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    Review of 'Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion'

    Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusionCrossref
    Thermal Imaging method will be helpful and can be extended in other research areas
    Average rating:
        Rated 4 of 5.
    Level of importance:
        Rated 4 of 5.
    Level of validity:
        Rated 4 of 5.
    Level of completeness:
        Rated 4 of 5.
    Level of comprehensibility:
        Rated 4 of 5.
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    Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion

    Porosity defects are currently a major factor that hinders the widespread adoption of laser-based metal additive manufacturing technologies. One common porosity occurs when an unstable vapor depression zone (keyhole) forms because of excess laser energy input. With simultaneous high-speed synchrotron x-ray imaging and thermal imaging, coupled with multiphysics simulations, we discovered two types of keyhole oscillation in laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. The highly accurate data labeling enabled by operando x-ray imaging allowed us to demonstrate a facile and practical way to adopt our approach in commercial systems. Laser fusion techniques build metal parts through a high-energy melting process that too often creates structural defects in the form of pores. Ren et al . used x-rays to track the formation of these pores while also making observations with a thermal imaging system. This setup allowed the authors to develop a high-accuracy method for detecting pore formation from that thermal signature with the help of a machine learning method. Implementing this sort of tracking of pore formation would help avoid building parts with high porosity that are more likely to fail. —BG Thermal imaging can detect pore formation during laser powder bed fusion, helping to ensure quality control.

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      Review text

      This study uses Thermal imaging techniques to identify pore formations which ensures extended quality control

      1. The pseudcode explaining how Machine Learning techniques deployed increases the readability and helps in conducting future research

      2. The stats could be added for ex. graph and tables which explains the accuracy using machine learning

      3. The recent papers relevant to the issue could be added as reference


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