Systems and methods for measuring radiant heat energy during additive manufacturing operations

Abstract

The present disclosure describes additive manufacturing methods, additive manufacturing systems, methods of operating additive manufacturing systems, and various methods and apparatus for characterizing additive manufacturing processes. A method for characterizing an additive manufacturing process may include: generating a scan of an energy source across a build plane; measuring the amount of energy radiated from the build plane during each scan using an optical sensing system, wherein the optical sensing system monitors two discrete wavelengths associated with a blackbody radiation curve of a powder layer; determining a change in temperature of an area of ​​the build plane traversed by the scan based on a ratio of sensor readings taken at the two discrete wavelengths; determining that the change in temperature exceeds a threshold range; and thereafter, adjusting subsequent scans of the energy source across or in the vicinity of an area of ​​the build plane.

Description

[0001] This application is a divisional application of the Chinese invention patent application with the application date of February 21, 2019, application number 2019800271818, and invention name “System and method for measuring radiant heat energy during additive manufacturing operations”.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to U.S. Provisional Patent Application No. 62 / 743,391 filed on October 9, 2018, U.S. Provisional Patent Application No. 62 / 643,457 filed on March 15, 2018, and U.S. Provisional Patent Application No. 62 / 633,487 filed on February 21, 2018. Background Art

[0004] Additive manufacturing or sequential assembly or construction of parts by adding a combination of material and applying energy has many forms and currently exists in many specific embodiments and examples. Additive manufacturing can be performed by using any of a variety of different processes involving virtually forming three-dimensional parts of any shape. The common point of various processes is to sinter, solidify or melt liquid, powdered or granular raw materials layer by layer using ultraviolet light, high-power lasers or electron beams, respectively. Unfortunately, the established process for determining the quality of the resulting parts manufactured in this way is limited. Conventional quality assurance testing usually involves post-processing measurements of the mechanical, geometric or metallurgical properties of the parts, which usually results in the destruction of the parts. Although destructive testing is a recognized way to verify the quality of parts, such testing cannot be applied to production parts for obvious reasons because it allows careful inspection of various internal features of the parts. Therefore, a way to non-destructively and accurately verify the mechanical, geometric and metallurgical properties of production parts produced by additive manufacturing is needed. Summary of the invention

[0005] The described embodiments relate to additive manufacturing, which involves the use of an energy source in the form of a moving area of ​​intense thermal energy. If the thermal energy causes physical melting of the added material, then these processes are widely known as welding processes. In a welding process, the material that is incrementally and sequentially added is melted by the energy source in a manner similar to fusion welding. Exemplary welding processes that the described embodiments are suitable for use include processes using a scanning energy source with a powder bed and wire-fed processes using arcs, lasers, and electron beams as energy sources.

[0006] When the added material takes the form of powder layers, after each incremental layer of powder material is sequentially added to the part being constructed, a scanning energy source is passed through the welding area of ​​the powder layer to melt the incrementally added powder, thereby forming a moving molten area, hereinafter referred to as a melt pool, so that once solidified, they become part of the previously sequentially added, melted and solidified layer located below the new layer to form the part being constructed. Because the additive machining process can be lengthy and include any number of passes through the melt pool, it is difficult to avoid at least slight variations in the size and temperature of the melt pool when the melt pool is used to solidify the part. The embodiments described herein reduce or minimize discontinuities caused by variations in the size and temperature of the melt pool. It should be noted that due to the high travel rate of the heating element and the complex patterns required to form the three-dimensional structure, the additive manufacturing process can be driven by one or more processors associated with a computer numerical control (CNC).

[0007] The overall purpose of the described embodiments is to apply optical sensing technology, such as quality inference, process control, or both, to additive manufacturing processes. Optical sensors can be used to track the evolution of physical phenomena in a process by tracking the evolution of variables associated with the physical phenomena in the process. As used herein, optical may include a portion of the electromagnetic spectrum, which includes near infrared (IR), visible, and near ultraviolet (UV). Typically, the wavelength of the spectrum is considered to be from 380nm to 780nm. However, the wavelengths of near UV and IR can extend as low as 1nm and as high as 3000nm, respectively. Sensor readings collected from optical sensors can be used to determine in process quality metrics (IPQM). One such IPQM is thermal energy density (TED), which helps characterize the energy applied to different areas of a part.

[0008] TED is a metric that is sensitive to user-defined laser powder bed fusion process parameters (e.g., laser power, laser speed, hatch spacing, etc.). This metric can then be used for analysis using IPQM to compare to a baseline dataset. The resulting IPQM can be calculated for each scan and displayed graphically or in three dimensions using a point-cloud. Additionally, comparison of the IPQM to a baseline dataset indicative of manufacturing defects can be used to generate control signals for process parameters. In some embodiments, where detailed thermal analysis is desired, thermal energy density can be determined for discrete portions of each scan. In some embodiments, thermal energy data from multiple scans can be partitioned into discrete grid regions of a grid, allowing each grid region to reflect the total amount of energy received at each grid region for a layer or a predetermined number of layers.

[0009] An additive manufacturing method is disclosed, comprising the steps of identifying a spectral peak associated with a batch of powder; selecting a first wavelength and a second wavelength spaced apart from the first wavelength, the first wavelength and the second wavelength offset from the identified spectral peak; generating multiple scans of an energy source across a layer of the batch of powder placed on a build plane during an additive manufacturing operation; measuring an amount of energy radiated from the build plane at the first wavelength; measuring an amount of energy radiated from the build plane at the second wavelength; determining a temperature change in an area of ​​the build plane traversed by the multiple scans based on a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength; determining that the temperature change is outside a threshold range; and thereafter, adjusting subsequent scans of the energy source across or near the area of ​​the build plane.

[0010] A method of additive manufacturing is disclosed, comprising the steps of identifying a spectral peak associated with a batch of powder; selecting a first wavelength and a second wavelength spaced apart from the first wavelength, the first wavelength and the second wavelength offset from the identified spectral peak; generating a plurality of scans of a layer of the batch of powder across a build plane with an energy source; generating a sensor reading during each of the plurality of scans using an optical sensing system that monitors the first wavelength and the second wavelength; determining a temperature change across the build plane during the plurality of scans using a ratio of the sensor readings collected at the first wavelength to the sensor readings collected at the second wavelength; determining when the temperature change exceeds a threshold range; and thereafter, adjusting an output of the energy source.

[0011] A method for additive manufacturing is disclosed, comprising the steps of: identifying a spectral peak associated with a batch of powder; selecting a first wavelength and a second wavelength spaced apart from the first wavelength, the first wavelength and the second wavelength offset from the identified spectral peak; generating multiple scans of an energy source across a layer of powder across a build plane; generating sensor readings during each of the multiple scans using an optical sensing system that monitors the first wavelength and the second wavelength during the multiple scans; for each of the multiple scans, mapping a portion of each sensor reading to a corresponding region in a plurality of regions of the build plane; for each of the plurality of regions: characterizing a temperature change within the region based on a ratio of a sensor reading taken at the first wavelength to a sensor reading taken at the second wavelength; determining that a temperature change associated with one or more of the plurality of regions is outside a threshold range; and thereafter, adjusting an output of the energy source. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present disclosure will be more easily understood through the following detailed description in conjunction with the accompanying drawings, in which like reference numerals represent like structural elements, and in which:

[0013] Figure 1Ais a schematic illustration of an optical sensing device used in an additive manufacturing system having an energy source, in this particular example, a laser beam;

[0014] Figure 1B is a schematic illustration of an optical sensing device used in an additive manufacturing system having an energy source, in this particular example, an electron beam;

[0015] Figure 2 shows a sample scan pattern used in the additive manufacturing process;

[0016] Figure 3 A flow chart representing a method for identifying portions of a part most likely to contain a manufacturing defect is shown;

[0017] 4A to 4H shows data associated with a step by step process to use thermal energy density to identify portions of a part most likely to contain manufacturing defects;

[0018] Figure 5 A flowchart is shown that describes in detail how to use scanlet data segregation to complete IPQM evaluation;

[0019] 6A to 6F Data associated with a step-by-step process is shown to use thermal energy density to identify portions of a part most likely to contain manufacturing defects; and

[0020] FIG. 7A to FIG. 7C Test results comparing IPQM metrics with post-processing metallography are shown;

[0021] Figure 8 An alternative process is shown in which data recorded by an optical sensor (such as a non-imaging photodetector) can be processed to characterize an additive manufacturing build process;

[0022] 9A to 9D A visual depiction indicating how multiple scans may contribute to the power introduced at a single grid area is shown;

[0023] Fig. 10A An exemplary turbine blade suitable for use in accordance with the described embodiments is shown;

[0024] Fig. 10B An exemplary manufacturing configuration is shown in which 25 turbine blades may be manufactured simultaneously atop build plane 1006;

[0025] FIG. 10C to FIG. 10D Shows Fig. 10B Different cross-sectional views of different layers of the configuration depicted in;

[0026] FIG. 11A to FIG. 11B shows cross-sectional views of the base of two different turbine blades;

[0027] Fig. 11C shows a picture illustrating the difference in surface consistency between two different bases;

[0028] Fig.12 illustrates thermal energy density for parts associated with a number of different builds;

[0029] Figures 13 to 14B illustrates an example of how thermal energy density can be used to control the operation of a part using in-situ measurements;

[0030] Fig. 14C Another power density diagram is shown that highlights the various physical effects caused by energy settings far outside the process window;

[0031] Fig.14D shows how the size and shape of the melt pool can vary depending on the laser power and scan speed settings;

[0032] FIG. 15A to FIG. 15F illustrates how meshes can be created dynamically to characterize and control additive manufacturing operations;

[0033] Fig.16 An exemplary control loop 1600 for establishing and maintaining feedback control of an additive manufacturing operation is shown;

[0034] Fig.17A The standard distribution of powder across the build plate is shown;

[0035] Fig. 17B shows how the thickness of the resulting powder layer can vary when the recoater arm reclaims insufficient powder and the insufficient powder spreads across the build plate;

[0036] Fig. 17C shows a black and white photograph of a build plate in which a small supply of powder results in only partial coverage of nine workpieces arranged on the build plate;

[0037] Fig.17D shows how the detected thermal energy density differs substantially when the energy source is scanned across all nine workpieces using the same input parameters;

[0038] Fig.18A An exemplary graph showing sensor readings taken by a spectrometer is shown;

[0039] Fig.18B An exemplary graph is shown depicting at least a portion of a sensor reading acquired by a spectrometer after a bandpass filter is placed on the spectrometer;

[0040] Fig.19A A graph showing a number of blackbody radiation curves representing various melt pool temperatures (ranging from 3500K to 5500K) is shown;

[0041] Fig.19B shows a graph and how a percentage change in power output affects the natural logarithm of the ratio of the intensities sensed at two discrete wavelengths;

[0042] Fig. 20A An exemplary additive manufacturing system is shown, which is equipped with three optical sensors to characterize the Figures 18A-19B the temperature changes described and the amount of energy added to the build plane;

[0043] Fig. 20B Shown with Fig. 20A A similar configuration, except that its sensor assembly can be connected to the laser's optics via a fiber optic cable; and

[0044] Fig.21 A block diagram illustrating a method for measuring light emissions during an additive manufacturing process is shown. DETAILED DESCRIPTION

[0045] Figure 1A An embodiment of an additive manufacturing system is shown that uses one or more optical sensing devices to determine thermal energy density. Thermal energy density is sensitive to changes in process parameters such as energy source power, energy source speed, and scan pitch. Figure 1A The additive manufacturing system uses a laser 100 as an energy source. The laser 100 emits a laser beam 101, which passes through a partially reflective mirror 102 and enters a scanning and focusing system 103, which then projects the beam to a small area 104 on a work platform 105. In some embodiments, the work platform is a powder bed. Light energy 106 is emitted from the small area 104 due to the high material temperature.

