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Directed Energy Deposition via Artificial Intelligence-Enabled Approaches

Author

Listed:
  • Utkarsh Chadha
  • Senthil Kumaran Selvaraj
  • Aakrit Sharma Lamsal
  • Yashwanth Maddini
  • Abhishek Krishna Ravinuthala
  • Bhawana Choudhary
  • Anirudh Mishra
  • Deepesh Padala
  • Shashank M
  • Vedang Lahoti
  • Addisalem Adefris
  • Dhanalakshmi S
  • Yu Zhou

Abstract

Additive manufacturing (AM) has been gaining pace, replacing traditional manufacturing methods. Moreover, artificial intelligence and machine learning implementation has increased for further applications and advancements. This review extensively follows all the research work and the contemporary signs of progress in the directed energy deposition (DED) process. All types of DED systems, feed materials, energy sources, and shielding gases used in this process are also analyzed in detail. Implementing artificial intelligence (AI) in the DED process to make the process less human-dependent and control the complicated aspects has been rigorously reviewed. Various AI techniques like neural networks, gradient boosted decision trees, support vector machines, and Gaussian process techniques can achieve the desired aim. These models implemented in the DED process have been trained for high-precision products and superior quality monitoring.

Suggested Citation

  • Utkarsh Chadha & Senthil Kumaran Selvaraj & Aakrit Sharma Lamsal & Yashwanth Maddini & Abhishek Krishna Ravinuthala & Bhawana Choudhary & Anirudh Mishra & Deepesh Padala & Shashank M & Vedang Lahoti &, 2022. "Directed Energy Deposition via Artificial Intelligence-Enabled Approaches," Complexity, Hindawi, vol. 2022, pages 1-32, September.
    • Handle: RePEc:hin:complx:2767371
    DOI: 10.1155/2022/2767371
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