Ultrasonic diagnostic for in situ control in metal additive manufacturing

Raffestin, Marc; Domashenkov, Alexey; Bertrand, Philippe; Faverjon, Pierre; Courbon, Cédric

doi:10.1016/j.measurement.2022.112244

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The images displayed notable differences in the surface morphology and crack development due to variations in the laser power and powder flow. Raffestin et al [38] explored in situ ultrasonic monitoring for internal defect detection during the laser powder bed fusion (LPBF) process, which is an advanced additive manufacturing technique. Their study was particularly focused on the use of an ultrasonic (US) system to monitor the formation and evolution of defects within bulk samples as they are manufactured, as depicted in Figure 14.…”

Section: Acousticsmentioning

confidence: 99%

Condition Monitoring in Additive Manufacturing: A Critical Review of Different Approaches

Khanafer,

Cao,

Kokash

2024

JMMP

View full text Add to dashboard Cite

This critical review provides a comprehensive analysis of various condition monitoring techniques pivotal in additive manufacturing (AM) processes. The reliability and quality of AM components are contingent upon the precise control of numerous parameters and the timely detection of potential defects, such as lamination, cracks, and porosity. This paper emphasizes the significance of in situ monitoring systems—optical, thermal, and acoustic—which continuously evaluate the integrity of the manufacturing process. Optical techniques employing high-speed cameras and laser scanners provide real-time, non-contact assessments of the AM process, facilitating the early detection of layer misalignment and surface anomalies. Simultaneously, thermal imaging techniques, such as infrared sensing, play a crucial role in monitoring complex thermal gradients, contributing to defect detection and process control. Acoustic monitoring methods augmented by advancements in audio analysis and machine learning offer cost-effective solutions for discerning the acoustic signatures of AM machinery amidst variable operational conditions. Finally, machine learning is considered an efficient technique for data processing and has shown great promise in feature extraction.

show abstract

Section: Acousticsmentioning

confidence: 99%