| Title |
Novel processing algorithm to improve detectability of disbonds in adhesive dissimilar material joints / |
| Authors |
Smagulova, Damira ; Mažeika, Liudas ; Jasiuniene, Elena |
| DOI |
10.3390/s21093048 |
| Full Text |
|
| Is Part of |
Sensors.. Basel : MDPI. 2021, vol. 21, iss. 9, art. no. 3048, p. 1-22.. ISSN 1424-8220 |
| Keywords [eng] |
NDT ; ultrasonics ; adhesive bonding ; dissimilar materials ; sensitivity analysis ; detectability |
| Abstract [eng] |
Adhesively bonded dissimilar materials have attracted high interest in the aerospace and automotive industries due to their ability to provide superior structural characteristics and reduce the weight for energy savings. This work focuses on the improvement of disbond-type defect detectability using the immersion pulse-echo ultrasonic technique and an advanced post-processing algorithm. Despite the extensive work done for investigation, it is still challenging to locate such defects in dissimilar material joints due to the large differences in the properties of metals and composites as well as the multi-layered structure of the component. The objective of this work is to improve the detectability of defects in adhesively bonded aluminum and carbon fiber-reinforced plastic (CFRP) by the development of an advanced post-processing algorithm. It was determined that an analysis of multiple reflections has a high potential to improve detectability according to results received by inspection simulations and the evaluation of boundary characteristics. The impact of a highly influential parameter such as the sample curvature can be eliminated by the alignment of arrival time of signals reflected from the sample. The processing algorithm for the improvement of disbond detectability was developed based on time alignment followed by selection of the time intervals with a significant amplitude change of the signals reflected from defective and defect-free areas and shows significant improvement of disbond detectability. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
