MScE Defence for Thomas McCarthy - Mechanical Engineering - FR

Event date(s): January 29, 2025
Time(s): 09:00 AM - 11:00 AM
Category: Fredericton
Location: Fredericton

Additive manufacturing, and more specifically laser powder bed fusion (LPBF), complements conventional manufacturing by producing a low volume of highly complex functional metallic components. The mode of laser emission, either continuous (c-LPBF) or pulsed (p-LPBF), has a pronounced impact on the resulting component. Although both have merits, the c-LPBF process dominates commercial machines and academic efforts. To promote further exploration of p-LPBF, the process must balance the component's quality and the industry's need for increased production. In this work, a multi-objective optimization framework is adopted to balance time and quality of p-LPBF produced Ti-6Al-4V as a function of key processing parameters. Lacking an analytical model, Bayesian inference of Gaussian process regression is utilized to relate laser power, exposure time, point distance, and hatch spacing to the as-built relative density, serving as a proxy for quality, while batch active learning efficiently samples the design space. In combination, the model accurately captures the relationship in a modest number of experiments and, in conjunction with NSGA-II, is able to determine a non-dominated set of equally valid solutions. Despite the model's accuracy, the current work highlights the need for a sufficiently large data set to accurately reflect the underlying mechanisms occurring in the p-LPBF process.

Building: Head Hall

Room Number: 224