SUBJECT: Ph.D. Proposal Presentation
BY: Masoumeh Aminzadeh
TIME: Tuesday, March 3, 2015, 11:00 a.m.
PLACE: Love Building, 109
TITLE: In-situ Identification of Defects and Errors in Additive Manufacturing Using Information Fusion
COMMITTEE: Dr. Thomas Kurfess, Chair (ME)
Dr. David Rosen (ME)
Dr. Hang J. Qi (ME)
Dr. Richard Cowan (ME (Manufacturing))
Dr. Chen Zhou (ISYE)


Laser powder-bed fusion (L-PBF) is an additive manufacturing (AM) process which enables fabrication of functional metal parts with near-net-shape geometries. The drawback to L-PBF is lack of precision and the high chance of formation of defects. Over the past two decades much research has been conducted on process control in order to produce parts of higher quality. This work proposes a framework for online quality monitoring system in L-PBF by direct monitoring the formation of defects and geometric errors. It is aimed to design an online quality monitoring system with the ability to detect, identify, and characterize different sorts of defects as well as geometric errors in each layer. High-resolution visual images and 2D height maps provided by interferometer and laser line scanner (LLS) are captured at each layer during the build process. A scheme for online detection system of defect and geometric error is proposed that consists of two subsystems of defect detection and geometric error detection. To achieve better precision, information from different sensors with different modalities and from images captured before and after powder fusion should be integrated effectively. To achieve better accuracy, integration of information from subsequent layers of build is suggested for the first time and appropriate techniques based on statistical estimation are developed.