Systems and methods for in-SITU sensing and data analysis for industrial processes

Abstract

Systems and methods are provided for in-situ sensing and data analysis for industrial processes (such as additive manufacturing processes). An eddy current sensor array may be repeatedly scanned across the part during its fabrication to collect in-situ data to assess the quality of the part. defects discovered by processing the scan data may be eliminated or mitigated by subsequent fabrication steps. A processing technique is described that can identify defects by filtering across the multiple scans. For example, during a laser powder bed fusion (LPBF) process, the sensor is scanned after each build layer; the resultant in-situ data is processed in the build or "z" direction using the disclosed filtering methods. The results are used to assess the quality of the part or adjust the fabrication process. Also disclosed are system configurations illustrating how an industrial process may be instrumented for such data collection.

Description

[0001] SYSTEMS AND METHODS FOR IN-SITU SENSING AND DATA ANALYSIS FOR INDUSTRIAL PROCESSES

[0002] RELATED APPLICATIONS

[0003] The present international patent application claims priority under the Patent Cooperation Treaty to U.S. Provisional Patent Application No. 63 / 632,696, filed on April 11, 2024; U.S. Provisional Patent Application No. 63 / 764,057, filed on February 27, 2025; and U.S. Provisional Patent Application No. 63 / 786,664, filed on April 10, 2025, each of which is hereby incorporated by reference in its entirety.

[0004] TECHNICAL FIELD

[0005] The present disclosure relates to the fields of non-destructive evaluation (NDE) and in- situ inspection.

[0006] BACKGROUND ART

[0007] The subject matter discussed in the background section should not be considered prior art merely because of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be considered to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves, may also correspond to claimed embodiments.

[0008] In U.S. Patent 11,268,933, issued March 8, 2022, which is incorporated herein by reference in its entirety, Goldfine and Dunford (hereinafter Goldfine 6) disclose a system and method for real-time measurement and feedback of metrology and metallurgical data during additive manufacturing (AM) part fabrication. A sensor is placed either in the rake / roller or following the rake / roller so that it has no impact on the process efficiency and can be used to provide real-time feedback and an archived digital map of the entire part volume. The solution provides non-contact sensing of AM layer's electrical conductivity in a high-temperature environment, metallurgical property verification, porosity imaging, local defect detection and sizing, local material temperature monitoring, and grain anisotropy imaging. Part geometry, the AM powder, and the laser / material interface are monitored in real-time. Real-time nonlinear control of the AM fabrication process is performed based on the sensor data. In U.S. Patent 11,268,931, issued March 8, 2022, which is incorporated herein by reference in its entirety, Goldfine and Dunford (hereinafter Goldfine 7) disclose a method including acts of placing a segmented field sensor proximal to a material under test, the material under test having a metal powder layer and a prior processed material, the segmented field sensor having a first sensing element at a first spatial wavelength and a second sensing element at a second spatial wavelength, the first and second sensing elements being inductive loops; exciting the segmented field sensor with an excitation signal having a first frequency; first measuring a first response of the first sensing element due to the excitation signal, the first response being a single scalar quantity; second measuring a second response of the second sensing element due to the excitation signal; and estimating a proximity of the segmented field sensor to the material under test and a property of the metal powder layer of the material under test from the first and second responses and no other substantially simultaneous response of the first sensing element due to the excitation signal at the first frequency that is independent of the first response.

[0009] In U.S. Patent No. 11,435,317, issued September 6, 2022, which is incorporated herein by reference in its entirety, Goldfine et al. (hereinafter “Goldfine 1”) describes a process for enhancing detection of defects having characteristic shapes provided in a signature library. Signatures, which may be obtained from actual sensor measurements from a known defect and then correlated with sensor measurements. A large correlation may be an indication that a defect is present at the material location where the measurement data was collected. Goldfine 1 further describes methods for obtaining signatures for the signature library, selecting an appropriate signature from the library for data, performing single and multichannel correlation, and flagging defect detections.

[0010] In International Application No. PCT / US24 / 55269 having an International Filing Date of November 8, 2024, which is incorporated herein by reference in its entirety, Goldfine et al. (hereinafter “Goldfine II”), further describe the use of signature libraries.

[0011] In International Publication No. WO 2023 / 192887 Al, published October 5, 2023, which is incorporated herein by reference in its entirety, Dunford and Washabaugh (hereinafter “Dunford I”) disclose an eddy current sensor with a remote current sense that has a drive conductor, current sense conductor, and one or more sense conductors. The drive conductor has first and second loop portions, the current sense conductor has a third loop portion, and the sense conductor has a sense loop portion. The first and third loop portions are proximal to each other to form the remote current sense. The sense loop portion and the second loop portion are proximal to each other to form a sense element. The remote current sense and sense element are suitably distant from one another to have separate environments of sensitivity. The sensor may be used by collecting transimpedance measurements from both the remote current sense and sense element under known conditions, and with the sense element under unknown conditions. These measurements are combined to provide a calibrated measurement result suitable for further analysis.

[0012] In U.S. Patent No. 6,188,218, issued February 13, 2001, which is herein incorporated by reference in its entirety, Goldfine et al. (hereinafter “Goldfine III”) describe calibration of an eddy current sensor “in air.”

[0013] In U.S. Patent No. 10,324,062, issued June 18, 2019, which is herein incorporated by reference in its entirety, Denenberg et al. (hereinafter “Denenberg”) describe a fully parallel, multi-channel impedance instrument.

[0014] In U.S. Patent No. 6,784,662, issued August 31, 2004, which is herein incorporated by reference in its entirety, Schlicker et al (hereinafter “Schlicker”), describes an eddy current sensor array.

[0015] In U.S. Patent No. 7,467,057, issued December 16, 2008, which is herein incorporated by reference in its entirety, Sheiretov et al (hereinafter “Sheiretov”), describes material property estimation using non-orthogonal responsive databases.

[0016] SUMMARY

[0017] Systems and methods are provided for in-situ sensing and data analysis for industrial processes (such as additive manufacturing processes). An eddy current sensor array may be repeatedly scanned across the part during its fabrication to collect in-situ data to assess the quality of the part, defects discovered by processing the scan data may be eliminated or mitigated by subsequent fabrication steps. A processing technique is described that can identify defects by filtering across the multiple scans. For example, during a laser powder bed fusion (LPBF) process, the sensor is scanned after each build layer; the resultant in-situ data is processed in the build or “z” direction using the disclosed filtering methods. The results are used to assess the quality of the part or adjust the fabrication process. Also disclosed are system configurations illustrating how an industrial process may be instrumented for such data collection.

[0018] One aspect relates to a method of monitoring a layer-by-layer additive manufacturing (AM) process, the method comprising acts of: (i) during the AM process, scanning and measuring an eddy current sensor array over a scan path, the scan path having a calibration region and a build region where layers are formed by the AM process; and (ii) calibrating the measurements over the build region with the measurements over the calibration region.

[0019] In some embodiments, the method further comprises an act of using in-situ sensing data, as the data is accumulated, begin processing in parallel with the build so that the part condition analysis can be completed within a fraction of the build time after the build is completed.

[0020] In some embodiments, wherein the scanning is first scanning, the method further comprises acts of (ii) second scanning and measuring with the eddy current sensor array over the build region after one or more additional layers are formed by the AM process; and (iii) calibrating the measurements over the build region from the second scanning with the measurements over the calibration region from the first scanning.

[0021] In some embodiments of the method, the calibration region comprises a reference material having an electrical conductivity greater than 1.0 %IACS. In some embodiments of the method, the calibration region comprises a reference material having an electrical conductivity greater than 0.1 %IACS.

[0022] In some embodiments, the method further comprises an act of processing the calibrated measurements to estimate properties of the build region, wherein the properties are normalized with reference to the reference material.

[0023] In some embodiments of the method, the calibration is in accordance with ASTM Standard E2338.

[0024] In some embodiments of the method, the calibration region is over the powder bed and the frequency of operation is below the frequency where the skin depth is larger than three times the average diameter of the metal powder.

[0025] In some embodiments of the method, the calibration region consists of a non-permeable and non-conductive material.

[0026] In some embodiments of the method, the calibration region comprises a first region comprising a reference material and a second region consisting of a non-permeable and non- conductive material.

[0027] In some embodiments of the method, the act of calibrating comprises calibrating based on measurements from the second regions and verifying calibration using measurements over the first region.

[0028] In some embodiments of the method, the reference material is a base portion of a build chamber for the AM process. In some embodiments of the method, the eddy current sensor array scans over the build region for at least 10% of the layers during AM process.

[0029] Another aspect relates to a method comprising acts of receiving measurement data comprising layer-by-layer measurements taken from an eddy current sensor array during an additive manufacturing (AM) process for a part; and rendering a three dimensional (3D) representation of the received measurement data, wherein the layer-by-layer measurements are spatially registered and offset in a build direction of the AM process.

[0030] In some embodiments, the method further comprises an act using in-situ sensing data, as the data is accumulated, begin processing in parallel with the build so that the part condition analysis can be completed within a fraction of the build time after the build is completed.

[0031] In some embodiments of the method, the offset is equal to the thickness of the process layer.

[0032] In some embodiments of the method, the rendering is done without another source of knowledge of a geometry of the part being manufactured by the AM process.

[0033] In some embodiments of the method, the AM process results in processed metal and unprocessed material, and the rendering comprises determining edge locations for the 3D representation by locating a transition between the processed metal and non-processed material using at least one ET sensing channel for a layer among the layer-by-layer measurements.

[0034] In some embodiments of the method, the rendering comprises defining at least a portion of a surface of the 3D representation from a plurality of layer-by-layer measurements in which each layer edge locations are determined.

[0035] In some embodiments, the method further comprises an act estimating a wall thickness from a spacing between the edge locations.

[0036] In some embodiments of the method, the location of the edge is determined using at least two sensing elements that are measured simultaneously.

[0037] In some embodiments, the method further comprises an act filtering the measurement data along a path that extends along a plurality of layers in the layer-by-layer measurements to detect a defect.

[0038] In some embodiments of the method, the visual representation of the defect is an ellipsoid whose size represents the dimension of the defect and whose color represents an electrical conductivity of the defect.

[0039] In some embodiments of the method, the path is a preferred filtering direction that is determined by minimizing the variation of an eddy current pattern along the path. In some embodiments of the method, selection of the preferred filtering direction comprises assimilating knowledge from a 3D numerical electromagnetics model combined with knowledge of the build geometry.

[0040] In some embodiments of the method, the minimization is performed in advance based on qualitative knowledge of eddy current patterns.

[0041] In some embodiments of the method, areas away from edges in regions of uniform material properties the default preferred direction will be in a z direction.

[0042] In some embodiments of the method, a filtering pipe is defined by an x-y area and a function of the sensor response is averaged over the x-y area to produce a scalar quantity where the scalar quantity is stored for each build layer along the preferred direction as a vector.

[0043] In some embodiments of the method, the vector is limited in the number of layers so that the length of the vector is less than 3 times longer than the length of a signature from a typical defect of a selected type.

[0044] In some embodiments of the method, the signature refers to a machine learning algorithm that is trained on multiple defect types and the trained signatures are then used to filter sensor responses to detect defects in a build.

[0045] In some embodiments of the method, the selected type of defect is a spherical void.

[0046] In some embodiments of the method, the signature is determined from one of the following: measurement data on actual defects and electromagnetic model outputs.

[0047] In some embodiments of the method, the minimization is a quantitative algorithm with interactive approval from an operator to iteratively establish full volumetric preferred direction filtering for the 3D printed object.

[0048] In some embodiments of the method, the algorithm uses an electromagnetic model of eddy current patterns and a quantitative measure of the variation between patterns relative to geometric features.

[0049] In some embodiments, the method further comprises an act providing a visual representation of a defect as part of the 3D representation.

