Additive-Manufacturing-feature-engineering

Repositry supporting two publications on LPBF process monitoring using acoustic emissions

• Analysis of time, frequency and time-frequency domain features from acoustic emissions during Laser Powder-Bed fusion process
• Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Journal link

https://doi.org/10.1080/17452759.2022.2028380

https://doi.org/10.1016/j.procir.2020.09.152

Overview

Sensor integration for in situ monitoring during additive manufacturing promises to enhance control over the process and assures quality in the fabricated workpieces. Acoustic emissions from the process zone of the laser powder-bed fusion process carry information about the events and failure modes of the printed workpiece. Analysis of acoustic signals emitted during different laser regimes, such as conduction, keyhole, etc. in time, frequency and time-frequency domains could provide quantitative information about the underlying physical mechanisms. This article reports a statistical analysis of the features in acoustic signals to perceive the characteristics of failure modes occurring during layering of stainless steel 316L. The visualization of the feature space distribution that corresponds to different failure modes shows the potentials of applying machine learning for in situ classification. The paper also proposes strategies in terms of data acquisition and preprocessing for building a comprehensive monitoring system..

Code

git clone https://github.com/vigneashpandiyan/Additive-Manufacturing-feature-engineering
cd  Additive-Manufacturing-feature-engineering

Citation

@article{pandiyan2020analysis,
  title={Analysis of time, frequency and time-frequency domain features from acoustic emissions during Laser Powder-Bed fusion process},
  author={Pandiyan, Vigneashwara and Drissi-Daoudi, Rita and Shevchik, Sergey and Masinelli, Giulio and Log{\'e}, Roland and Wasmer, Kilian},
  journal={Procedia CIRP},
  volume={94},
  pages={392--397},
  year={2020},
  publisher={Elsevier}
}
@article{drissi2022differentiation,
  title={Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning},
  author={Vigneashwara Pandiyan et al.},
  journal={Virtual and Physical Prototyping },
  volume={17},
  pages={181-204},
  year={2022},
  publisher={Taylor and Francis}
}