[4A5] Eddy currents and ultrasonic testing data fusion for wire + arc additive manufacturing
E Mohseni¹, V Tunukovic¹, S McKnight¹, R Zimmerman¹, R Pyle¹, A Poole¹, R Gomez¹, S Pierce¹, M Rizwan¹, R Vithanage¹, C MacLeod¹, E Foster¹, J Ding² and S Williams²
¹University of Strathclyde, UK
²Cranfield University, UK
Advanced manufacturing methods, such as high-deposition rate wire + arc additive manufacturing (WAAM), have seen an increasing uptake in high-value manufacturing of aerospace components owing to their capability to produce large components in a short time. The process is automated, involving industrial robots that enable layer-by-layer deposition on substrates through arc-based welding processes. The components are prone to the typical welding defects such as lack of fusion, keyhole and porosities and therefore non-destructive evaluation (NDE) is crucial to ensure their fitness for service. Traditionally, they are inspected post-manufacturing; however, the fully automated WAAM deposition process unlocks the opportunity to also integrate and deliver NDE during manufacturing after the deposition of every few layers, leading to process cost-effectiveness and the opportunity for early process intervention and remedial rework.
A dual-sensor concept for in-process inspection of WAAM components is presented in this work. The two sensor modalities deployed here are: (a) a high-temperature phased array ultrasonic testing (UT) roller probe; and (b) a high-temperature flexible eddy current (EC) array. The design and fabrication are presented for the robotically deployable dual-sensor head. To test the performance of the dual-sensor, titanium calibration blocks with artificial defects and geometries similar to WAAM were manufactured. A range of side-drilled holes and flat-bottomed holes located at different depths were fabricated. The phased array UT and array EC scans were performed at once using the dual-sensor head through a fully integrated software encoding the data from both arrays with robot pose information.
C-scan images from both UT and EC arrays were produced during the scan. The images went through image registration, preprocessing and fusion stages. Different preprocessing methodologies were devised and applied to the EC C-scan images to overcome the lift-off noise bands caused by the poor probe contact to the surface close to the corner fillets of the calibration sample. Two distinct data fusion approaches, namely the ramping method and the novelty detection statistical method, followed by pixel-level summation, multiplication and max pooling, were applied to the filtered C-scans and their performance was compared. For the statistical approach, distributions of a clean section of UT noise and EC differentials per channel across the scan were fitted. The resulting cumulative distribution function was then used with an acceptable criterion for false indications (for example 0.1%) to give thresholded images. These images can then be combined with either max pooling or pixel summation. The results of data fusion showcased the complementary nature of the two inspection methods through successful detection of all the artificial defects in the calibration blocks.
A dual-sensor concept for in-process inspection of WAAM components is presented in this work. The two sensor modalities deployed here are: (a) a high-temperature phased array ultrasonic testing (UT) roller probe; and (b) a high-temperature flexible eddy current (EC) array. The design and fabrication are presented for the robotically deployable dual-sensor head. To test the performance of the dual-sensor, titanium calibration blocks with artificial defects and geometries similar to WAAM were manufactured. A range of side-drilled holes and flat-bottomed holes located at different depths were fabricated. The phased array UT and array EC scans were performed at once using the dual-sensor head through a fully integrated software encoding the data from both arrays with robot pose information.
C-scan images from both UT and EC arrays were produced during the scan. The images went through image registration, preprocessing and fusion stages. Different preprocessing methodologies were devised and applied to the EC C-scan images to overcome the lift-off noise bands caused by the poor probe contact to the surface close to the corner fillets of the calibration sample. Two distinct data fusion approaches, namely the ramping method and the novelty detection statistical method, followed by pixel-level summation, multiplication and max pooling, were applied to the filtered C-scans and their performance was compared. For the statistical approach, distributions of a clean section of UT noise and EC differentials per channel across the scan were fitted. The resulting cumulative distribution function was then used with an acceptable criterion for false indications (for example 0.1%) to give thresholded images. These images can then be combined with either max pooling or pixel summation. The results of data fusion showcased the complementary nature of the two inspection methods through successful detection of all the artificial defects in the calibration blocks.
