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<p>Porosity in additive manufacturing (AM), often regarded as a defect, is now being intentionally utilized to open new frontiers in material design and functionality. This review explores how engineered porosity can be harnessed across various applications, including lightweight structures, acoustic insulation, biomedical implants, and advanced filtration systems. Techniques such as templating, gas foaming, sacrificial materials, and parameter control are discussed for their ability to achieve tailored porosity. Furthermore, the manuscript reviews cutting-edge measurement and characterization techniques, including X-ray Computed Tomography (XCT), Scanning Electron Microscopy (SEM), and Machine Learning-based porosity prediction. The paper concludes with a discussion of the challenges and future directions in the controlled use of porosity to enhance functional performance in AM parts. This work aims to bridge the gap between porosity as a defect and porosity as a design feature, providing valuable insights for materials engineers, biomedical designers, and AM practitioners.</p>

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Porosity in additive manufacturing: purposeful design, applications, and characterization methods — a review

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • Suhas Alkunte,
  • Abhijeet Mali,
  • Anas Ullah Khan,
  • Nitin More,
  • Nikhil Ingle,
  • Swapnil Nalawade,
  • Nilima Sinha,
  • Madhur Gupta,
  • Kishor B. Shingare,
  • Kin Liao,
  • Ismail Fidan

摘要

Porosity in additive manufacturing (AM), often regarded as a defect, is now being intentionally utilized to open new frontiers in material design and functionality. This review explores how engineered porosity can be harnessed across various applications, including lightweight structures, acoustic insulation, biomedical implants, and advanced filtration systems. Techniques such as templating, gas foaming, sacrificial materials, and parameter control are discussed for their ability to achieve tailored porosity. Furthermore, the manuscript reviews cutting-edge measurement and characterization techniques, including X-ray Computed Tomography (XCT), Scanning Electron Microscopy (SEM), and Machine Learning-based porosity prediction. The paper concludes with a discussion of the challenges and future directions in the controlled use of porosity to enhance functional performance in AM parts. This work aims to bridge the gap between porosity as a defect and porosity as a design feature, providing valuable insights for materials engineers, biomedical designers, and AM practitioners.