I recently built a relatively large (100cm × 80cm) 3D printed artwork, which features a painted relief of mathematical functions. Read how I prepared the data, converted it into 3D printable tiles and converted them into printable gcode.

All the images and previews are from the proof of concept I created. The final artwork is quite different, but I used the same code and methods to develop it.

In this short tutorial, I will focus on the technical aspects to create the 3d printed tiles, including the scripts I used. I will only briefly cover the assembly and painting process, as I think this is too much out of the scope of my blog.

## My Vision

Since working with computers, I have always liked two or three dimensions function plots of mathematical formulas. When I got a 3D printer, I tried various ways to create something physical from mathematical data. After creating smaller art pieces, I had to test something larger.

My idea was to visualize three different functions on a larger canvas. The abstract artwork has three distinct sections, and each of these sections has its colour and mathematical formula.

## Define the Picture Dimension

As all the calculations and generation of the data and models will be complex, I first set the final dimensions of the artwork. **I found 100cm × 80cm is a good size**, large enough to impress, but still light enough to hang on a wall.

Next, I tested various **data resolutions** and how they look printed: A one-millimetre data resolution was just a little bit too large to create a smooth surface, so I set it to **0.5mm**. Yet, I also found that larger data resolutions of 10mm create a unique effect, and it is something I will try at a later time.

## Prepare the Function Data

I started creating the functions. Here, I used a simple python script to create a two-dimensional preview image of the function.

import numpy as np import imageio as iio from math import sin, pi, sqrt, radians from pathlib import Path # Settings DATA_WIDTH = 2000 # points DATA_HEIGHT = 1600 # points PROJECT_DIR = Path(__file__).parent PREVIEW_IMG_PATH = PROJECT_DIR / 'function_preview.png' class WorkingSet: def __init__(self): self.out_img = np.zeros(shape=(DATA_HEIGHT, DATA_WIDTH, 3), dtype=np.uint8) @staticmethod def get_z(x: float, y: float) -> float: """ Function to generate the z height. @param x The input in the range 0.0-1.0 @param y The input in the range 0.0-1.0 @return The result in the range 0.0-1.0. """ d = sqrt((1-x)**2 + y**2) d1 = sqrt((1-x)**2 + y**2) d2 = sqrt((1-x-0.6)**2 + (y+0.1)**2) f1 = sin(radians(d1*16*360)) f2 = sin(radians(d2*17*360)) f = (((f1 + f2*0.3)/2.0)+1.0)/2.0 z = f * ((d/sqrt(2)*0.78)+0.5) return z def run(self): """ Run this working set. """ print('Calculate function data...') out = np.zeros(shape=(DATA_HEIGHT, DATA_WIDTH)) for py in range(DATA_HEIGHT): for px in range(DATA_WIDTH): z = self.get_z(px/(DATA_WIDTH-1), 1.0-py/(DATA_HEIGHT-1)) if z > 1.0: z = 1.0 if z < 0.0: z = 0.0 out = z print('Create visualization...') for y in range(DATA_HEIGHT): for x in range(DATA_WIDTH): z = out[y, x] self.out_img[y, x] = [np.uint8(z*255), np.uint8(z*255), np.uint8(z*255)] iio.imwrite(str(PREVIEW_IMG_PATH), self.out_img) def main(): working_set = WorkingSet() working_set.run() if __name__ == '__main__': main()

The script has a simple “settings” section, where I define the primary parameters of the preview. The shown case is 2000 by 1600 pixels, which match the 0.5mm resolution for the 100cm × 80cm picture.

In `get_z`

I define the function to create the Z-value for each image pixel. It takes normalized values from 0.0 to 1.0 and returns values in the range of 0.0 to 1.0.

I used this simple preview script to create all three formulas. Yet, the visualization is not perfect because a grayscale image does not correctly visualize height levels.

## Prepare the Sections

To visualize the colours and sections of the final image, I created a simplified representation in a vector drawing program. Then, I made an even simpler version for the calculations, only consisting of red, blue, and green areas. Next, I exported it as a PNG image with 2000 × 1600 pixels without antialiasing.

