Indiana University-Purdue University Indianapolis (IUPUI) / Material jetting (MJ) additive manufacturing (AM) has experienced an increased adoption in several industry areas and as well as research applications. One of MJ’s distinct benefits is the ability to print tunable composites, digital materials (DM) by carefully adjusting the ratio of droplets of heterogeneous base-polymeric inks. However, the lack of material information usable in computer simulations has hampered its acceptance in some end-use applications. For these materials to be used in Finite Element Analysis (FEA) simulations the mechanical properties of the DMs need to be characterized into usable material models. DMs printable with an MJ printer has a wide variety of materials properties, ranging from flexible silicone rubber to rigid Acrylonitrile Butadiene Styrene (ABS). Therefore, to cohesively express the mechanical behavior of the DMs it is necessary to utilize non-linear material models. The objective this research is to conduct physical testing to characterize the mechanical behavior of DMs printable with an MJ. Subsequently, to validate the effectiveness of the material models for multi-DM prints. Utilizing the newly characterized material models two use cases were investigated, with the goal of improving the performance of printed parts through simulation. In this study, an MJ printer was used to fabricate the test specimens as well as the components used in the use case studies. The study was focused on the family of six DMs printable from the mixture of the base polymers Tango Black+ (TB+) and Vero White+ (VW+). To characterize the mechanical properties of the materials a tensile test was conducted utilizing the KS-M6518 standard as a basis. The mechanical properties of the DMs were then fitted into four non-linear models and the results compared. The fitted models were, the Neo Hookean model, a two-parameter, three-parameter, and a five-parameter Mooney Rivlin model. To confidently use the material models for multi-DM prints FEA simulations need to validate the accuracy to which they can predict the deformation of the samples under load. To compare the results of the computer simulations and the physical test, strain maps for both results were analyzed. Four different test specimens were printed and tested. A baseline single material samples were compared to three multi-material samples with different embedded structures. The results confirmed the validity of the material models even when used for multi-DM prints. The recently characterized models are utilized in two use case studies which showcase the potential of DMs. The first use case was focused on printing multi-DM substrates for the use of stretchable electronics. The second use case investigated the benefits of utilizing multiple materials to create 3D conductive traces utilizing a new method, the “swollen-off” method. Both case studies showed the benefits of utilizing DMs as well as the applicability of the material models in predictive simulations.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第18318号 / 農博第2043号 / 新制||農||1021(附属図書館) / 学位論文||H26||N4825(農学部図書室) / 31176 / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 本田 与一, 教授 星野 敏, 教授 縄田 栄治 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第19201号 / エネ博第321号 / 新制||エネ||65(附属図書館) / 32193 / 京都大学大学院エネルギー科学研究科エネルギー応用科学専攻 / (主査)准教授 中嶋 隆, 教授 大垣 英明, 教授 作花 哲夫 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DGAM

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In this research, a tribocharging model based on the prominent condenser model was used in combination with an Eulerian-Lagrangian CFD model to simulate particle tribocharging in particle-laden flows. The influence of different parameters on particle-wall interactions during particle transport in a particle-laden pipe flow was elucidated. An artificial neural network was developed for predicting particle-wall collision numbers based on a database obtained through CFD simulations. The particle-wall collision number from the CFD model was validated against experimental data in the literature. The tribocharging and CFD models were coupled with the experimental tribocharging data to estimate the contact potential difference of powders, which is a function of contact surfaces' work functions and depends on the physicochemical properties of materials. While the contact potential difference between the particles and wall is an essential parameter in the tribocharging models, the accurate measurement of the property is a complex process requiring a highly controlled environment and special equipment. The results from this research also confirm that particle tribocharging is very much dependant on the particle-wall collision number influenced by various parameters, such as particle size and density, air velocity, and pipe dimensions. Plotting the experimentally measured charge-to-mass ratios against the calculated contact potential differences for samples with different protein contents uncovered a linear trend, which opens a novel approach for protein quantification of powders for a given particle size. Therefore, an algorithm is proposed for rapid quantification of protein content and particle size determination of samples during transport in particle-laden flows based on the triboelectric charge measurement. The algorithm requires a CFD-based artificial neural network to estimate the particle-wall interactions based on the hydrodynamic characteristics of the particles and flow systems. / Thesis / Master of Applied Science (MASc)