[0050] The foregoing methods may be implemented through the systems described herein.

[0051] The foregoing is a non-limiting summary of the invention, which is defined by the attached claims.

[0052] BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

[0054] FIG. 1 A is block diagram of a system for inspecting a test object, according to some embodiments;

[0055] FIGs. 1B-C show eddy current array sensors, according to some embodiments;

[0056] FIG. 2 is a block diagram of an instrument for measuring and analyzing sensor measurements, according to some embodiments;

[0057] FIG. 3 is an laser powder bed fusion (LPBF) build chamber and illustration of the orientation of an X, Y, Z coordinate system, according to some embodiments;

[0058] FIG. 4A is an illustration of the paths used for arbitrary direction filtering of a tubular component, according to some embodiments;

[0059] FIG. 4B is an expanded view of the paths used for arbitrary direction filtering of a tubular component, according to some embodiments;

[0060] FIG. 4C is a further expanded view of paths used for arbitrary direction filtering of a tubular component highlighting overlapping paths, according to some embodiments;

[0061] FIG. 4D-4E are illustrations of how in-situ measurement data may be organized into pipes for arbitrary direction filtering, according to some embodiments;

[0062] FIG. 5 is a flow chart illustrating the analysis steps associated with arbitrary direction filtering, according to some embodiments;

[0063] FIG. 6 is a flow chart illustrating the analysis steps associated with arbitrary direction filtering for use during the build process, according to some embodiments;

[0064] FIG. 7 is a flow diagram of a method 700 used for applying shape filtering methods in the build direction during the build process (z-directed filtering), according to some embodiments;

[0065] FIGs. 8A-8E show perspective views of a LPBF system 800 with a instrument and sensor mounted to the recoater, according to some embodiments;

[0066] FIG. 9A is a cross-sectional view of a test specimen 900 fabricated with a LPBF machine, according to some embodiments;

[0067] FIG. 9B is a representative B-scan plot response for a single sense element along the length of a test specimen at an excitation frequency of 5.24 MHz and at several different build layers, according to some embodiments;

[0068] FIG. 9C is a representative arbitrary direction plot for a single indication, according to some embodiments; and FIG. 9D is a representative arbitrary direction plot for several indications, according to some embodiments.

[0069] DETAILED DESCRIPTION

[0070] The inventors have recognized and appreciated the need for improved defect detection during manufacturing, as part of manufacturing quality control, and during the useful life of manufactured articles.

[0071] The Detailed Description is organized as follows. Section 1 provides an overview for an inspection system according to some embodiments. Section 2 provides a description of an arbitrary direction filter, exemplary applications, and systems and methods for implementing the same. Section 3 proves an example system installation in an LPBF machine that may be used to collect in-situ data during an LPBF process that may be used for assessing the quality of the part being built and / or real-time control of the LPBF process based, for example, on the application of the described arbitrary direction filtering technique. Section 4 presents measurement data from a tested embodiment of the system in an LPBF application. Finally, Section 5 provides a closing discussion.

[0072] Section 1: Overview of an Inspection System

[0073] Aspects of some embodiments relate to the use of a system 100 for inspecting a test object 130. System 100 is shown as a block diagram in FIG. 1A. System 100 includes an instrument 110 and a sensor cartridge 140. In some embodiments, system 100 includes a scanner 150 for providing mechanical support for instrument 110, sensor cartridge 140, and / or test object 130 as well as facilitating relative motion between sensor cartridge 140 and test object 130.

[0074] Instrument 110 may be housed in a housing 107; in some embodiments the housing is substantially cylindrical in shape such as that described in U.S. Patent No. 10,416,118, Measurement system and method of use, by Goldfine et al. issued September 17, 2019 and herein incorporated by reference in its entirety (hereinafter “Goldfine IV”). Sensor cartridge 140 may have a rigid connector which interfaces both mechanically and electrically with an instrument side connector 105.

[0075] In some embodiments, sensor cartridge 140 is connected to instrument side connector 105 via cable 180, passing data, power, and communication along conductors 321. Cable 180, shown in FIGS. 4A-4D may be of arbitrary length in accordance with the requirements of the application. Although cable 180 is shown with only excitation signals 181 and response signals 183 passing through it, it should be appreciated that cable 180 may also convey other signals (including power). For example, power and / or measurement signals for position encoder 103 may be conveyed through cable 180. Similarly, power and / or control signals for actuator 101 may be conveyed through cable 180. Cable 180 may include a protective covering 325 that is flexible enough to enable inspection for the features of interest and durable enough to prevent kinking, impact damage, tearing and other such failure or damage modes. Cable 180 may also have rigid housings 323 to protect the connector and maintain connection. These housings may be of clamshell design clamping around the connectors 322. Cable 180 may also include mechanical strain relief 324 at each connecting end, and clamshell housing 323 may clamp the strain relief and protective covering to alleviate any mechanical strain on cable. Cable 180 may include mechanical connections 326 to remove strain from the connectors and conductors. Strain relief may extend beyond the clamshell housings to provide mechanical support to the cable where it is contained by the clamshells. Cable 180 conductors 321 may be discrete wires or flexible printed circuit. Cable 180 may have bends 327 in housing 323 to support use of flexible printed circuit construction.

[0076] In some other embodiments, sensor 120 is directly connected to instrument side connector 105. Sensor cartridge 140 in some embodiments also includes a flexible sensor 120, and a mechanical support 141 to which the sensor is attached. Sensor 120 may be attached to mechanical support 141 with glue, tape, double sided tape, or in any suitable way. In some embodiments, sensor 120 is integrally manufactured with mechanical support 141. For example, electrical traces of sensor 120 may be printed (or otherwise formed) directly on mechanical support 141 and possibly buried within mechanical support 141 by adding additional material of mechanical support 141 over such traces. Instrument 110 is configured to provide excitation signals 181 to sensor 120 and measure the resulting response signals 183 of sensor 120. Response signals 183 may be measured and processed to estimate properties of interest, such as electromagnetic properties (e.g., electrical conductivity, permeability, and permittivity), geometric properties (e.g., layer thickness, sensor liftoff), material condition (e.g., fault / no fault, crack size, layer to layer bond integrity, porosity, residual stress level, temperature), or any other suitable property or combination thereof including properties of the fabricated part and the powder. (Sensor liftoff is a distance between the sensor and the closest surface of the test object for which the sensor is sensitive to the test object’s electrical properties.) This may include qualification of a repair procedure for the additive manufacturing process, by inspecting the defect area before and after the repair process to guide the repair process by measuring features of the defect such as location and size estimation and to verify the repair was performed properly and without defects. This may include detection of defects such as excess porosity, voids, variation in metallurgical structure, or variation in surface roughness of built-up material.

[0077] Instrument 110 may include a processor 111, a user interface 113, memory 115, an impedance analyzer 117, and a network interface 119. Though, in some embodiments of instrument 110 other combinations of components may be included. While instrument 110 is drawn with housing 107, it should be appreciated that instrument 110 may be physically realized as a single mechanical enclosure; multiple, operably-connected mechanical enclosures, or in any other suitable way. For example, in some embodiments it may be desired to provide certain components of instrument 110 as proximal to sensor 120 as practical, while other components of instrument 110 may be located at greater distance from sensor 120.

[0078] Processor 111 may be configured to control instrument 110 and may be operatively connected to memory 115. Processor 111 may be any suitable processing device such as for example and not limitation, a central processing unit (CPU), digital signal processor (DSP), controller, addressable controller, general or special purpose microprocessor, microcontroller, addressable microprocessor, programmable processor, programmable controller, dedicated processor, dedicated controller, or any suitable processing device. In some embodiments, processor 111 comprises one or more processors, for example, processor 111 may have multiple cores and / or be comprised of multiple microchips. Processing of sensor data and other computations such as for control may be performed sequentially, in parallel, or by some other method or combination of methods.

[0079] Memory 115 may be integrated into processor 111 and / or may include “off-chip” memory that may be accessible to processor 111, for example, via a memory bus (not shown). Memory 115 may store software modules that when executed by processor 111 perform desired functions. Memory 115 may be any suitable type of non-transient computer-readable storage medium such as, for example and not limitation, RAM, a nanotechnology-based memory, optical disks, volatile and non-volatile memory devices, magnetic tapes, flash memories, hard disk drive, circuit configurations in Field Programmable Gate Arrays (FPGA), or other semiconductor devices, or other tangible, non-transient computer storage medium.

[0080] Instrument 110 may have one or more functional modules 109. Modules 109 may operate to perform specific functions such as processing and analyzing data. Modules 109 may be implemented in hardware, software, or any suitable combination thereof. Memory 115 of instrument 110 may store computer-executable software modules that contain computerexecutable instructions. For example, one or more of modules 109 may be stored as computer- executable code in memory 115. These modules may be read for execution by processor 111. Though, this is just an illustrative embodiment and other storage locations and execution means are possible.

[0081] Instrument 110 provides excitation signals for sensor 120 and measures the response signal from sensor 120 using impedance analyzer 117. Impedance analyzer 117 may contain a signal generator 112 for providing the excitation signal to sensor 120. Signal generator 112 may provide a suitable voltage and / or current waveform for driving sensor 120. For example, signal generator 112 may provide a sinusoidal signal at one or more selected frequencies, a pulse, a ramp, or any other suitable waveform. Signal generator 112 may provide digital or analog signals and include conversion from one mode to another. Denenberg provides a discussion of an impedance analyzer that may be used in some embodiments. See, for example, the discussion in connection with FIG. 19a which provides a discussion on how impedance analyzer 117 can take a measurement. Goldfine III provides further discussion on how such impedance measurements may be calibrated to remove certain systematic bias from the measurements, according to some embodiments.

[0082] In some embodiments, impedance analyzer 117 has a current sensor 109 that is used to measure a current leaving signal generator 112. Current sensor 109 may be any suitable sensor for measuring such current. For example, current sensor 109 may include a known series resistance in the drive current signal path and current sensor 109 may measure the voltage across such known resistance such that the current may be calculated using Ohm’s Law. As another example, current sensor 109 may measure the voltage induced on an inductive pick-up coil having a well known transimpedance.

[0083] Sense hardware 114 may comprise multiple sensing channels for processing multiple sensing element responses in parallel. As there is generally a one to one correspondence between sense elements and instrumentation channels these terms may be used interchangeably. It should be appreciated that care should be used, for example, when multiplexing is used to allow a single channel to measure multiple sense elements. For sensors with a single drive and multiple sensing elements such as the MWM®- Array eddy current array available from JENTEK® Sensors, Inc., the sensing element response may be measured simultaneously at one or multiple frequencies including simultaneous measurement of real and imaginary parts of the transimpedance (or mathematically equivalent measurements / representations such as the magnitude and phase of the transimpedance or the in-phase and quadrature components of the transimpedance). Though, other configurations may be used. For example, sense hardware 114 may comprise multiplexing hardware to facilitate serial processing of the response of multiple sensing elements and for eddy current arrays. Some embodiments of sensor 120 use certain MWM-Array formats to take advantage of the linear drive and the ability to maintain a consistent eddy current pattern across the part using such a linear drive. Sense hardware 114 may measure sensor transimpedance for one or more excitation signals at one or more sense elements 123 of sensor 120. It should be appreciated that while transimpedance (sometimes referred to simply as impedance), may be referred to as the sensor response, the way the sensor response is represented is not critical and any suitable representation may be used. In some embodiments, the output of sense hardware 114 is stored along with temporal information (e.g., a time stamp) to allow for later temporal correlation of the data, and positional data correlation to associate the sensor response with a particular location on test object 130. Instrumentation may also operate in a pulsed mode with time gates used to provide multiple sensing outputs and multiple channels used to acquire data from multiple sensing elements. If these sensing elements 123 have different drive-sense gaps (distance between a drive construct 122 and the sense elements 123, then this is referred to as a segmented field sensor. Thus, sensor operation can be at a single frequency, multiple frequencies, or in a pulsed mode where the drive is turned on and off in a prescribed manner or switched between two or more modes of excitation.

