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<p>The friction stirs additive manufacturing (FSAM) is an emerging solid-state additive manufacturing technique. However, the multi-layer deposition in the process is difficult. The built quality of FSAM is majorly affected by the defects, which can be studied by using a non-destructive technique. The present study is based on the multi-layer deposition through FSAM by using SS304 stainless steel rods. The study investigates force components along the <i>x</i>, <i>y</i>, and <i>z</i>-axes, along with spindle torque and temperature dynamics. Employing discrete wavelet transformation (DWT) and statistical analysis for defect detection, the research identifies surface defects, highlighting the potential of force variations as indicators of build quality in FSAM. Advanced signal processing techniques, including the strategic selection of optimal mother wavelets, demonstrated superior defect identification in torque, Y-load, and Z-load signals. The integration of spindle torque and Y-load signals further expanded the scope of defect detection, providing key indicators of structural issues in the FSAM build. Subsequently, internal defects such as cracks, voids, and cavities were further examined using X-ray micro-computed tomography and SEM. Further, the mechanical properties and microstructure of the FSAM build have been investigated. The strength of the builds of the successive layers increased (15%) at the cost of strain (5%). Maximum hardness (195 Hv) was found at the center of the FSAMed build. Overall, these achievements contribute to an enhanced and accurate defect identification process in multi-layer FSAM, advancing the solid-state additive manufacturing technologies.</p>

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A study on build quality of friction stir deposition–based additive manufacturing: mechanical properties and defects

  • 期刊论文

  • 发表日期：

    2025年4月29日

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  • Raju Prasad Mahto,
  • S. Pavan Kumar Reddy,
  • Souvik Karmakar

摘要

The friction stirs additive manufacturing (FSAM) is an emerging solid-state additive manufacturing technique. However, the multi-layer deposition in the process is difficult. The built quality of FSAM is majorly affected by the defects, which can be studied by using a non-destructive technique. The present study is based on the multi-layer deposition through FSAM by using SS304 stainless steel rods. The study investigates force components along the x, y, and z-axes, along with spindle torque and temperature dynamics. Employing discrete wavelet transformation (DWT) and statistical analysis for defect detection, the research identifies surface defects, highlighting the potential of force variations as indicators of build quality in FSAM. Advanced signal processing techniques, including the strategic selection of optimal mother wavelets, demonstrated superior defect identification in torque, Y-load, and Z-load signals. The integration of spindle torque and Y-load signals further expanded the scope of defect detection, providing key indicators of structural issues in the FSAM build. Subsequently, internal defects such as cracks, voids, and cavities were further examined using X-ray micro-computed tomography and SEM. Further, the mechanical properties and microstructure of the FSAM build have been investigated. The strength of the builds of the successive layers increased (15%) at the cost of strain (5%). Maximum hardness (195 Hv) was found at the center of the FSAMed build. Overall, these achievements contribute to an enhanced and accurate defect identification process in multi-layer FSAM, advancing the solid-state additive manufacturing technologies.