[0046] In some embodiments, the scanning and focusing system 103 may be configured to collect some of the optical energy 106 emitted from the beam interaction region 104. The partially reflective mirror 102 may reflect the optical energy 106, as depicted by an optical signal 107. The optical signal 107 may be interrogated by a plurality of on-axis optical sensors 109, each of which receives a portion of the optical signal 107 through a series of other partially reflective mirrors 108. It should be noted that in some embodiments, the additive manufacturing system may include only one on-axis optical sensor 109 with a fully reflective mirror 108.

[0047] It should be noted that the collected optical signal 107 may not have the same spectral content as the optical energy 106 emitted from the beam interaction region 104 because the signal 107 undergoes some attenuation after passing through multiple optical elements, such as the partially reflective mirror 102, the scanning and focusing system 103, and a series of other partially reflective mirrors 108. Each of these optical elements may have its own transmission and absorption characteristics, resulting in varying amounts of attenuation, thereby limiting certain portions of the spectrum of energy radiated from the beam interaction region 104. The data generated by the coaxial optical sensor 109 may correspond to the amount of energy applied to the work platform.

[0048] Examples of coaxial optical sensors 109 include, but are not limited to, light to electrical signal transducers such as pyrometers and photodiodes (i.e., photodetectors). Optical sensors may also include spectrometers and low or high speed cameras operating in the visible, ultraviolet, or infrared spectrum. The coaxial optical sensor 109 is located in a reference frame that moves with the beam, i.e., when the laser beam 101 is scanned on the work platform 105, the coaxial optical sensor 109 sees all areas touched by the laser beam and is able to collect optical signals 107 from all areas of the work platform 105 that are touched. Since the path of the light energy 106 collected by the scanning and focusing system 103 is nearly parallel to the laser beam, the sensor 109 can be considered a coaxial sensor.

[0049] In some embodiments, the additive manufacturing system may include off-axis sensors 110, which are in a fixed reference frame relative to the laser beam 101. These off-axis sensors 110 will have a given field of view 111, which may be very narrow or may include the entire work platform 105. Examples of these sensors may include, but are not limited to, pyrometers, photodiodes, spectrometers, high-speed or low-speed cameras operating in the visible, ultraviolet or infrared spectrum, etc. Off-axis sensors 110 that are not aligned with the energy source are considered off-axis sensors. Off-axis sensors can also be sensors that combine a series of physical measurement modalities, such as laser ultrasonic sensors, which can actively excite or "ping" the deposit with a laser beam, and then use laser interferometers to measure the resulting ultrasonic waves or the "near transient high-frequency resonant response (ringing)" of the structure to measure or predict the mechanical properties or mechanical integrity of the deposit while the deposit is being built. Laser ultrasonic sensor / interferometer systems can be used to measure the elastic properties of materials, which can provide a deep understanding of, for example, the porosity and other material properties of the material. Additionally, a laser ultrasonic / sensor interferometer system can be used to measure defect formation that causes material vibrations.

[0050] In addition, there may be contact sensors 113 on the mechanical device, the recoater arm 112 that spreads the powder. These sensors may be accelerometers, vibration sensors, etc. Finally, there may be other types of sensors 114. These sensors may include contact sensors such as thermocouples that measure macroscopic thermal fields, or may include acoustic emission sensors that can detect cracking and other metallurgical phenomena that occur in the deposit when the deposit is being built. These contact sensors can be used to characterize the operation of the recoater arm 112 during the powder addition process. The data collected by the coaxial optical sensor 109 and the off-axis sensor 110 can be used to detect process parameters associated with the recoater arm 112. Therefore, the system can detect and solve the unevenness in the surface of the spread powder. The rough surface caused by changes in the powder spreading process can be characterized by the contact sensor 113 to anticipate possible problem areas or unevenness in the resulting parts.

[0051] In some embodiments, the laser beam 101 may melt peaks in the powder spread, causing subsequent powder layers to have corresponding peaks. At some point, the peaks may contact the recoater arm 112, thereby potentially damaging the recoater arm 112 and causing other spread powder inhomogeneities. Thus, embodiments of the present invention may detect inhomogeneities in the spread powder before they cause inhomogeneities in the build area on the work platform 105. Those of ordinary skill in the art will recognize many variations, modifications, and alternatives.

[0052] In some embodiments, the on-axis optical sensor 109, the off-axis optical sensor 110, the contact sensor 113, and the other sensors 114 may be configured to generate in-process raw sensor data. In other embodiments, the on-axis optical sensor 109, the off-axis optical sensor 110, the contact sensor 113, and the other sensors 114 may be configured to process the data and generate reduced order sensor data.

[0053] In some embodiments, a computer 116 including a processor 118, a computer readable medium 120, and an I / O interface 122 is provided and coupled to appropriate system components of the additive manufacturing system to collect data from various sensors. The data received by the computer 116 may include raw sensor data and / or reduced-order sensor data in the process. The processor 118 may use the raw sensor data and / or reduced-order sensor data in the process to determine the power and control information of the laser 100, including the coordinates relative to the work platform 105. In other embodiments, the computer 116 including the processor 118, the computer readable medium 120, and the I / O interface 122 may provide control of various system components. The computer 116 may send, receive, and monitor control information associated with the laser 100, the work platform 105, and the recoater arm 112 to control and adjust the corresponding process parameters for each component.

[0054] The processor 118 can be used to perform calculations using data received by various sensors to generate in-process quality metrics. In some embodiments, data generated by the coaxial optical sensor 109 and / or the off-axis sensor 110 can be used to determine the thermal energy density during the build process. The processor can receive control information associated with the movement of the energy source across the build plane. The processor can then use the control information to associate data from one or more coaxial optical sensors 109 and / or off-axis sensors 110 with corresponding positions. The associated data can then be combined to calculate the thermal energy density. In some embodiments, the processor 118 can generate control signals for process parameters (e.g., laser power, laser speed, scanning spacing, and other process parameters) using thermal energy density and / or other metrics in response to other metrics or thermal energy density that exceed the expected range. In this way, problems that may otherwise damage production parts can be improved. In embodiments where multiple parts are generated at a time, timely corrections to process parameters in response to metrics that exceed the expected range can prevent adjacent parts from receiving too much or too little energy from the energy source.

[0055] In some embodiments, the I / O interface 122 may be configured to transmit the collected data to a remote location. The I / O interface 122 may be configured to receive data from a remote location. The received data may include a benchmark data set, historical data, post-processing inspection data, and classifier data. The remote computing system may use the data transmitted by the additive manufacturing system to calculate the in-process quality metric. The remote computing system may transmit information to the I / O interface 122 in response to a specific in-process quality metric.

[0056] In the case of electron beam systems, Figure 1B Possible configurations and sensor arrangements are shown. An electron beam gun 150 generates an electron beam 151 which is focused by an electromagnetic focusing system 152 and then deflected by an electromagnetic deflection system 153, thereby forming a precisely focused and directed electron beam 154. The electron beam 154 forms a thermal beam-material interaction zone 155 on a workpiece 156. From the workpiece 156 radiates optical energy 158 which can be collected by a series of optical sensors 159, each of which has its own respective field of view 160, which can be further isolated locally to the interaction zone 155, or can encompass the entire workpiece 156. In addition, the optical sensors 159 can have their own tracking and scanning systems which can follow the electron beam 154 as it moves across the workpiece 156.

[0057] Whether or not the sensor 159 has optical tracking, the sensor 159 may be implemented as a pyrometer, a photodiode, a spectrometer, and a low or high speed camera operating in the visible, UV, or IR spectral regions. The sensor 159 may also be a sensor that combines a range of physical measurement modalities, such as a laser ultrasonic sensor, which may utilize a laser beam to actively excite or "hit" the deposit, and then use a laser interferometer to measure the resulting ultrasonic waves or the "near-transient high-frequency resonant response" of the structure to measure or anticipate the mechanical properties or mechanical integrity of the deposit as the deposit is being built. In addition, there may be contact sensors 113 on the recoater arm. These sensors may be accelerometers, vibration sensors, etc. Finally, there may be other types of sensors 114. These sensors may include contact sensors such as thermocouples that measure macroscopic thermal fields, or may include acoustic emission sensors that can detect cracking and other metallurgical phenomena occurring in the deposit as the deposit is being built. In some embodiments, one or more thermocouples may be used to calibrate the temperature data collected by the sensor 159. It should be noted that in combination with Figure 1A and Figure 1B The described sensors may be used in the manner described to characterize the performance of any additive manufacturing process involving sequential material accumulation.

[0058] Figure 2Possible scanning patterns for scanning an energy source across a powder bed are shown. In 200, an area of ​​a workpiece is processed by scanning an energy source along a long path length that alternates in a certain direction. In this embodiment, a scanning spacing 204 is shown between a first scan 206 and a second scan 208. In 202, an area of ​​the workpiece is divided into smaller checkerboards 214, which can be scanned from left to right and from top to bottom by a first scan 210 and a second scan 212. In other embodiments, the scanning order for each checkerboard can be randomized. Many scanning patterns can be utilized in conjunction with the additive manufacturing process disclosed herein. Those of ordinary skill in the art will recognize many variations, modifications, and alternatives.

[0059] Figure 3 A flow chart illustrating an exemplary process 300 is shown that uses data generated by an additive manufacturing system to determine thermal energy density and identify portions of a part that are most likely to contain manufacturing defects. Data generated by the on-axis optical sensor 109 and the off-axis optical sensor 110 may be used alone or in combination to determine thermal energy density. At 302, a raw photodiode data trace is received. The raw photodiode data trace may be generated using, for example, voltage data generated by the sensor in response to detected emitted thermal energy. At 304, a portion of the raw photodiode trace that corresponds to a particular scan scan is identified. i In some embodiments, a single photodiode data trace may be separated from the rest of the sensor readings by reference to the energy source drive signal data (the drive signal responsible for steering and actuating the energy source). At 306, the raw photodiode data trace for the scan i The area (hereinafter referred to as pdon i ). In some embodiments, pdon i represents the integrated photodiode voltage. In some embodiments, pdon i Indicates that in scan i At 308, the identification is performed with scan i The part identified at 308 may also have an associated area A of the part. p . You can use pdon i These two values ​​are determined in association with the energy source position data as described above. The process may calculate the total scan count at 310. At 312, the scan count may be determined. i The length of the associated link L i . L can be calculated using formula (1) i , where x1 i ,y1i and x2 i ,y2 i Indicates that it is used for scan i The corresponding start and end positions.

[0060]

[0061] At 314, the total length Lsum of all scans used to produce the part may be determined. p The length L of each scan associated with the part can be i Sum to determine Lsum on the part p At 316, the scanned proportional area A may be determined. i A can be calculated using formula (2): i :

[0062]

[0063] At 316, the th Scanning proportional thermal energy density (TED) TED i TED i is an example of a set of descending process features. The TED is calculated using raw photodiode data. From this raw sensor data, the TED calculation extracts the descending process features from the raw sensor data. TED i It is sensitive to all user-defined laser powder bed fusion process parameters (e.g., laser power, laser speed, scan spacing, etc.). TED can be calculated using Equation (3) i . :

[0064]

[0065] For the purposes of this discussion, "reducing" refers to one or more of the following: data compression, i.e., there are fewer data in a feature than in the original data; data reduction, i.e., systematic analysis of the original data to derive process metrics or other figures of merit; data aggregation, i.e., clustering the data into discrete groups and smaller sets of variables that characterize clusters in contrast to the original data themselves; data transformation, i.e., mathematical manipulation of the data using a transformation law or algorithm to map the original data linearly or nonlinearly into another variable space of lower dimensionality; or any other related such technique that will have the net effect of reduced data density, reduced data dimensionality, reduced data size, conversion of the data into another reduced space, or all of these effects occurring simultaneously.

