Patent Description:
Aerospace engine components, may incur defects or imperfections during the manufacturing process. Non- destructive testing (NDT) inspections are performed during different stages of the manufacturing process to identify defective parts. Inspection methods include, but are not limited to, visual inspection, X- Ray, thermography, and ultrasonic testing. It is particularly difficult to inspect components that have an internal structure using only external observations. Forms of flaws such as porosity and inclusions in metallic parts are particularly difficult to detect. These types of defects can grow and damage the part in service. Such internal defects are often detected by some form of excitation of the structure (ultrasonic, thermoacoustic, and the like), sensing of the excitation, and manual interpretation of the sensor signals, see for example <FIG>. This manual inspection process is tedious, time consuming, and error prone.

What is needed is a mechanism that can identify scan data distractors with high accuracy, thus being successful at classifying indications as actual part defects.

Prior art includes <CIT> which discloses a method and system for assessing quality of spot welds.

<CIT> discloses ultrasonic inspection of a part in a liquid filled tank.

According to an aspect of the present invention, there is provided a system for detecting a sub-surface defect as claimed in claim <NUM>.

Optionally, the scan comprises transmitting ultrasonic energy to the part and receiving the ultrasonic energy from the part.

Optionally, the distractor comprises at least one of a bubble, a floater, or a surface condition.

Optionally, the system further comprises determining, by the processor, a defect characteristic including at least one of a defect size, a defect location, and a defect type.

Optionally, the system further comprises verifying, by the processor, that the indication is cleared and dimensioning the indication.

Optionally, the system further comprises providing, by the processor, an A-scan pre-processing function utilizing the learning phase module and the inference phase module.

Optionally, the system further comprises providing, by the processor, a disposition of one of an indication being a defect or an indication being a distractor.

According to another aspect of the present invention, there is provided a process for detecting a sub-surface defect as claimed in claim <NUM>.

Optionally, the process further comprises classifying by the processor, each the distractor indication; and determining by the processor, if a rescan is required.

Optionally, the process further comprises analyzing, by the processor, an A-scan associated with the indication as additional scan data to identify quality issues in the scan data.

Optionally, the process further comprises alerting by the processor, an inspector responsive to the nature and severity of the distractor; or requesting by the processor, a rescan of the part.

Optionally, the step of determining an indication in the scan data that indicates a distractor is responsive to data augmentation techniques comprises at least one of shifting, scaling, locally added noise, and neural network-based approaches including sequence or LSTM auto encoders, and generative adversarial networks.

Optionally, determining the indication in the scan data utilizes a temporal ML model trained using previously and newly obtained A-scans in the learning phase module for classifying the distractor.

Optionally, the process further comprises providing, by the processor, a disposition of one of an indication being a defect or an indication being a distractor.

Other details of the process are set forth in the following detailed description and the accompanying drawings wherein like reference numerals depict like elements.

Referring now to <FIG>, the exemplary ultrasonic testing (UT) inspection system <NUM> is shown. The UT inspection system <NUM> includes several functional units, such as a pulser/receiver <NUM>, transducer <NUM>, and display devices <NUM>. A pulser/receiver <NUM> is an electronic device that can produce high voltage electrical pulses <NUM>. Driven by the pulser <NUM>, the transducer <NUM> generates high frequency ultrasonic energy (sound energy) <NUM>. The sound energy <NUM> is introduced and propagates in the form of waves through a liquid coupling medium materials <NUM> in the UT tank <NUM>, such as water, and the part being inspected <NUM>, like an engine disk. When there is a discontinuity <NUM> in the part <NUM>, such as a crack, located in the wave path, a portion of the energy <NUM> will be reflected back from the discontinuity (indication) <NUM> surface. The transducer <NUM> can detect the reflected energy wave. The reflected wave signal is transformed into scan data <NUM> relayed in the form of an electrical signal <NUM> by the transducer <NUM> and relayed to a first processor <NUM> and displayed on a screen <NUM> (Computer <NUM> in <FIG>). A second processor <NUM> (Computer <NUM> in <FIG>) is configured to automatically analyze the scan data <NUM> to distinguish between actual part defects and spurious indications of defects.

A more detailed schematic of the interactions between first processor <NUM> and the UT tank <NUM> is shown in <FIG>. The first processor <NUM> is responsible for multiple functions. One of those functions includes sending scan plan information <NUM> to a robot controller <NUM>. The first processor <NUM> can communicate with a motor controller <NUM>. The motor controller <NUM> is configured to operate a motor <NUM> to rotate a turntable <NUM> supporting the inspected part <NUM>. The first processor <NUM> can also receive scan signal data <NUM> from the pulser/receiver <NUM> received from the transducer <NUM> coupled to a robot <NUM> to be displayed for review by an inspector <NUM>.