[0084] Sensor 120 is shown as an eddy-current sensor, though other sensor types may be used with system 100. For example, in some embodiments, sensor 120 is one or more of an eddy current sensor, an optical sensor, an ultrasonic testing (UT) sensor, a thermographic sensor, and a radiography sensor.

[0085] FIGs. 1B-1C show some eddy current array embodiments of sensor 120. Sensor 120 has a drive construct 122, a sense element 123 (or multiple sense elements), each of which is discussed further herein. In some embodiments sensor 120 provides temperature measurement, voltage amplitude measurement, strain sensing or other suitable sensing modalities or combination of sensing modalities. In some embodiments, sensor 120 is an eddy-current sensor such as an MWM, MWM-Rosette, or MWM-Array sensor available from JENTEK Sensors, Inc., Marlborough, MA. A discussion of some MWM-Array sensors may be found, for example, in Schlicker. Sensor 120 may be a magnetic field sensor or sensor array such as a magnetoresistive sensor (e.g., MR-MWM-Array sensor available from JENTEK Sensors, Inc.), a segmented field MWM sensor, and the like. Segmented field sensors have sensing elements at different distances from the drive winding to enable interrogation of a material to different depths at the same drive input frequency. Sensor 120 may have a single or multiple sensing and drive elements. Sensor 120 may be scanned across, mounted on, or embedded into test object 130.

[0086] In FIG. IB, sensor 120 is an eddy current array having an array of sensing elements 123 and a drive winding 121. Drive winding 121 has a single rectangular drive construct 122. Drive construct 122 has a linear segment along which sensing elements 123 are each equidistant. The distance 124, shown as the distance from the nearest linear drive segment to the center of sensor elements may be defined as a drive-sense gap. Other definitions of drive-sense gap may be found in literature or used - for example, the distance between the nearest linear drive segment and the nearest segment of the sense element coil.

[0087] In FIG. IB, sensor 120 has sensing elements 123 within the confines of rectangular drive construct 122. Sensor 120 shown in FIG. 1C is essentially identical to sensor 120 shown in FIG. IB except that sensing elements 123 outside the confines of rectangular drive construct 122. The drive-sense gap, distance 124, may be defined in the same way for both designs.

[0088] In FIG. ID, sensor 120 has a drive winding 121 where the drive construct 122 is a dual- rectangular drive construct. In some embodiments the dual rectangular drive constructs are connected such that current flows in the same direction in the two adjacent drive segments.

[0089] In some embodiments, the computer-executable software modules 109 may include a sensor data processing module that, when executed, estimates properties of test object 130. The sensor data processing module may utilize multi-dimensional precomputed databases that relate one or more frequency transimpedance measurements to properties of test object 130 to be estimated. The generation of suitable databases and the implementation of suitable multivariate inverse methods are described, for example, in Sheiretov. The sensor data processing module may take the precomputed database and sensor data and, using a multivariate inverse method, estimate material properties for the processed part or the powder. Though, the material properties may be estimated using any other analytical model, empirical model, database, lookup table, or other suitable technique or combination of techniques.

[0090] User interface 113 may include devices for interacting with a user. These devices may include, by way of example and not limitation, keypad, pointing device, camera, display, touch screen, audio input and audio output.

[0091] Network interface 119 may be any suitable combination of hardware and software configured to communicate over a network. For example, network interface 119 may be implemented as a network interface driver and a network interface controller (NIC). The network interface driver may be configured to receive instructions from other components of instrument 110 to perform operations with the NIC. The NIC provides a wired and / or wireless connection to the network. The NIC is configured to generate and receive signals for communication over the network. In some embodiments, instrument 110 is distributed among a plurality of networked computing devices. Each computing device may have a network interface 119 for communicating with other computing devices forming instrument 110.

[0092] In some embodiments, multiple instruments 110 are used together as part of system 100. Such systems may communicate via their respective network interfaces. In some embodiments, some components are shared among the instruments. For example, a single computer may be used to control all instruments. In one embodiment multiple areas on the test object are scanned using multiple sensors simultaneously or in an otherwise coordinated fashion to use multiple instruments and multiple sensor arrays with multiple integrated connectors to inspect the test object surface faster or more conveniently.

[0093] Actuator 101 may be one or more actuators used to position sensor cartridge 140 with respect to test object 130 and ensure that the liftoff of the sensor 120 is in a desired range relative to the test object 130. For example, actuator 101 may drive the movement of mechanical components of scanner 150 that in turn move the sensor 120 relative to test object 130. Actuator 101 may be an electric motor, pneumatic cylinder, hydraulic cylinder, or any other suitable type or combination of types of actuators for facilitating movement of sensor cartridge 140 with respect to test object 130. Actuator 101 may be controlled by motion controller 118. Motion controller 118 may control actuator 101 to move sensor cartridge 140 and sensor 120 relative to test object 130.

[0094] Regardless of whether motion is controlled by motion controller 118 or directly by the operator, position encoder 103 and motion recorder 116 may be used to record the relative positions of sensor 120 and test object 130. This position information may be recorded with impedance measurements obtained by impedance analyzer 117 so that the impedance data may be spatially registered.

[0095] For some applications the performance of system 100 depends (among other things) on the proximity of sensor 120 to test object 130; that is to say the sensor liftoff may be critical to performance for such applications. For example, crack detection in an aerospace application may require cracks 0.5 mm (0.02 inches) in length be reliably detectable in test object 130 (e.g., a turbine disk slot). In order to achieve reliable detection of a small crack, sensor 120’s liftoff may need to be kept to under 0.25 mm (0.010 inches). Further, for such an application, sensor 120 may preferably be a sensor array, thus the liftoff of each element in the array may need to be kept to under 0.25 mm (0.010 inches). (It should be appreciated that these dimensions are illustrative and the specific requirements will be dictated by the details of the application.) Measurements may be complicated when test object 130 has a complex curved surface that may change along a measurement scan path.

[0096] To permit high-performance operation at higher excitation frequencies, use of current sensor 109 to measure the current in drive winding 121 may not be sufficient. The inventors have recognized and appreciated that measurement performance may be improved by measuring the current in drive winding 121 closer to the portion of the drive conductor that is inductively coupling to sense element 123. Specifically, and as described in Dunford I, a current sense element located on sensor 120 can be used to much more accurately measure the current in drive winding 121 that is inductively coupling to sense element 123. This is contrasted with measurement of the drive current much further from sense element 123 using current sensor 109 which is typically within instrument housing 107. Although the electrical impedance of cable 180 may alter the current at the instrument, the local measurement can account for any variation of the current due to the cable.

[0097] FIG. 2 shows embodiments of instrument 110 with a specific focus on data collection and analysis. It should be appreciated that other aspects of instrument 110 discussed in connection with FIG. 1 A or elsewhere may also be part of such an embodiment.

[0098] Prior to using instrument 110 to collect and analyze sensor data as part of system 100, instrument 110 may be configured for a specific measurement application. An instrument control module 230 may be used to configure instrument 110 for a specific measurement application. Instrument control module 230 may utilize a session file 210 to store an instrument configuration 211, a measurement sequence instructions 212, and an interpolation configuration 213.

[0099] Instrument configuration 211 may store information identifying the type of sensor to be used, the excitation frequencies and their respective amplitudes, specific grids within precomputed database 203 for impedance data interpolation, the type of calibration to be used, the modules that are used as part of the measurement such as the specific signatures within signature library 205 for data analysis, and other information for configuring instrument 110 for a measurement application. The calibration typically uses an air calibration or an air with a one point reference measurement calibration. The term “air calibration”, as used herein, is calibration away from any conducting or magnetic materials (e.g., in a gas, air, vacuum, or in the presence of non-conducting, non-magnetic materials). This requires that conducting or magnetic materials are far enough away from the sensor so as to not impact the sensor response. For an air calibration itself, a measurement of the sensor response in air is used to adjust the measurement impedances to known and reproducible values. This approach does not require the use of reference standards for the instrument adjustment, but measurements on a reference part or material is recommended for verification of the calibration itself. To reduce channel -to-chann el variations in the sense element responses and improve consistency of the conductivity measurement, a second measurement point can be used as part of the calibration. This second measurement is usually for a reference material with known electrical properties. This provides consistency with other standard procedures for conductivity measurements. Note that one or more reference point measurements could be used but this tends to be less robust than including a measurement response in air since the reference part measurement for calibration requires knowledge of the conductivity of the reference material. The instrument configuration 211 typically also includes information about the data acquisition rate and the configuration of auxiliary information that could be associated with each measurement such as position encoder information, temperature, strain gages, etc.

[0100] Measurement sequence instructions 212 may define the sequence of actions that are to take place for a measurement. Instructions 212 may specify motor control, triggers, changes to the instrument configuration, and prompt user actions. For example, instructions 212 may indicate that after initializing a measurement, a first motor is to move at a certain speed during measurement collection and, after reaching an end point, measurement is to stop. As another example, after a first measurement is taken the instructions 212 may indicate the user is to be prompted to take an action (e.g., lay a non-conducting layer between the test object and the sensor to increase sensor liftoff) and then wait until a user initiated trigger is received. As yet another example, after taking first measurements the instructions may cause instrument 110 to be reconfigured to an alternate instrument configuration (e.g., having different excitation frequencies or other configuration properties).

[0101] Measurement sequence instructions 212 may also include definitions of the views to be presented to the end user. These views may be read by graphics generation module 270 to affect the graphical presentation to the user. Note that the graphics generation could also be in the form of data tables.

[0102] In some embodiments, an inverse interpolation module 220 is used to process impedance data 201 obtained from sensor 120 by impedance analyzer 117. Inverse interpolation module 220 utilizes a grid database 203 to estimate physical properties from impedance data 201. Physical properties estimated may include properties such as layer and gap thicknesses, electrical conductivity as a function of spatial position, and magnetic permeability as a function of spatial position. For example, the physical properties estimated by inverse interpolation module 220 for a sensor scanning a coated substrate material may include (i) liftoff, (ii) coating thickness, (iii) coating electrical conductivity, and (iv) substrate electrical conductivity. Secondary properties may also be estimated using the output of the inverse interpolation module, such as layer thicknesses, gaps between layers, the size of a chamfer, and further to estimate defect sizes or to identify types of anomalies.

[0103] Interpolation configuration 213 of session file 210 may be used to specify aspects of the inverse interpolation. For example, in some embodiments a hierarchical approach can be used to increase numerical stability and accuracy of the multiple unknown inversion. Property effects can be systematically separated from one another by using specific excitation frequencies and / or segmented fields to estimate the properties they are most sensitive to. For example, a coating conductivity property may be estimated using only a high frequency excitation measurement, and then both the high and a low frequency used to determine coating thickness and substrate conductivity (with the coating conductivity in this second step assigned the value determined from the high frequency alone). This may be useful for example for inspection through a bushing. In one embodiment of this invention, the use of multiple frequencies and the inverse interpolation module along with the ability to scan the internal surface of the busing in a hole, is used to detect cracks in the stackup / layers / skins through a bushing. This can utilize other aspects of this invention, after accounting for the thickness and properties of the busing to estimate the conductivity or magnetic permeability of the “substrate” which in this case is the properties of the structural layers, aircraft skin, material that is inspected through the busing.

[0104] Further discussion of the operation of inverse interpolation module 220 may be found in Sheiretov.

