Laser Ultrasonic Detection and Signal Enhancement of Internal Microdefects in LPBF Ti6Al4V with Anisotropic Microstructure: Simulations and Experiments

Laser Powder Bed Fusion (LPBF) has revolutionized high-end manufacturing, particularly in aerospace and biomedical fields. However, internal defects such as pores, cracks, and inclusions compromise the structural integrity and service reliability of LPBF components. Laser ultrasonics, a non-contact, broadband non-destructive testing (NDT) method, offers a promising solution for detecting and characterizing these defects. This study systematically investigated laser ultrasonic testing technology for LPBF-fabricated Ti6Al4V using a combined approach of physics-driven simulation modeling and experimental validation. To accurately model material anisotropy, a finite element model was developed that integrated Voronoi algorithm-generated polycrystalline microstructures with orientation-dependent elastic tensors, providing a comprehensive representation of the material’s microstructural heterogeneity. Simulation results revealed that while sub-100-μm defects yield weak ultrasonic scattering signals, the Synthetic Aperture Focusing Technique (SAFT) markedly improves the detection and imaging performance for such small-scale defects. Experimental validation using a laser ultrasonic system identified a 90 μm internal defect in the LPBF Ti6Al4V specimen, though a 75 μm defect was undetectable. This highlights the need for enhanced sensitivity. A signal processing method combining time-truncation principal component analysis (PCA) with targeted noise reduction and SAFT was proposed to reduce high-frequency noise and improve high-resolution imaging, enhancing defect detection accuracy. This study provides theoretical foundations and technical support for high-precision defect detection in metal additive manufacturing components, with significant implications for quality control in high-end equipment manufacturing.

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Publication Details

Journal
Micromachines
Published
2026-08-28
DOI
https://doi.org/10.3390/mi17091025
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
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article

Laser Ultrasonic Detection and Signal Enhancement of Internal Microdefects in LPBF Ti6Al4V with Anisotropic Microstructure: Simulations and Experiments

Xingyu Zhou, Ping Hu, Jia Xie, Yixuan He
Micromachines
Additive Manufacturing Materials and Processes
article

Laser Ultrasonic Detection and Signal Enhancement of Internal Microdefects in LPBF Ti6Al4V with Anisotropic Microstructure: Simulations and Experiments

Xingyu Zhou, Ping Hu, Jia Xie, Yixuan He
article en

Abstract

Laser Powder Bed Fusion (LPBF) has revolutionized high-end manufacturing, particularly in aerospace and biomedical fields. However, internal defects such as pores, cracks, and inclusions compromise the structural integrity and service reliability of LPBF components. Laser ultrasonics, a non-contact, broadband non-destructive testing (NDT) method, offers a promising solution for detecting and characterizing these defects. This study systematically investigated laser ultrasonic testing technology for LPBF-fabricated Ti6Al4V using a combined approach of physics-driven simulation modeling and experimental validation. To accurately model material anisotropy, a finite element model was developed that integrated Voronoi algorithm-generated polycrystalline microstructures with orientation-dependent elastic tensors, providing a comprehensive representation of the material’s microstructural heterogeneity. Simulation results revealed that while sub-100-μm defects yield weak ultrasonic scattering signals, the Synthetic Aperture Focusing Technique (SAFT) markedly improves the detection and imaging performance for such small-scale defects. Experimental validation using a laser ultrasonic system identified a 90 μm internal defect in the LPBF Ti6Al4V specimen, though a 75 μm defect was undetectable. This highlights the need for enhanced sensitivity. A signal processing method combining time-truncation principal component analysis (PCA) with targeted noise reduction and SAFT was proposed to reduce high-frequency noise and improve high-resolution imaging, enhancing defect detection accuracy. This study provides theoretical foundations and technical support for high-precision defect detection in metal additive manufacturing components, with significant implications for quality control in high-end equipment manufacturing.

MicromachinesVol. 17(9)
Wuhan University (CN), China XD Group (China) (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Additive Manufacturing Materials and Processes
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