The scan plan <NUM> contains instructions <NUM> for moving a robotic arm <NUM> and positioning the transducer <NUM> around the inspected part <NUM> for collection of scan data <NUM>. The data <NUM> can be collected by scanning every surface <NUM> of the part <NUM> until the totality of surfaces <NUM> of the part <NUM> that cover the entirety of the part <NUM> volume have been scanned. In order to generate a scan plan <NUM>, the inspector <NUM> configures the scan by setting parameters <NUM> in a UT tank vendor software <NUM> installed on the first processor <NUM>. The values of such parameters <NUM> depend on the inspected part <NUM>; some parameters <NUM> and their representative values include water path length, that is, the distance between the tip of the transducer <NUM> and the inspected part <NUM> of for example, <NUM>.

The pulser/receiver <NUM> produces outgoing electrical pulses <NUM> to the transducer <NUM> and receives/amplifies returning pulses <NUM> from the transducer <NUM>. The robotic arm <NUM> aides in the translation (spatial coordinates) and angulation (tilting) of the transducer <NUM> according to the scan plan <NUM>. A single transducer <NUM> generates and receives sound wave signals <NUM> that traverse the liquid medium <NUM> and the inspected part <NUM>.

One of the main uses of the UT inspection system <NUM> is for detecting and evaluating flaws or defects in physical parts <NUM>, such as turbine components of gas turbine engines. A defect can be defined as a region of a material or a structure that has different physical properties from its neighborhood (causing a discontinuity in that region), and those differences in properties are not intended during manufacturing. Defects can occur during manufacturing or if the physical properties are altered over time. Some examples of defects detected by ultrasonic inspection are inclusions (e.g., non-metallic, metallic, reactive inclusions), or cracks. An indication is how those defects show up in the signals coming out from the immersion pulse-echo ultrasonic system. Not all indications detected are defects because there might be false positives, but the premise from the inspection method is that all defects conforming to NDT specifications are detected as indications. Defect identification is performed by scanning parts <NUM> by pulser/receiver <NUM>, transducer <NUM>, and display devices <NUM>, <NUM>. Ultrasonic data <NUM> of the scanned part <NUM> can be formatted into three presentations: A-scan, B-scan, and C-scan. The A-scan presentation is a one dimension, <NUM>-D plot that displays the amount of received ultrasonic energy (vertical axis) as a function of time (horizontal axis). The B-scan presentation is a cross-sectional, two dimension, <NUM>-D profile of the time-of-flight (time travel or depth) of the sound energy in the vertical axis and the linear position of the transducer <NUM> in the horizontal axis. Lastly, the C-scan presentation is also a <NUM>-D plot that captures a plan-type view of the location and size of the part; plots for either relative signal amplitude or time-of-flight may be generated. Multiple presentation scans can be used together for more accurate determinations of the condition of the part <NUM>.

Absence of indications <NUM> in A/B/C-scans represents a clean part <NUM> without defect. The existence of indications <NUM> can be due to two main reasons: <NUM>) part defects or <NUM>) distractors. Distractors <NUM> is a general term corresponding to any artifact which appears as a false indication/defect and includes for example, floaters (debris that cause interruption of the sound beam), electrical noise (interfering electrical currents), and surface flare (resonance between wave length of sound beam and the profile of the machining grooves). Other factors such as transducer noise, material noise, and miss alignment between transducer and part, could also impact the ultrasonic data quality and generate false alarms. Depending on the nature and degree of distractors <NUM>, a part <NUM> rescan may be required. Nevertheless, manual analyses of scan data, and especially across different presentations, are tedious, time-consuming, imprecise, and error prone. Disclosed herein is a mechanism that can identify these distractors <NUM> with high accuracy, to be used in classifying indications as actual part defects. A system and method for assessing the quality of UT scan data, from detecting and classifying distractors to deciding the appropriate course of action to eliminate them is disclosed.

Referring also to <FIG>, in an exemplary embodiment of a process map is shown. The process <NUM> includes the use of a processor <NUM> which can include one or more processors <NUM> (e.g., computer systems having a central processing unit and memory) for recording, processing and storing the data received. The processor <NUM> may include a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof.

The processor <NUM> can operate within the process <NUM> to assess the quality of UT scan data by leveraging computer vision techniques and subject matter expert criteria applied to C-scan presentations coupled with temporal machine learning (ML) models for pattern recognition of associated A-scan presentations.

