Verification of data compression focusing on continuity in 3D printing

Satoshi Kodama — Mon, 23 Dec 2024 00:00:00 +0000

Recently, 3D printers have become capable of producing relatively large, high-resolution models. Unlike simple shapes, it is becoming possible to print large complex shapes with high accuracy. However, the data size of complex models is also large, and the slice data required for printing is also large. Thus, in this study, we investigated reducing the data size by focusing on the characteristics of the slice data required for 3D printing. The proposed method focuses on the continuity of each layer and the top/bottom layers of the cross-section used to print the 3D model. Preliminary experiments were conducted to determine whether the data size could be reduced by applying the difference method. Here, the results obtained from the continuity were output as text data, and various metadata, e.g., lamination pitch data, required for printing were ZIP compressed. Then, we compared conventional file formats as a format that can be converted as a printable file as lossless compression. The results demonstrated that the file size can be reduced for 3D complex shapes with a large number of vertices, which are difficult to handle. We found that the proposed difference method was effective for relatively large files that require a general-purpose graphics processing unit to create slice data.

Determination of spherical coordinates of sampled cosmic ray flux distribution using Principal Components Analysis and deep Encoder-Decoder network

Tomasz Hachaj, Marcin Piekarczyk, Łukasz Bibrzycki, Jarosław Wąs — Mon, 23 Dec 2024 00:00:00 +0000

In this paper we propose a novel algorithm based on the use of Principal Components Analysis for the determination of spherical coordinates of sampled cosmic ray flux distribution. We have also applied a deep neural network with encoder-decoder (E-D) architecture in order to filter-off variance noises introduced by sampling. We conducted a series of experiments testing the effectiveness of our estimations. The training set consisted of 92250 images and validation set of 37800 images. We have calculated mean absolute error (MAE) between real values and estimations. When E-D is applied, the number of cases (estimations) where MAE < 10 increases from 48% to 79% for θ and from 62% to 65% for ϕ, MAE < 5 increases from 24% to 45% for θ and from 47% to 52% for ϕ, MAE < 1 increases from 6% to 9% for θ and from 12% to 16% for ϕ, where θ is the zenith angle, and ϕ is the azimuthal angle. This is a significant change and it demonstrates the high utility of the E-D network use and shows the accuracy of the PCA-based algorithm. We also publish the source code used in our research in order to make it reproducible.

Machine Graphics and Vision

Verification of data compression focusing on continuity in 3D printing

Determination of spherical coordinates of sampled cosmic ray flux distribution using Principal Components Analysis and deep Encoder-Decoder network