Panel Rationalization (OWL)

never has panel rationalization been so straighforward! k-means clustering to sort similar panels!

panels are sorted by two parameters:

  • panel area
  • surface normals

and then replaced with set panel dimensions (an average of each cluster) + 20mm offset. the results are pretty decent, with minimal overlap even at the steep bits of the surface.

panel types

number of clusters (types of rectangles) from 2 – 50, iterations = 3

clustering iterations

k (number of clusters) = 25, iterations running from 1 – 30

EDIT : a little extra definition showing colour clustering to reduce the number of colour variations needed from 1124 to 10-50.

18217830_10154861225159064_1385605154_n

colour variations from 10-30, 30 iterations

18197623_10154861722884064_1987930800_n

10 colours, iterations from 10-30, showing how the clustering works in realtime

plugins used: OWL

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Author: Tang Li Qun

Design Architect, Computational Designer, 3D Generalist, Machine Learning enthusiast, Tinkerer

2 thoughts on “Panel Rationalization (OWL)”

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