NA - Integrated framework for quality assurance of additive manufacturing
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Abstract
The major challenges are represented by: 1. Real-time response Computational costs of the artificial intelligence algorithms are constrained by the necessity of detecting defects on a layer-by-layer basis. To tackle this problem, we will consider software/hardware acceleration strategies. 2. Need for large number of training images and datasets ML techniques are built on top of a pre-annotated training sets. This set should be: Massive ? in case of deep learning, huge amount of experimental data Balanced ? same number of examples of different experimental conditions (faulty/not faulty, different categories of defects, different geometries of manufacts, different materials, etc.
) 3. Need for heterogeneous data integration The algorithms need to integrate data from different sources (cameras, sensors, CAD, post-processing tests, user manuals, etc.), which may be structured (e.g. images) or unstructured (e.g. manuals) and have different formats and granularities
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| Total cost: | € 43,000.00 |
|---|---|
| Total contribution: | € 43,000.00 |
| PoliTo total cost: | € 43,000.00 |
| PoliTo contribution: | € 43,000.00 |