A Statistical Transfer Learning Perspective for Modeling Shape Deviations in Additive Manufacturing

@article{Cheng2017AST,
  title={A Statistical Transfer Learning Perspective for Modeling Shape Deviations in Additive Manufacturing},
  author={Longwei Cheng and Fugee Tsung and Andi Wang},
  journal={IEEE Robotics and Automation Letters},
  year={2017},
  volume={2},
  pages={1988-1993}
}
Quality control of additive manufacturing applications is required to improve the shape fidelity of the products, which relies on increasing the predictive performance of statistical deviation models for any new shape. Building a single comprehensive model for a wide range of shapes is a very challenging problem, since the error generating mechanism of additive manufacturing applications is usually of high complexity, the amount of training data is usually limited, and the connection among… CONTINUE READING

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