Machine learning with fast feature generation for selective laser melting print parameter optimization
Abstract:
A method includes identifying machine process parameters for an additive manufacturing process to produce a part, providing a real-world sensor to sense a characteristic associated with a real-world version of the additive manufacturing process, receiving sensor readings from the real-world sensor while the machine is performing the real-world version of the additive manufacturing process, generating, with a computer-based processor, physics-based features associated with the additive manufacturing process, and training a machine-learning software model based at least in part on the machine process parameters, the sensor readings, and the physics-based features to predict a behavior of the real-world sensor.
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