## Calculate the Picture Relief

Next, I calculated the Z-data for the whole picture, combining all three functions into one. I used the following python script for this calculation:

import enum from typing import Optional import numpy as np import imageio as iio from math import sin, pi, sqrt, radians from pathlib import Path # Settings PICTURE_WIDTH = 1.0 # m PICTURE_HEIGHT = 0.8 # m RELIEF_HEIGHT = 0.03 # m RELIEF_BASE_THICKNESS = 0.002 # m PICTURE_RESOLUTION = 0.0005 # m DATA_WIDTH = 2000 # points DATA_HEIGHT = 1600 # points PROJECT_DIR = Path(__file__).parent SECTION_WIDTH = DATA_WIDTH # px SECTION_HEIGHT = DATA_HEIGHT # px SECTION_PATH = PROJECT_DIR / 'sections.png' PREVIEW_IMG_PATH = PROJECT_DIR / 'preview_image.png' Z_DATA_PATH = PROJECT_DIR / 'z_data.npy' SECTION_BLEND_PATH = PROJECT_DIR / 'section_blend_data.npy' SECTION_BLEND_SIZE = 3 # delta pixels class Section(enum.Enum): NONE = 0 RED = 1 GREEN = 2 BLUE = 3 class WorkingSet: def __init__(self): self.img: Optional[np.ndarray] = None self.blend: Optional[np.ndarray] = None self.out_img = np.zeros(shape=(DATA_HEIGHT, DATA_WIDTH, 3), dtype=np.uint8) @staticmethod def get_blue_z(x: float, y: float) -> float: """ Function to generate the z height for the blue section. @param x The input in the range 0.0-1.0 @param y The input in the range 0.0-1.0 @return The result in the range 0.0-1.0. """ def wave(v: float): n = (v + 0.5) % 1.0 return (n if n < 0.5 else 1.0-n) * 2.0 d = (x + y*1.46) * 15 f1 = (sin(radians(d*360))+1.0)/2.0 f2 = wave(d) z = ((f1 * y) + (f2 * (1.0-y))) * ((1.0-y)*0.3+0.7) return z @staticmethod def get_red_z(x: float, y: float) -> float: """ Function to generate the z height for the red section. @param x The input in the range 0.0-1.0 @param y The input in the range 0.0-1.0 @return The result in the range 0.0-1.0. """ def wave(v: float): n = v % 1.0 return (n if n < 0.5 else 1.0-n) * 2.0 h = abs(sin(radians(x*1.2*180)))*0.8+0.2 d = sin(radians((x+0.1)*360))*0.7 return wave(x*25+d)*h @staticmethod def get_green_z(x: float, y: float): """ Function to generate the z height for the green section. @param x The input in the range 0.0-1.0 @param y The input in the range 0.0-1.0 @return The result in the range 0.0-1.0. """ d = sqrt((1-x)**2 + y**2) d1 = sqrt((1-x)**2 + y**2) d2 = sqrt((1-x-0.6)**2 + (y+0.1)**2) f1 = sin(radians(d1*16*360)) f2 = sin(radians(d2*17*360)) f = (((f1 + f2*0.3)/2.0)+1.0)/2.0 z = f * ((d/sqrt(2)*0.8)+0.5) return z def get_section(self, px: int, py: int) -> Section: """ Get the section based on a pixel in the section image. :param px: The pixel in the x axis (0-image width) :param py: The pixel in the y axis (0-image height) :return: 0 for no section (out of bounds), 1, 2, 3 for a coloured section. """ if px < 0 or px >= SECTION_WIDTH or py < 0 or py >= SECTION_HEIGHT: return Section.NONE p = self.img if p[0] >= 128: return Section.RED if p[1] >= 128: return Section.GREEN return Section.BLUE def calculated_blended_row(self, py: int) -> np.ndarray: """ Calculate a row from the section file, blending the surrounding pixels into a function weighting map. :param py: The pixel row in the y axis. :return: An array with function weights for all three functions. """ row = np.zeros(shape=(DATA_WIDTH, 3)) for px in range(DATA_WIDTH): found_pixels = 0 for y in range(py - SECTION_BLEND_SIZE, py + SECTION_BLEND_SIZE + 1): for x in range(px - SECTION_BLEND_SIZE, px + SECTION_BLEND_SIZE + 1): m = self.get_section(x, y) if m == Section.NONE: continue found_pixels += 1 row[px, m.value-1] += 1 if found_pixels > 0: row[px] /= found_pixels return row def get_z(self, px: int, py: int) -> float: """ Calculate the z height for a given pixel. :param px: The x coordinate for the pixel. :param py: The y coordinate for the pixel. :return: The z value for the given pixel coordinates. """ m = self.blend x = px / (DATA_WIDTH - 1) y = 1.0 - (py / (DATA_HEIGHT - 1)) z = (self.get_red_z(x, y) * m[0]) + (self.get_green_z(x, y) * m[1]) + (self.get_blue_z(x, y) * m[2]) z /= (m[0]+m[1]+m[2]) if z < 0: z = 0 if z > 1.0: z = 1.0 return z def run(self): """ Run this working set. """ if SECTION_BLEND_PATH.is_file(): print('Reading blend data...') self.blend = np.load(str(SECTION_BLEND_PATH)) else: print('Generating blend data...') self.img = iio.imread(SECTION_PATH) self.blend = np.ndarray(shape=(DATA_HEIGHT, DATA_WIDTH, 3)) for py in range(DATA_HEIGHT): print(f'at row {py}...') self.blend = self.calculated_blended_row(py) np.save(str(SECTION_BLEND_PATH), self.blend) print('Calculate functions...') out = np.zeros(shape=(DATA_HEIGHT, DATA_WIDTH)) for py in range(DATA_HEIGHT): for px in range(DATA_WIDTH): out = self.get_z(px, py) np.save(str(Z_DATA_PATH), out) print('Create visualization') for y in range(DATA_HEIGHT): for x in range(DATA_WIDTH): z = out[y, x] self.out_img[y, x] = [np.uint8(z*255), np.uint8(z*255), np.uint8(z*255)] iio.imwrite(str(PREVIEW_IMG_PATH), self.out_img) def main(): working_set = WorkingSet() working_set.run() if __name__ == '__main__': main()