[0105] In some embodiments, instrument 110 is also equipped with a forward model module 240 for precomputing grids for grid database 203 using a sensor-material model. The model may be a physics-based model, an empirical model based on prior measurements, or any other suitable type of model for creating measurement grids. In some embodiments, forward model module 240 is not made a part of instrument 110 and only grids are stored in grid database 203 of instrument 110. For example, forward model module 240 may be a software application run on a computer to produce grids which are then stored in grid database 203. In some embodiments, instrument 110 includes a signature definition module 250 for defining characteristic responses (“signatures”) of a feature to be enhanced or suppressed in measurement data. In some embodiments signatures and their use may be similar to those described in Goldfine I and Goldfine II. Signature definition module 250 may allow a user to identify signatures and store them in a signature library; alternatively or additionally, signatures may be identified in an automated or semi -automated way. For example, a crack defect signature may appear in the electrical conductivity response measured by a sensor scanning over the crack. In the case of a sensor array, the response may be observed on a single or multiple adjacent channels. A signature may be identified as a single channel response or a multi-channel response. Signature definition module 250 may standardize signatures prior to storing them in library 205. For example, signatures may be standardized to a specific number of points or a specific amplitude range. Signatures may also include metadata that provide additional information about the signature such as the size of the defect the signature was obtained from.

[0106] Detection and sizing module 260 may be used to detect and size defects in measurement data from a test object using signatures from signature library 205. Module 260 may evaluate the correlation between a measurement and a signature. If the correlation exceeds a threshold a detection may be flagged. The threshold may be set based on the detection and false alarm requirements of the application. Signature library 205 may contain multiple signatures that may be tested against measurement data. The signature having the greatest similarity with the measurement may also be used to size a detected defect. For example, the defect size may be estimated to be the same as the size of the defect the signature.

[0107] Module 260 may also be used to suppress features that are not of interest such as fasteners or through holes. For example, a through hole in a plate typically has a significant effect on the estimated electrical conductivity of the substrate material if a planar model is used to estimate conductivity. The shape of the conductivity response with respect to position as the sensor is scanned over the hole depends upon the actual electrical conductivity of the substrate material, the excitation frequency, and the geometry (e.g., sense element size and spatial wavelength) of the sensor. However, for a given sensor array, because the conductivity response of the through hole is consistent, it may be removed from the conductivity estimate. For example, module 260 may identify a highly correlated through hole signature with the conductivity response from measurement. The conductivity response may then be updated to remove the signature. This will flatten the conductivity response and may also allow for the hole location to be accurately estimated from the measurement data. While this example discussed suppressing the response for processed data such as the estimated conductivity of the material this approach can also be used for unprocessed data such as the sensor impedance or transinductance.

[0108] Graphics generation module 270 may provide a graphical representation to the user to assist the user in the data collection and / or analysis process. Module 270 may present such a graphical presentation on a video display integral to and / or separate from instrument 110. Information may be presented as tables, A-scans, B-scans, C-scans, or any suitable way. In some embodiments, module 270 configures the graphical environment based on instructions 212. In this way a consistent presentation of information can be provided to the user.

[0109] Report Generation Module 280 may be included to facilitate review of measurement results outside of the graphical environment of instrument 110. For example, report generation module 280 may produce a report of measurement data in pdf, docx, rtf, xlsx, or other suitable format. Session file 210 may specify the report format which may be used by module 280 to generate reports for measurement data.

[0110] In some embodiments, the output includes a decision with regards to the future disposition of the test object. Modules 270 and / or 280 may present such a decision. Examples include pass / fail decisions on the quality of a component, or the presence of flaws. As another example, it may be determined whether the test object may be returned to service, repaired, replaced, scheduled for more or less frequent inspection, and the like. If it is determined that the application was not determinative, instrument 110 may re-perform the procedure(if automated), or advise the user to re-perform the procedure. A procedure may need to be re-performed, for example, if all requirements of the procedure were not met. For example, the procedure may require the liftoff of the sensor to be below a threshold amount over the inspection surface and require re-performance if the liftoff requirement is not met.

[0111] Software implementations are focused on increasing data analysis, speed and large file handling. In one such embodiment, software is upgraded from 32 bit to 64 bit to improve the speed of data analysis and rendering of multiple images for viewing analyzed data. In some embodiments, multithreading, vector processing, or other methods for implementing rapid data analysis for multivariate inverse methods, intelligent filtering, or AI / ML implementations are utilized to improve the speed for data analysis and viewing. Inspection speed may also be affected by the scan speed of motors and the data acquisition rate. The scan speed of motors and data acquisition rate are set to provide a minimum number of data points on a prescribed defect size (e.g., 50x50 mil comer cracks, or 50x25 mil midwall cracks) where the minimum number of data points required is determined from empirical data taken at two or more scan speeds / data rate combinations. Scan speeds and data rate should also take into account the type of data analysis to be implemented (e.g., MIM, intelligent filtering, AI / ML). Intelligent filtering is the use of signature libraries and shape filtering as described, for example, in Dunford I.

[0112] In some embodiments the measurement results are used to control a process. For example, a property measurement may be fed back into a control circuit that controls a process. Section 2: Arbitrary Direction Filter

[0113] The inventors have recognized and appreciated that in applications where multidimensional data is available characterizing test objects it may be beneficial to filter the data along an arbitrary path to detect features of interest. Thus, in this filtering approach, data may be analyzed in a direction relevant to the test object.

[0114] In the case of LPBF, for example, the data is generally analyzed in the build direction (“z-direction”), where the layers of material (in the x- and y-directions) are sequentially added to the test object. This is illustrated in FIG. 3, which shows a LPBF system 300 with sensor 120 attached to recoater 303. The x-direction is taken to be the scan direction 302 and the y-direction is perpendicular to the scan direction and in the plane of the ET array. If the sense elements of the array are aligned perpendicular to scan direction 302, this corresponds to the y-direction. Again, the z-direction is then normal to scan direction 302. Recoater 303 passes over build plate 301 on which the test object is built by the LPBF process.

[0115] The filtering direction may follow a feature in the test object, such as a corner or edge that is linear or curved. This is illustrated in FIG. 4A where several of these analysis paths or “pipes” 401 are shown near the corner of a tubular structure 400. FIG. 4B shows an expanded view of the comer of the tubular structure 400 where the corner region 403 has been subdivided to contain many individual paths to provide a small cross-sectional area 405 compared to the area of the corner region to provide a higher spatial resolution in the region of interest. This illustrates that for finer spatial analysis additional pipes can be used that follow a path that is adjacent to the original pipes. This is similar to the generation of meshes for finite element methods. FIG. 4C shows a further expanded view of tubular structure 400 showing that pipes may overlap as do pipes 411 and 413. Note that this filtering approach can be called z-directed filtering since conventional nondestructive eddy current testing (ET) generally focuses on scanning of the sensor in one or two directions so that the position information is in the x- direction or possibly the x-y-direction and the z-direction is perpendicular to this scan direction.

[0116] FIGs. 4D and 4E illustrate the pipes concept in another way for two build geometries. In these figures, the build geometry is shown and then filled or partially filled by circular shapes that represent the regions over which data is to be averaged or otherwise combined to represent a point within the build. A sequence of, for example, adjacent circles can be used to define a pipe or the path for the arbitrary direction filtering described herein. The circles may be overlapping or non-overlapping in some embodiments. It should be appreciated that the circles are intended to be illustrative and the exact shape of the region used for defining a points value may be an arbitrarily defined surface or volume within the three dimensional in-situ measurement data.

[0117] For LPBF additive manufacturing the layer thickness may be very small compared to the dimensions of the sensor or sensor array. This provides the possibility for higher spatial resolution in the build direction that has not been possible with conventional ET methods. The spatial resolution can be described in terms of the inverse of the data spacing in a direction. For example, the layer thickness depends upon the powder dimensions and processing conditions but can be on the order of 0.05 mm. In contrast, the dimensions of the sense elements in an ET array often range from 1 mm to 5 mm while the dimensions of the drive windings range from 70 mm to 200 mm in the array direction (or more depending upon the LPBF machine) and 3 mm to approximately 13 mm in the direction perpendicular to the array elements. While data can be acquired rapidly to provide a data resolution in the scan direction of order 0.1 mm, the spatial resolution is often highest in the build direction. By analyzing the data with a component of the filtering direction in the highest spatial resolution direction, which is the build direction for this example, smaller features of interest can be observed compared to filtering in the directions with less spatial resolution.

[0118] FIG. 5 shows a flow chart illustrating an inspection process 500 for detecting indications of interest in an additive manufacturing (AM) process using arbitrary direction filtering, according to some embodiments. Process 500 may be implemented using system 100 or in any suitable way. Process 500 is generally described in connection with data collected during laser powder bed fusion (LPBF) fabrication process where the test object 130 (FIG. 1) is a component being fabricated. Though, process 500 may be applied to any suitable application. Recall that the LPBF process involves fabricating a part or component inside a build chamber by placing a powder onto a build plate and using a laser to selectively heat portions of the powder according to a prespecified build pattern. After the heating process, the build plate is lowered, powder is reapplied by passing a recoater or spreader across the build plate, and then applying the laser heating. This is the fabrication process for each layer of the built-up material of the component or part. Process 500 involves filtering or analyzing measurement data in a direction that follows a feature of interest to detect indications of interest. Features of interest include a comer or edge of the built-up material but could also be a constant location relative to the plane of the face of the build plate. The indication of interest, in some embodiments, could be porosity or a void, a variation in metallurgical structure, or a variation in surface roughness of the built-up material. Once an indication is detected and sized, an evaluation step is performed to determine if this is a relevant indication, such as a defect of interest exceeding a size threshold, or a non-relevant indication, such as fine structural details intended in the built-up material.

[0119] Inspection process 500 begins at step 501 with receiving a selection of a feature of the test object for analysis and a full set of inspection data for each of the relevant layers of the build process. The selection of the feature could be provided through human input, or via a software module that automatically identifies the feature using a predefined requirement. In some embodiments, the received feature is a corner of the test object, a location along an edge of the test object, or an area of the test object where stress is expected to concentrate during the design use of the test object (and thus is particularly susceptible to damage). As an example, for a vertical wall of the built-up material in the test object a constant planar location (e.g., scan position and sense element channel) could be used and selected manually. As another example, the intended build pattern could be searched for corners in the built-up material and one or more of these corners could be selected for the inspection process automatically by a software module. In some embodiments, the received feature selection is one or more sensor array responses associated with a processing condition of interest, such as the presence of porosity or voids. It should be appreciated that process 500 is not limited to receiving a single feature selection, and in other embodiments, process 500 may receive multiple feature selections such as, for example, all comers and edges as well as the volume within the build-up material. Furthermore, the features of interest can be divided into regions that are more likely to form defects than other regions and regions that are more critical to component performance if defects are formed. Given the ranking of regions based on severity criteria that includes both likelihood to form defects and criticality of locations, in one implementation the resolution of the pipes would be adjusted higher in more critical or more likely defect areas. In one such embodiment, only these higher ranked areas are processed in real-time to enable adjustment of the build process to repair detected defects. This full set of inspection data is appropriate for a post-fabrication analysis and part qualification. The starting layer for the analysis is also selected. This starting layer may not be the first layer of the set of inspection data. For example, often the first layers (e.g., up to approximately 80) are used structurally for holding the part onto the build plate and are machined off of the part after the additive manufacturing fabrication process has been completed. In that case, the starting layer would be layer 80.

[0120] At step 503, after receiving the feature selection and a full set of inspection data for the build, the subset of inspection data associated with the starting layer and the next layer are selected for analysis. Analysis could be performed on the data for the starting layer itself, such as detection of material edges and shape filtering as described below. However, for analysis focused on variations in the direction of the build, inspection data for more than one layer is required.