[0066] TED iCan be used for analysis during comparison of process quality metrics (IPQM) to a benchmark data set. The resulting IPQM can be calculated for each scan. At 318, the IPQM quality benchmark data set and the calculated TED can be compared. i In areas of the part where the difference between the calculated TED and the benchmark data set exceeds a threshold, these areas can be identified as potentially including one or more defects and / or further processing can be performed on the areas in near real time to improve any defects caused by the change in TED relative to the benchmark data set. In some embodiments, a classifier can be used to identify portions of the part that may contain defects. The classifier is capable of grouping the results as nominal or non-nominal and can be represented through graphical and / or text-based media. The classifier can use a variety of classification methods, including but not limited to: statistical classification of both univariate and multivariate; heuristic-based classifiers; expert system-based classifiers; lookup table-based classifiers; classifiers based only on upper control limits or lower control limits; classifiers that work in conjunction with one or more statistical distributions, which classifiers can establish nominal and non-nominal thresholds based on confidence intervals and / or consideration of degrees of freedom; or any other classification scheme that is implicit or explicit and is capable of distinguishing whether a set of feature data is nominal or non-nominal. For purposes of this discussion, "nominal" will refer to a set of process results within predefined specifications that result in post-process measured properties of the parts so manufactured falling within a range of values ​​considered acceptable, or any other quantitative, semi-quantitative, objective, or subjective method for confirming an "acceptable" part. Additional description of the classification of IPQMs is provided in US patent application Ser. No. 15 / 282,822, filed Sep. 30, 2016, the disclosure of which is incorporated herein by reference in its entirety for all purposes.

[0067] You will understand that Figure 3 The specific steps illustrated in the figure provide a specific method for collecting data and determining thermal energy density according to an embodiment of the present invention. Other sequences of steps may also be performed according to alternative embodiments. In addition, Figure 3 Each step illustrated in the figure may include multiple sub-steps, which can be performed in various orders suitable for the individual steps. In addition, other steps can be added, or existing steps can be removed, depending on the specific application. Those skilled in the art will recognize many variations, modifications and alternatives.

[0068] 4A to 4H Illustrated are the steps used in process 300 to determine TED and identify any portions of a part that may contain defects. Figure 4ACorresponding to step 302, and showing the raw photodiode signal 402 for a given scan length. The x-axis 450 indicates the time in seconds and the y-axis 460 indicates the photodiode voltage. In some embodiments, optical measurements may be made by a pyrometer instead or in addition. Signal 402 is the photodiode raw voltage. The rise 404 and fall 406 of the photodiode signal 402 can be clearly seen, as well as the scattering and variation 408 of the signal during the time when the laser is turned on. Data is collected at a given number of samples per second. The variation 408 in the photodiode signal 402 may be caused by a variation in the powder melted on the powder bed. For example, one of the secondary troughs of the photodiode signal 402 may be caused by the energy absorbed by the larger particles in the particle bed as they transition from a solid state to a liquid state. Typically, the number of data points in a given segment of the photodiode signal between a rise event and a fall event may be related to the scan duration and the sampling rate.

[0069] Figure 4B A photodiode signal 402 and a laser drive signal 410 are shown. The energy source drive signal data may be generated using Figure 4B , in this case, the laser drive signal 410 or command signal tells the laser to turn on or off for a particular scan length. The photodiode signal 402 is superimposed on the laser drive signal 410. The rise 421 and fall 414 of the laser drive signal 410 correspond to the rise 404 and fall 406 of the photodiode signal 402. Figure 4B The data illustrated in FIG. 4 can be used at step 304 to identify the portion of the raw photodiode signal 402 corresponding to the scan. In some embodiments, the laser drive signal 410 is ˜0 V when the laser is off and ˜5 V when the laser is on. Step 304 can be accomplished by isolating all data associated with the photodiode signal when the laser drive signal 410 is above a certain threshold (e.g., 4.5 V) and excluding all data by analyzing when the laser is below the threshold.

[0070] Figure 4C One embodiment of step 306 is shown, which includes determining an area 416 under the raw photodiode signal 402. In some embodiments, the area under the curve may be calculated using equation (4):

[0071]

[0072] The integrated photodiode voltage 418 may be used to determine the voltage for TED i Calculated PDO i .

[0073] Figure 4DShown are the location of the scan 420 relative to the part and the total scan count 424. Two values ​​may be used to determine the TED corresponding to the scan location on the part. Figure 4E A rendered area of ​​interest is shown. Figure 4E A number of other parts 428 and a witness coupon 430 are also shown. Figure 4E All parts in are depicted as being located on a powder bed 432.

[0074] Figure 4F A trace associated with a portion of the photodiode data and the laser drive signal data corresponding to four scans is shown, which can be used with the remainder of the photodiode data to determine a total sample count 434. The total sample count can be used to calculate the total scan length LSum on the part p The total sample count is determined by summing the laser on time periods 436. In some embodiments, the total scan length may be determined using the sum of the laser on time periods and the average speed at which the energy source was scanned during the laser on time periods.

[0075] After the scan data is collected, the TED for each layer can be calculated using the TED associated with each laser scan and then displayed in a graph 440, as shown. Figure 4G . Graph 440 shows TED values ​​within a nominal region 442 and a non-nominal region 444. The TED regions are divided by a reference threshold 438. In this way, layers in a part that may contain defects can be easily identified. Further analysis can then be focused on layers with non-nominal TED values.

[0076] Figure 4H It is shown how the TED values ​​for each scan may be displayed in three dimensions using a point cloud 446. The point cloud 446 illustrates the location of the TED values ​​from the nominal region 442 and the non-nominal region 444 in three-dimensional space by displaying the non-nominal values ​​as a different color or intensity than the nominal values. The non-nominal values ​​indicate the portions of the part that are most likely to contain manufacturing defects, such as porosity resulting from keyhole formation or voids resulting from lack of fusion. In some embodiments, the system may generate and transmit a control signal that will change one or more process parameters based on the TED.

[0077] Figure 5A flow chart illustrating an exemplary process 500 is shown that uses data generated by an additive manufacturing system to determine thermal energy density and identify portions of a part that are most likely to contain manufacturing defects. Data generated by the on-axis optical sensor 109 and the off-axis optical sensor 110 may be used alone or in combination to determine thermal energy density. At 502, photodiode time series data may be collected. The photodiode time series data may be generated using, for example, voltage data associated with the sensor. At 504, laser drive time series data is collected. The laser drive time data may be associated with additive manufacturing process parameters such as laser power, laser speed, scan spacing, xy position, etc. The process at 506 may slice the photodiode time series data by discarding portions of the photodiode time series data corresponding to portions of the laser drive time series data indicating a laser off state. In some embodiments, the laser drive signal is ~0V when the laser is off and ~5V when the laser is on. The process at 506 can be performed by isolating all data where the laser drive signal 410 is above a certain threshold (e.g., 4.5V) and excluding from analysis all data where the laser is below that threshold. In some embodiments, the photodiode signal that periodically drops to ~0.2V can be included in the sample sequence data because these sample sequence data are when the laser is just turned on and the laser is heating the material.

[0078] The process at 506 may output only laser-on photodiode data 508. The process at 510 may use the laser-on photodiode data to convert the time series data into sample series data. The process at 510 segments the laser-on photodiode data into "N" sample segments. The use of 20 sample segments is intended to provide an example of one embodiment of the present invention. Any number of sample segments may be used with varying degrees of accuracy / resolution. In some embodiments, the group of sample segments may be referred to as a sub-scan 520, because multiple sub-scans 520 are typically required to make up a single scan. The process at 512 may count the number of samples 516. The process at 514 may render the area of ​​the laser-processed part. In some embodiments, the laser-processed part area 518 may be determined using the number of pixels associated with the laser-processed part in the display. In other embodiments, the area may be calculated using data associated with process parameters and the number of scans. At 522, the process normalizes the sub-scan data using the total sample count, the laser-processed part area 518, and the sub-scan data 520. In the illustrated embodiment, the sub-scan metric data 524 is the thermal energy density for the portion of the part associated with each sub-scan. In some embodiments, the scan data may also be subdivided by scan type. For example, an additive manufacturing machine may utilize scans with different features. In particular, a contour scan or a scan designed to finish the outer surface of a part may have much greater power than a scan designed to sinter the inner area of ​​a part. Therefore, more consistent results may also be obtained by isolating the data by scan type. In some embodiments, the identification of the scan type may be based on scan intensity, scan duration, and / or scan position. In some embodiments, the scan type may be identified by associating the detected scan with a scan specified by a scan plan (which is associated with the part being built).

[0079] Next, process 528 receives the reference scan measurement data and thermal energy density and outputs an IPQM quality assessment 530. The IPQM quality assessment 530 can be used to identify the portion of the part that is most likely to contain a manufacturing defect. Process 528 may include a classifier as discussed earlier in this specification. In addition to the above methods and systems, process 528 may also use Mahalanobis distance to compare candidate data (e.g., sub-scan measurement data 524) and reference sub-scan measurement data. In some embodiments, the Mahalanobis distance for each sub-scan may be calculated using the reference sub-scan measurement data. Although the Mahalanobis distance for each sub-scan may be calculated using the reference sub-scan measurement data, the process 528 may also use Mahalanobis distance to compare candidate data (e.g., sub-scan measurement data 524) and reference sub-scan measurement data. Figure 5 The disclosed embodiments have discussed the use of lasers as an energy source, but it will be apparent to those skilled in the art that many modifications and variations are possible in light of the above teachings, for example, electron beams or other suitable energy sources may be used in place of lasers.

[0080] It should be understood that Figure 5 The specific steps shown in provide a specific method for determining thermal energy density and identifying parts that are most likely to contain manufacturing defects according to another embodiment of the present invention. According to alternative embodiments, other step sequences may also be performed. For example, alternative embodiments of the present invention may perform the steps outlined above in a different order. In addition, in Figure 5 The various steps shown in the embodiment may include multiple sub-steps, which may be performed in various orders suitable for the individual steps. In addition, other steps may be added or removed depending on the specific application. Those of ordinary skill in the art will recognize many variations, modifications and alternatives.

[0081] Fig. 6A The photodiode time series data 602 is shown. Figure 2 Various on-axis or off-axis sensors shown in FIG. 602 collect photodiode time series data. The x-axis 604 indicates time in seconds and the y-axis 606 indicates the voltage generated by the sensor. The voltage generated by the sensor is associated with the energy emitted from the build plane, which may impinge on one or more sensors. Sample 606 is illustrated on the trace of photodiode time series data 602. Figure 4B A process is described at 506 where the photodiode data is associated with the laser drive signal.

[0082] Figure 6B Laser-on photodiode data is shown. The x-axis represents the number of samples 608 and the y-axis 610 represents the voltage of the raw sensor data. The drop in voltage 612 is included in the analysis because, although the voltage is substantially lower, the laser is still actively contributing to heating the material.

