Systems and methods for additive manufacturing operations
This disclosure describes various system and methods for monitoring photons emitted by a heat source of an additive manufacturing device. Sensor data recorded while monitoring the photons can be used to predict metallurgical, mechanical and geometrical properties of a part produced during an additive manufacturing operation. In some embodiments, a test pattern can be used to calibrate an additive manufacturing device.
1. A method for calibrating an additive manufacturing device, the method comprising:
monitoring photons emitted as energy impacts metal powder spread across a build plane during a first additive manufacturing operation performed by a first additive manufacturing device, the monitoring being performed using one or more optical sensors;
recording sensor data captured by the one or more optical sensors;
identifying one or more actuations of a heat source of the first additive manufacturing device using changes in intensity from the recorded sensor data;
counting a number of actuations of the heat source;
calculating a first best fit curve over time based on the changes in intensity and counted number of actuations;
comparing the first best fit curve to a second best fit curve based on sensor data collected during a second additive manufacturing operation performed by a second additive manufacturing device;
determining differences between the first and second best fit curves; and
calibrating the second additive manufacturing device using the determined differences.
2. The method as recited in claim 1 , wherein at least one optical sensor of the one or more optical sensors is a photon to electrical signal transducer.
3. The method as recited in claim 1 , further comprising:
extracting a heating rate, cooling rate and average intensity associated with energy impacting the metal powder from the sensor data.
4. The method as recited in claim 3 , wherein comparing the first and second best fit curves comprises determining whether or not the second best fit curve falls within a predetermined threshold range of the first best fit curve.
5. The method as recited in claim 4 , wherein the predetermined threshold range is a 95% statistical confidence level.
6. The method as recited in claim 1 , wherein the first additive manufacturing device is different than the second additive manufacturing device.
7. The method as recited in claim 1 , wherein the additive manufacturing operations comprise a test pattern.
8. The method as recited in claim 7 , wherein an intensity of the emitted photons varies over the course of the test pattern.
9. The method as recited in claim 1 , further comprising:
comparing an internal grain structure of parts produced by both the first and second additive manufacturing operations to verify the calibration of the second additive manufacturing device.
10. The method as recited in claim 1 , wherein the one or more optical sensors comprises an off-axis photodiode and an on-axis photodiode.
11. A method for an additive manufacturing device, comprising:
conducting an additive manufacturing operation to build a test part during which operating parameters of the additive manufacturing device are varied;
absorbing photons emitted from a build plane of the additive manufacturing device using a photodiode;
extracting features from variations in a number of photons absorbed by the photodiode, extracting features comprising:
identifying one or more actuations of a heat source of the additive manufacturing device using changes in intensity recorded by the photodiode;
counting a number of actuations of the heat source; and
calculating a best fit curve over time for the number of actuations; and
comparing the extracted features to baseline features.
12. The method as recited in claim 11 , wherein the baseline features are extracted by carrying out the same additive manufacturing operation on another additive manufacturing device.
13. The method as recited in claim 11 , wherein the photodiode and the heat source of the additive manufacturing device use the same optical path.
14. The method as recited in claim 11 , wherein the extracted features are selected from a group consisting of an average heat source intensity, a peak temperature, a cooling rate and a heating rate.
15. The method as recited in claim 11 , wherein a pyrometer with a field of view covering a portion of the build plane is also used to monitor heat emitted from the build plane.
16. The method as recited in claim 11 , wherein the baseline features were extracted by carrying out the same additive manufacturing operation on the same additive manufacturing device at an earlier time.
17. A calibration system configured to measure performance of an additive manufacturing device having a heat source and a build plane, the calibration system comprising:
an optical sensor suite configured to absorb photons emitted from the build plane during an additive manufacturing operation;
a data storage device configured to store readings taken by the optical sensor suite; and
a processor configured to:
extract features from the stored readings by identifying one or more actuations of the heat source of the additive manufacturing device using changes in intensity recorded by the optical sensor suite, counting a number of actuations of the heat source, and calculating a best fit curve over time for the counted number of actuations;
compare the extracted features to a set of baseline features associated with another additive manufacturing device or with the same additive manufacturing device.
18. The calibration system as recited in claim 17 , wherein the optical sensor suite comprises an off-axis photodiode and an on-axis photodiode.
19. The calibration system as recited in claim 17 , wherein data associated with the extracted features is combined and standardized to characterize the performance of the additive manufacturing device.
20. The calibration system as recited in claim 17 , wherein the processor is further configured to generate updated operating parameters for the additive manufacturing device.