Machine Learning-Based Analysis of Laser Focal Offset Effects on Surface Quality in Laser Powder Bed Fusion

Author

• Hameem Berenjkar Gorabi, The University of Texas Rio Grande Valley

Date of Award

7-1-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

First Advisor

Maysam Pournik

Second Advisor

Farid Ahmed

Third Advisor

Tamer Oraby

Abstract

Laser Powder Bed Fusion (LPBF) grapples with tradeoffs in build quality and ease of production, often relying on post-processing to reach design standards or the mechanical properties of traditional fabrication. Remelt post-processing offers an in-situ post-processing solution requiring no extra equipment, acting as a second stage of production. Due to the inherent stochasticity and variability of these processes robust maps of process parameter of the remelt post process is desirable. Machine Learning regression is suited to bridge this gap, and has a successful track record in the optimization and process mapping of LPBF materials. This work’s key contribution is the creation of process maps for the effects of laser power and focal offset on the surface roughness of remelted specimens through the machine learning framework to account for complexities of the LPBF process.

Comments

Copyright 2026 Hameem Hamed Berenjkar Gorabi. All Rights Reserved. https://proquest.com/docview/3409025102

Recommended Citation

Gorabi, H. B. (2026). Machine Learning-Based Analysis of Laser Focal Offset Effects on Surface Quality in Laser Powder Bed Fusion [Master's thesis, The University of Texas Rio Grande Valley]. ScholarWorks @ UTRGV. https://scholarworks.utrgv.edu/etd/1978