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Detecting voids in 3D printing using melt pool time series data

Abstract

Powder Bed Fusion (PBF) has emerged as an important process in the additive manufacture of metals. However, PBF is sensitive to process parameters and careful management is required to ensure the high quality of parts produced. In PBF, a laser or electron beam is used to fuse powder to the part. It is recognised that the temperature of the melt pool is an important signal representing the health of the process. In this paper, Machine Learning (ML) methods on time-series data are used to monitor melt pool temperature to detect anomalies. In line with other ML research on time-series classification, Dynamic Time Warping and k-Nearest Neighbour classifiers are used. The presented process is effective in detecting voids in PBF. A strategy is then proposed to speed up classification time, an important consideration given the volume of data involved.

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Notes

  1. https://aconity3d.com/products/aconitymini/

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Acknowledgements

This publication has resulted from research supported in part by a grant from Science Foundation Ireland (SFI) under Grant Number 16/RC/3872 and is co-funded under the European Regional Development Fund.

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Correspondence to Pádraig Cunningham.

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Mahato, V., Obeidi, M.A., Brabazon, D. et al. Detecting voids in 3D printing using melt pool time series data. J Intell Manuf 33, 845–852 (2022). https://doi.org/10.1007/s10845-020-01694-8

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  • DOI: https://doi.org/10.1007/s10845-020-01694-8

Keywords

  • Process monitoring
  • Classification
  • Time-series