Using Voxelisation-Based Data Analysis Techniques for Porosity Prediction in Metal Additive Manufacturing
Abraham George,
Marco Trevisan Mota,
Conor Maguire
et al.
Abstract:Additive manufacturing workflows generate large amounts of data in each phase, which can be very useful for monitoring process performance and predicting the quality of the finished part if used correctly. In this paper, a framework is presented that utilises machine learning methods to predict porosity defects in printed parts. Data from process settings, in-process sensor readings, and post-process computed tomography scans are first aligned and discretised using a voxelisation approach to create a training … Show more
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