Effect of Process Parameters on the Performance of Drop-On-Demand 3D Inkjet Printing: Geometrical-Based Modeling and Experimental Validation

Abstract

As additive manufacturing has evolved, 3D inkjet printing (IJP) has become a promising alternative manufacturing method able to manufacture functional multi-material parts in a single process. However, issues with part quality in terms of dimensional errors and lack of precision still restrict its industrial and commercial applications. This study aims at improving the dimensional accuracy of 3D IJP parts by developing an optimization-oriented simulation tool of droplet behavior during the drop-on-demand 3D IJP process. The simulation approach takes into consideration the effect of droplet volume, droplet center-to-center distance, coverage percentage of jetted droplets, the contact angle of the ink on the solid substrate and coalescence performance of overlapping droplets, in addition to the number of printed layers. Following the development of the simulation tool using MATLAB, its feasibility was experimentally validated and the results showed a good agreement with a maximum deviation of 2.25% for horizontal features. In addition, the simulated horizontal features are compared with the results of “Inkraster” software, which also illustrates droplet behavior, however, only in 2D. For vertical features, a dial gauge indicator is used to measure the sample height, and the validation results show that the simulation tool can predicate the height of the sample with an average error of 10.89% for a large droplet diameter and 8.09% for a small diameter. The simulation results were found to be in a good agreement with the dimensions of the printed parts. The developed tool was then used to elucidate the effect of resolution of processed TIFF image and droplet diameter on the dimensional accuracy of 3D IJP parts.