Metal additive manufacturing (AM) is an innovative manufacturing technology that uses a high-power laser for the layer-by-layer production of metal components. Despite many achievements in the field of AM, few studies have focused on the nondestructive characterization of microstructures, such as grain size and porosity. In this study, various microstructures of additively manufactured metal components were characterized non-destructively using linear/nonlinear ultrasonic techniques. The contributions of this study are as follows: (1) presenting correlation analyses of various microstructures (grain size and texture, lack of fusion, and porosity) and ultrasonic properties (ultrasonic velocity, attenuation, and nonlinearity parameters), (2) development of nondestructive microstructural characterization techniques for additively manufactured components; and (3) exploring the potential for the online monitoring of AM processes owing to the nondestructive nature of the proposed technique. The performance of the proposed technique was validated using additively manufactured samples under varying laser beam speed conditions. The characteristics of the target microstructures characterized using the proposed technique were consistent with the results obtained using destructive optical microscopy and electron back-scattered diffraction methods.

Ultrasonic testing (UT) has been actively studied to evaluate the porosity of additively manufactured parts. Currently, ultrasonic measurements of as-deposited parts with a rough surface remain problematic because the surface lowers the signal-to-noise ratio (SNR) of ultrasonic signals, which degrades the UT performance. In this study, various deep learning (DL) techniques that can effectively extract the features of defects, even from signals with a low SNR, were applied to UT, and their performance in terms of the porosity evaluation of additively manufactured parts with rough surfaces was investigated. Experimentally, the effects of the processing conditions of additive manufacturing on the resulting porosity were first analyzed using both optical and scanning acoustic microscopy. Second, convolutional neural network (CNN), deep neural network, and multi-layer perceptron models were trained using time-domain ultrasonic signals obtained from additively manufactured specimens with various levels of porosity and surface roughness. The experimental results showed that all the models could evaluate porosity accurately, even that of the as-deposited specimens. In particular, the CNN delivered the best performance at 94.5%. However, conventional UT could not be applied because of the low SNR. The generalization performance when using newly manufactured as-deposited specimens was high at 90%.

The absolute acoustic nonlinearity parameter β is defined by the displacement amplitudes of the fundamental and second-order harmonic frequency components of the ultrasonic wave propagating through the material. As β is a sensitive index for the micro-damage interior of industrial components at early stages, its measurement methods have been actively investigated. This study proposes a laser-ultrasonic detection method to measure β. This method provides (1) the β measurement in a noncontact and nondestructive manner, (2) inspection ability of different materials without complex calibration owing to direct ultrasonic displacement detection, and (3) applicability for the general milling machined surfaces of components owing to the use of a laser interferometer based on two-wave mixing in the photorefractive crystal. The performance of the proposed method is validated using copper and 6061 aluminum alloy specimens with sub-micrometer surface roughness. The experimental results demonstrated that the β values measured by the proposed method for the two specimens were consistent with those obtained by the conventional piezoelectric detection method and the range of previously published values.