Patent ID: 11958251
Assignee: UNIVERSITY OF TENNESSE RESEARCH FOUNDATION
Field: Measurement (Instruments)
Classification: CPC B  A  G  Y | IPC A  B  G

Claim 1:
2. An additive manufacturing system comprising:
at least one electronic nose comprising:
a housing having openings on corresponding ends thereof to enable air flow, the housing having an air channel for air to flow between the ends;
a plurality of thin film gas sensors, each having an active sensor portion in the air channel; and
a mount configured to mount the housing to an extruder head of an additive manufacturing device, and

a processor configured to:
supply power to the plurality of thin film gas sensors to bias the sensors;
receive output from each of the plurality of thin film gas sensors;
determine whether there is an abnormality in an additive manufacturing process manufacturing a product from one or more materials or composite material based on one or more combinations of the output from each of the plurality of thin film gas sensors during the additive manufacturing process and from a deployed machine learning model; and
generate a report for the additive manufacturing process containing the abnormality determination, wherein the received the output from each of the plurality of thin film gas sensors is in response to heating materials or composite materials at different temperatures, one of the materials or composite materials at a time and wherein the processor is configured to:
generate randomly a first dataset for training and a second dataset for testing a plurality of models using the received output;
train and test the plurality of models using one or more combinations of the output from each of the plurality of thin film gas sensors, the plurality of models is generated using a plurality of different machine learning techniques, the training based on the first dataset and the testing based on the second dataset; and
evaluate a prediction accuracy of each of the plurality of models using an
evaluation parameter and select a model from among the plurality of models to deploy as the deployed machine learning model for detecting the abnormality in the additive manufacturing process based on a comparison of the evaluation parameter for each of the plurality of models.