[0083] Figure 6C The laser-on photodiode sample sequence data discussed with respect to step 510 is shown. The 20 sample segments 620 can be of any size. Twenty samples correspond to a laser travel distance of ~400μm with a laser travel speed of 1000mm / s. The noise of the XY signal itself is about ~150μm. In some embodiments, with less than 20 sample segments, for example, 2 sample segments, the measured distance and noise will be in a ratio such that the position of the point cannot be determined with confidence. In some embodiments, a limit of 50 samples can be used for a spatial resolution of less than 1mm. Therefore, for 50KHz data with a laser travel speed of 1000mm / s, the number of samples used to segment the data should be in the range of 20≤N≤50. It will be apparent to those skilled in the art that many modifications and variations are possible in view of the above teachings.

[0084] Fig.6D522 and illustrates an embodiment in which an average value 618 for each sub-scan is determined. In some embodiments, the inputs to process 522 include total sample count 516, laser processed portion area 518, and sub-scan line data 520. Using these inputs, the average value can be used to determine the area under the curve (AUC), as shown in equation (5):

[0085] AUC = V (average) * N (samples) Formula (5)

[0086] Where V is the average voltage determined for each subscan and N is the number of samples. Fig.6D In the example, since the width of the data is fixed, the average voltage of the 20 sample segments is equal to integrating the signal.

[0087] Fig. 6E The laser processed area A for a single sub-scan 622 and for all scans 624 is shown. i In addition to the area, the length L of the scan can also be calculated i and L on the entire part i The sum of LSum p L can be calculated using formula (6): i :

[0088]

[0089] The x and y coordinates for the scan start and scan end may be provided or may be determined based on one or more direct sensor measurements.

[0090] Fig. 6F A rendered area 626 of the laser-machined portion associated with a layer in the build plane is shown. In some embodiments, once the pdon is determined i 、Part area A p , the scan length L i and the total length LSum p , we can use formula (7) to calculate TED:

[0091]

[0092] TED is sensitive to all user-defined laser powder bed fusion process parameters (e.g., laser power, laser speed, scan spacing, etc.). TED values ​​can be used for analysis using IPQM to compare with a benchmark dataset. The resulting IPQM can be determined for each laser scan and displayed graphically or in 3D using a point cloud. Figure 4G An exemplary graph is shown. Figure 4H An exemplary point cloud is shown.

[0093] FIG. 7A to FIG. 7C Post-process porosity measurements and corresponding normalized in-process TED measurements are shown. This figure shows that in-process TED measurements can be an accurate IPQM predictor of porosity and other manufacturing defects. Fig. 7A TED metric data is shown compared to a benchmark data set. The graph shows the value of each photodiode in the IPQM metric individually and in combination. On-axis photodiode data 702 may be from a sensor aligned with an energy source. Off-axis photodiode data 704 may be collected by a sensor that is not aligned with the energy source. The combination of on-axis and off-axis photodiode data 706 produces the highest sensitivity to changes in process parameters. The x-axis 708 shows the build plane layer of the part; the y-axis 710 shows the Mahalanobis distance between the calculated TED and the benchmark metric.

[0094] The Mahalanobis distance can be used to normalize the TED data. The Mahalanobis distance indicates how many standard deviations each TED measurement differs from the nominal distribution of TED measurements. In this case, the Mahalanobis distance indicates how many standard deviations each TED measurement differs from the average TED measurement collected when constructing the control layer 526-600. Fig. 7A The lower graph also shows how TED varies with global energy density (GED) and porosity. Specifically, for this set of tests, TED can be configured to predict the porosity of the part without the need for destructive inspection.

[0095] In some embodiments, the performance of the additive manufacturing apparatus may be further validated by comparing quantitative metallographic characteristics (e.g., size and shape of pores or intermetallic particles) and / or mechanical performance characteristics (e.g., strength, toughness, or fatigue) of the formed metal parts while performing the test runs. Typically, the presence of unmelted metal powder particles in the test part indicates insufficient energy applied, while a test part that receives too much energy is prone to developing internal cavities, both of which can compromise the integrity of the formed part. Porosity 714 may represent these defects.

[0096] In some embodiments, for generating Fig. 7AThe nominal value will be taken from a previous test. In some embodiments, the nominal value may also be taken from a subsequent test, since calculations do not need to be performed during the additive manufacturing operation. For example, when attempting to compare the performance of two additive manufacturing devices, the nominal value may be identified by using the first run test in the additive manufacturing device. The performance of the second additive manufacturing device may then be compared to the nominal value defined by the first additive manufacturing device. In some embodiments, the two machines may be expected to have comparable performance when the performance of the two additive manufacturing devices is within a predetermined threshold of five standard deviations. In some embodiments, the predetermined threshold may be a 95% statistical confidence level derived from an inverse chi squared distribution. This type of testing method may also be used to identify performance that changes over time. For example, after calibrating the machine, the results of the test pattern may be recorded. After the device performs a certain number of manufacturing operations, the additive manufacturing device may be operated again. The initial test pattern performed immediately after calibration may be used as a baseline to identify any changes in the performance of the additive manufacturing device over time. In some embodiments, the settings of the additive manufacturing device may be adjusted to restore the additive manufacturing device to its post-calibration performance.

[0097] Figure 7B Shown is a post-processed metallography of a part constructed using an additive manufacturing process. Figure 7B Component 718 and a corresponding cross section 720 of the component are shown. Segments 1 to 11 correspond to Fig. 7A 720. The changes in process parameters and the resulting changes in porosity can be seen in the cross-sectional view 720 of the part. In particular, the porosity of segments 2 and 3 is the highest, at 3.38% and 1.62%, respectively. By increasing the number of defect marks 722 in the sample part, higher porosity is shown in the cross section.

[0098] Figure 7C The IPQM results with the corresponding cross sections determined during metallography are shown. Each cross section includes 2714. The energy density 724 and porosity 714 are in units of 1. The sample with the largest number of defect marks 722 corresponds to the TED measurement with the largest normalized distance from the reference. The figure illustrates that a low normalized distance predicts a higher density and low porosity metallography, while a high normalized Mahalanobis distance is highly associated with high porosity and poor metallography. For example, a low power setting used to generate a layer around layer 200 resulted in a high porosity 714 and a large number of defect marks 722. In contrast, using the middle of the road settings on or around layer 600 resulted in no identifiable defect marks 722 and the lowest porosity value recorded was 0.06%.

[0099] Figure 8 An alternative process is shown in which data recorded by an optical sensor (such as a non-imaging photodetector) can be processed to characterize an additive manufacturing build process. At 802, raw sensor data is received, which can include associated build plane intensity data and an energy source drive signal. At 804, by comparing the drive signal and the build plane intensity data, each scan can be identified and positioned within the build plane. Typically, the energy source drive signal will provide at least a start position and an end position, from which the area spanned by the scan extension can be determined. At 806, the raw sensor data associated with the intensity or power of each scan can be classified into corresponding X and Y grid areas. In some embodiments, the raw intensity or power data can be converted into energy units by associating the dwell time of each scan in a particular grid area. In some embodiments, each grid area can represent a pixel of an optical sensor monitoring the build plane. It should be noted that different coordinate systems such as polar coordinates can be used to store grid coordinates, and the storage of coordinates should not be limited to Cartesian coordinates. In some embodiments, different scan types can be classified separately so that analysis can be performed only on specific scan types. For example, an operator may wish to focus on contour scans if those scan types are most likely to contain unwanted variations. At 808, the energy input at each grid region may be summed, thereby determining the total amount of energy received at each grid region using equation (8).

[0100]

[0101] This summing may be performed before a new powder layer is added to the build plane or, alternatively, the summing may be delayed until a predetermined number of powder layers have been deposited. For example, the summing may be performed only after portions of five or ten different powder layers have been deposited and fused during the additive manufacturing process. In some embodiments, the sintered powder layers may increase the thickness of the part by approximately 40 microns; however, this thickness will vary depending on the type of powder being used and the thickness of the powder layers.

[0102] At 810, the standard deviation of the samples detected and associated with each grid area is determined. This can help identify grid areas with small or large changes in power readings. Changes in the standard deviation can indicate problems with sensor performance and / or the following: one or more scans are missing or the power level of one or more scans is far outside the nominal operating parameters. The standard deviation can be determined using equation (9).

[0103]

[0104] At 812, the total energy density received at each grid area can be determined by dividing the power reading by the total area of ​​the grid area. In some embodiments, the grid area can have a square geometry with a length of approximately 250 microns. The energy density for each grid area can be determined using equation (10).

[0105]

[0106] When more than one part is being built, different grid regions may be associated with different parts at 814. In some embodiments, the system may include stored part boundaries that may be used to quickly associate each grid region and its associated energy density with its corresponding part using the coordinates of the grid region and the boundaries associated with each part.

[0107] At 816, the area of ​​each layer of the part may be determined. In the case where a layer contains voids or helps define an internal cavity, a large portion of the layer may not receive any energy. Therefore, the affected area may be calculated by summing only the grid areas identified as receiving some amount of energy from the energy source. At 818, the total amount of power received by the grid areas within the portion of the layer associated with the part may be summed and divided by the affected area to determine the energy density for that layer of the part. The area and energy density may be calculated using equations (11) and (12).

[0108]

[0109]

[0110] At 820, the energy density of each layer can be summed together to obtain a metric indicating the total amount of energy received by the part. The overall energy density of the part can then be compared to the energy density of other similar parts on the build plane. At 822, the total energy from each part is summed. This can allow for a high level comparison between different builds. Build comparisons can help identify system differences, such as powder differences and differences in overall power output. Finally at 824, the summed energy values ​​can be compared to other layers, parts, or build planes to determine the quality of the other layers, parts, or build planes.

[0111] It should be understood that Figure 8 The specific steps illustrated in FIG. 1 provide a specific method for characterizing an additive manufacturing build process according to another embodiment of the present invention. According to alternative embodiments, other sequences of steps may also be performed. For example, alternative embodiments of the present invention may perform the steps outlined above in a different order. In addition, in Figure 8 Each step illustrated in the figure may include multiple sub-steps, which can be performed in various orders suitable for the individual steps. In addition, other steps can be added or removed depending on the specific application. Those of ordinary skill in the art will recognize many variations, modifications and alternatives.

[0112] 9A to 9D A visual depiction indicating how multiple scans may contribute to the power introduced at a single grid area is shown. Fig. 9A A grid pattern consisting of a plurality of grid regions 902 is depicted, which are distributed across a portion of a part built by an additive manufacturing system. In some embodiments, the size of each individual grid region can be between 100 and 500 microns, however, it should be understood that slightly smaller or larger grid regions are also possible. 9A also depicts a first energy scan pattern 904 extending diagonally across the grid region 902. The first energy scan pattern 904 can be applied by a laser or other strong source of thermal energy scanning across the grid region 902. It should be noted that although the energy scan is depicted as having a uniform energy density in some embodiments, a Gaussian distribution can be used instead to model the energy density of the scan. The Gaussian distribution can be used to more accurately model the heat distribution within each scan because the heat is highly concentrated at the point of incidence between the energy source (e.g., a laser or electron beam) and the powder layer on the build plane, and then gradually weakens in intensity toward the edge of the scan. By more accurately modeling the beam, grid areas 902 on the edges of the energy scan 904 are assigned substantially smaller and more precise amounts of energy, while grid areas falling within the center portion of the energy scan 904 are assigned proportionally larger amounts of energy.

