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 are automated or aided methods for detecting defects.

The 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.

In accordance with 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 flaw detection algorithm is based on at least one inspection technique sheet.

Optionally, determining the part disposition is responsive to at least one of acceptance criteria defined in the technique sheet and the scan data.

In accordance with the present invention, there is provided a process for detecting a sub-surface defect as claimed in claim <NUM>.

Optionally, the process further comprises: analyzing, by the processor, C-scan data for quality issues, wherein upon an indication that the C-scan data is acceptable, the processor executes an algorithm to identify indications in the scan data.

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: detecting, by the processor, that at least one of the C-scan data and the A-scan data is bad, executing, by the processor, an error handling loop to troubleshoot and resolve the quality issues in the scan data.

Optionally, the process further comprises: detecting, by the processor, that both of the C-scan data and the A-scan data are good, the indication is classified and sorted by a severity value.

Optionally, the process further comprises: confirming, by the processor, the indication by collecting additional A-scan data at different angulations of the transducer.

Optionally, the process further comprises: assessing, by the processor, a confirmed indication and providing a disposition for the indication; wherein the disposition comprises a combination of subject-matter- experts that identify features of interest in a detected indication that can be a defect, and a machine learning method that uses historical defect characteristics to get bounds that determine a likelihood of an indication to be a subsurface defect.

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 the materials in the UT tank <NUM>, such as water <NUM>, 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 <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>.

Referring also to <FIG>, the scan resolution in both index axis dimension <NUM> and scanning axis dimension <NUM> is shown. In an exemplary embodiment, a value of <NUM> each, a scan direction <NUM> of moving the transducer <NUM> from the outer diameter (O. ) to the inner diameter (I. ) of the inspected part <NUM>, the rotation speed of the turntable <NUM> supporting the part <NUM>, of for example <NUM>/s, and the gate (that is, an electronic means of selecting a segment of the time base range for monitoring or further processing) start and end positions of for example <NUM> and <NUM>, respectively, as well as its trigger level of <NUM>% (that is, amplitude level above or below which the inspected part <NUM> is accepted or rejected) can be predetermined. The scanning procedure of one part surface <NUM> to generate the C-scan is depicted in <FIG>.

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>.

Referring also to <FIG>, an illustration of the inspection system <NUM>, is shown. The sensitivity and the detection capability of the system <NUM> can be a result of the technology used for generating the inspection data. Detection of the subsurface defects <NUM> from the inspection data <NUM> is primarily performed by human inspectors <NUM> which is a labor intensive and error prone process leading to inconsistencies and cost of poor quality.

<FIG> shows a system and method for automatic defect recognition (ADR) processor <NUM> for automatic inspection of engine parts <NUM> using immersion pulse-echo inspection technology as described above. The automatic defect recognition processor <NUM> 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 process is shown in <FIG> where the ADR processor <NUM> uses the communication with Sonic Tank block <NUM> to initiate the collection of data <NUM> from the inspection of the part <NUM> in the tank <NUM>. After the data collection, the ADR processor <NUM> runs an indication (potential flaws) flaw detection algorithm <NUM> based on at least one inspection technique sheet <NUM>. The ADR processor <NUM> further provides instructions to the transducer <NUM> to maximize a signal-to-noise ratio to localize identified indications more accurately. The indications represent a sensed flaw or defect or discontinuity <NUM> in the part <NUM>. A technique sheet <NUM> can be provided as an external input to the ADR processor <NUM> through the technique sheet specs block <NUM>. Using the acceptance criteria defined in the technique sheet <NUM> and detected indications data <NUM>, at block <NUM> the ADR processor <NUM> can issue a part level disposition <NUM> and automatically fills out quality notification (QN) reports <NUM> that can be communicated to the Factory <NUM> at block <NUM> for storage and archiving in quality databases <NUM> of the factory infrastructure <NUM>. At certain points during the inspection, the ADR processor <NUM> can involve the human inspector <NUM> for assistance or/and verification of the detected indications <NUM>. This is accomplished based on a confidence system that the ADR processor uses to self-assess its own disposition. The interaction of the human inspector with the ADR processor <NUM> is through a user interface <NUM>.

Referring also to <FIG>, an exemplary automatic defect recognition system process flow diagram is shown. There can be two types of data that ADR processor <NUM> uses in the inspection process <NUM>, C-scans and A-scans as detailed above. <FIG> shows the detailed flow of the processing of those scans <NUM> and description of situations requiring human <NUM> intervention (Inform Operator Block). The inspection process <NUM> starts at <NUM> by analyzing the C scan data for quality issues (for example, electrical noise, bubbles in the tank and the like). If the data is good, ADR processor <NUM> executes an algorithm to identify indications in the scan data at <NUM>. At this point a second quality detector (per indication quality detector) <NUM> uses the A-scan associated with the identified indications as additional data to identify quality issues in the data.

