In recent years, casting molds made by sand-type additive manufacturing (AM) technology are increasingly being used to build prototypes and small-lot production casting products. The development of this technology aiming at mass-production applications in the future is progressing through the advancement of AM technologies and casting technologies. The application to mass production is expected to increase the potentials of the whole casting, by realizing more complicated internal structure, reducing product thickness and weight by improving the cavity precision, etc. One method which can meet such needs is the current AM technique using furan sand molds. It is performed on large molding machines and is based on the organic self-hardening process utilizing mainly furan binder. This 3D AM sand mold using furan binder (hereafter referred to as “furan AM sand mold”) contains sulfurous acid gas generated by the thermal decomposition of the catalyst used or organic gases generated by the thermal decomposition of the cured binder during pouring. In some cases, it is necessary to take measures such as removal of the gases from the molds. Other issues also need to be improved in terms of the working environment and casting quality. Examples of practically applied 3D AM methods using inorganic binders are ; ink jetting water to plaster or cement, or ink jetting water to coated sand (laminated sand coated with water glass). However, various challenges are met with these methods, such as lack of high-speed large 3D printers and difficulty of mold collapsibility after pouring.

  Under these circumstances, we attempted to fill a furan AM sand mold with an inorganic binder consisting of phosphate and sulfate, and then sinter the sand mold in an atmosphere of 850℃ so that the furan AM sand mold changes into inorganic mold. With this mechanism, strong bonded layers are formed by the formation of polyphosphoric acid by the high molecularization of phosphate and the fusion of sulfate, while the thermal decomposition of the cured furan binder is progressing. As a result, this transforms the sand mold into a mold composed of only the inorganic binder. Th resultant mold has been found to be sufficiently practical with almost no gas generation even under the condition of 1000℃. As for the problem of mold breakage, when water is added to the mold, the sulfate that forms the adhesion layer dissolves in the water, and this reaction allows the mold to be collapsed easily. These results confirm that this mold has a balanced combination of good heat resistance, reduced harmful gases, and good mold collapsibility.

  In order to accelerate the calculation of the microstructure formation simulation of multi-component alloys, we have developed a numerical model of the microstructure simulation based on the cellular automaton (CA) method, which applies deep learning to the calculation of the equilibrium concentration at the solid-liquid interface. As a result of verifying the estimation accuracy of the equilibrium concentration calculation by deep learning, it was confirmed that the estimation accuracy is very high and that it is effective as a method of equilibrium concentration calculation. Three-dimensional dendritic growth simulations of Al-5%Si-4%Cu ternary alloy was performed using CA models to which deep learning was applied and in which the CALPHAD method was coupled. As a result, the growth behavior of the dendrite tip was in good agreement between the two models. In addition, the calculation speed per step was about 100 times faster than the CA model in which thermodynamic calculations by the CALPHAD method were coupled. These findings confirm that the alternative to thermodynamic calculation by deep learning is a very effective method for accelerating the calculation of solidification structure simulation of multi-component alloys.