Metal Additive Manufacturing of Plastic Injection Molds with Conformal Cooling Channels.

Conformal cooling channels (CCCs) are widely used in the plastic injection molding process to improve the product quality and operational performance. Tooling that incorporates CCCs can be fabricated through metal additive manufacturing (MAM). The present work focuses on the MAM of a plastic injection mold insert with different CCC types that are circular, serpentine, and tapered channels with/without body-centered cubic (BCC) lattices. The entire manufacturing process of the mold insert is explained from the design step to the final printing step including the computational thermal & mechanical simulations, performance assessments, and multiobjective optimization. Compared to the traditional channels, conformal cooling channels achieved up to 62.9% better cooling performance with a better thermal uniformity on the mold surface. The optimum mold geometry is decided using the multiobjective optimization procedure according to the multiple objectives of cooling time, temperature non-uniformity, and pressure drop in the channel. Direct Metal Laser Sintering (DMLS) method is used for manufacturing the molds and the quality of the printed molds are analyzed with the X-ray Computed Tomography (X-ray CT) technique. The errors between the design and the printed parameters are less than 5% for the circular and tapered channels while the maximum deviation of the strut diameters of the BCC is 0.06 mm.

Transient Prediction of Nanoparticle-Laden Droplet Drying Patterns through Dynamic Mode Decomposition.

Nanoparticle-laden sessile droplet drying has a wide impact on applications. However, the complexity affected by the droplet evaporation dynamics and particle self-assembly behavior leads to challenges in the accurate prediction of the drying patterns. We initiate a data-driven machine learning algorithm by using a single data collection point via a top-view camera to predict the transient drying patterns of aluminum oxide (Al2O3) nanoparticle-laden sessile droplets with three cases according to particle sizes of 5 and 40 nm and Al2O3 concentrations of 0.1 and 0.2 wt %. Dynamic mode decomposition is used as the data-driven learning model to recognize each nanoparticle-laden droplet as an individual system and then apply the transfer learning procedure. Along 270 s of droplet drying experiments, the training period of the first 100 s is selected, and then the rest of the 170 s is predicted with less than a 10% error between the predicted and the actual droplet images. The developed data-driven approach has also achieved the acceptable prediction for the droplet diameter with less than 0.13% error and a coffee-ring thickness over a range of 2.0 to 6.7 µm. Moreover, the proposed machine learning algorithm can recognize the volume of the droplet liquid and the transition of the drying regime from one to another according to the predicted contact line and the droplet height.