Quantum fluctuation-driven 3D printing generation

Abstract

In conventional 3D printing, noise functions using pseudo-random numbers have periodicity and reproducibility, making it difficult to reproduce non-periodic, unique, minute bump patterns and internal structures found in nature. [Solution] This invention is a technique that uses quantum random numbers as the initial seed for a noise function and sequentially adds non-periodic fluctuations based on quantum random numbers to the output of the noise function. This makes it possible to impart non-periodic and natural microstructures to the surface and interior of 3D printed models, achieving high-quality and unique textures. The generated noise displacement information is reflected as the displacement of the 3D printed model, and 3D printer data is created after slicing.

Description

Technical Field

[0001] The present invention relates to 3D printing technology, and in particular, to a technology for generating an initial seed of a noise function using quantum random numbers, and further adding fluctuations based on quantum random numbers to the output of the noise function, thereby reflecting a fine uneven pattern on the surface or internal structure of a 3D print model. In this specification, a "3D print model" refers to three-dimensional digital data used for 3D printing, and is to be treated separately from printer command data such as G-code generated by slicing.

Background Art

[0002] In conventional 3D printing, in order to impart randomness to the surface and internal structure, noise functions based on pseudo-random numbers such as Perlin noise and simplex noise have been used. However, since these pseudo-random numbers are computationally reproducible, periodicity and predictability remain in the noise pattern, and there are limitations in generating fine structures with true randomness. Description of the Invention

[0003] The present invention is characterized in that it is applied when using quantum random numbers having non-deterministic and true randomness as the initial seed value of a noise function, when sequentially adding fluctuations based on quantum random numbers to the output of the noise function, or both. By using quantum random numbers in these two steps, the periodic characteristics of the pseudo-random number-based noise function can be suppressed, and a more natural, aperiodic, and unique uneven pattern and structure can be reflected in the 3D print model. Pseudo-code is shown as an example. seed = get_quantum_random() base_noise = perlin_noise(seed, x, y) quantum_offset = get_quantum_random_float(-offset_range, offset_range) noise_with_fluctuation = base_noise + quantum_offset In the first line, a non-deterministic true random number is obtained from a quantum random number generator or similar source and set as the "initial seed" for the noise function. In the second line, the seed value is used to calculate the basic pattern of Perlin noise (smooth and continuous noise values) corresponding to the coordinates (x, y). In the third line, quantum random numbers are obtained again, and small random fluctuations (offset values) are generated within the range of -offset_range to +offset_range. In the fourth line, by adding fluctuations derived from these quantum random numbers to the basic noise value, a "non-periodic and natural fluctuation" that cannot be obtained with simple pseudo-random number patterns is expressed. In this way, by adding true random fluctuations not only to the initial seed but also to the noise output itself, a complex and unique uneven pattern that is closer to that of nature is achieved. The 3D printing model generation method according to the present invention includes, for example, the following steps. (1) Step to obtain quantum random numbers: The device either incorporates a quantum random number generator or obtains quantum random numbers via an external device or API. (2) Noise function generation step: In pseudorandom-based noise functions (such as Perlin noise, Simplex noise, and fractal noise), the acquired quantum random numbers are used as the initial seed values ​​for internal use. Furthermore, by adding quantum random number-based fluctuations to the output value of the noise function, a non-periodic and natural randomness is imparted. (3) 3D printed model displacement processing step: The values ​​obtained using a noise function based on quantum random numbers (including fluctuations) are reflected as displacement amounts for the surface vertices and internal voxel data of the 3D printed model, adding non-periodic micro-irregularities and cavity structures. (4) 3D Print Model Generation Step: Based on the displacement-processed 3D printed model, slicing is performed to generate G-code for 3D printers or an equivalent output data format for three-dimensional printing devices. Example of an embodiment

[0004] The present invention is not limited to the following embodiments, but also includes variations and applications. Example 1: Manufacturing of small decorative items By using quantum random numbers as the initial seed and fluctuations for Perlin noise, a non-periodic and unique microscopic irregularities are imparted to the surfaces of rings, pendants, and other items. Example 2: Manufacturing of building material exterior panels By using quantum random numbers as the initial seed and fluctuations for fractal noise, a non-periodic uneven pattern that mimics the natural texture of stone is formed, improving slip resistance. Example 3: High-end furniture decorative panel By using quantum random numbers as the initial seed and fluctuations for Simplex noise, the three-dimensional feel and tactile sensation of the furniture panel surface are improved. Example 4: Exterior design of high-end home appliances Quantum random numbers are used as the initial seed and fluctuations for Perlin noise to give the smart speaker casing a matte texture with fine irregularities. Example 5: Decoration of automotive interior parts By using quantum random numbers as the initial seed and fluctuations for Simplex noise, a high-quality, non-periodic uneven pattern is formed on vehicle instrument panels and other surfaces. The scope of the present invention is defined, but is not limited to, the appended claims. Effects of this embodiment

[0005] According to this embodiment, the following effects can be obtained. By suppressing the periodic characteristics of noise functions generated by conventional pseudorandom numbers and adding non-periodic fluctuations generated by quantum random numbers, it is possible to impart true non-periodicity and unique, subtle patterns. • High-quality textures and internal structures that reproduce the regularity and irregularity of nature are obtained, contributing to an enhanced sense of luxury in the product. By using a two-stage approach—adding quantum random numbers as an initial seed and adding fluctuations to the noise output—we can achieve highly efficient non-deterministic noise generation while maintaining reproducibility compared to using only pseudorandom numbers, thereby reducing the computational load. • The application of unique, one-of-a-kind micro-patterns to each product makes it easy to manufacture one-of-a-kind items. • Easy to integrate into existing 3D printing environments, enabling a wide range of applications. Furthermore, since the quantum random number-driven microstructure generation according to the present invention forms a unique pattern that cannot be reproduced with each manufacturing process, the structure can also be used as a physical feature for individual identification and proof of authenticity.

Claims

1. The quantum random numbers obtained by the quantum random number generation means Used as the initial seed for the noise function, The noise function generates displacement information that is applied to either the surface structure, the internal structure, or both of the 3D printed model. A method for generating three-dimensional digital data of a 3D printed model that reflects non-periodic and unique noise displacement by incorporating the displacement information into the 3D printed model.

2. The quantum random numbers obtained by the quantum random number generation means Used in the step of adding aperiodic fluctuations to the output of the noise function, The noise function generates displacement information that is applied to either the surface structure, the internal structure, or both of the 3D printed model. A method for generating three-dimensional digital data of a 3D printed model that reflects non-periodic and unique noise displacement by incorporating the displacement information into the 3D printed model.

3. The quantum random numbers obtained by the quantum random number generation means The quantum random numbers obtained by the quantum random number generation means are used as the initial seed for the noise function, and / or in the step of adding aperiodic fluctuations to the output of the noise function. The noise function generates displacement information that is applied to either the surface structure, the internal structure, or both of the 3D printed model. A method for generating three-dimensional digital data of a 3D printed model that reflects non-periodic and unique noise displacement by incorporating the displacement information into the 3D printed model.

4. A computer program for causing a computer to perform the method described in any one of claims 1 to 3.

5. A computer-readable recording medium having the computer program described in claim 4 recorded on it.