<p>The development of efficient manufacturing processes for tailored wick structures are essential to design efficient two-phase thermal management systems in miniaturized power electronics, energy, automotive, and spacecraft applications. The desired wicks require highly-thermal-conducting, liquid-permeable porous structures, i.e., porous metallic structures with interconnected micro-size pore network. However, manufacturing a tailored wick structure, which is usually complex and non-uniform in nature, is challenging using conventional manufacturing processes due to limited manufacturability and poor cost effectiveness. Additive manufacturing is an ideal alternative approach to overcome these technical challenges as it offers a layer-by-layer manufacturing process for wicks derived from computer-aided design (CAD) tools. However, state-of-the-art metal three-dimensional printers have poor manufacturability when depositing pre-designed porous wick structures with pore sizes below 100 um. Interconnected porous structures often lack rigidity and crumble due to poor coalescence. In this paper, a powder bed fusion 3D printer (Matsuura Lumex Avance-25) was employed to fabricate metallic wick structures through partial sintering for pore sizes below 100 um with data-driven control of process parameters. Four AM process parameters were considered: laser power, laser spot diameter, hatch spacing, and scan speed. Due to the unavailability of process maps between various print process parameters and properties of printed metallic wick structures, a Gaussian process surrogate-based approach was employed to identify the combinations of the four additive manufacturing process parameters that result in improved manufacturability of wick structures. Since the generation of training points for surrogate model training through experimentation is expensive and time-consuming, Bayesian optimization was used for sequential and intelligent selection of training points that provide maximum information gain regarding the relationships between the process parameters and the manufacturability of a 3D printed wick structure. In addition to the Gaussian process surrogate model, an analysis using a support vector machine (SVM) model was performed to understand the effect of the surrogate model on the required number of training points for reliable predictions of 3D wick structure manufacturability. The relationship between the required number of training points and model prediction accuracy was investigated. The AM parameters’ ranges were discretized using six values of hatch spacing, eight values of scan speed, four values of laser power, and two values of spot diameter, which resulted in a total of 384 combinations across four parameters. Preliminary results conclude that 80% prediction accuracy is achievable with approximately forty training points (only 10% of total combinations). This study provides insights into the selection of optimal process parameters for the desired additively-manufactured wick structure performance.</p>

Process Mapping of Additively-Manufactured Metallic Wicks Through Surrogate Modeling

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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