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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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Monitoring and Control of the Direct Energy Deposition (DED) Additive Manufacturing Process Using Deep Learning

Yonghui Liu1,2,3, Haonan Ren1,2, Qi Zhang1,2

  • 1College of Engineering, Ocean University of China, Qingdao 266400, China.

Materials (Basel, Switzerland)
|January 10, 2026
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Summary
This summary is machine-generated.

This review explores how deep learning (DL) enhances Directed Energy Deposition (DED) manufacturing processes like laser DED and WAAM. AI-driven optimization and real-time monitoring are key for improving quality and efficiency in aerospace applications.

Keywords:
additive manufacturingdeep learningdirected energy depositionin situ monitoringprocess control

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Area of Science:

  • Additive Manufacturing
  • Artificial Intelligence
  • Materials Science

Background:

  • Directed Energy Deposition (DED), including laser DED and WAAM, is crucial for aerospace components.
  • Traditional DED relies on process parameter optimization, facing challenges with complex parameter interactions.
  • Deep learning (DL) offers advanced solutions for AI-driven optimization and real-time monitoring in DED.

Purpose of the Study:

  • To systematically review the application of DL techniques in laser DED and WAAM processes.
  • To compare AI-driven monitoring strategies across various data sources and accuracy metrics.
  • To explore the potential of DL for closed-loop control and reinforcement learning in DED.

Main Methods:

  • Outlining DL frameworks like ANNs, RNNs, CNNs, and RL and their relevance to DED data.
  • Comparing AI applications in DED process monitoring based on parameters, optical, thermal, acoustic signals, and sensor fusion.
  • Investigating DL for closed-loop parameter adjustment and reinforcement learning control.

Main Results:

  • DL frameworks show compatibility with DED data for advanced process optimization.
  • AI-driven monitoring offers improved accuracy across diverse data inputs compared to traditional methods.
  • DL demonstrates significant potential for real-time control and quality enhancement in DED.

Conclusions:

  • DL is essential for the intelligent upgrade of DED processes, addressing complex challenges.
  • Future research should focus on overcoming data quality and model interpretability bottlenecks.
  • AI integration promises enhanced efficiency and quality in aerospace manufacturing using DED.