[0113] Fig. 9BIt is shown how the energy introduced on the part is represented in each grid area 902 by a single grayscale color representing the amount of energy received, where darker shades of gray correspond to larger amounts of energy. It should be noted that in some embodiments, the size of the grid area 902 can be reduced to obtain higher resolution data. Alternatively, the size of the grid area 902 can be increased to reduce the use of memory and processing power.

[0114] Fig. 9C A second energy scanning pattern 906 is shown that overlaps at least a portion of the energy scan of the first energy scanning pattern. Figure 8 As discussed in the text of , where a first energy scan pattern and a second energy scan pattern overlap, the grid area is shown with a darker shade to illustrate how the energy from the two scans increases the amount of energy received at the overlapping scan pattern. Obviously, the method is not limited to two overlapping scans, and may include many other additional scans that will be added together to fully represent the energy received at each grid area.

[0115] Fig. 10A An exemplary turbine blade 1000 suitable for use in accordance with the described embodiments is shown. The turbine blade 1000 includes a plurality of different surfaces and includes many different features requiring many different types of complex scans to produce. In particular, the turbine blade 1000 includes a hollow blade portion 1002 and a tapered base portion 1004. Fig. 10B An exemplary manufacturing configuration is shown in which twenty-five turbine blades 1000 may be manufactured simultaneously atop build plane 1006 .

[0116] FIG. 10C to FIG. 10D Shows Fig. 10B Different cross-sectional views of different layers of the depicted configuration with grid-based TED visualization of the layers. Fig. 10C Showing layer 14 of turbine blade 1000, the TED-based visualization layer illustrates how the lower end of the selected portion of base 1004 may define multiple voids in turbine blades 1000-1, 1000-2, and 1000-3. Because the energy density data is associated with the discrete mesh regions, these voids, which would otherwise be completely hidden within the turbine blade, are clearly visible in this mesh-based TED visualization. Fig. 10D The upper end of the base 1004 is shown to also define a plurality of hidden cavities within the turbine blades 1000-1, 1000-2, and 1000-3, which can be seen from Fig. 10D The grid-TED-based visualization layer depicted in FIG. 4 clearly distinguishes the multiple hidden holes.

[0117] FIG. 11A to FIG. 11BCross-sectional views of base 1004 - 1 and base 1004 - 2 of two different turbine blades 1000 are shown. Fig.11A A base 1004-1 produced using nominal manufacturing settings is shown. The outer surface 1102 and the outer surface 1104 of the base 1004-1 receive substantially more energy than the interior 1106 of the base 1004-1. The increased energy input into the outer surface provides a more uniform hardened surface, resulting in an annealing effect along the outer surface 1102 and the outer surface 1104. This additional energy can be introduced using a higher energy profile scan for the outer surface 1102 and the outer surface 1104. Fig. 11B Base 1004-2 is shown, which was produced in the same manufacturing settings as 1004-2, except that the profile scans were omitted. Although all scans utilized in the manufacturing operations to produce base 1004-2 were also included in producing base 1004-1, the summation of the energy density input for all scans covering each grid area allows the operator to clearly see the differences between bases 1004-1 and 1004-2.

[0118] Fig. 11C The difference in surface consistency between base 1004-1 and base 1004-2 is shown. Clearly, omitting the profile scan for base 1004-2 has a substantial impact on the overall exterior surface quality. In terms of consistency, the exterior surface of base 1004-1 is smoother and has fewer holes. The annealing effect on base 1004-1 should also make it substantially stronger than base 1004-2.

[0119] Fig.12 Thermal energy density for parts associated with multiple different builds is shown. Builds A through G each include thermal emission data for approximately 50 different parts (represented by discrete circles 1202) built during the same additive manufacturing operation. The figure shows how thermal emission data can be used to track differences between different builds. For example, Batch A, Batch B, and Batch C all have similar TED distributions; however, Build D, Build E, and Build F, while still within tolerance, have consistently smaller thermal emission data. In some cases, these types of variations can be attributed to variations in powder batches. In this way, thermal emission data can be used to track systematic errors that may have a negative impact on overall output quality. It should be noted that while this chart depicts average TED values ​​based on a grid TED, a similar chart can be constructed for a scan-based TED method.

[0120] Figures 13 to 14B An example of how thermal energy density can be used to control the operation of a part using in-situ measurements is illustrated. Fig.13It is shown how parts produced using different combinations of energy source power and scan speed can be produced and then destructively analyzed to determine the resulting part density in grams per cubic centimeter, as depicted. In this experiment, a part associated with part density 1302 was produced using the manufacturer's recommended scan and laser power settings, which was 4.37 g / cc. Based on the resulting density readings, the location of dashed line 1304 can be determined. Line 1304 indicates that the resulting reduced energy input causes the portions of the powder that are not fully heated to fuse together, causing the part density to drop below the threshold density level. When too much energy is added to the system, keyhole-shaped holes are formed in the part due to the powder being vaporized rather than melted during the manufacturing operation, the part density can also be reduced. Dashed line 1306 can be determined experimentally based on the density data, and in this example, dashed line 1306 is identified by dropping to a part density as low as 4.33 g / cc. Line 1308 represents an optimal energy density profile along which energy density and part geometry remain substantially constant. Density testing shows how the average density of parts created using settings distributed along line 1308 remains relatively consistent.

[0121] FIG. 14A to FIG. 14B A thermal energy density contour is shown superimposed on a portion of a power density graph and illustrates how thermal density measurements collected during an additive manufacturing operation vary depending on the settings used for the energy source. FIG. 14A to FIG. 14BAs depicted in , darker shading indicates higher thermal energy density and lighter shading indicates lower thermal energy density. Through part density testing and thermal energy density profiles, control limits for specific parts can be determined. In this case, control limits indicated by ellipse 1402 have been determined, and the power and scan speed parameters are allowed to vary by up to 3σ along line 1308 and up to 1σ along a line perpendicular to line 1308 as the settings for producing part 1302 vary. In some embodiments, allowable variations in power and speed allow in-process adjustments to be made to maintain the desired thermal energy density during the production run. Ellipse 1404 indicates the entire process window that can accommodate further deviations outside the control limits. In some embodiments, the process window can be used to identify deviations that will still allow the resulting part to be verified using only in-process data. It should be noted that although the control and process windows depicted are shown as ellipses, other shapes and sizes of process windows are possible and may be more complex depending on, for example, the geometry of the part and the eccentricity of the material. During manufacturing operations, thermal energy density can be determined in situ using optical sensor readings that measure thermal radiation. In the event that the thermal energy density falls outside of the expected range while maintaining the laser power and scan speed within the depicted control limits indicated by ellipse 1402, the portion of the part with an abnormal thermal energy density reading can be flagged as having the potential for a defect.

[0122] In some embodiments, the process window may be incorporated into a modeling and simulation program that models one or more optical sensors that collect sensor readings for determining thermal energy density. Once the modeling and simulation system iterates to a first approximation of the instruction set for the workpiece, the expected thermal energy density may be output to the additive manufacturing machine for further testing. When the additive manufacturing machine includes an optical sensor and a computing device configured to measure thermal energy density, the additive manufacturing machine may use the modeled thermal energy density data for further testing and validation. A comparison of the modeled thermal energy density to the measured thermal energy density may be used to confirm how closely the execution of the instruction set matches the expected results in situ.

[0123] Fig. 14CAnother power density graph is shown that highlights various physical effects caused by energy source settings far beyond the process window 1404. For example, the power density graph shows that adding a large amount of laser power at a low scan speed results in the formation of keyhole holes in the workpiece. The formation of keyhole holes occurs due to partial evaporation of the powder material due to receiving too much energy. In addition, low power and high scan speed combinations can result in the powder not being able to fuse together. Finally, high power levels combined with high scan speeds result in agglomeration of melted metal during the build operation. It should be noted that changing the thickness of the deposited power layer can result in a deviation from the line separating the conduction mode zone from the keyhole formation zone and the lack of fusion zone. For example, increasing the thickness of the powder layer has the effect of increasing the slope of the line separating the keyhole area and the lack of fusion zone from the conduction mode zone, because thicker layers generally require more energy to undergo liquefaction.

[0124] Fig. 14C It is also shown how the power and scan speed settings corresponding to the conduction mode region generally do not cause any of the above-mentioned serious defects; however, by maintaining the laser settings corresponding to the values ​​within the process window 1404, the grain structure and / or density of the resulting part can be optimized. Another benefit of keeping the energy source settings within the process window 1404 is that these settings should keep the thermal energy density readings within a narrow range of values. Any thermal energy density value that falls outside the predetermined range during the manufacturing operation can indicate a problem in the manufacturing process. These problems can be solved by moving the settings closer to the center area of ​​the process window and / or by updating the process window for subsequent parts. In some embodiments, the manufacturer can discover that the failure is caused by defective powder or some other infrequent abnormal behavior (aberration) that should not be considered for inclusion in the subsequent manufacturing process. It should be appreciated that the depicted process window may not be the same for all parts of the part and may vary greatly depending on what area or even specific layer on the part is being processed at a specific time. In some embodiments, the size and / or shape of the process window 1404 may also vary according to other factors such as scan pitch, scan length, and scan direction.

[0125] Fig.14D It is shown how the size and shape of the melt pool can vary depending on the laser power and scan speed settings. Exemplary melt pools 1406 to 1418 present exemplary melt pool sizes and shapes for various laser power and speed settings. In general, it can be seen that larger melt pools result from higher laser power amounts and lower scan speeds. However, for this particular configuration, the size of the melt pool depends more on the laser power than the speed.

[0126] FIG. 15A to FIG. 15F Illustration of how meshes can be created dynamically to characterize and control additive manufacturing operations. Fig.15A A top view of cylindrical workpiece 1502 is shown positioned on a portion of build plane 1504. Workpiece 1502 is shown as it undergoes an additive manufacturing operation. Fig. 15B It shows how a cylindrical workpiece 1502 is divided into a plurality of tracks 1506 along which an energy source can melt powder distributed on the upper surface of the cylindrical workpiece 1502. In some embodiments, the energy source can alternate the direction 1506 as depicted, while in other embodiments, the energy source can always move in one direction. In addition, the direction of the track 1506 can vary from layer to layer to further randomize the orientation of the scans used to build the workpiece 1502.

[0127] Fig. 15C An exemplary scanning pattern for an energy source is shown when the energy source forms a portion of a workpiece 1502. As shown by arrow 1508, the direction of movement of the exemplary energy source across the workpiece 1502 is diagonal. A single scan 1510 of the energy source may be oriented in a direction perpendicular to the direction of movement of the energy source along the track 1506 and extend completely across the track 1506. The energy source may be briefly turned off between consecutive individual scans 1510. In some embodiments, the duty cycle of the energy source may be approximately 90% as the energy source traverses each of the tracks 1506. By employing this type of scanning strategy, the energy source may cover the width of the track 1506 as the energy source traverses the workpiece 1502. In some embodiments, the strip 1510 may have a width of approximately 5 mm. This may significantly reduce the number of tracks required to form the workpiece 1502 because, in some embodiments, the width of the molten pool produced by the energy source may be on the order of approximately 80 microns.

