Characterization and Machine Learning-Based Prediction of Aerosol Jet Printing Behavior on Inclined Substrates

Abstract

Aerosol Jet Printing (AJP) has emerged as a versatile additive manufacturing process for producing high-resolution electronic features across a wide range of materials and substrates. While the technique is well established for planar surfaces, its full potential for conformal and hybrid additive manufacturing remains underdeveloped. As the field advances toward printing on complex three-dimensional geometries, a deeper understanding of the influence of surface orientation and deposition angle on print quality becomes essential. In this study, we present a comprehensive experimental investigation of AJP on inclined surfaces to quantify the effects of geometric parameters on printed line morphology and electrical performance. A series of various line orientations was deposited at surface inclination angles ranging from 0° to 60° and orientation angles from 0° (horizontal) to 90° (vertical), systematically evaluating the effect of line orientation on inclined surfaces for the first time. The resulting line characteristics, including but not limited to width, thickness, and resistivity, were systematically measured and analyzed. The results reveal that both the surface inclination and print orientation significantly affect the deposited line geometry, with line width generally increasing at higher inclination angles due to altered gas flow rate and droplet spreading behavior. However, resistivity exhibits a nonlinear dependence on deposition geometry, indicating a complex interplay between droplet deposition dynamics, ink redistribution, and post-deposition sintering. To capture these relationships quantitatively, a Gaussian Process Regression model was trained on the experimental dataset. The model achieved a prediction error as low as 1.58 $\%$, demonstrating strong potential for predictive process planning and optimization. This data-driven approach provides a foundation for developing adaptive path planning strategies for conformal AJP on non-planar surfaces. Overall, this work advances the understanding of geometric effects in aerosol jet printing and provides key insights for improving the reliability and precision of conformal and hybrid additive manufacturing of electronics. The outcomes contribute toward enabling robust, high-performance, and geometry-aware AJP processes for next-generation three-dimensional electronic systems.