The process <NUM> begins with the collection of scan data <NUM> particularly both amplitude and time-of-flight C-scans at step <NUM>. The process <NUM> at <NUM>, includes use of the processor <NUM> within a module I <NUM> that processes any indication in the scan data <NUM> that adheres to a subject-matter expert (SME)-defined criteria for indications <NUM> (for example, amplitude values above evaluation threshold). Module I <NUM> is the main distractor classifier to discriminate indications <NUM> into either distractors <NUM> with the correct label for example, bubble, floater, surface condition, or actual defects that are then passed to a dimensioning module II <NUM> to determine defect characteristics such as, the defect size, location, and type at step <NUM>. Module II <NUM> serves a dual purpose of verification that the actions performed as output of the decision model in Module I <NUM> have cleared out the indication <NUM> and also allows for dimensioning of actual defect indications <NUM> based on such factors as size, location, and type of defect, for defect indication <NUM> reporting purposes at <NUM> as shown in <FIG>.

Referring also to <FIG>, the schematic workflow of the process <NUM> shows details utilized to detect those distractors <NUM> in module I <NUM>. Module I <NUM> includes instructions for the processor <NUM> to begin with the extraction of A-scan data <NUM> for the pixel with highest signal amplitude value in each of the detected indications <NUM>. The next steps within module I <NUM> include A-scan pre-processing <NUM> which can support a hybrid approach to perform a learning phase at a module I. B <NUM> (for example, build predictive ML models), and run an inference phase at module I. C <NUM> (for example, predict the class of each distraction indication <NUM>) as illustrated in <FIG>.

During the inference phase <NUM>, the processor <NUM> utilizes a temporal ML model that had been trained using previously and newly obtained A-scans in the learning phase <NUM> is used to classify the nature of the distraction indication <NUM> (for example, bubbles, floaters, electrical noise, and surface flare). Such models can be variants of deep recurrent neural networks, including long short-term memory (LSTM) and gated recurrent network (GRU). Prior to feeding A-scans to the processor <NUM>, each A-scan signal must be pre-processed (for example, by resampling) so that all data samples in the network input layer have the similar shape.

The model learning phase in module I. B <NUM> is aided by data augmentation techniques to enrich a training dataset <NUM> and make the ML model robust by reducing the possibility of overfitting. Such techniques include shifting, scaling, locally added noise, and neural network-based approaches (for example, sequence or LSTM auto encoders, and generative adversarial networks). Once a final temporal ML model is obtained and new UT scan data are available, inference can be performed in the module I. C <NUM>, where each distraction indication <NUM> is classified and a determination is made whether a rescan is required. Finally, in module I. D <NUM>, depending on the nature and severity of the distractor indication <NUM>, an automated decision is made to eliminate source of distractor in the ultrasonic data, for instance, to automatically remove bubble using a robotic actuator, initiate a calibration procedure to align transducer orientation w. to scanned part, request partial/full rescan, and/or alert the UT tank operator/inspector <NUM> with details about distractor, who can then take appropriate corrective action e.g. remove bubble/floaters or machine the part surface to remove profile of the machining grooves and eliminate surface flare.

A technical advantage of the process described is a mechanism utilized for an increased yield in part inspections with minimal human intervention.

Another technical advantage of the disclosed process can include leveraging two scan presentations, subject matter expertise, and analytics for the classification of distraction indicators.

Another technical advantage of the disclosed process can include using classification of distractors to take more informed trouble shooting actions to eliminate scan data quality issues.

Claim 1:
A system (<NUM>) for detecting a sub-surface defect (<NUM>) comprising:
a tank (<NUM>) containing a liquid (<NUM>);
a transducer (<NUM>) configured to fluidly couple to a part (<NUM>) located in the tank (<NUM>) and further configured to transmit ultrasonic energy (<NUM>), said transducer (<NUM>) configured to scan said part (<NUM>) to create scan data (<NUM>) of the scanned part (<NUM>);
a pulser/receiver (<NUM>) coupled to said transducer (<NUM>) configured to receive and transmit said scan data (<NUM>);
a processor (<NUM>) coupled to said pulser/receiver (<NUM>), said processor (<NUM>) configured to communicate with said pulser/receiver (<NUM>) and collect said scan data (<NUM>); and said processor (<NUM>) configured to detect said sub-surface defect;
a tangible, non-transitory memory configured to communicate with said processor (<NUM>), the tangible, non-transitory memory having instructions stored therein that, in response to execution by the processor (<NUM>), cause the processor (<NUM>) to perform operations comprising:
receiving, by the processor (<NUM>), said scan data for said part from said transducer (<NUM>);
collecting, by the processor (<NUM>), the scan data (<NUM>);
determining, by the processor (<NUM>), a defect indication (<NUM>) in the scan data (<NUM>) that indicates a distractor, wherein said indication is based on a learning phase module and an inference phase module that the processor (<NUM>) uses to self-assess said indication; and
creating, by the processor (<NUM>), a defect indication report (<NUM>).