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1. Design, simulation, and experiments for direct thixotropic metal 3D printing

   Yifan Fei; Jie Xu; Donggang Yao; Richard Chiou; Jack Zhou (March 2022, Materials science in additive manufacturing)

   Chee Kai Cha (Ed.)

   Compared with current powder-based 3D metal printing, thixotropic metal 3D printing has great potentials and advantages in equipment cost, product quality, and process efficiency. In this paper, detailed problem statement, technique challenge, and development method regarding thixotropic metal 3D printing are discussed. A shear mixing and extruding prototype machine for thixotropic alloy fabrication was designed. We developed a direct thixotropic metal 3D printing machine and conducted a modeling and simulation process for the system. The printability of this direct metal 3D printing machine was studied. At the end, conclusions and future directions are also presented. 

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2. From semisolid metal processing to thixotropic 3D printing of metallic alloys

   Yifan Fei; Jie Xu; Donggang Yao; and Jack Zhou (March 2022, Virtual and physical prototyping)

   Chee Kai Cha (Ed.)

   Semisolid metal processing is a well-known technology that can be used to enhance manufacturing product quality in broad industries. The technology controls the thixotropic properties of alloys within their solidus and liquidus temperature ranges. In general, most known alloys can generate semisolid slurries at high solid fractions, and the remaining can form slurries at low solid fractions. This has provided opportunities for many metals and alloys to be processed as semisolid slurries in today’s casting industry. However, only a few researchers studied this technology potentially for a new metal-based additive manufacturing or 3D printing process. This article reviewed literature and findings from thixotropy rheology and semisolid metal processing methods, finally to thixotropic metal 3D printing. The survey shows that more future work is needed, including the investigation of thixotropic metal flow mechanics, the modelling and simulation of semisolid metal extrusion and further development of a fully thixotropic 3D printing system. 

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3. Rheological Study of Low-Viscous Hydrogels for 3d Bio-Printing Processes

   Tuladhar, Slesha; Nelson, Cartwright; Habib, Ahasan. (July 2021, Proceedings of the 2021 IISE Annual Virtual Conference)

   The promising success of 3D printing technique with synthetic polymers like nylon, ABS, PLA and epoxy motivates the researchers to put efforts into fabricating constructs with biocompatible natural polymers. The efforts have been broadened into various fields such as bioengineering, manufacturing, and regenerative medicine. Additive biomanufacturing commonly known as 3D bioprinting shows a lot of potential in tissue engineering with those natural polymers. Some challenges such as achieving printability, maintaining geometry in post printing stage, comforting encapsulated cells, and ensuring high proliferation are to be resolved to turn this process into a successful trial. Appropriate design of experiments with a detail rheological investigation can identify useful mechanical properties which is directly related to shape fidelity of 3D bio-printed scaffolds. As candidate natural polymers, Alginate-low viscous Carboxymethyl Cellulose (CMC) was used restricting the solid content 8% (w/v). Various rheological tests, such as the Steady Rate Sweep Test, Thixotropic (3ITT), Amplitude, and Frequency test were performed. The result indicated that rheological properties are CMC dependent. Printability and shape fidelity were analyzed of the filaments and scaffolds fabricated with all the combinations. The rheological results were co-related with the printability and shape fidelity result. 

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4. 3D Printed CO ₂ ‐Based Triblock Copolymers and Post‐Printing Modification

   https://doi.org/10.1002/anie.202208355  

   Wei, Peiran; Bhat, Gulzar A.; Cipriani, Ciera E.; Mohammad, Hamza; Schoonover, Krista; Pentzer, Emily B.; Darensbourg, Donald J. (August 2022, Angewandte Chemie International Edition)

   Abstract We report the facile synthesis and 3D printing of a series of triblock copolymers consisting of soft and hard blocks and demonstrate that alkene pendant groups of the hard block can be covalently modified. The polymers are prepared using a salenCo(III)TFA/PPNTFA binary catalyst system and 1,2‐propanediol as a chain transfer agent, providing an efficient one‐pot, two‐step strategy to tailor polymer thermal and mechanical properties. Thixotropic inks suitable for direct ink write printing were formulated by dissolving the block copolymers in organic solvent and dispersing NaCl particles. After printing, porous structures were produced by removing solvent and NaCl with water to give printed structures with surfaces that could be modified via UV‐initiated thiol‐ene click reactions. Alternatively, a tetra‐thiol could be incorporated into the ink and used for cross‐linking to give objects with high solvent resistance and selective degradability. 

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5. Machine Learning-Based Modeling of Electric-Field-Assisted Direct Ink Writing (EDIW) Process

   https://doi.org/10.1115/MSEC2023-106095  

   Chen, Yinong; Deshpande, Anupam Ajit; Joyee, Erina Baynojir; Pan, Yayue (June 2023, American Society of Mechanical Engineers)

   Abstract Direct ink writing (DIW) is an extrusion-based additive manufacturing technology. It has gained wide attentions in both industry and research because of its simple design and versatile platform. In electric-field-assisted Direct Ink Writing (eDIW) processes, an external electric field is added between the nozzle and the printing substrate to manipulate the ink-substrate wetting dynamics and therefore optimize the ink printability. eDIW was found effective in printing liquids that are typically difficult to print in the conventional DIW processes. In this paper, an eDIW process modeling system based on machine learning (ML) algorithms is developed. The system is found effective in predicting eDIW printing geometry under varied process parameter settings. Image processing approaches to collect experiment data are developed. Accuracies of different machine learning algorithms for predicting printing results and trace width are compared and discussed. The capabilities, applications and limitations of the presented machine learning-based modeling approach are presented. 

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