SYSTEM AND METHOD FOR REAL-TIME VISUALIZATION OF FOREIGN OBJECTS WITHIN A MATERIAL
The present disclosure provides a system and method for real-time visualization of a material during ultrasonic non-destructive testing. The system is capable of producing A-scans, B-scans, and C-scans of the test object and automatically highlighting potential foreign objects within the test object based on the scan data. The system includes a graphical user interface (GUI) capable of displaying a three-dimensional (3-D) image of a composite laminate constructed of a series of two-dimensional (2-D) cross sections. In one embodiment, the system includes an artificial intelligence module capable of highlighting foreign objects in order to provide size data, shape data, and/or depth data of the foreign object.
1 . A system for non-destructive testing in identification of foreign objects, comprising:
at least one ultrasonic transducer in communication with at least one processor;
wherein the at least one ultrasonic transducer is operable to emit ultrasonic waves to and receive reflected ultrasonic waves from a test object to produce signal data;
wherein the at least one processor generates A-scans for a plurality of spatial locations of the test object based on the signal data;
wherein the at least one processor constructs a plurality of C-scan slices based on the A-scans;
wherein the at least one processor implements k-means clustering for each of the plurality of C-scan slices and determines a candidate C-scan slice based on the k-means clustering; and
wherein the at least one processor determines a geometric perimeter, an effective radius, and/or an area of at least one foreign object based on a binarized version of the candidate C-scan slice.
2 . The system of claim 1 , wherein the at least one processor utilizes Gaussian bilateral filtering on the candidate C-scan slice before generating the binarized version of the candidate C-scan slice.
3 . The system of claim 1 , wherein the plurality of C-scan slices are constructed based on a maximum amplitude value for an associated gated region for each C-scan slice, an average amplitude value for the associated gated region for each C-scan slice, and/or an integral of the associated gated region for each C-scan slice.
4 . The system of claim 1 , wherein the at least one ultrasonic transducer is deposed within a portable transducer housing device.
5 . The system of claim 1 , wherein the candidate C-scan slice is determined based on detection of a significant decrease in largest cluster size of the k-means clustering.
6 . The system of claim 1 , wherein the at least one processor generates a depth of the at least one foreign object based on the candidate C-scan slice.
7 . The system of claim 1 , wherein the at least one processor determines a plurality of candidate C-scan slices, indicating a plurality of foreign objects positioned at different depths.
8 . The system of claim 1 , wherein the test object is a carbon fiber laminate.
9 . A method for identifying foreign objects with non-destructive testing, comprising:
at least one ultrasonic transducer communicating with at least one processor;
the at least one ultrasonic transducer emitting ultrasonic waves to and receiving reflected ultrasonic waves from a test object to produce signal data;
the at least one processor generating A-scans for a plurality of spatial locations of the test object based on the signal data;
the at least one processor constructing a plurality of C-scan slices based on the A-scans;
the at least one processor implementing k-means clustering for each of the plurality of C-scan slices and determining a candidate C-scan slice based on the k-means clustering; and
the at least one processor determining a geometric parameter, an effective radius, and/or an area of at least one foreign object based on a binarized version of the candidate C-scan slice.
10 . The method of claim 9 , further comprising the at least one processor utilizing Gaussian bilateral filtering on the candidate C-scan slice before generating the binarized version of the candidate C-scan slice.
11 . The method of claim 9 , further comprising constructing the plurality of C-scan slices based on a maximum amplitude value for an associated gated region for each C-scan slice, an average amplitude value for the associated gated region for each C-scan slice, and/or an integral of the associated gated region for each C-scan slice.
12 . The method of claim 9 , wherein the at least one ultrasonic transducer is deposed within a portable transducer housing device.
13 . The method of claim 9 , further comprising determining the candidate C-scan slice based on detection of a significant decrease in largest cluster size of the k-means clustering.
14 . The method of claim 9 , further comprising the at least one processor generating a depth of the at least one foreign object based on the candidate C-scan slice.
15 . The method of claim 9 , further comprising the at least one processor determining a plurality of candidate C-scan slices, indicating a plurality of foreign objects positioned at different depths.
16 . The method of claim 9 , wherein the test object is a carbon fiber laminate.
17 . A system for non-destructive testing in identification of foreign objects, comprising:
at least one ultrasonic transducer in communication with at least one processor;
wherein the at least one ultrasonic transducer is operable to emit ultrasonic waves to and receive reflected ultrasonic waves from a test object to produce signal data;
wherein the at least one processor generates A-scans for a plurality of spatial locations of the test object based on the signal data;
wherein the at least one processor constructs a plurality of C-scan slices based on the A-scans;
wherein the at least one processor implements k-means clustering for each of the plurality of C-scan slices and determines a candidate C-scan slice based on the k-means clustering; and
wherein the at least one processor generating a depth of at least one foreign object based on the candidate C-scan slice.
18 . The system of claim 17 , wherein the at least one processor utilizes Gaussian bilateral filtering on the candidate C-scan slice.
19 . The system of claim 17 , wherein the candidate C-scan slice is determined based on detection of a significant decrease in largest cluster size of the k-means clustering.
20 . The system of claim 17 , wherein the at least one processor determines a plurality of candidate C-scan slices, indicating a plurality of foreign objects positioned at different depths.