Defect identification using machine learning in an additive manufacturing system
An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder.
1. An additive manufacturing system comprising:
a recoater arm arranged to distribute layer of metallic powder across a build plane;
a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part; and
a processor configured to:
access a part geometry for the part;
steer the beam of energy across the build plane based on the part geometry;
receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder;
convert the received data into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder;
select a machine learning algorithm based on at least in part on the part geometry;
use the one or more parameters as input into the machine learning algorithm to generate an output from the machine learning algorithm; and
use the output of the machine learning algorithm to detect one or more defects in the fused metallic powder.
2. The additive manufacturing system of claim 1 wherein the machine learning algorithm is further configured to determine a type of the one or more defects.
3. The additive manufacturing system of claim 2 wherein the type of the one or more defects includes at least one of a lack of fusion defect, a porosity defect or an inclusion defect.
4. The additive manufacturing system of claim 1 wherein the one or more parameters includes determining a thermal emission density (TED) that includes measuring an amount of energy radiated from the build plane during one or more scans and determining area of the build plane traversed during the one or more scans.
5. The additive manufacturing system of claim 1 wherein the one or more parameters includes identifying spectral peaks associated with material properties of a batch of powder and selecting a first wavelength and a second wavelength spaced apart from the first wavelength, and determining an amount of energy radiated from the build plane based upon a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.
6. The additive manufacturing system of claim 1 wherein the machine learning algorithm includes one or more training parameters based on a known-defective part.
7. The additive manufacturing system of claim 6 wherein the one or more training parameters are derived from a known-defective part having at least one void.
8. The additive manufacturing system of claim 6 wherein the one or more training parameters are derived from a known-defective part having at least one inclusion.
9. The additive manufacturing system of claim 1 wherein the one or more sensors includes an on-axis photodetector.
10. The additive manufacturing system of claim 1 wherein the detecting one or more defects further comprises a processor configured to determine whether the defect can be remedied.
11. The additive manufacturing system of claim 1 wherein the part geometry is received as a stereolithography (STL) file.