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PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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Real-Time Defect Identification in Automotive Brake Calipers Using PCA-Optimized Feature Extraction and Machine

Juwon Lee1, Ukyong Woo1,2, Myung-Hun Lee1

  • 1School of Architecture, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

A new non-contact system uses impact-acoustics to detect defects in automotive brake calipers. This automated quality control method achieves over 95% accuracy, ensuring safer Electric Parking Brake systems.

Keywords:
NDTautomotive brake caliperdefect identificationfeaturemachine learningprincipal component analysis

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Area of Science:

  • Mechanical Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Automotive brake calipers are critical for Electric Parking Brake (EPB) systems.
  • Manufacturing defects can compromise caliper performance, leading to braking issues.
  • Real-time, non-contact defect detection is needed for quality control.

Purpose of the Study:

  • Develop a non-contact automated impact-acoustic measurement system (AIAMS).
  • Enable real-time detection of manufacturing defects in brake calipers.
  • Improve the reliability and safety of EPB systems.

Main Methods:

  • Implemented an automated impact hammer and microphone-based system.
  • Utilized Fast Fourier Transform (FFT) and Principal Component Analysis (PCA) for feature extraction.
  • Employed machine learning algorithms (SVM, KNN, DT) for defect classification.

Main Results:

  • Shannon Entropy (SE) identified as the most discriminative feature via PCA.
  • High classification accuracies achieved: SVM (99.2%/97.5%), KNN (98.8%/98.0%), DT (99.2%/96.5%).
  • Field tests confirmed over 95% average defect classification accuracy.

Conclusions:

  • The AIAMS is effective for real-time, non-contact quality control of brake calipers.
  • The system enhances manufacturing defect detection for EPB components.
  • Demonstrated practical applicability in industrial settings with high accuracy.