Nesting algorithms deal with placing two-dimensional shapes on the given canvas. In this paper a binary way of solving the nesting problem is proposed. Geometric shapes are quantized into binary form, which is used to operate on them. After finishing nesting they are converted back into original geometrical form. Investigations showed, that there is a big influence of quantization accuracy for the nesting effect. However, greater accuracy results with longer time of computation. The proposed knowledge base system is able to strongly reduce the computational time.
2D allocation; Algorithm; Geometry shapes; Knowledge base; Nesting; Quantization; Simulation
Algebraic Geometry | Electrical and Computer Engineering | Engineering | Geometry and Topology
Quantization with Knowledge Base Applied to Geometrical Nesting Problem.