[0128] Figures 15D to 15EIt is shown how to dynamically generate grid areas 1512 along each track 1506, and how to set the size of grid areas 1512 to accommodate the width of each individual scan 1510. The system can predict the exact location of subsequent scans by referencing the energy source drive signal on the way to the energy source. In some embodiments, the width of the grid 1512 can match the length of the individual scan 1512 or be within 10% or 20% of the length of the individual scan 1512. Again, the scan length of the individual scan 1512 can be expected by referencing the energy source drive signal. In some embodiments, the shape of the grid area 1512 can be square or rectangular. As the energy source continues along the track 1506, the thermal energy density can be determined for each of the grid areas 1512. In some embodiments, the thermal energy density reading within the grid area 1512-1 can be used to adjust the output of the energy source within the next grid area (in this case, the grid area 1512-2). For example, if the thermal energy density readings produced by the individual scans 1510 within the grid area 1512-1 are substantially higher than expected, the power output of the energy source may be reduced, the speed at which the energy source scans across the individual scans 1510 may be increased, and / or the spacing between the individual scans 1510 may be increased within the grid area 1512-2. These adjustments may be made as part of a closed loop control system. Although only five individual scans 1510 are shown within each area, this is done for exemplary purposes only, and the actual number of individual scans within the grid area 1512 may be substantially higher. For example, in the case where the melt zone produced by the energy source is approximately 80 microns wide, approximately 60 individual scans 1510 may need to be performed to allow all of the powder within the 5 mm square grid area 1512 to fall within the melt zone.

[0129] Fig.15FThe edge region of the workpiece 1502 is shown once the energy source has completed traversing the pattern of track 1506. In some embodiments, the energy source may continue to add energy to the workpiece 1502 after most of the powder has been melted and resolidified. For example, the contour scan 1514 may be tracked along the outer periphery 1516 of the workpiece 1502 to apply a surface finish to the workpiece 1502. It should be appreciated that the contour scan 1514 as depicted is substantially shorter than the individual scans 1510. To this end, the grid area 1518 may be substantially narrower than the grid area 1512. It should also be noted that the shape of the grid area 1518 is not purely rectangular because, in this case, the shape of the grid area 1518 follows the contour of the outer periphery of the workpiece 1502. Another situation that may cause a difference in scan length may be that the workpiece includes a wall of varying thickness. The variable thickness wall may cause the scan length to vary within a single grid area. In such a case, the area of ​​each grid area may be kept consistent by increasing the length of the grid area while narrowing the width to accommodate the variation in the length of each scan.

[0130] Fig.16 A closed loop control example is shown, which shows a feedback control loop 1600 for establishing and maintaining feedback control of an additive manufacturing operation. At box 1602, a baseline thermal energy density for the next grid area to be traversed by the energy source is input into the control loop. The baseline thermal energy density reading can be confirmed from a modeling and simulation program and / or from a previously run experiment / test run. In some embodiments, the baseline thermal energy density data can be adjusted by an energy density deviation block 1604, which includes energy density readings for various grid areas recorded during a previous layer. The energy density deviation block 1604 may include adjustments to the baseline energy density block in the case where the previous layer receives too much or too little energy. For example, in the case where the optical sensor reading indicates that the thermal energy density in one area of ​​the workpiece is lower than the nominal value, the energy density deviation value can increase the value of the baseline energy density for the grid area overlapping the grid area that is lower than the nominal thermal energy density reading. In this way, the energy source is able to melt other powders that were completely melted during the previous layer or layers.

[0131] Fig.16Also shown is how the inputs from boxes 1602 and 1604 work together to create an energy density control signal received by a controller 1606. The controller 1606 is configured to receive the energy density control signal and generate energy source input parameters, which are configured to generate the desired thermal energy density in the current grid area. The input parameters may include power, scanning speed, scanning spacing, scanning direction, and scanning duration. Then, the energy source 1608 receives the input parameters, and the energy source 1608 adopts any changes in the input parameters for the current grid area. Once the optical sensor measures the scan of the energy source 1608 that constitutes the current grid area, at box 1610, the thermal energy density for the current grid area is calculated and compared with the energy density control signal. If the two values ​​are the same, the energy density control signal will not be changed based on the optical sensor data. However, if the two values ​​are different, the difference is added or subtracted from the energy density control signal for the scan performed in the next grid area.

[0132] In some embodiments, the grid area for the current layer and all previous layers can be a dynamically generated grid area that is oriented according to the path and scan length / width of the scan performed by the energy source. In this type of configuration, both the baseline energy density and the energy density deviation can be based on the dynamically generated grid area. In other embodiments, the grid area for the current layer can be dynamically generated, and the energy density deviation data 1604 can be based on the energy density reading associated with the static grid area defined before the additive manufacturing operation begins, resulting in the static grid area remaining fixed throughout the part and unchanged in position, size or shape. When a Cartesian grid system is required, the grid can be evenly shaped and spaced, but it can also take the form of a grid area that constitutes a polar coordinate grid system. In other embodiments, the grid area for the current layer can be statically generated before the build operation is performed, and the energy density deviation data can also be statically generated and share the same grid for the current layer.

[0133] In some embodiments, the control loop 1600 may be used to use thermal emission density instead of thermal energy density. In addition to thermal energy density, thermal emission density may also refer to other factors. For example, thermal emission density may be a weighted average of multiple features, including thermal energy density and one or more other features (such as peak temperature, minimum temperature, heating rate, cooling rate, average temperature, standard deviation from average temperature, and rate of change of average temperature over time). In other embodiments, one or more other features may be used to verify that the scans that make up each of the grid regions are reaching the desired temperature, heating rate, or cooling rate. In such embodiments, the verification feature may be used as a flag to indicate that input parameters for the energy source may need to be adjusted within a defined control window to achieve the desired temperature, heating rate, or cooling rate. For example, if the peak temperature within a grid region is too low, the power may be increased and / or the scan speed may be reduced. Although the aforementioned control loop 1600 is discussed in relation to various types of grid TEDs, it should be noted that those skilled in the art will also understand that the scan TED metric may also be used in similar loop configurations.

[0134] TED analysis for small quantity supply of recoater arm

[0135] Fig.17A A standard distribution of powder 1702 across build plate 1704 is shown. This depiction shows how the powder is spread evenly without any variation in height. In contrast, Fig. 17B 1702. As an insufficient amount of powder 1702 is recovered by the recoater arm, the thickness of the powder layer 1702 can vary greatly. Once the recoater arm begins to run out of powder 1702, the thickness of the layer of powder 1702 gradually decreases until a portion of the build plate 1704 is completely free of powder 1702. As this error can have a very negative impact on the overall quality of the part, early detection of this phenomenon is important for accurate defect detection.

[0136] Fig. 17C A black and white photograph of a build plate is shown in which a short supply of powder 1702 results in nine workpieces arranged on the build plate being only partially covered. In particular, three of the workpieces on the right side of the photograph are completely covered, while three of the workpieces on the left side are almost completely uncovered.

[0137] Fig.17D1706 produces a substantially higher thermal energy density reading due to the substantially higher emissivity of the powder and the thermal conductivity of the build plate or solidified powder material being greater than the effective thermal conductivity of the powder. The higher thermal conductivity reduces the amount of energy available to radiate back to the optical sensor, thereby reducing the detected thermal radiation. In addition, the lower emissivity of the material itself also reduces the amount of thermal energy radiated.

[0138] Thermal Energy Density vs. Total Energy Density

[0139] The power delivered by the energy source into the workpiece causes the melting of the material from which the workpiece is made, but this power can also be dissipated by several other heat and mass transfer processes during the additive manufacturing process. The following formula describes the different processes that can absorb the emitted power when the energy source is a laser that is scanned across the powder bed:

[0140] P 总激光功率 =P 在激光处的光学损耗 +P 腔室气体的吸收 +P 反射 +P 颗粒和羽流相互作用 +P 维持熔化池所需的功率 +P 传导损耗 +P 辐射损耗 +P 对流损耗 +P 蒸发损耗 Formula (13)

[0141] Optical losses at the laser refer to power losses due to imperfections in the optical system responsible for transmitting and focusing the laser on the build plane. Imperfections result in absorption and reflection losses of the laser light emitted within the optical system. Absorption by chamber gases refers to power losses due to the absorption of a small portion of the laser power by the gases within the build chamber of the additive manufacturing system. The impact of this power loss will depend on the absorptivity of the gas at the laser wavelength. Reflection losses refer to power losses due to light escaping the laser optics and not being absorbed by the powder bed. Particle and plume interactions refer to the interaction between the laser and the ejected plume and / or particles during the deposition process. While power losses due to these effects can be ameliorated by protecting the gases that are circulating through the build chamber, it is generally not possible to completely avoid a small power reduction. Power required to maintain the melt pool refers to the portion of the laser power absorbed by the work material to melt the powder and ultimately overheat the powder to whatever temperature the melt pool eventually reaches. Conduction losses refer to the portion of the power absorbed by conduction to the solidifying metal beneath the powder and beneath the powder bed itself. In this way, the powder bed and solidifying material that make up the part conduct heat away from the melt pool. Conductive transfer of this heat energy is the dominant form of energy loss from the melt pool. Radiative losses refer to the portion of the laser power emitted by the melt pool and material surrounding the melt pool that is hot enough to emit thermal radiation. Convective losses refer to the losses caused by the transfer of thermal energy to the gases circulating through the build chamber. Finally, evaporative losses refer to the small portion of powder material that will evaporate under laser irradiation. The latent heat of vaporization is very large, so this has a strong cooling effect on the melt pool and can become a non-negligible source of energy loss as the total laser beam power becomes higher.

[0142] The thermal energy density (TED) metric is based on the measurement of optical light, which is the result of the radiation of light from the heated area, the transmission of the light back through the optical device, the collection of the light by the detector, and the conversion of the light into an electrical signal. The Stefan-Boltzmann equation shown below in equation (14) gives the equation that governs blackbody radiation over all possible frequencies:

[0143]

[0144] The variables from equation (14) are shown below in Table (1).

[0145]

[0146] Table (1)

[0147] There are other interfering factors that affect the radiated light before it is collected by the sensor and before it results in a voltage used to calculate the TED metric. These are summarized in equation (15):

[0148] V TED所使用的电压 = {P 辐射 –P V观察因子 –P 以辐射的波长的光学损耗 –P 传感器损耗因子}*(sensor scale factor) Formula (15)

[0149] These various terms from equation (15) are explained below in Table (2).

[0150]

[0151]

[0152] Table (2)

[0153] Typically in additive manufacturing, the figure of merit used is the global energy density (GED). This is a parameter that combines various process inputs, as shown in equation (16):

[0154] GED = (electron beam power) / {(travel speed) * (scanning pitch)} Formula (16)

[0155] We note that the unit of GED energy per area is: (joule / SEC) / {(CM / SEC)*(CM)}=joule / CM 2 . However, it should be noted that while GED may have the same units as TED, GED and TED are not generally equivalent. For example, TED is the radiant power from a high temperature region divided by the area, while GED is a measure of input power. As described herein, TED relates to a response or process output, while GED relates to a process input. Therefore, the inventors believe that TED and GED are different measurements from each other. In some embodiments, the area used to determine TED is different from the melt pool area. Therefore, some embodiments do not have a direct correlation between TED and the melt pool area. Advantageously, TED is sensitive to a variety of factors that directly affect the additive manufacturing process.

[0156] Thermal energy density based on monitoring of discrete wavelengths of light

[0157] Fig.18AAn exemplary graph 1800 is shown showing sensor readings taken by a spectrometer during a laser sintering additive manufacturing process using a powdered aluminum alloy. In some embodiments, the spectrometer may have a range between 0-1500 nm, which covers both the visible and near infrared spectra. Peak 1802, centered around 1064 nm, corresponds to the wavelength of the ytterbium-doped laser that serves as the energy source for the additive manufacturing process. Even when a baffle is mounted on the laser, the amplitude of peak 1802 may be artificially increased because light from the laser is reflected off other surfaces within the build chamber before being sensed by the spectrometer. Due to the amplitude of peak 1802, the spectrometer is saturated with light from that wavelength, causing light at other frequencies that could otherwise be captured by the spectrometer to be suppressed or, in some cases, completely blocked. For example, while peak 1804 may correspond to a blackbody radiation curve, the amplitude of the signal is too low for feature extraction due to saturation of the spectrometer by the laser.

