Dear Colleague, The AM industry has seen many important advancements in quality monitoring and Sigma…
Machine learning facilitates the continuous advancement of computing through exposure to new scenarios, testing and adaptation while employing pattern and trend detection for improved decisions in subsequent situations.
Sigma Labs has proven that machine learning can accurately predict where anomalies in 3D printed metal parts are likely to occur.
In a recently published quality assurance case study, our team at Sigma Labs used in-process metrics from specific parts builds to train machine learning models to diagnose how well the model predictions compared to each of four training labels (lack of fusion, gas porosity, keyhole, and tungsten inclusions). We found that the models could be well trained to recognize their own anomaly types and avoid flagging other anomaly types.
Sigma’s Labs’ PrintRite3D® software, using machine learning, can accurately predict the size and position of anomalies previously identified by CT scanning. Prediction metrics like these can make it possible in the future to close the loop on controlling the build process. When production managers can predict anomalies, they can decide whether to interrupt or adjust the build process or even redesign the part. This will ultimately save time, money, and materials, enhancing the manufacturer’s bottom line.