I use three functions `get_blue_z`

, `get_red_z`

and `get_green_z`

to for the different z-profiles of the three sections.

After the start, I read the section data from the image sections.png, adding a small “blur effect” and converting them into a weight map. This map contains three values for each data point. Each value controls the amount used from each function. Converting these values can take quite a while. Therefore I store them in the file `section_blend_data.npy`

for later use.

Next, I use the weight map to calculate the final z-values for all data points. From these values, I create another preview image `preview_image.png`

and also store them in the file `z_data.npy`

.

The preview image looks like this:

## Convert the Data into 3D Objects

Next, I switched to Blender to create 3D objects from the data. Here I used a simple script, creating individual tiles from the data:

import bpy import numpy as np # Settings RELIEF_HEIGHT = 0.03 # m RESOLUTION = 0.0005 # m BASE_THICKNESS = 0.0006 # m TILE_SPACING = 0.0002 # m Z_DATA = np.load('<path>/z_data.npy') Z_DATA_WIDTH = 2000 # px Z_DATA_HEIGHT = 1600 # px def get_z(x: float, y: float) -> float: """ Get the Z value for a data point. :param x: The x coordinate in data space. :param y: The y coordinate in data space. :return: The resulting Z value in model space. """ if x < 0: x = 0 if y < 0: y = 0 if x >= (Z_DATA_WIDTH - 1): x = Z_DATA_WIDTH - 1 if y >= (Z_DATA_HEIGHT - 1): y = Z_DATA_HEIGHT - 1 z = float(Z_DATA[Z_DATA_HEIGHT - y - 1, x]) * RELIEF_HEIGHT return z def create_tile(sx: int, sy: int, width: int, height: int, name: str): """ Create a tile with start sx, sy and width, height in shape units """ vert_width = width + 1 vert_height = height + 1 def get_vert_index(x: int, y: int) -> int: """ Get the vertice index for one coordinate. """ return x * vert_width + y def face(x: int, y: int): """ Get one face tuple on the surface. """ return ( get_vert_index(x, y), get_vert_index(x + 1, y), get_vert_index(x + 1, y + 1), get_vert_index(x, y + 1)) def get_point(x: int, y: int): """ Get one single point in model space. """ rx = (sx+x) * RESOLUTION if x == width: rx -= TILE_SPACING ry = (sy+y) * RESOLUTION if y == height: ry -= TILE_SPACING rz = get_z(sx+x, sy+y) return rx, ry, rz # Create the vertices for the surface. vertices = [get_point(x, y) for x in range(vert_width) for y in range(vert_height)] faces = [face(x, y) for x in range(width) for y in range(height)] # Add four vertices for the bottom bt_index = len(vertices) # Store the index of these four bottom vertices. x1 = sx * RESOLUTION x2 = (sx+width) * RESOLUTION - TILE_SPACING y1 = sy * RESOLUTION y2 = (sy+height) * RESOLUTION - TILE_SPACING vertices.extend([ (x1, y1, -BASE_THICKNESS), (x2, y1, -BASE_THICKNESS), (x2, y2, -BASE_THICKNESS), (x1, y2, -BASE_THICKNESS)]) # add all other sides # front top_edge = [get_vert_index(vert_width - x - 1, 0) for x in range(vert_width)] top_edge.extend([bt_index, bt_index+1]) faces.append(top_edge) # back top_edge = [get_vert_index(x, vert_height-1) for x in range(vert_width)] top_edge.extend([bt_index+2, bt_index+3]) faces.append(top_edge) # left top_edge = [get_vert_index(0, y) for y in range(vert_height)] top_edge.extend([bt_index+3, bt_index]) faces.append(top_edge) # right top_edge = [get_vert_index(vert_width - 1, vert_height - y - 1) for y in range(vert_height)] top_edge.extend([bt_index+1, bt_index+2]) faces.append(top_edge) # bottom faces.append([bt_index, bt_index+3, bt_index+2, bt_index+1]) # Create Mesh Datablock mesh = bpy.data.meshes.new(name) mesh.from_pydata(vertices, [], faces) # Create Object and link to scene obj = bpy.data.objects.new(name, mesh) bpy.context.scene.collection.objects.link(obj) def main(): tile_width = 400 tile_height = 400 for x in range(5): for y in range(4): create_tile(tile_width * x, tile_height * y, tile_width, tile_height, f'tile{y}{x}') if __name__ == '__main__': main()

