Long Short-Term Memory Neural Network for Temperature Prediction in Laser Powder Bed Additive Manufacturing

  title={Long Short-Term Memory Neural Network for Temperature Prediction in Laser Powder Bed Additive Manufacturing},
  author={Ashkan Mansouri Yarahmadi and Michael Breu{\ss} and Carsten Hartmann},
. In context of laser powder bed fusion (L-PBF), it is known that the properties of the final fabricated product highly depend on the temperature distribution and its gradient over the manufacturing plate. In this paper, we propose a novel means to predict the temperature gradient distributions during the printing process by making use of neural networks. This is realized by employing heat maps produced by an optimized printing protocol simulation and used for training a specifically tailored… 

Long Short-Term Memory

A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.

Adam: A Method for Stochastic Optimization

This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

Dropout: a simple way to prevent neural networks from overfitting

It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.

Three-dimensional finite element analysis simulations of the fused deposition modelling process

Abstract Fused deposition modelling (FDM) is a layer-manufacturing technology that has been widely used for rapid prototyping applications in product design and development. Owing to the intensive