Classification of Topological Discrepancies in 3D Printing with ArrayFire

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Researchers from the Palo Alto Research Center in California credit ArrayFire in a paper published in the Journal of Computer-Aided Design. The paper is titled “A Classification of Topological Discrepancies in Additive Manufacturing” and showcases a novel approach for classification of local shape deviations in topological terms than can be used to improve 3D printing processes. The OpenCL version of ArrayFire on an NVIDIA GTX 1080 GPU was used for FFT-based convolutions and superlevel set operations.

A design’s manufacturability via an additive manufacturing (AM) process is largely determined by the AM machine’s ability to print the shape within ‘acceptable limits’. The notion of geometric dimensioning and tolerancing has been used successfully to define and check these limits for conventionally manufactured parts, but it is challenging to define features of size for AM, and efforts are ongoing. Nonetheless it is clear that combinations of manufacturing plans and 3D printer resolutions will produce deviations between as-designed and as-manufactured shapes, with no systematic procedure to check and control this deviation.

Under-depositing (UD) and over-depositing (OD) lead to topological errors, as shown in the paper’s Figure 1.

The algorithm of the paper includes 3 steps: 1) computing the topological difference between the as-manufactured sample and the as-designed ideal, 2) analyzing the under-deposited regions, and 3) analyzing the over-deposited regions. An example representation of this problem is shown in Figure 9 for synthetic data.

Results on a real model using ArrayFire is shown in Figure 4.

As the overlap measurement ratio threshold (OMRT) decreases, over-depositing of the manufactured structure increases.

Performance Results

Figure 12 illustrates the CPU times for parallel computation of ECC for UD/OD features. Notice that as the allowance for over-deposition is increased by decreasing the OMRT, the time increases almost linearly with the overlap measure, which is proportional to the volume (and number of voxels) for OD deviations from design.

Thanks to these researchers for sharing their great work with us!

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