[0158] Fig.18B An exemplary graph 1850 is shown depicting a portion of the sensor readings corresponding to the previously indicated peak 1804 acquired by the spectrometer after adding a bandpass filter to the spectrometer that blocks frequencies around the peak 1802. For example, the bandpass filter can be configured to remove frequencies of light between 1000 and 1100 nm. Removing these frequencies from the spectrometer causes the graph 1850 to follow the general shape of the blackbody radiation curve, except for certain spectral feature peaks 1852. The spectral peaks 1852 are caused by the material properties of the powder subjected to laser irradiation. In some embodiments, these spectral peaks may appear due to the presence of neutral atoms that make up the powder, ionized powder, and electrons from ionization. These spectral peaks 1852 are generally maintained at a fixed wavelength. This allows the selection of wavelengths 1854 and wavelengths 1856 at frequencies that deviate from the spectral peaks 1852. Although the size of the spectral peaks 1852 can vary with temperature, the wavelength range they cover remains essentially the same over a large temperature range. In some embodiments, it may also be desirable to establish a trend line that follows the shape of a black body curve and select wavelengths that are consistently on the trend line that defines the black body curve. This further helps prevent the selected wavelength 1854 or one of the wavelengths 1856 from being placed on a spectral feature that could negatively impact the accuracy of the temperature data derived from the sensor monitoring wavelengths 1854 and 1856.

[0159] The optical sensor monitoring wavelength 1854 and wavelength 1856 can be configured to monitor a narrow range of wavelengths between 0.5 nm and about 10 nm centered on the selected wavelength. The size of the range can depend on the application and characteristics of the powder and the energy source used. In some embodiments, two different optical sensors can be used to collect light emitted at wavelengths 1854 and 1856. The optical sensor can take the form of a photodetector, or more specifically, a photodiode with a dielectric multilayer wavelength notch filter that limits the light reaching the photodiode to a narrow range of wavelengths centered on wavelengths 1854 and 1856, respectively. Although wavelengths 1854 and 1856 are on opposite sides of the blackbody curve, it should be noted that the wavelengths can also be on the same side of the curve as long as the wavelengths do not overlap.

[0160] The ratio of the light intensity at wavelength 1854 to the light intensity at wavelength 1856 can be used to characterize changes or deviations in temperature at the build plane. These measurements are driven by thermal radiation from the melt pool and a glowing plume near the melt pool that is caused by the vaporization of a small portion of the metal powder. Most of the measurements come from the glowing plume because the glowing plume tends to mask the blackbody emission from the melt pool. This configuration of monitoring a very small range of light emanating from the build plane can prevent most of the inaccuracies caused by broad spectrum monitoring. For example, this monitoring method greatly reduces inaccuracies caused by reflections of the laser from the walls of the additive manufacturing apparatus. It should be noted that the wavelength of the blackbody radiation curve can vary substantially depending on the type of powdered metal being used. For example, while Fig.18A and 18B Corresponding to aluminum, which has a black body radiation curve extending between 400 nm and 900 nm, the black body radiation curve of titanium may be located between about 1400 nm and 1700 nm. For this reason, when the metal powder alloy is changed, the wavelengths 1854 and 1856 will be reselected. It may also be desirable to reselect the wavelengths 1854 and 1856 when the desired operating temperature changes. Operators may change the operating temperature of parts having the same type of metal alloy when a different material grain structure is desired.

[0161] Fig.19AA graph 1900 is shown showing a number of black body radiation curves representing various melt pool temperatures from 3500K to 5500K, and the graph is a representation of Planck's radiation equation. In the event that it is desired to maintain a melt pool temperature of 4000K throughout the additive manufacturing operation, light wavelengths 1902 and 1904 may be selected on either side of the peak of the black body radiation curve 1906. The ratio of the energy density recorded by a first sensor monitoring light wavelength 1902 to the energy density recorded by a second sensor monitoring light wavelength 1904 may be used to characterize the temperature and heat within the additive manufacturing system. When the ratio of these energy densities begins to increase, it may be determined that the temperature of the melt pool is increasing. Similarly, a decrease in the ratio value indicates a decrease in the build plane / melt pool temperature. Fig.19A It is shown that for blackbody radiation curve 1906, the ratio of intensities can be about 1.4, while for blackbody radiation curve 1908, the ratio of intensities can be about 1.5, and for blackbody radiation curve 1910, the ratio of energy density can be about 2.7. In some embodiments, this energy density ratio can be used in place of the raw intensity / energy density readings when calculating thermal energy density in order to more accurately characterize additive manufacturing operations.

[0162] In addition to providing a useful feature that scales predictably with changes in the melt pool temperature, the relative intensity of the light can also be used to directly measure temperature. The Stefan-Boltzman law (see equation (14) above) describes the relationship between power and temperature, and Planck's law describes the spectral density of electromagnetic radiation emitted by a black body in thermal equilibrium at a given temperature T. The law is named after Max Planck, who proposed it in 1900. The law can be adapted to solve for temperature as shown in equation (17) below:

[0163]

[0164] In equation (17), T corresponds to the temperature of the melt pool and h is Planck's constant (6.62 x 10 -34 J s), c is the speed of light in a vacuum, k B is the Boltzmann constant (1.38x10 -23 J / K), λ1 and λ2 are two discrete wavelengths of light, and is the light intensity at the two corresponding wavelengths. This equation allows the calibration temperature to be determined from the readings taken by the two sensors sampling the two discrete wavelengths of light.

[0165] Fig.19BGraph 1950 is shown, along with how the percentage change in power output affects the natural logarithm of the ratio of the intensity sensed at the first wavelength and the second wavelength. Graph 1950 shows how the percentage change in power is directly proportional to the natural logarithm of the light intensity sensed at the first wavelength and the second wavelength. This consistent correlation allows for the development of a lookup table that accurately tracks changes in energy input on the powder bed. In this way, any substantial change in the intensity ratio of this logarithm can correspond to undesirable problems in the build process.

[0166] Fig. 20A An exemplary additive manufacturing system is shown equipped with three optical sensors, wherein two optical sensors monitor discrete wavelengths of light to characterize temperature changes occurring on a build plane in real time, and a third optical sensor is configured to measure the temperature of the material as described above with respect to FIG. 18A to FIG. 19B The thermal energy density is sensitive to changes in process parameters such as energy source power, energy source speed, and scanning spacing. Fig. 20A The additive manufacturing system of uses a laser 2000 as an energy source. Laser 2000 emits a laser beam 2001, which passes through a partially reflective mirror 2002 and then enters a scanning and focusing system 2003, which then projects the beam to an area 2004 on a build plane 2005. In some embodiments, build plane 2005 is a powder bed. Due to the high material temperature and the emissivity properties of the material receiving the irradiation by laser beam 2001, light energy 2006 is emitted from area 2004.

[0167] In some embodiments, the scanning and focusing system 2003 can be configured to collect some of the light energy 2006 emitted from the region 2004. In some embodiments, the molten pool and the luminous plume can cooperatively emit black body radiation from within the region 2004. The molten pool is the result of the liquefaction of the metal powder due to the energy applied by the laser beam 2001, and it causes most of the emission of light energy 2006 to be reflected back to the focusing system 2003. The luminous plume is generated by the vaporization of a portion of the metal powder. The partially reflective mirror 2002 can reflect most of the light energy 2006 received by the focusing system 2003. This reflected energy is indicated as light energy 2007 in FIG. 20. The light energy 2007 can be interrogated by the coaxial optical sensors 2009-1 and 2009-2. Each coaxial optical sensor 2009 receives a portion of the light energy 2007 through the reflectors 2008-1 and 2008-2. In some embodiments, the reflectors 2008 can be configured to reflect only wavelengths λ1 and λ2, respectively. In some embodiments, the optical sensors 2009-1 and 2009-2 receive a total of 80-90% of the light reflected by the optical system. The optical sensors 2009-1 and 2009-2 may also include notch filters configured to block any light outside of the respective wavelengths λ1 and λ2. The third optical sensor 2009-3 may be configured to receive light from the partially reflective mirror 2002. As depicted, additional mounting points may be included to allow the third optical sensor 2009-3 to be mounted. In some embodiments, the optical sensors 2009-1 and 2009-2 may be covered by notch filters, while the third optical sensor 2009-3 may be configured to measure a wider range of wavelengths. In some embodiments, the optical sensors 2009-1 or 2009-2 may be replaced by a spectrometer configured to perform an initial characterization of a blackbody radiation curve associated with a batch of powder used to perform an additive manufacturing process. This characterization can then be used to determine how to configure the wavelength filters of the optical sensors 2009-1 and 2009-2 to deviate from and avoid any spectral peaks associated with the blackbody curve characterized by the spectrometer. This characterization is performed prior to performing a full additive manufacturing operation.

[0168] It should be noted that the collected optical energy 2007 may not have the same spectral content as the optical energy 2006 emitted from the beam interaction region 2004 because the optical energy 2007 has undergone some attenuation after passing through multiple optical elements (e.g., partial reflector 2002, scanning and focusing system 2003, and one or more of partial reflectors 2008). These optical elements may each have their own transmission and absorption characteristics, resulting in varying amounts of attenuation, thereby limiting certain portions of the spectrum of energy radiated from the beam interaction region 2004. The data generated by the coaxial optical sensor 2009 can correspond to the amount of energy applied to the work platform. This allows the notch characteristic wavelength to be selected to avoid frequencies that are overly attenuated by the absorption characteristics of the optical elements.

[0169] Examples of coaxial optical sensors 2009 include, but are not limited to, light to electrical signal transducers (i.e., photodetectors), such as pyrometers and photodiodes. Optical sensors may also include spectrometers, and low or high speed cameras operating in the visible, ultraviolet, or infrared spectrum. Coaxial optical sensors 2009 are in a reference frame that moves with the beam, i.e., they see all areas touched by laser beam 2001 as it scans across build plane 2005, and are able to collect light energy 2007 from all areas of build plane 2005. Because the light energy 2006 collected by scanning and focusing system 2003 travels nearly parallel to the path of the laser beam, sensors 2009 may be considered coaxial sensors.

[0170] In some embodiments, the additive manufacturing system may include off-axis sensors in a fixed reference frame relative to the laser beam 2001. Additionally, there may be contact sensors on the recoater arm that are configured to spread the metal powder across the build plane 2005. These sensors may be accelerometers, vibration sensors, etc. Finally, there may be other types of sensors, such as thermocouples for measuring macroscopic thermal fields, or acoustic emission sensors may be included that can detect cracks and other metallurgical phenomena that occur in the deposit being built.

[0171] In some embodiments, a computer 2016 including a processor 2018, a computer readable medium 2020, and an I / O interface 2022 is provided, which is coupled to appropriate system components of the additive manufacturing system to collect data from various sensors. The data received by the computer 2016 may include raw sensor data and / or reduced-order sensor data in processing. The processor 2018 may use the raw sensor data and / or reduced-order sensor data in processing to determine power and control information for the laser 2000, including coordinates relative to the build plane 2005. In other embodiments, the computer 2016 (including the processor 2018, the computer readable medium 2020, and the I / O interface 2022) may provide control of various system components. The computer 2016 may send, receive, and monitor control information associated with the laser 2000, the build plane 2005, and other associated components and sensors.