[0121] At step 505, the spatial position, such as the x and y coordinates relative to the build plate region, of the selected feature within the layer data is located. The selected feature may be located in any suitable way. In some embodiments, the location is predetermined within the vertical built-up material walls. This could involve choosing the sense element or channel for the sensor array as well as the array scan position and then determining the subset of data within the area around the feature. In some embodiments, information from the build process parameters are used to determine the intended location of the feature and then choosing the sense element and scan position accordingly. In some other embodiments, an expected pattern in the layer data is used to search the layer data for a matching pattern and thereby determine the appropriate sense element and scan position for the feature. For example, a known spatial variation in the data response in the vicinity of a corner could be the basis of the search. In general, the sense element and scan positions for the feature changes with each layer. This spatial direction of the feature with successive build layers is arbitrary because it follows the feature itself and, in contrast, the layer direction is only in the z-direction.

[0122] At step 507, inspection data for the layer and around the feature is assessed to determine the condition of the build material. Preferably inspection data from more than one layer is used in the analysis so that standard signal processing techniques can be applied. The analysis can take a variety of forms, such as a simple difference between the current sets and one or more prior sets, a difference between several of the recent sets and prior sets, or a shape matching filter to determine if the response variation with set number matches a reference shape, such as a void or a crack. Again, for a simple LPBF example, consider a fixed location in the sense element and scan direction. An average of the subset data response in this region can be performed, such as the average over a scan position within + / - 2.5 mm of the feature center position for the sense element of interest. The response could be the raw data response, such as the real or imaginary parts of the complex sensor response, the magnitude or phase of the complex sensor response, or a processed parameter value such as the effective conductivity or lift-off. Assuming the condition being analyzed is the effective conductivity, if a reduction in conductivity is observed with the recent set it may indicate the presence of a void or increased levels of porosity. Note that this analysis could also include an initial step of determining edges in the material being built up.

[0123] At step 509, an evaluation decision is made. If the variation in the inspection data for the current layer suggests an indication of interest, process 500 proceeds to step 510. If there is no indication of interest, process 500 proceeds to step 511.

[0124] At step 510, based on the finding of an indication of interest, the indication may be flagged for further evaluation. This evaluation is to determine if the indication is relevant, such as a defect or flaw, or non-relevant so that the disposition for the part, such as accept as-is, rework, repair, or dispose, can be determined. The volume coordinates (e.g., layer number and x-y position) should be recorded. If the indication indicates a negative disposition and disposal of the part, the process 500 can be halted. Otherwise process 500 proceeds to step 511.

[0125] At step 511, an evaluation decision is made. If the full set of build data contains additional layers, process 500 proceeds to step 513. If all of the layers in the full set of build data have been processed, process 500 proceeds to step 515. In step 513 the next layer for the analysis is selected. Step 515 provides an opportunity for additional analysis. For example, each of the indications in isolation may be acceptable but if there is a collection of indications in close proximity to each other the part quality may not be acceptable.

[0126] Whereas FIG. 5 showed a flow chart appropriate for a post-fabrication inspection analysis, FIG. 6 shows a flow chart illustrating an inspection process 600 for detecting indications of interest during an additive manufacturing (AM) process, according to some embodiments. Process 600 may be implemented using system 100 or in any suitable way. Process 600 is generally described in connection with data collected during a LPBF fabrication process where the test object 130 (FIG. 1) is a component being fabricated. Though, process 600 may be applied to any suitable application.

[0127] Inspection process 600 begins at step 601 with receiving a selection of a feature of the test object for analysis and a full set of inspection data for each of the relevant layers of the build process. As with step 501 of FIG. 5, in step 601 the selection of the feature could be provided through human input, or via a software module that automatically identifies the feature using a predefined requirement. At step 603, after receiving the feature selection, inspection data is acquired from the sensor (e.g., sensor 120) for a build layer of the fabrication process. Sensor 120 may be mounted, for example, to the recoater or spreader of an LPBF machine. Data may thus be collected by measuring the sense element responses of the sensor array as the recoater is moving across the build region.

[0128] At step 605, process 600 locates the selected feature within the inspection data collected at step 603 for the current build layer. The selected feature may be located in any suitable way. As with step 505 of FIG. 5, in step 605 the spatial position, such as the x and y coordinates relative to the build plate region, of the selected feature within the layer data is located.

[0129] In some embodiments, in order to improve consistency of the responses across the sensor array a recalibration step can be included. This recalibration involves adjusting the response of each sense element to a known set of conditions for the scan. These known conditions could come from the response to an insulating material such as air or even a powder containing region if the sensor array is not sensitive to the powder properties, or a uniform material layer such as the base of the build chamber. In some embodiments, an independent temperature measurement is used as a known condition. In some embodiments, the recalibration involves calculating an offset and scale factor to adjust the response to an expected value for the reference condition.

[0130] At step 607, inspection data for the layer and around the feature is assessed to determine the condition of the build material. In general, the sense element and scan positions for the feature changes with each layer. This spatial direction of the feature with successive build layers is arbitrary because it follows the feature itself and, in contrast, the layer direction is only in the z-direction. In one embodiment the data being analyzed contains several series of values including an inspection value associated with the feature in a given layer, such as the average effective conductivity, as well as the x and y positions of the feature for each layer. The index for these series is the layer number. Standard signal processing methods can be applied to the series of inspection values, such as analysis methods described previously in reference to step 507 of FIG. 5 or the shape filtering approach described in FIG. 7.

[0131] At step 609, an evaluation decision is made. If the variation in the recent set or sets of data suggest an indication of interest, process 600 proceeds to step 611. If there is no indication of interest, process 600 proceeds to step 613.

[0132] At step 611, based on the finding of an indication of interest, the build process is adjusted. In some embodiments, the indication of interest is fatal to the build process and process 600 may terminate the build process (and process 600) accordingly. In some embodiments, the build process is adjusted to repair the site of the indication of interest. For example, if the indication of interest is an indication of excess porosity, in fabrication of the following layer the laser in an LPBF build process may provide extra power and heat to the affected area. With the build process adjusted (and assuming it is not terminated), process 600 proceeds to step 613. It is noted that successful adjustments to the build process may still result in subsequent negative determinations at step 609 regarding an indication of interest with respect to that particular feature location. Further evaluation can determine if the indication is relevant or non-relevant.

[0133] In some embodiments of process 600, steps 605, 607, 609, 613, and 615 are adapted to complete prior to further material processing (e.g., laser sintering) without slowing the build process. In some embodiments, steps 605, 607, 609, 613, and 615 are adapted for relatively improved performance, or lower processing cost, such that the build process is slowed to allow these data processing steps. In some embodiments, steps 605, 607, 609, 613, and 615 are implemented on a computer that supports multithreading. Threading may be used to process data (e.g., while the recoater is returning to a rest position). In some embodiments, data processing is run on a computer with a user terminal in a separate thread or as an asynchronous task such that the user interface for the computer terminal remains responsive to user inputs while the layer is being processed. In some embodiments, multiple threads may be used to run various aspects of the data processing simultaneously. For example, one processing thread may work on grid lookup, while another runs shape filter algorithms on raw data, while still another runs the raw data through artificial intelligence or machine learning (AI / ML) algorithms, such as the shape filter described in reference to FIG. 7.

[0134] In some embodiments, data acquisition in step 603 may be run on its own processor thread. In some embodiments, the data processing steps 605 and 607 may begin in parallel with step 603 for measurements that have already been acquired while the sensor is still scanning.

[0135] At step 613, based on a finding of no indication of interest or if build process condition modification requires reheating of a material layer, process 600 determines if the build process is complete. If the build process is complete, process 600 continues to step 615. If the build process is not complete, process 600 returns to step 603.

[0136] In some embodiments, at step 615, once the build process is complete, post-build- analysis may be performed. Assuming that only a limited number of features were selected in step 601 in order to provide real-time or near real-time feedback as part of the fabrication process, this post-build-analysis could provide for a more thorough analysis, i.e., of many more features throughout the volume of the part, since the time for analysis would not be restricted by the fabrication process. This post-build analysis could follow the approach of process 500 in

[0137] FIG. 5.

[0138] In some embodiments of process 600, for a LPBF fabrication process, the data acquisition step 603 combines the use of an eddy current sensor array with a drive conductor and at least two sensing elements, a drive current frequency that produces a depth of penetration in a metal processed AM part that is, for example, less than 10 times deeper than the vertical extent of the indication of interest, which may be a defect in the part. This shallow depth of penetration is needed to provide sensitivity to the presence of the indication. Repeat scans with resolution of less than two times the vertical extent of the indication of interest is also needed. This can be for every processed layer (i.e., each time a new layer of powder is sintered into a metal) or it can be for every two or every five layers depending on the requirements of the part. As part of the data acquisition step 603 it may be necessary to spatially register the scans between the sets to account for drift or lag of position encoder information. This can be accomplished by choosing a reference feature, such as the edge of the build plate or a known feature of the build material, and adjusting the position information to ensure consistency between the scans. Furthermore, the filtering analysis at step 607 that processes inspection data from at least two passes of the sensing array (as the AM process builds the part, e.g., after two sequential layers) to enable detection of the indication of interest. This same method can also be used to estimate the vertical extent of the indication.

[0139] In some embodiments of step 515, multithreading and parallel processing are used to divide data processing tasks across multiple CPU cores. Depending on the relative size of the data sets (which can include both the full set of inspection data as well as subsets of the inspection data for a limited number of layers being used for the analysis of a given layer) and the inspection data for the processing to be done, the division of data into separate processing threads varies. For example, to estimate property values for numerous layers at a time that contain a similar number of measurements, thread pools may be utilized to process numerous data sets (layers) simultaneously, speeding up processing. For shape filters and z-directed filtering, threads may be used to process multiple channels or frequencies simultaneously, in contrast to full data sets. In cases where the number of measurements per set varies widely (maybe certain sections of the build require different scan speeds), it may not be the most efficient to process multiple sets at a time. In this case, it may be better to group data by a similar number of measurements and process these simultaneously. Algorithms are developed to assign multithreaded tasks in a way that is most efficient for a given scenario. As mentioned above, the spatial resolution in the build direction is determined by the powder layer height and the processed layer height (note the processed layer height may be less than the powder layer height due to the sintering process) where the sensor is scanned across the powder bed each time a powder layer is processed by the power source (which may be a laser for sintering to produce a solid metal layer from the powder layer, an electron beam or other means). For example, a powder layer and resulting metal layer height of 10 microns, would enable detection of indications with vertical extent of more than 20 microns using this invention (since at least two passes or scans are required to cross the indication along the z direction). In one embodiment at least three scans of the sensor are used and the differences between the scans provide the presence of an indication. In this embodiment the smallest indication detectable has a vertical extent of at least three layer heights.

[0140] In another embodiment a physics model is used to estimate the liftoff and one or more additional properties such as conductivity. In this embodiment, the additional property, such as conductivity, at a location in the x-y plane is estimated at a single frequency and subtracted from the prior scan and compared to the difference between the prior scan conductivity and the scan prior to the prior scan as a means to detect an indication with vertical extent of only two layers. For indications with vertical extent of more than three layers, one embodiment processes data from the most recent scan and at least one scan at a z-direction distance greater than the vertical extent of the indication. This enables comparison to a baseline conductivity for the metal to enhance the signal-to-noise for the indication detection. In one such embodiment a more sophisticated filter is constructed from two or more scans that cross a suspected indication and two or more scans that cross a region below the suspected indication in the z direction. In one such embodiment the average response at an x-y position (averaged over a predefined x-y planar area) for at least two scans or layers at different z direction positions is compared to a similar average of responses offset in the z direction. In one such embodiment one of these averages is for the most recent scan and the prior scan. In one such embodiment a shape filter is used in the z direction using 5 or more responses at the same approximate x-y position. In one such embodiment the x-y position average is adjusted using a priori knowledge of the build progression to follow a geometric feature such that the geometric feature remains essentially constant in shape at a fixed distance from the x-y position, as the part is built in the x-y plane for each layer at the x-y location. The x-y position for the z-directed filtering, in one embodiment, is adjusted so that the x-y position is the same distance from an edge or other geometric feature such as a corner or hole. The z-directed shape filtering enhances indication responses and suppresses other inconsequential responses, such as gradual property changes. The impact of the geometric feature on the response is removed by baseline subtraction or some other means, by using a prior layer response for the x-y area at each x-y position, when the distance from said geometric feature is adjusted to remain the same.

