Additive Manufacturing

3D Printing with Drones

For most of my PhD research at the University of Texas at Austin, I explored the feasibility of integrating underactuated multicopters (a special type of drone) with 3D printing to enable large-scale and fully remote additive manufacturing. My …

Towards Online Monitoring and Data-Driven Control: A Study of Segmentation Algorithms for Laser Powder Bed Fusion Processes

An increasing number of laser powder bed fusion machines use off-axis infrared cameras to improve online moni- toring and data-driven control capabilities. However, there is still a severe lack of algorithmic solutions to properly process the …

Geometrical Analysis of Simple Contours Deposited by a 3D Printing Hexacopter

Current limitations in vertical and horizontal mobility for ground robots in 3D printing of medium to large-scale objects have recently led to the development of a 3D printing hexacopter testbed at the University of Texas at Austin. This testbed can …

A 3D Printing Hexacopter: Design and Demonstration

3D printing using robots has garnered significant interest in manufacturing and construction in recent years. A robot's versatility paired with the design freedom of 3D printing offers promising opportunities for how parts and structures are built in …

GAIN Mechanical Engineering Department Award 2021

Awardee of the Mechanical Engineering Department Award for the 2021 Graduate and Industry Networking Event by the University of Texas at Austin for outstanding research.

First 3D Printing Attempt with a Drone

Building a drone from scratch is certainly not for the faint of heart. Trying to make the drone deposit material while flying is even more challenging. Here is one of my first attempts.

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether …

Predictive Iterative Learning Control

During my Master’s research at the University of Texas at Austin, I focused on enhancing the precision and quality of Selective Laser Sintering (SLS) through advanced data-driven control techniques. My work introduced a Predictive Iterative Learning …

Predictive Iterative Learning Control with Data-Driven Model for Optimal Laser Power in Selective Laser Sintering

Building high quality parts is still a key challenge for Selective Laser Sintering machines today due to a lack of sufficient process control. In order to improve process control, we propose a Predictive Iterative Learning Control (PILC) controller …

Won Sandia National Laboratories Grant

Pitched and secured five-figure funding from Sandia National Labs for a novel laser pre-scan process that enhances part quality in selective laser sintering (3D printing).