Fused Deposition Modeling (FDM) 3D printers can print a structure called a ”bridge,” which is a thin structure formed in mid-air with both ends fixed. In this study, we focus on the thickness of each bridge to further develop this structure for artistic printing. In conventional 3D printing, bridges have a uniform thickness from beginning to end. By controlling the amount of material and the printing speed, it is possible to change the thickness of the bridge within a range of about 0.5-0.2 mm in diameter. We can also create a gray-scale representation similar to hatching by densely printing the bridges with controlled thickness at intervals of approximately 1.0 mm. It is also confirmed that moir´e patterns can be created by printing multiple bridge layers while changing the angle of the printing direction. This paper examines the feasibility of controlling bridge thickness, explores the parameters that contribute to our technique, discusses calibration when materials or 3D printers change, and describes efforts to achieve stable printing. We also present a design system and examples, and discuss future work of our technique.

Ancient stone tools excavated from ruins are important materials for archaeological research. The manufacturing process of stone tools is revealed by frequently assembling and disassembling the joining materials. Each stone tool is stored in a bag with its identification number and photo. However, storage and management of stone tools may occur human errors, such as mis-recognition and mistaking of the storage bag. This study proposes an image-based identification method to match each stone tool with its corresponding storage bag. Two commonly used stable postures were defined for each stone tool. The collected image dataset is used for deep learning of an untrained CNN model and seven pre-trained models. The experimental results showed better accuracy and processing speed than those of previous research. Finally, three of the models with high classification performances are selected to construct the detector with the YOLO framework for practical scenario.

Reassembling fragmented stone tools from the Paleolithic era remains a significant archaeological challenge. Our study addresses this challenge by introducing a matching algorithm with a primary emphasis on enhancing computational efficiency and refining the flake surface matching process. Our method builds upon previous study, specifically targeting reductions in the computation time. The algorithm was tested on a dataset comprising 43 stone models. A critical aspect of this study is flake surface matching which is a fundamental aspect of stone tool reassembly. By optimizing the computational cost, we aim to provide archaeologists with a more efficient and accurate tool for reconstructing archaeological artifacts.

This is a description of the implementation of the "Three-dimensionalization of Dithering on Stacked Concave-Convex Map" that was exhibited at NICOGRAPH 2020 and a study of its extension. At NICOGRAPH 2020, we exhibited an uneven picture constructed by stacking objects printed on a single-color printer by dividing a plane of 18 cm square into 64 x 64 blocks. However, as the resolution increases or the pattern of the picture becomes more complex, the time and memory required to compute the stacked shapes increases, making it difficult to find a solution using width-first or depth-first search. In this study, we investigated search methods for computing the shape, and succeeded in obtaining an approximate but sufficiently accurate and fast solution using the anytime beam search method. In addition, there is a practical need to control the order of stacking. We propose a method to control the stacking order by using a directed graph structure during search. Furthermore, we propose a method to reduce the thickness of printed materials.