[0172] The processor 2018 can be used to perform calculations using data collected by various sensors to generate in-process quality metrics. In some embodiments, the data generated by the coaxial optical sensor 2009 can be used to determine the thermal energy density during the build process. The processor can receive control information associated with the movement of the energy source across the build plane. The processor can then use the control information to associate the data from (one or more) coaxial optical sensors 109 and / or (one or more) off-axis optical sensors 110 with the corresponding position. The associated data can then be combined to calculate the thermal energy density. In some embodiments, the processor 2018 can generate control signals for process parameters (such as laser power, laser speed, hatch spacing, and other process parameters) using thermal energy density and / or other metrics in response to thermal energy density or other metrics exceeding the expected range. In this way, problems that may otherwise damage the production of parts can be improved. In embodiments where multiple parts are generated at a time, timely correction of process parameters in response to metrics exceeding the expected range can prevent adjacent parts from receiving too much or too little energy from the energy source.

[0173] In some embodiments, the I / O interface 2022 can be configured to transmit the collected data to a remote location. The I / O interface 2022 can be configured to receive data from a remote location. The received data may include a baseline data set, historical data, post-processing inspection data, and classifier data. The remote computing system can use the data transmitted by the additive manufacturing system to calculate in-process quality metrics. The remote computing system can transmit information to the I / O interface 122 in response to a specific in-process quality metric. It should be noted that the sensors described in conjunction with FIG. 20 can be used in the manner described to characterize the performance of any additive manufacturing process involving sequential material accumulation.

[0174] Although the embodiments described herein have used data generated by optical sensors to determine thermal energy density, the embodiments described herein may be implemented using data generated by sensors that measure other manifestations of physical variables in the process. Sensors that measure manifestations of physical variables in the process include, for example, force and vibration sensors, contact thermal sensors, non-contact thermal sensors, ultrasonic sensors, and eddy current sensors. It will be apparent to those of ordinary skill in the art that many modifications and variations are possible in light of the above teachings.

[0175] Fig. 20B Shown with Fig. 20A A similar configuration, except that the sensor assembly 2010 can be attached to the optics of the laser 2000 via an optical cable 2012. This allows the sensor assembly 2010 to be separated from the additive manufacturing device. In some embodiments, the optical cable can include a splitter that replaces the optical device 2008, thereby allowing each of the optical sensors 2009-1, 2009-2, and 2009-3 to be supplied by a separate optical cable. The splitter can also be configured to distribute the appropriate portion of the light 2007 to each optical sensor. In this way, the sensors can be further separated. The sensor assembly 2010 will still be in electrical communication with the computer 2016, so that the various types of control described above can be fully implemented regardless of the location of the sensor assembly 2010.

[0176] Fig.21A block diagram illustrating a method for measuring light emission during an additive manufacturing process is shown. At 2102, during the additive manufacturing process, radiation from a molten pool is collected using a spectrometer. Light emission from an energy source used to create the molten pool would typically saturate the readings taken by the spectrometer. At 2104, a wavelength range corresponding to light emission performed by the energy source and recorded by the spectrometer is identified. In some embodiments, the range is determined by including any wavelength where the measured intensity exceeds a predetermined intensity threshold. After this wavelength range is identified, a bandpass filter can be added to the spectrometer that prevents light emission from the energy source from being picked up or sensed by the spectrometer. Once the bandpass filter is added to the spectrometer, the spectrometer can take new readings. Now that the spectrometer is no longer saturated with light emission from the energy source, the wavelengths spanned by the blackbody radiation curve can be identified. At 2106, spectrometer data corresponding to the identified blackbody radiation curve can be isolated. At 2108, spectral features within the identified blackbody radiation curve can be identified. These spectral features can take the form of discontinuities, such as sharp peaks or valleys, which differ greatly from the shape of the blackbody curve and are caused by various material properties of the metal powder. In optional step 2110, the wavelength corresponding to the peak of the blackbody radiation curve for a typical operating temperature is identified. From the blackbody curve, a trend line can be generated to help identify the peak. Identifying the blackbody peak at a particular operating temperature may be helpful when the operator wishes to select a wavelength pair that is on opposite sides of the blackbody curve.

[0177] Fig.21Including 2112, which describes the selection of wavelength pairs that avoid spectral characteristics and / or spectral peaks associated with the blackbody radiation curve. The selected wavelength pair can take the form of a 10nm wavelength range. In an embodiment where it is desired to place the wavelength pair on opposite sides of the blackbody curve, the wavelength of the first wavelength of the wavelength pair is shorter than the wavelength corresponding to the peak of the blackbody curve, and the wavelength of the second wavelength of the wavelength pair is longer than the wavelength corresponding to the peak of the blackbody curve. Although a single wavelength can be used to identify each wavelength in the wavelength pair, these pairs will generally adopt a narrow wavelength range with a width between 0.5nm and 10nm. At 2114, two photodetectors that can take the form of photodiodes are configured with notch filters that limit the sensing of the optical sensor to the wavelength of the corresponding one of the wavelength pairs. At 2116, the photodiodes can be calibrated and / or balanced so that the response curve of each is equivalent. Calibration can include measuring the performance of various test operations to confirm that the ratio of the intensity of the sensor readings obtained by the two sensors is consistent with the expected value. At 2118, the two sensors are configured to monitor and characterize the additive manufacturing process in real time. A processor responsible for making setting changes to the additive manufacturing process can be configured to adjust those settings in response to unexpected changes in the ratio of the intensities from the two optical sensors. For example, the ratio of the intensities or the natural logarithm of the ratio of the intensities can be used as a closed-loop feedback control system (similar to Fig.16 It should be noted that in the attached Figure 18A-21 The dual wavelength features discussed in the description of can be applied to any polymerization scheme described in other parts of this application.

[0178] In addition, it is usually performed any time the type or characteristics of the powder used change Fig.21 The calibration scheme described in . Even powders from the same manufacturer may change over time, resulting in slightly different compositions of the powders received. Changes in composition may result in additional spectral features / peaks, which may necessitate a shift in one or both of a pair of wavelengths in order to produce accurate temperature data.

[0179] Various aspects, embodiments, implementations or features of the described embodiments may be used alone or in any combination. Various aspects of the described embodiments may be implemented by software, hardware, or a combination of hardware and software. The described embodiments may also be implemented as computer-readable code on a computer-readable medium for controlling manufacturing operations, or may be implemented as computer-readable code on a computer-readable medium for controlling a production line. The computer-readable medium is any data storage device that can store data that can be subsequently read by a computer system. Examples of computer-readable media include read-only memory, random access memory, CD-ROM, HDD, DVD, magnetic tape, and optical data storage devices. The computer-readable medium may also be distributed on a network-coupled computer system so as to store and execute the computer-readable code in a distributed manner.

[0180] For the purpose of explanation, the foregoing description uses specific terms to provide a thorough understanding of the described embodiments. However, it will be apparent to those skilled in the art that specific details are not required to practice the described embodiments. Therefore, for the purpose of illustration and description, the foregoing descriptions of specific embodiments are presented. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It will be apparent to those skilled in the art that many modifications and variations are possible in view of the above teachings.

Claims

1. An additive manufacturing method, comprising: identifying at least one spectral peak associated with a material property of the powder; selecting at least a first wavelength that deviates from the identified at least one spectral peak; scanning an energy source across at least a portion of the layer of powder; measuring energy radiated from at least a portion of the layer of the powder at the first wavelength; A temperature of at least the portion of the layer of the powder is determined based at least in part on the energy measured at the first wavelength and the energy measured at the second wavelength.

2. The additive manufacturing method according to claim 1, wherein: The second wavelength is offset from the at least one spectral peak.

3. The additive manufacturing method according to claim 2, further comprising: Energy radiated from at least the portion of the layer of the powder at the second wavelength is measured.

4. The additive manufacturing method according to claim 3, further comprising: The temperature of at least the portion of the layer of the powder is determined based on a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.

5. The additive manufacturing method according to claim 3, further comprising: A temperature change of at least the portion of the layer of the powder is determined based on a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.

6. The additive manufacturing method according to claim 5, further comprising: It is determined that the temperature change is outside a threshold range.

7. The additive manufacturing method according to claim 1, further comprising: The area of ​​at least the portion of the layer of powder is determined by: determining a starting point for a first scan of the energy source; determining an endpoint of the first scan; as well as The length of the first scan is determined by calculating the distance between the starting point and the end point.

8. The additive manufacturing method according to claim 7, further comprising: Thermal energy density is mapped to locations within a part formed by the additive manufacturing method by the following steps: receiving energy source drive signal data indicating a path of the energy source across at least the portion of the layer of powder; as well as The scanned position is determined using the energy source drive signal data.

9. An additive manufacturing system, comprising: an energy source configured to direct an energy beam at a build plane including the powder layer; a sensor that identifies at least one spectral peak associated with a material property of the powder; a detector that measures an amount of energy radiated from the build plane as the energy source is scanned across an area of ​​the build plane, wherein the detector measures energy at a first wavelength that is offset from the at least one identified spectral peak; as well as A processor is configured to receive data from the detector and determine a temperature of the region of the build plane based at least in part on the energy measured at the first wavelength and the energy measured at the second wavelength.

10. The additive manufacturing system according to claim 9, wherein: The detector is a first detector that measures energy having a first wavelength, and the system further includes: a second detector that measures energy having a second wavelength, wherein the second wavelength is offset from the at least one spectral peak.

11. The additive manufacturing system according to claim 10, wherein: The processor is configured to receive data from the first detector and the second detector and determine a temperature of the region of the build plane based on a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.

12. The additive manufacturing system according to claim 10, wherein: The processor is configured to determine a temperature change in the region of the build plane based on a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.

13. The additive manufacturing system according to claim 12, wherein: The processor is configured to determine whether the temperature change is outside a threshold range.

14. The additive manufacturing system according to claim 13, wherein: The processor is configured to generate an alarm in response to the temperature change being outside of the threshold range.

15. The additive manufacturing system according to claim 9, further comprising: The area of ​​the region of the construction plane is determined by: determining a starting point for a first scan of the energy source; determining an endpoint of the first scan; as well as The length of the first scan is determined by calculating the distance between the starting point and the end point.

16. The additive manufacturing system according to claim 9, further comprising: Thermal energy density is mapped to locations within a part formed by the additive manufacturing system by the following steps: receiving energy source drive signal data indicating a path of an energy source across the region of the build plane; as well as The scanned position is determined using the energy source drive signal data.

17. A method of operating an additive manufacturing system, the method comprising: scanning a laser beam across an area of ​​a build plane, wherein the build plane includes a layer of powder configured to be fused by the laser beam, and the powder has at least one spectral peak associated with a material property of the powder; measuring energy radiated from the region of the build plane during the scan at a first wavelength, wherein the first wavelength is offset from at least one spectral peak of the powder; A temperature of the region of the build plane during the scan is determined based at least in part on the energy measured at the first wavelength and the energy measured at the second wavelength.

18. The method according to claim 17, wherein: The at least one spectral peak is based on material properties of the powder when the powder is subjected to laser irradiation.

19. The method according to claim 17, wherein: The at least one spectral peak and the first wavelength are determined using a spectrometer.

20. The method of claim 17, further comprising: Energy radiated from the region of the build plane during the scan at a second wavelength is measured, wherein the second wavelength is offset from the at least one spectral peak of the powder.

21. The method according to claim 20, further comprising: The temperature of the region is determined based on a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.

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