[0141] In one such embodiment the response of the sensor is recalibrated at least one additional time after the initial calibration performed prior to the start of the build. In one such embodiment this recalibration is accomplished for every scan at each z position. In one such embodiment the recalibration uses data taken over air as the array is scanned across a region in the build plate or adjacent to the build plate (i.e., in the frame area) that does not have any metal or electrically conducting material. In another such embodiment an area adjacent to the build area that has a relatively constant conductivity is used to perform a reference calibration (see ASTM E2338). In one such embodiment, both an air and reference calibration similar to that described in ASTM E2338 are used to calibrate the sensor array data for each sensing element. In one such embodiment only the recalibrated impedance data is used to process data for detection of local indications, measurement of geometric features, or determination of material properties.

[0142] In one such embodiment a stored library of signatures is compared to the response for at least three scans where the response comprised of measured sensor data for more than three scans that are spatially registered in the x-y plane, and measurements are made at a frequency that is high enough that the depth of penetration is less than 10 times the vertical extent of the smallest indication of interest and the processed layer thickness is less than 25 percent of the vertical extent of the indication of interest to enable at least three sensor passes to cross the indication.

[0143] In one embodiment a measure of a property away from the indication location in the x-y plane is used to normalize the indication location response for each scan of the sensor to enable comparison of repeated scans and to build the z-direction response. In one such embodiment, normalization is accomplished both in the z-direction using a scan below the indication being detected, as well as normalization in the x-y plane. Normalization within the x-y plane can be performed by dividing all data in the scan direction (x direction) by the average of data in the scan direction (x direction) over a defined normalization range. Alternatively x-y plane normalization can be performed by subtracting all data in the scan direction (x direction) by the average of data in the scan direction (x direction) over a defined normalization range. Normalization in the z direction is also performed in one embodiment where z-direction normalization is defined as either division at each position in a scan by data at the same position in another scan, or by subtraction at each position in a scan from data at the same position in another scan. In each of these normalization examples an auto-normalization methodology is described that selects the normalization range based on a predetermined set of criteria. This enables the normalization range in the scan direction or the z-direction normalization range to vary in the y direction or other direction to achieve a consistent normalization result. The objective of the normalization range selection is to identify ranges with approximately constant responses or properties.

[0144] In one embodiment the angle of a metal part edge in the z-direction is estimated based on at least two sensor responses at two different z-direction heights corresponding to two different layers or times. In one such embodiment, this edge location is then used to register responses and detect indications at a fixed distance from the edge by comparing at least two responses at different z-direction heights. In another embodiment, multiple scans are used to estimate the roughness of a metal part edge that is not in the x-y plane, but has a significant component in the z-direction. In one such embodiment this is done for a vertical oriented edge (i.e. the edge is perpendicular to the x-y plane). In another such embodiment the edge is at an angle relative to the x-y plane that is not 90 degrees and both this angle and the roughness of the part surface along this edge are estimated from a series of scans.

[0145] In another embodiment the responses at two or more z-direction heights for two or more sensing elements are used to estimate the edge orientation for a metal part in the x-y plane. In one such embodiment this edge position is then used with a physics based model response to estimate the wall thickness for the metal part in the x-y plane. In one such embodiment the response at multiple z-direction heights is then compared to a signature library to search for defect-like responses and detect defects with z-direction extent of more than three layer thicknesses. In another such embodiment the multiple z-directed scans are used to estimate quantitative features of the part geometry. In one such embodiment these features include wall thickness, edge location, radius of curvature at a corner, or other such dimensions.

[0146] In one embodiment the resolution of data is improved by taking additional scans shifted by half a sensing element width in the y direction (direction perpendicular to the scan direction in the x- y plane. In one such embodiment every other scan is shifted by half a sensing element width so that the effective image resolution is doubled when the depth of penetration is significantly more than one layer height. This includes coordination for triggering of the sensor data acquisition.

[0147] In each embodiment that requires x-y position information, it is necessary to acquire this information during each scan, including the start and stop positions and times. In one embodiment installation is on a vendor machine without access to this data from the operating software. In this embodiment another means such as adding sensors or switches or observing position optically may be required. In another embodiment access to the operating software and internal motor control control data is available and used to determine position accurately at all times for the array.

[0148] In another invention, an apparatus and method is disclosed for monitoring the metallurgical conditions, such as heat treatment, of a metal AM part by imaging the conductivity for each individual processed layer, and using an independent temperature measurement to correct the conductivity measurement to estimate a metallurgical condition. In one such embodiment the depth of penetration for the eddy current conductivity measurement is greater than the thickness of the most recent processed layer and the estimate of the conductivity of the most recent processed layer is normalized by the conductivity estimates for the material more than two depths of penetration below the most recent processed layer. In one such embodiment, signature libraries of spatial responses are used to improve the estimate of conductivity for a thin walled part. In another such embodiment the monitoring data is used to alter the processing to improve the metallurgical properties. In each of the above embodiments the metallurgical property may be a measure of heat treat quality. In one such embodiment multiple frequencies are used to verify not only the properties of the most recent layer, but also the properties of prior layers. In each of the above embodiments, the data is stored at high resolution in the z-direction and responses from at least two passes of the sensor at a vertical separation less than one depth of penetration for the applied frequency are combined to estimate the metallurgical condition of the near surface region and build a stored data set that represents the AM part properties in three dimensions.

[0149] In one embodiment, the innovation includes using a flexible cable to connect all channels of a sensor array, such as an MWM-Array, to an instrument that provides a current to at least one drive conductor and measures a response for at least two sensing elements in the array. In one such invention all electronics are located outside the processing chamber, with only the flex cable and the MWM-Array in the chamber; the flex cable being at least 3 feet long. In another such embodiment a means is added to measure the drive current close to the sensor array with a lead going back to the electronics to record the drive current and a means for adjusting the drive current to a prescribed value. In one such embodiment, additional electronics are added near the sensor and protected from the processing environment to enhance the drive current measurement capability. In another such embodiment, additional electronics are added near the sensor array to enable operation at frequencies above 2 MHz with the long flex cable. Note that this is needed because of the long flex cable behavior and the challenges of operating at high frequencies with such long cables.

[0150] As shown in FIG. 4A, preferred direction “pipes” are constructed in a direction that is at an angle from the z build direction at a fixed distance from a geometric feature such that the eddy current pattern along the preferred filtering direction (or pipe) is essentially the same, where in-situ sensing data from at least two build layers is used to determine the presence of a defect.

[0151] The feature may be an edge, a corner, a hole, or any other complex feature where a pipe can be created from data in multiple build layers where along this pipe, at a fixed distance from the feature, the eddy current pattern without a defect would be essentially the same.

[0152] In some embodiments, the feature geometry in the plane of the build does not change rapidly in the build direction. For example, a gradual curvature of the edge in FIG. 4A, in the build direction is allowed as long as the rate of change of the feature position in the x-y plane is very small compared to the build layer thickness. In some embodiments, it could be a curved edge of the component in FIG. 4A where the pipe would follow the edge at a fixed distance.

[0153] Regarding FIG. 4A, the pipe may be used to fill the space of the part being built. Smaller pipes would be used in regions with complex geometry changes and larger pipes would be used in regions away from geometric features such as edges. This is similar to building a finite element mesh, but the pipes must be overlapping to fill the space. Note that the cross section of the pipes may be square, rectangular, trapezoidal, parallelogram, or any convenient cross section to allow overlap and filling of the space. The length of the pipes in the build direction will be longer in regions with gradual changes in the build direction, and shorter in regions with more rapidly changing features in the build direction. In one such embodiment, the length of the pipes in the build direction are adjusted to conveniently span a space while limiting the length of the pipe to cover regions for each pipe with consistent eddy current patterns. For geometric features that are rapidly changing in the z-direction, the preferred filtering direction may be a curved pipe that provides the highest correlation of the eddy current patterns in the build direction. It is understood that for these features the intelligent filtering along the pipe will be less capable than for pipes with more slowly varying eddy currents. For example, the probability of detecting a defect of a certain size may be lower than along a more gradually varying feature. For such features, one preferred embodiment is to compare the build data to nominal data for a prior build for another essentially identical feature in the same build to improve defect detection capability. For example, there may be a small number of fatigue critical areas with geometries that change rapidly in the build direction. For these areas additional computation may be necessary through comparison with similar build features from prior builds or from the same build. This additional computation may be limited to the features that cannot use sufficiently long pipes over which the eddy current pattern is essentially the same.

[0154] In some embodiments a method for building the pipe distribution in a part using a mathematical function that selects pipe features (length, cross sectional area, shape, and position) is used to minimize the eddy current pattern variation along the pipe where a second mathematical function is used to ensure that the pipes fill the space of the part in a manner that minimizes the computation time while enabling the intelligent filters to provide a target signal to noise for a select defect size.

[0155] As shown in the example data of Section 4, normalizing unprocessed and / or processed data in the z-direction can improve the visualization of indications and defects. In one embodiment, the normalization is done on conductivity data that is compensated for lift off. In another embodiment, the normalization is done on raw impedance data, but in a path where the eddy current pattern is not varying substantially (meaning less than 5% of the variation caused by edges or other geometric features). This preferred path is defined as a path along which the eddy current pattern is not varying and is not necessarily vertical. In some embodiments, the path follows an edge of the metal part being built in the build progression. In some embodiments the data is averaged in the x-y plane over an area that is smaller when you are near a complex feature and larger when you are away from complex features to minimize the amount of computation necessary to process data from an entire volume of a part being built.

[0156] Use of two or more channels of data to locate an edge in the x-y plane and track vertical or non-vertical edges in the build direction (not necessarily the z-direction; following a contour that may not be straight; the contour defines the transition from processed metal (e.g., sintered, e-beam) to unprocessed material (powder, air), where the conductivity of the processed metal is typical of a metal (e.g., nickel alloy ~1%IACS) and the conductivity of the unprocessed material at the applied frequency is essentially that of air when measured by an eddy current sensor, where the contour is not necessarily straight or vertical). In some embodiments, the edge location in the x-y plane is plotted for each process layer along a contour and compared to the results of a smoothing function along that contour performed on the same data (e.g., a low pass filter output with outlier rejection), in order to estimate the surface roughness along that contour. In some embodiments, the response from two or more eddy current array sensing elements is used to estimate the location of the edge of the metal and nonmetal in the x-y plane. In some embodiments, an algorithm uses a-priori knowledge of the surface orientation in the x-y plane that includes that contour where the surface is the surface of the part being built. Other uses of a- priori knowledge are obvious for identifying and estimating the nominal contour for comparison to the printed contour of the actual part for the purpose of estimating surface roughness.

[0157] In some embodiments, the printed contour is compared to the planned contour for the purpose of estimating geometric accuracy. Multiple contours may be used to construct a feature of a part such as a corner or a radius in the x-y plane to estimate the accuracy of the geometric feature. In another embodiment, a wall thickness is estimated using sense elements that are less than 50% larger than the wall thickness. In a preferred embodiment the sensing element width is less than the smallest wall thickness that must be measured. One pair of sensing elements may be used to estimate the location of one edge of a thin wall in the x-y plane and a different pair of sensing elements is used to estimate the location of the second edge of a thin wall and these two edge locations are subtracted to estimate the wall thickness. A contour in the x-y plane may be built from multiple edge locations to form one edge of a thin wall and a second contour may be built in the x-y plane to form the opposite edge of the same thin wall so that the thickness along that contour can be estimated. The minimum estimated thickness along the wall may be compared to a minimum allowed thickness for the purpose of part qualification.

