Three physics-informed data-driven methods monitor two-photon lithography machine health by predicting structure dimensions from process parameters and applying statistical tests on data from six parameter sets and two health states.
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The method detects unknown faults in ultrasonic metal welding at 96% accuracy and incorporates new fault types from only five labeled samples to reach 98% classification accuracy.
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Physics-informed data-driven machine health monitoring for two-photon lithography
Three physics-informed data-driven methods monitor two-photon lithography machine health by predicting structure dimensions from process parameters and applying statistical tests on data from six parameter sets and two health states.
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Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding
The method detects unknown faults in ultrasonic metal welding at 96% accuracy and incorporates new fault types from only five labeled samples to reach 98% classification accuracy.