Abstract: A computer-implemented system and method of rapidly predicting at least one of residual stress and distortion of a quenched aluminum casting. Input data corresponding to at least one of topological features, geometrical features and quenching process parameters associated with the casting is operated upon by the computer that is configured as a neural network to determine output data corresponding to at least one of the residual stress and distortion based on the input data. The neural network is trained to determine the validity of at least one of the input data and output data and to retrain the network when an error threshold is exceeded. Thereby, residual stresses and distortion in the quenched aluminum castings can be predicted using the embodiments in a tiny fraction of the time required by conventional finite-element based approaches.

Abstract: A computer-implemented system and method of rapidly predicting at least one of residual stress and distortion of a quenched aluminum casting. Input data corresponding to at least one of topological features, geometrical features and quenching process parameters associated with the casting is operated upon by the computer that is configured as a neural network to determine output data corresponding to at least one of the residual stress and distortion based on the input data. The neural network is trained to determine the validity of at least one of the input data and output data and to retrain the network when an error threshold is exceeded. Thereby, residual stresses and distortion in the quenched aluminum castings can be predicted using the embodiments in a tiny fraction of the time required by conventional finite-element based approaches.