If one of the quality detectors flags the data as bad, the process <NUM> goes into an error handling loop <NUM> to troubleshoot and resolve the data quality issues. If any of the scan data defects (resulting from <NUM> or <NUM>) can be automatically resolved <NUM>, then ADR processor <NUM> will send a command to the sonic tank <NUM> for resolving this issue (for instance, brushing the surface in case of a bubble issue). After the automatic resolution is executed successfully, ADR processor <NUM> will request a rescan <NUM> from the sonic tank <NUM>. On the other hand, if the scan data defects cannot be resolved automatically or the there was an issue with automatically resolving it, the ADR processor <NUM> will involve the operator <NUM> at block <NUM> in order to perform some physical action (for example, brush bubbles off the part <NUM>) before ADR processor <NUM> requests a rescan at <NUM>. In the case where the operator cannot resolve the issue or did not respond on time (at "Wait for operator" blocks), the process <NUM> will go into the error state where ADR processor will stop the current part inspection and move on to the next part. There are other error handling mechanisms that take care of network issues that may arise and impact the communication of the ADR processor <NUM> and the sonic tank <NUM>.

If both quality detectors pass the data, the indications are classified and sorted by severity at block <NUM>. Indications are further confirmed at block <NUM> by collecting additional A-scan data at different angulations of the transducer <NUM>. Finally, at <NUM> the disposition and confidence module assesses each confirmed indication and comes up with a disposition, for example reject the part as the amplitude of at least one indication is above prescribed rejection threshold.

An exemplary characteristic of the ADR processor <NUM> is the built-in confidence system <NUM> shown in "Disposition and Confidence Assessment" module <NUM> in <FIG>. The confidence <NUM> in ADR processor <NUM> disposition is a combination of subject-matter- experts (SME) that identify the features of interest <NUM> in a detected indication that can possibly be a defect <NUM>, and machine learning methods <NUM> that use historical defect characteristics <NUM> to get bounds that determine likelihood of an indication <NUM> to be a real subsurface defect as shown in <FIG>. This knowledge provides the basis of the confidence system <NUM> that can provide a disposition such as high confidence pass/fail or low confidence pass/fail of the inspected part <NUM>. The indication confidence model <NUM> depicted in <FIG> provides two outputs: <NUM>) confidence: low or high and <NUM>) a priority score which is a real number that provides a closeness metric of the evaluated indication to those indications from the historical training set <NUM>.

In an exemplary embodiment one metric that can be used as a priority score is a p-value of the statistical model learned by the machine learning (ML) process <NUM>. For a given number N of features (N-dimensional features), using the historical inspection data <NUM>, the machine learning procedure <NUM> learns a statistical distribution function for the values , (for example, fitting mean and standard deviations of a multi-variate Gaussian distribution). For an observed indication with feature values equal to Z<NUM>, one can use the p-value defined as p-value = 2Prob [Z>|z<NUM>|] as a measure of how close this indication is to a real defect model learned using ML. The higher the p-value, the closer the indication features are to the feature learned by the ML model, hence the closer the value is to a real defect or discontinuity. As an illustration for the p-value metric, <FIG> provides a visual interpretation <NUM> of the p-value on a <NUM>-D feature.

The confidence/prioritization model <NUM> can be described from a statistical standpoint. One can utilize the machine learning <NUM> process to learn a model for defects as a function of their features value; setup a null hypothesis (H<NUM>): ADR detected indication follows the ML distribution; test the statistic: features values; the threshold of significance: define confidence threshold (for example, <NUM>); observation o: an indication detected by the ADR system <NUM>; Calculate p-value of observation O; reject the null hypothesis if the calculated p-value is below the threshold of significance and mark the indication as low confidence, or else accept the null hypothesis and mark the indication as high confidence.

A technical advantage of the process described for the confidence/scoring system <NUM> can be used for historical data analysis <NUM> to rank probability of a scanned part to have an ADR indication that is a real defect, hence human inspector <NUM> resources can be used efficiently for re-inspection purposes.

Another technical advantage of the disclosed process can include an efficient automated process which minimizes user involvement and invokes that only when necessary.

Another technical advantage of the disclosed process can include a unique method for confidence assessment along with indication scoring framework which allows for prioritization of user attention.

Claim 1:
A system 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 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>) in electronic communication with 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 (<NUM>);
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 to perform operations comprising:
receiving, by the processor (<NUM>), said scan data (<NUM>) for said part from said transducer (<NUM>);
running, by the processor (<NUM>), a flaw detection algorithm;
determining, by the processor (<NUM>), a part disposition wherein said part disposition is based on a confidence system (<NUM>) that the processor uses to self-assess said part disposition;
providing, by the processor, further instructions to the transducer (<NUM>) to maximize a signal-to-noise ratio to localize identified indications, wherein the indications represent at least one of a sensed flaw, defect, and discontinuity (<NUM>) in the part (<NUM>); and
creating, by the processor (<NUM>), a report.