[0158] A method for characterizing a layer of powder on top of a metal where an ET array is used at a frequency high enough that the skin depth is less than 3 times the average powder particle diameter. Powders of different materials may be used to build multi-material constructs, and the ET array can be used to confirm proper location of the powders prior to sintering. In some embodiments, powder clumps can be detected by differentiating the frequency response of clumps from properly laid powder. The inspection at a lower frequency through the powder may be used to determine if a detected clump resulted in an indication or defect in the next processed metal layer. The consistency of the powder layer may be estimated for nonferrous powders. In another embodiment, the powder layer is characterized for ferrous powders.

[0159] A method for detecting anomalies such as powder clumps and correlating the occurrence of such anomalies with anomalies in the sintered metal. One such embodiment where these anomalies in the sintered metal are verified through an alternative means such as metallography. One such embodiment where these occurrences are used to train an AI / ML model or other intelligent filtering algorithm. An embodiment is where anomalies are intentionally created as part of the build process. A method of calibrating in air where the calibration is performed over the powder in either the reservoir or the powder bed. This can be accomplished by selecting an excitation frequency for the eddy current instrument where the response to the powder is substantially smaller than the response to the sintered metal. Substantially smaller can be taken as 0.1% of the response is associated with the powder. This is quantifiable by determining the error caused by calibrating in air versus calibrating over the powder. This error in the desired measurement should be less than 5% or other required accuracy. In one embodiment this error is in a conductivity estimate. In another, this error is in response to a defect of interest. In one embodiment the entire sensor array is over only powder at one scan location for all sensing elements. In another embodiment the sensor may not have access to a region of unprocessed powder for all of the sense elements of the array. For this situation, the calibration information can be used from several regions where the sense elements are over unprocessed powder in separate regions and the measurement information from these separate regions are assembled to create a set of calibration information for the sensor array. This information may be assembled from different layer scans as well.

[0160] FIG. 7 is a flow diagram of a method 700 used in some embodiments for applying shape filtering methods to the variation of scan data in the build direction during a LPBF process, i.e., z-directed filtering. Although method 700 may be implemented in any suitable way, in some embodiments method 700 is implemented as a module, such as modules 109 in FIG. 1.

[0161] At step 701 scan data is collected for the newly deposited layer during a LPBF process. In some embodiments the scan data may be the real and imaginary parts of the transimpedance measurements obtained from the sensor. In other embodiments, the scan data may be material properties such as electrical conductivity or sensor liftoff. For example, impedance measurements may first be obtained from the sensor and then processed in a suitable way to provide material property estimates. In some embodiments a multivariate inverse method is used in combination with a database of precomputed sensor responses to estimate material properties. In some embodiments the scan data is interpolated onto a grid of x and y positions with uniform spacing.

[0162] At step 703, if this layer was the first layer deposited and scanned then the method returns to step 701 once a new layer has been deposited. Otherwise the method advances to step 705.

[0163] At step 705 the scan data for the current layer is processed in a suitable way so as to bring the data into spatial alignment with previous layers. In some embodiments the recorded scan-direction (i.e., x-direction) positions of the most recent scan data are adjusted to accomplish the alignment. In some embodiments the alignment is performed in both the scan direction (x- direction) and the direction transverse to the scan direction (y-direction). In some embodiments the alignment might be performed relative to a feature of the build plate (e.g., an edge) that is included in the scan path of the sensor. In this case a single global alignment may be performed relative to the scan data from a fixed layer (e.g., the first layer) since features of the build plate are stationary. In other embodiments separate alignments might be performed relative to particular features of the component that is being built (e.g., corners). In this case, once the positions of the features have been found the scan area can be divided into a fixed number of cells corresponding to the cross sections of the fixed number of z-directed pipes which will be used in subsequent data processing steps. In this case the alignment may be performed relative to the scan data from the immediately preceding layer.

[0164] In some embodiments data for a single selected property (e.g., electrical conductivity) is used to perform the alignment. In some embodiments the position values of the scan data are adjusted to make the location of a minimum of the selected property, within a specified position search window, coincide with the position of the corresponding minimum in the scan data for the reference layer. In some embodiments a local maximum of the selected property is used in performing the alignment. In other embodiments the position where the selected property value first crosses a threshold value, within a specified position search window, is used in performing the alignment. In other embodiments shape filtering may be used (as described in Goldfine II in some embodiments) to identify the location of the feature to be used as the alignment reference.

[0165] At step 707, if a sufficient number of layers have been scanned for further processing to be performed, the method advances to step 709. Otherwise the method returns to step 701 once a new layer has been deposited. The number of layers required for further processing depends upon the particular way the method will perform scan data averaging and image subtraction at steps 709 and 711. Different embodiments will perform the processing at these steps in different ways as described below.

[0166] At step 709 averaging is performed. In some embodiments this is done solely for the purpose of smoothing the data. In other embodiments, such as when implementing a “pipes” approach, the averaging is done separately over each of the cells determined at step 705 so that there is a single property value for the current layer corresponding to each pipe.

[0167] In some embodiments the averaging is limited to the data from the current layer, applying a moving average in the x and y directions. In other embodiments the moving average is also performed in the z direction. In this case a sufficient number of layers will need to have been scanned before the averaging can be performed for the current layer.

[0168] At step 711 an image difference is calculated. The averaged property values from a scan of a previous layer are subtracted from the averaged property values from the current layer. In some embodiments this difference is performed at all corresponding x and y positions. In other embodiments this difference is performed for the corresponding cells of each pipe. In some embodiments the layer subtracted is the immediately preceding layer. In other embodiments the method skips over one or more previous layers to select the layer to be subtracted. The output of the image difference calculation is stored and used as input to the subsequent z-directed shape filtering described below. In some embodiments no image subtraction is performed and the subsequent z-directed filtering is performed directly on the averaged property values.

[0169] At step 713, if a sufficient number of layers have been scanned for z-directed shape filtering to be performed, the method advances to step 715. Otherwise the method returns to step 701 once a new layer has been deposited. The number of layers required for the shape-filter processing depends upon the sizes of the signatures in the signature library to be used by the shape filter.

[0170] At step 715, z-directed shape filtering is performed to find indications corresponding to flaws for which there are signatures in the signature library. In some embodiments shape filtering may be implemented using the method described in Goldfine II, though any suitable shape filtering methodology may be used. In some embodiments shape filtering is performed separately at each x-y position in the plane of the current layer. A vector of scalar values is constructed at each x-y position by taking the corresponding output value from step 711 for the current layer and a sufficient number of preceding layers. In other embodiments shape filtering is performed separately for each pipe. A vector of scalar values is constructed for each pipe by taking the output value from step 711 in that pipe for the current layer and a sufficient number of preceding layers. In all such embodiments each vector of scalar values is then input into the shape filtering algorithm.

[0171] At step 717, if the build is complete then the algorithm ends. Otherwise the method returns to step 701 once a new layer has been deposited.

[0172] Section 3: Example installation in LPBF machine

[0173] The in-situ sensing system electronics may be mounted in, on, or in close proximity to the AM machine. For LPBF machines with recoaters, the sensor may be mounted to the recoater and scan over the build at each layer as the recoater passes over the build plate. In some embodiments, as shown in FIGs. 8A-8E, the system electronics 810 (e.g., instrument 110 of FIG. 1) as well as the sensor 820 (e.g., sensor 120 of FIG. 1) are mounted to the recoater 803 in LPBF system 800. FIG. 8 A shows a perspective view of the LPBF system 800 where build plate 801 is also visible.

[0174] In some embodiments, as detailed in FIG. 8B, system electronics 810 consist of modular stacking units 811 and 813 to scale the number of sensing elements that can be supported. Sensor electronics 810 are shown securely mounted to recoater 803 using a bracket 817. The instrument electronics may include a connector 815 to provide power and pass data to a computer for real-time processing (e.g., inside or outside of the build chamber of LPFB system 800. Electronics 810 may have coverings 812 to protect from exposure to molten metal, sparks, or other hot debris produced in the AM process. FIG. 8C shows that system electronics 810 may include cooling devices 814 such as fans, ducting, heat pipes, and the like to provide sufficient cooling for the system.

[0175] As shown in FIG. 8D, LPBF system 800 may be fitted with a sensor mounting bracket 821, sensor mounting block 823, and sensor 820. Sensor mounting bracket 821 and mounting block 823 may include fine adjustments to set the distance from sensor 820 to the last build layer and correct for minor tilt. System electronics 810 include a connector 816 for attaching sensor 820. A cable or flexible printed circuit 830 carries signals between system electronics 810 and sensor 820. As shown in FIG. 8E, sensor 820 captures data of the parts 805 being fabricated on build plate 801 by LPBF system 800 each time recoater 803 passes over the build plate area.

[0176] It should be appreciated that while the system electronics 810 were attached to recoater 803 in some of the embodiments discussed in connection with FIGs. 8A-8E, other embodiments are also contemplated where, for example, the system electronics are at a fixed location within or outside the build chamber and the cable 830 is adapted to connect sensor 820 and system electronics 810. In another embodiment, system elements 810 are mounted within or outside the build chamber on a mobile platform (e.g., wheels, rails) that moves along with the recoater such that cable 830 is substantially unflexed as the sensor 820 and system electronics 810 move in tandem.

[0177] Section 4: Example layer-by-layer inspection results from LPBF machine

[0178] Aspects of this invention are illustrated through an example LPBF fabrication that was monitored with an eddy current sensor array mounted to a spreader. Several test specimens and a complex shaped integrally bladed rotor were fabricated. Several of the test specimens intentionally had voids of varying dimension and orientation within the built-up material. FIG. 9A shows a cross-sectional view of a test specimen 900 fabricated using the LPBF process. The material was Inconel 718 and the build process included 249 layers. Dashed lines 903 and 905 represent the location of layers 150 and 200, respectively in test specimen 900. This specimen had dimensions of 62 mm x 4.6 mm x 10 mm with a series of intended voids built into the specimen. This particular specimen had horizontal voids with each void being 3 mm in length with a 7 mm spacing. The void diameters were 0.04, 0.08, 0.16, 0.40, and 0.80 mm. The approximate levels in the specimen for two of the layers, which correspond to the scan number as well, are indicated. The thickness of each built-up layer was 0.04 mm.

[0179] For this example, the sensor array had 39 sense elements or channels, with each sense element being 1.5 mm wide (in the channel direction) and 1.0 mm long (in the scan direction). Excitation frequencies of 655 kHz, 1.31 MHz, 5.24 MHz were used; the powder diameter was small (by at least a factor of 10) compared to the skin depth so that substantial eddy currents were not induced in the powder particles at these frequencies and the response of the sensor array to the powder was essentially the same as the response to air itself. The linear motion rate for the spreader was approximately 0.1 m / s and the eddy current data acquisition rate yielded a data spacing of approximately 0.1 mm / s. For the calibration, a reference measurement was taken prior to the build being performed with the sensor array over an open region in the base of the build chamber. The response of each sense element was then adjusted to match a model of the predicted response for the sensor array at each frequency. The same model of predicted responses was also used to convert the measurement response for each sense element at each location into an estimate of the effective conductivity of the inspected material and lift-off. Displays of the raw complex impedance data, in the form of the real and imaginary (in-phase and out-of-phase, respectively) parts or in the form of a magnitude and phase, were evaluate along with the data processed into the effective properties. For this example, only the effective conductivity results are provided in the following.

