Ceramics are high-strength and high-temperature resistant materials that are used in various functional parts. However, due to the high strength and brittleness properties, there are many difficulties in the fabrication of complex shapes. Therefore, there are many studies related to the fabrication of ceramic parts using 3D printing technology optimized for complex shapes. Among them, studies using photo-polymerization (PP) 3D printing technology with excellent dimensional accuracy and surface quality have received the most widespread attention. To secure the physical properties of sintered ceramic, the content and distribution of materials are important. This study suggests a novel 3D printing process based on a high-viscosity composite resin that maximizes the content of zirconia ceramics. For reliable printing, the developed 3D printers that can adjust the process environment were used. To minimize warpage and delamination, the divided micro square pattern images were irradiated in two separate intervals of 1.6 s each while maintaining the internal chamber temperature at 40 °C. This contributed to improved stability and density of the sintered structures. Ultimately, the ceramic parts with a Vickers hardness of 12.2 GPa and a relative density of over 95% were able to be fabricated based on a high-viscosity resin with 25,000 cps.

Due to the high hardness and brittleness of ceramic materials, conventional cutting methods result in poor quality and machining difficulties. Additive manufacturing has also been tried in various ways, but it has many limitations. This study aims to propose a system to monitor surface defects that occur during the printing process based on high-viscosity composite resin that maximizes ceramic powder content in real time using image processing and convolutional neural network (CNN) algorithms. To do so, defects mainly observed on the surface were classified into four types by form: pore, minor, critical, and error, and the effect of each defect on the printed structure was tested. In order to improve the classification efficiency and accuracy of normal and defective states, preprocessing of images obtained based on cropping, dimensionality reduction, and RGB pixel standardization was performed. After training and testing the preprocessed images based on the DenseNet algorithm, a high classification accuracy of 98% was obtained. Additionally, for pore and minor defects, experiments confirmed that the defect surfaces can be improved through the reblading process. Therefore, this study presented a defect detection system as well as a feedback system for process modifications based on classified defects.

The purpose of this study is to show more diverse texture modifications by changing the material of a food 3D-printed structure conducted only with soft materials (in this case, potatoes and chocolate) to a hard material (in this case, maltitol here). However, unlike previous 3D-printed food materials, sweetener materials such as sucrose and maltitol are sensitively caramelized at a high melting temperature. As such, there is no commercialized printing equipment. Therefore, a printing process experiment was conducted first in this case. To do this, a high-temperature syringe pump-based extrusion device was designed, and process tests according to the temperature and environment were conducted. An assessment of the internal structural changes according to the infill patterns and infill percentages was conducted based on the acquired process conditions. The texture strength increased as the infill percentage increased. Depending on the infill patterns, the texture strength increased in the order of the Hilbert curve, honeycomb, and rectilinear samples here. As a result, a change in the texture strength was determined through a change in the internal structure of a hard food material using 3D printing, which showed a wider range of change than in conventional soft food materials.