Publications by Rita Drissi Daoudi

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This study investigates the use of a low cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF). Three processing ...

The Laser Power Bed Fusion (LPBF) process is of high interest to many industries, such as motors and vehicles, robotics, biomedical applications, aerospace, and others. LPBF workpieces can indeed achieve near full density and high resistance. However, a la ...

The defective regimes in metal-based Laser Powder Bed Fusion (LPBF) processes can be minimized by deploying in-situ monitoring strategies comprising Machine learning (ML) algorithms and sensing techniques. So far, algorithms trained for monitoring a partic ...

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

Selective Laser Melting (SLM) or Laser Powder Bed Fusion (L-PBF) is the most studied laser-based additive manufacturing process for metals and alloys. One important issue in SLM is the time-consuming identification of a process window leading to quasi full ...

Rita Drissi Daoudi

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Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Acoustic emission for the prediction of processing regimes in Laser Powder Bed Fusion, and the generation of processing maps

Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

An effective rule for translating optimal selective laser melting processing parameters from one material to another