Influence of surface roughness from additive manufacturing on laser ultrasonics measurements

Bakre, Chaitanya; Hassanian, Mostafa; Lissenden, Cliff J.

doi:10.1063/1.5099713

Cited by 21 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Laser ultrasonic measurement can be highly skewed if the sample surface is rough owing to effects such as speckle noise and nonuniform reflectivity. 36 Normally, a surface roughness of several micrometers is required to ensure that ultrasonic waves can be properly measured. 37 Considering the feasibility of in situ operations during the DED process, laser polishing was adopted for the surface treatment of the prepared samples.…”

Section: Experimental Validation Of Spc In Swtmentioning

confidence: 99%

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Liu

Yang

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

Nonlinear ultrasonic techniques have the benefit of high sensitivity to micro or early-stage defects. Among the various nonlinear techniques, the newly proposed sideband peak count (SPC) technique investigates defect-induced nonlinearity by counting the spectral sidebands from a broadband ultrasonic response in the frequency domain. In this study, SPC analysis is transformed into the time–frequency plane through synchrosqueezed wavelet transform (SWT) for transient nonstationary ultrasonic signals. The proposed new SPC technique was then adopted for in situ porosity monitoring in directed energy deposition (DED)—a typical metal additive manufacturing process. Porosity is one of the most critical defects in DED and has detrimental effects on the mechanical properties and fatigue performance of products. For in situ porosity monitoring, a fully noncontact ultrasonic measurement was achieved with a laser ultrasonic system, and its detectability was improved by laser polishing. Time and frequency windows were properly selected to suppress the effects of wave characteristic variations on the SPC analysis in the SWT domain. The performance of the proposed technique was verified by monitoring porosity in stainless steel 316L samples manufactured in the DED process. The test results demonstrated that the proposed nonlinear technique is much more sensitive to porosity than conventional linear techniques, and hence, is more suitable for in situ porosity monitoring.

show abstract

Section: Experimental Validation Of Spc In Swtmentioning

confidence: 99%

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Liu

Yang

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…The method has been deployed to detect subsurface defects of AM components produced using laser powder bed fusion [25] and on WAAM components detecting surface cracks, flat bottom holes and through holes [26]. However, laser ultrasonic setups have historically featured very high initial costs, slow generation, acquisition and data processing times, while also being sensitive to surface finish [27] and safety considerations.…”

Section: 3: Inspection Of Additive Manufactured Componentsmentioning

confidence: 99%

Multi-layer ultrasonic imaging of as-built Wire + Arc Additive Manufactured components

Zimermann

Mohseni

Lines

et al. 2021

Additive Manufacturing

View full text Add to dashboard Cite

“…The presence of air gaps owing to the imperfect contact results in impedance mismatch and also multi-reflections of the incident ultrasonic waves. Consequently, except for the "smooth condition", the additional changes in the properties of the incident ultrasonic waves make it difficult to evaluate the porosity of rough surfaces by using conventional UT such as ultrasonic velocity and ultrasonic attenuation coefficient measurements [22].…”

Section: Ultrasonic Measurementsmentioning

confidence: 99%

“…To overcome this problem, previous studies commonly used various surface polishing methods as a post-processing step to prepare the surface of as-deposited AM parts whose roughness exceeded a certain level. However, this post-processing step often resulted in an increase in the overall time and cost of the AM process [22][23][24][25]. The aforementioned discussion highlights the need to develop an innovative UT that can evaluate porosity without surface polishing, that is, even under rough surface conditions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Ultrasonic Testing to Evaluate the Porosity of Additively Manufactured Parts with Rough Surfaces

Park

Hong

et al. 2021

Metals

View full text Add to dashboard Cite

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%.

show abstract

Influence of surface roughness from additive manufacturing on laser ultrasonics measurements

Cited by 21 publications

References 12 publications

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Multi-layer ultrasonic imaging of as-built Wire + Arc Additive Manufactured components

Deep Learning-Based Ultrasonic Testing to Evaluate the Porosity of Additively Manufactured Parts with Rough Surfaces

Contact Info

Product

Resources

About