New Mexico Patent of the Month – January 2023
Additive manufacturing is a rapidly growing industry, with a wide range of applications and processes used to create 3D parts. One of the major challenges in this field is the detection and characterization of defects that can occur during the manufacturing process. This can lead to reduced material properties and premature failure of the finished part. Sigma Labs, Inc. is tackling this problem using machine learning.
Sigma Labs’ technology follows an additive manufacturing process wherein metallic powder is poured and melted together layer by layer. This sintering is achieved by a power source that emits a beam of energy to fuse the powder. A processor then converts data from a set of sensors into parameters used as input into a machine learning algorithm. The machine learning algorithm is trained to detect different types of in-process defects that occur during the manufacturing process, such as lack of fusion, porosity, or inclusion defects. The processor calculates parameters based on the electromagnetic energy emitted during the fusing of the metallic powder, such as thermal emission density (TED) or thermal emission profile (TEP).
The machine learning algorithm used by Sigma Labs is based on a random forest model and is trained using known-defective parts. This allows the algorithm to recognize and distinguish between different types of defects and determine the best course of action for each situation. For example, if a lack of fusion defect is detected, the operator can decide to repair the part or discontinue building it.
This technology provides several benefits over conventional techniques, including the ability to detect defects in-process and correct them, resulting in improved manufacturing efficiency. The defect identification also enables the operator to make a more informed decision during the build, improving the quality and reliability of the finished part. With Sigma Labs’ solution, additive manufacturing can become a more reliable and efficient process, leading to greater adoption and growth in the industry.
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