CAD designers constantly face challenges in optimizing the design space to reduce cost and improve aesthetics without compromising the components’ functionality in an assembly. Specifically, in the aerospace industry, there is a need to continuously accommodate more components in lesser space and also consume less weight. With an increased use of 3D CAD models to represent complex designs and assemblies in industries like automotive, aerospace, and in various electronic products, there is a need for a tool to review the CAD assemblies for space optimization at every stage of the design.

For effective space optimization, there is a need to quickly get an idea of the consumed and available space in the assembly. This will help the designer take effective decisions in placement of components such that the available space is effectively optimized. Currently designers adopt various tools like repeated sectioning, component box computation and summation to get an estimate of the available versus consumed space. The first approach is time consuming while the latter is not as accurate as required.

In this paper, we have adopted an approach based on voxelization or volumization of the 3D CAD models to compute the volume space occupied by the assembly and its components and also identify void spaces between the components in an assembly. The volume space is computed by fitting a voxel grid enclosing the 3D Model with uniform number of voxels along coordinate axes. The voxels are examined to identify whether there is material (or no material) and classify them as inside (material) or outside (no material) voxels and are grouped into finite regions. Once the model is discretized, the output can be post-processed to generate data for various applications — computing the wrap volume or consumed space of the complete assembly, identifying significant void spaces in the assembly, etc. A wrap volume is the volume of the package which would result if we hypothetically wrap the model under consideration with wrapping/packaging paper. Consumed-space is different from the material volume in that it includes additional space due to voids in the model, small inaccessible spaces, etc.

The approach is useful for design improvements through optimization of void spaces between components depending on the related use cases. In the present study, this methodology has been successfully tested on a few industrial components. Greater accuracy in space discretization can be achieved by increasing voxel resolution. This approach handles complex geometry including freeform surfaces and turns out to be useful in addressing the space optimization problems in industries dealing with complex assemblies.

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