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Comparison of early stopping neural network and random forest for in-situ quality prediction in laser based additive manufacturing
Matthew Behnke
,
Shenghan Guo
, Weihong Guo
Research output
:
Contribution to journal
›
Conference article
›
peer-review
17
Scopus citations
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Dive into the research topics of 'Comparison of early stopping neural network and random forest for in-situ quality prediction in laser based additive manufacturing'. Together they form a unique fingerprint.
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Keyphrases
Additive Manufacturing Process
66%
Complex Parts
11%
Early Stopping
100%
Engineering Application
11%
In Situ
100%
In Situ Techniques
11%
In-situ Monitoring
11%
Large Array
11%
Laser Additive Manufacturing
100%
Melt Pool
11%
Melt Pool Characteristics
11%
Multiple Functionalities
11%
Neural Network
100%
Porosity
11%
Porosity Defects
11%
Pyrometer
11%
Quality Prediction
100%
Random Forest
100%
Random Forest Classifier
11%
Speed Monitors
11%
Engineering
Additive Manufacturing
100%
Additive Manufacturing Process
100%
Engineering Application
16%
Melt Pool
33%
Porosity
33%
Random Forest
100%
Chemical Engineering
Neural Network
100%
Material Science
Three Dimensional Printing
100%