This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.

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Acoustic measurement data from Multilayer Ceramic Capacitors (MLCCs). Contains preprocessed data from intact and damaged MLCCs for damage detection (classification) purposes.

Instructions: 

Contains acoustic measurement data from 180 multilayer ceramic capacitors (2220 case size, 22 uF, 24V), soldered onto two test circuit boards. The measurements were performed by placing a piezoelectric point contact sensor on top of each capacitor, and subjecting the MLCC to a voltage frequency sweep from 100 Hz to 2 MHz over a duration of 100 ms. The resulting acoustic waveforms have been denoised, bandpass filtered, and downsampled. Furthermore, instantaneous phase response was calculated for each MLCC.

The dataset contains measurements from both intact and mechanically damaged components for quality assurance purposes (classification task). The acoustic signature of each MLCC is represented by an eight-dimensional feature vectror in the file inputs.mat:

  1. Acoustic emission amplitude at the highest resonace peak
  2. Frequency of the highest resonance peak
  3. Amplitude of the second-highest resonance peak
  4. Frequency of the second-highest resonance peak
  5. Total phase shift during frequency sweep
  6. Median amplitude of 10 of the highest resonance peaks
  7. Median frequency of 10 of the highest resonance peaks
  8. Mean group delay ripple calculated from the phase response of each component

The labels (0=no damage; 1=damage) for each component are found in targets.mat. Note that the labelling process was done by cross-sectioning each component and inspecting the sample visually under a microscope. Therefore, the labels may not be completely accurate, as the signs of damage can be difficult to observe.

 

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