Abstract

Molten Metal Jetting (MMJ) has become a promising pathway for next-generation metal additive manufacturing because it avoids the need for powders or high-power energy sources and enables precise, drop-on-demand deposition of structural alloys. Despite this promise, the field lacks a predictive thermal modeling framework that can describe the full temperature history of a part as thousands of droplets accumulate. Without such a model, it is difficult to control heat buildup, to understand how bonding conditions evolve across layers, and to avoid defects such as incomplete fusion, porosity, and geometrical distortion. This dissertation presents the first validated macroscale thermal modeling framework for the MMJ process, combining experimental characterization, analytical modeling, and a graph-theory numerical approach to capture the full spatiotemporal thermal behavior of MMJ builds. The work begins with a detailed review of single-droplet physics, thermal interactions, and heat accumulation phenomena, followed by an experimental study performed on the Xerox ElemX system that measures top-surface temperatures for one, three, and five simultaneous builds. These measurements reveal how interlayer cooling time increases when multiple parts are printed in parallel, and they provide a controlled dataset for validating the proposed model. Additional thermocouple measurements on printed aluminum parts quantify the influence of natural convection, platform motion, argon shielding flow, and printhead proximity. These data are used to compute realistic convective heat transfer coefficients, addressing a long-standing gap in MMJ modeling where convective losses were previously simplified or ignored. Using this experimental foundation, the dissertation develops an analytical model based on fin theory to identify dominant cooling mechanisms and to determine height-dependent convection coefficients for MMJ. Building on these results, a graph-theory thermal model is created to simulate part-scale heat conduction during sequential droplet deposition. The model represents the part as a network of thermal nodes that exchange heat through conductive pathways while losing heat through spatially varying convection. Sequential droplet inputs are superimposed in time to generate a fully resolved thermal history without the computational burden of high-fidelity CFD or conventional finite element simulations. Model predictions are validated against thermal camera measurements for single-part, three-part, and five-part cases. The comparisons show that single-part builds experience a gradual surface temperature drop as a function of part height due to minimal cooling time between layers, while multi-part build surface temperatures cool down more quickly due to extended interlayer delays. The model captures these trends accurately and predicts cooling rates, temperature gradients, and peak temperatures across full part heights. The results also highlight how droplet frequency, build layout, and convection strength jointly control the thermal state that governs droplet spreading, remelting behavior, and microstructural development. This dissertation provides three primary contributions to the field: the first part-scale thermal model for MMJ, the first experimentally grounded convection analysis for MMJ environments, and the first demonstration that build sequencing can be used intentionally to modulate interlayer cooling. The framework presented here establishes a foundation for predictive process planning, supports future microstructure modeling, and enables the development of real-time control strategies for molten metal jetting.

Library of Congress Subject Headings

Liquid metals--Thermal properties--Mathematical models; Additive manufacturing--Mathematical models; Graph theory

Publication Date

12-2025

Document Type

Dissertation

Student Type

Graduate

Degree Name

Mechanical and Industrial Engineering (Ph.D)

Department, Program, or Center

Industrial and Systems Engineering

College

Kate Gleason College of Engineering

Advisor

Denis Cormier

Advisor/Committee Member

Rui Liu

Advisor/Committee Member

Zipeng Guo

Campus

RIT – Main Campus

Plan Codes

MIE-PHD

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