Microstructural Characterization of Additively Manufactured Metal Components Using Linear and Nonlinear Ultrasonic Techniques

Abstract

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.

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

“…Furthermore, predicting and altering the process parameters necessitate advanced ML algorithm techniques to integrate within the in-situ monitoring system capable of instantaneous feedback [ 221 ]. In relation to this, challenge remains on the structural health monitoring (SHM) system of the AM techniques [ 222 ] for microstructural characterization [ 223 ] of additively manufactured components. A list of commonly used ML algorithms, sensing principles and detected defects is shown in Figure 25 .…”

Prediction of Manufacturing Parameters of Additively Manufactured 316l Steel Samples Using Ultrasound Fingerprinting

“…These features can be extracted through various signal processing techniques, such as time-domain and frequency-domain analyses, waveform analysis, and imaging algorithms. The specific features extracted will depend on the application and the type of ultrasound NDE method being used [24].…”

The use of defects and compositional variations to elucidate physical phenomena in electron beam melted Ti-6Al-4V across scan strategies

“…These bands appear flatter and more uniform than those observed surrounding pores and defects, and fewer ripples (or turbulence) are observed, giving insights into the interactions of defects and bands. The observation of extended compositional fluctuations around pores is novel and may provide new methods of identifying and interpreting porosity using nondestructive evaluation techniques [76,[150][151][152], as the volume of distinctively different material is larger than the pore. However, the insight is not unexpected: LOF pores are formed while layers are still molten and interact as objects with the fluid flow of the melt pools (somewhat equivalent to solid, unmelted particles).…”

“…The work on the interaction of defects with the compositional variations (Section 4.2), as well as the compositional variations themselves, also poses interesting questions regarding the characterization of AM parts. Recent work in the literature has begun to examine the possibility of detecting compositional variations through non-destructive techniques [150][151][152]. While the techniques are still in their initial stages, and the resolutions used may not be fine enough to detect the compositional variations presented in this work, there is promise for the future.…”