### What didn’t Work

Originally I started with a different approach, where I created one solid object with the profile. Then, I made boxes in the correct sizes of the tiles and boolean operations to convert the relief into smaller entities.

While this approach seemed more straightforward and accurate, Blender could not produce correct geometrical results. The created tiles always had defects, which caused problems in the slicer. Repairing these defects was time-consuming, but I also had to fix the objects after every rebuild of the geometries manually.

My Blender script is as simple as possible. It loads the generated Z-data into a NumPy array, creating separate tiles.

Instead of working with actual dimensions and dividing them into the data points, I define the distance between the data points using the variable `RESOLUTION`

.

I create each tile by generating a grid of vertices, using the Z-values multiplied with the desired relief size. Next, I add faces between all the vertices to build the top surface of the tile.

Now I add four vertices more at the four bottom corners of the object. Then I create faces on the sides, connecting all the vertices from the side of the top surface with two vertices at the bottom.

I move the vertices on the top and right sides by a tiny amount to create a minimal gap between the tiles. This gap is required to compensate for printing variations and the glue.

### Reduce the Mesh Size?

I found reducing the mesh size is not worth the time and effort. The slicer software can efficiently deal with complex objects like these. Also, because the grid never perfectly represents the functions, I found changing the faces will often make these imperfections more visible.

## Export the Tiles from Blender

Exporting all the tiles from Blender is simple. The export dialogue has a batch function, where you can save a selection of objects into individual files.

So, I export all the generated tiles in individual STL files. I name them `picture_tileXX.stl`

. In the Blender export dialogue, I use `picture_.stl`

as filename, where it automatically adds the name of the objects at the end of this name.

## Slicing the Tiles

Next, I slice all the objects and convert them into gcode. I first import one of the STL files in Prusa Slicer and configure all required parameters. After I am happy with the slicing results, I store the configuration with “File”->”Export”->”Export Config…” into a `config.ini`

file.

Now I can use the following Python script to convert all files into gcode automatically:

import subprocess from pathlib import Path PROJECT_DIR = Path('<project path>/') PRUSA_CMD = '/Applications/PrusaSlicer.app/Contents/MacOS/PrusaSlicer' PRUSA_CONFIG = PROJECT_DIR / 'config.ini' STL_DIR = PROJECT_DIR STL_PATTERN = 'picture_tile*.stl' def main(): for path in STL_DIR.glob(STL_PATTERN): print(f'Processing: {path.name}') cmd = [ PRUSA_CMD, '--export-gcode', '--load=', str(PRUSA_CONFIG), '--output=', str(path.with_suffix('.gcode')), '--slice', str(path) ] subprocess.run(cmd) if __name__ == '__main__': main()

## Print the Tiles

Printing all the 20 tiles took some time, but watching how the relief emerges layer by layer is lovely.

After each print, I wrote the tile number on its back, which was a great help with the assembly.

## Assemble and Paint the Tiles

I used a slightly larger sheet of plywood to assemble the tiles into the painting. I put all the tiles into place, using a fast curing epoxy glue.

Next, I smoothed the surface with sandpaper. Smoothing the surface also removed most of the small tips from the seams. Where required, I filled the small gap between the tiles with wood filler. Adding a primer further smoothed the surface.

I used acrylic colours to paint the relief with the colours I wanted. These colours nicely separated the different reliefs and added more depth and detail.

## Conclusion

I hope this short tutorial gave you the inspiration to create your own large 3d painting. The final result at this scale is very impressive and with the large structures, the painting looks very different depending on the lights and its shadows.

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