[0180] FIG. 9B shows plot 910 which illustrates example responses for scans across the build region at a single excitation frequency. These responses are for a single sense element that was scanned over the test specimen 900. The effective conductivity response for several representative layers is shown. Scans 160 and 249 are for material layers which did not have voids intentionally present. The scans show the gradually varying along the length of the bar and dropping rapidly at then end of the bar at a position of approximately -16.6 in. The gradual variation in the conductivity along the length is likely caused by temperature variations in the build up material; it is well known that the electrical conductivity of metals typically varies with the temperature. For the intermediate scans for layers from 165 to 179 voids were fabricated into the test specimen. These voids appear, in this case, as a local reduction in the effective conductivity, with a larger void diameter of 0.8 mm present between positions -16.9 and -17.2 in. and a smaller void diameter of 0.4 mm present between positions -17.3 and -17.5 in. Note that the local decrease of the effective conductivity for the larger void visibly increases with the number of layers containing the void; this is consistent with the eddy current response being sensitive to the depth of the surface-breaking void. Similar results were obtained with the other excitation frequencies.

[0181] FIG. 9C shows plot 920 which illustrates an example response for an arbitrary direction plot. In this case the selected region of interest spanned the largest voids in test specimen 900 for a single sense element channel. The average response over this region was obtained for each layer or scan for each excitation frequency. The direction of this plot is illustrated as 901 of FIG. 9A. Solid material for each layer was present for layers 150 to 160 and for layers 180 to 200. This particular void was formed between layers 161 and 179. Since this void was a horizontal cylinder in shape, the width of the void within each built-up layer varied with the largest width at layer 170 and the smallest widths at layers 161 and 179. However, the eddy current response shows a reduction in effective conductivity. The reduction becomes larger as the void becomes deeper and the volume of the void becomes larger (i.e., with increasing layer number starting from layer 161). Starting with layer 180 the current build layer is solid and covers the void. The effective conductivity from the highest excitation frequency, which has the smallest skin depth and sees the shortest distance into the test material, returns to the value of a thick solid material rapidly, within several layers. However, for the lower excitation frequencies, which have a larger skin depth that see farther into the solid test material, the reduction in conductivity associated with the presence of the void remains for 10 to twelve more layers. This is consistent with each layer being relatively thin (on the order of 0.04 mm) and the depth of penetration of the magnetic field into the test material. Note that while optical methods can only provide information about the current or surface layer, the eddy current sensor can provide information about both the surface and subsurface characteristics of the void.

[0182] FIG 9D shows plot 930 which illustrates an example response for an arbitrary direction plot but this example includes several indications. As with FIG. 9C the selected region of interest spanned the largest voids in test specimen 900 for a single sense element channel using the direction 901. The average response over this region was obtained for each layer or scan for each excitation frequency. To account for minor differences in the conductivity estimates between the frequencies, a subset of the data (layers 60 - 80) were used to normalize the responses. The normalization involved obtaining an average response over this region for each frequency, comparing this average to a reference value of 1.1%IACS, determining a correction factor as the difference between the average response and reference response, and then adjusting the response data by subtracting the correction factor. Solid material was built for each layer except those indicated as containing voids. The void containing layers (111-129, 161-179, and 211-229) had a portion of the layer that was not sintered by the laser during the build process. The eddy current response shows a reduction in effective conductivity when the void is present. As in the discussion for FIG. 9C, the lower excitation frequencies continue to show the presence of the void even after it has been covered by several layers of solid material. This example shows that multiple indications may be present along path direction.

[0183] Section 5: Closing Discussion

[0184] Having thus described several aspects of at least one embodiment of this invention, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure and are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description and drawings are by way of example only.

[0185] It should be appreciated that the connections between the hydraulic components shown in the drawings and described with reference to embodiments of control systems, liquid supply systems, conditioning systems, and the like may be achieved by any suitable pipe, hose, tube, conduit, or other mechanism for conveying liquid under pressure. Where such connections have been described as a specific hydraulic conveyance it should be appreciated that other embodiments may use hose, tube, conduit, or any other suitable hydraulic conveyance.

[0186] It should be appreciated that any suitable liquid may be used in embodiments of the systems and method disclosed herein. For example, in some embodiments, the liquid is water, or water containing additives such as glycol to improve certain aspects of performance. Though any suitable liquid may be used.

[0187] It should be appreciated that while some embodiments were described with respect to cooling a conditioned space, the embodiments may be applicable to heating a conditioned space. Those of skill in the art will appreciate that some embodiments may be used for heating without modification or with only minor modifications.

[0188] It should be appreciated that all mechanical and end electrical equipment will have functional limitations. Generally, the ideal behavior has been described so as to not unnecessarily distract from the general operation and description of the embodiments. Those of skill in the art will recognize and appreciate the need to consider both ideal and non-ideal behavior in designing specific embodiments just as with any electrical or mechanical device. It should also be appreciated that the descriptions of components having the same name or same reference number appear in multiple drawings so as to avoid having to describe the common aspects of a component multiple times. It should be clear to those of skill in the art whether such descriptions made with reference to one embodiment are applicable to another embodiment.

[0189] Various aspects of the present invention may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments. The above-described embodiments of the present invention can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.

[0190] Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

[0191] Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.

[0192] Such computers may be interconnected by one or more networks in any suitable form, including as a local area network or a wide area network, such as an enterprise network or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks. Also, the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and / or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

[0193] In this respect, the invention may be embodied as a computer readable medium (or multiple computer readable media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.

[0194] In this respect, it should be appreciated that one implementation of the above-described embodiments comprises at least one computer-readable medium encoded with a computer program (e.g., a plurality of instructions), which, when executed on a processor, performs some or all of the above-discussed functions of these embodiments. As used herein, the term “computer-readable medium” encompasses only a computer-readable medium that can be considered to be a machine or a manufacture (i.e., article of manufacture). A computer-readable medium may be, for example, a tangible medium on which computer-readable information may be encoded or stored, a storage medium on which computer-readable information may be encoded or stored, and / or a non-transitory medium on which computer-readable information may be encoded or stored. Other non-exhaustive examples of computer-readable media include a computer memory (e.g., a ROM, a RAM, a flash memory, or other type of computer memory), a magnetic disc or tape, an optical disc, and / or other types of computer-readable media that can be considered to be a machine or a manufacture.

[0195] The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the present invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.

[0196] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0197] Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

[0198] Also, the invention may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

[0199] For the purposes of describing and defining the present disclosure, it is noted that terms of degree (e.g., “substantially,” “slightly,” “about,” “comparable,” etc.) may be utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. Such terms of degree may also be utilized herein to represent the degree by which a quantitative representation may vary from a stated reference (e.g., about 10% or less) without resulting in a change in the basic function of the subject matter at issue. Unless otherwise stated herein, any numerical values appearing in this specification may be modified by a term of degree thereby reflecting their intrinsic uncertainty.

[0200] Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

[0201] Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including," "comprising," or “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

Claims

CLAIMS1. A method of monitoring a layer-by-layer additive manufacturing (AM) process, the method comprising acts of:(i) during the AM process, scanning and measuring an eddy current sensor array over a scan path, the scan path having a calibration region and a build region where layers are formed by the AM process; and(ii) calibrating the measurements over the build region with the measurements over the calibration region.

2. The method of claim 1, further comprising using in-situ sensing data, as the data is accumulated, begin processing in parallel with the build so that the part condition analysis can be completed within a fraction of the build time after the build is completed.

3. The method of claim 1, wherein the scanning is first scanning, the method further comprising acts of:(ii) second scanning and measuring with the eddy current sensor array over the build region after one or more additional layers are formed by the AM process; and(iii) calibrating the measurements over the build region from the second scanning with the measurements over the calibration region from the first scanning.

4. The method of claim 1, wherein the calibration region comprises a reference material having an electrical conductivity greater than 1.0 %IACS.

5. The method of claim 1, wherein the calibration region comprises a reference material having an electrical conductivity greater than 0.1 %IACS.

6. The method of claim 5, further comprising acts of processing the calibrated measurements to estimate properties of the build region, wherein the properties are normalized with reference to the reference material.

7. The method of claim 5, wherein the calibration is in accordance with ASTM Standard E2338.

8. The method of claim 1, wherein the calibration region is over the powder bed and the frequency of operation is below the frequency where the skin depth is larger than three times the average diameter of the metal powder.

9. The method of claim 1, wherein the calibration region consists of a non-permeable and non- conductive material.

10. The method of claim 1, wherein the calibration region comprises a first region comprising a reference material and a second region consisting of a non-permeable and non-conductive material.

11. The method of claim 10, wherein the act of calibrating comprises calibrating based on measurements from the second regions and verifying calibration using measurements over the first region.

12. The method of claim 10, wherein the reference material is a base portion of a build chamber for the AM process.

13. The method of claim 1, wherein the eddy current sensor array scans over the build region for at least 10% of the layers during AM process.

14. A method comprising: receiving measurement data comprising layer-by-layer measurements taken from an eddy current sensor array during an additive manufacturing (AM) process for a part; and rendering a three dimensional (3D) representation of the received measurement data, wherein the layer-by-layer measurements are spatially registered and offset in a build direction of the AM process.

15. The method of claim 14, further comprising using in-situ sensing data, as the data is accumulated, begin processing in parallel with the build so that the part condition analysis can be completed within a fraction of the build time after the build is completed.

16. The method of claim 14, wherein the offset is equal to the thickness of the process layer.

17. The method of claim 14 wherein the rendering is done without another source of knowledge of a geometry of the part being manufactured by the AM process.

18. The method of claim 14, wherein the AM process results in processed metal and unprocessed material, and the rendering comprises determining edge locations for the 3D representation by locating a transition between the processed metal and non-processed material using at least one ET sensing channel for a layer among the layer-by-layer measurements.

19. The method of claim 18, wherein the rendering comprises defining at least a portion of a surface of the 3D representation from a plurality of layer-by-layer measurements in which each layer edge locations are determined.

20. The method of claim 18, further comprising estimating a wall thickness from a spacing between the edge locations.

21. The method of claim 18, where the location of the edge is determined using at least two sensing elements that are measured simultaneously.

22. The method of claim 14, further comprising filtering the measurement data along a path that extends along a plurality of layers in the layer-by-layer measurements to detect a defect.

23. The method of claim 22, wherein the visual representation of the defect is an ellipsoid whose size represents the dimension of the defect and whose color represents an electrical conductivity of the defect.

24. The method of claim 22, wherein the path is a preferred filtering direction that is determined by minimizing the variation of an eddy current pattern along the path.

25. The method of claim 24, wherein selection of the preferred filtering direction comprises assimilating knowledge from a 3D numerical electromagnetics model combined with knowledge of the build geometry.

26. The method of claim 24, wherein the minimization is performed in advance based on qualitative knowledge of eddy current patterns.

27. The method of claim 26, wherein areas away from edges in regions of uniform material properties the default preferred direction will be in a z direction.

28. The method of claim 27, wherein a filtering pipe is defined by an x-y area and a function of the sensor response is averaged over the x-y area to produce a scalar quantity where the scalar quantity is stored for each build layer along the preferred direction as a vector.

29. The method of claim 28, wherein the vector is limited in the number of layers so that the length of the vector is less than 3 times longer than the length of a signature from a typical defect of a selected type.

30. The method of claim 19, wherein the signature refers to a machine learning algorithm that is trained on multiple defect types and the trained signatures are then used to filter sensor responses to detect defects in a build.

31. The method of claim 19, wherein the selected type of defect is a spherical void.

32. The method of claim 19, wherein the signature is determined from one of the following: measurement data on actual defects and electromagnetic model outputs.

33. The method of claim 24, wherein the minimization is a quantitative algorithm with interactive approval from an operator to iteratively establish full volumetric preferred direction filtering for the 3D printed object.

34. The method of claim 33, wherein the algorithm uses an electromagnetic model of eddy current patterns and a quantitative measure of the variation between patterns relative to geometric features.

35. The method of claim 14 further comprising providing a visual representation of a defect as part of the 3D representation.

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