Process Quality Prediction Algorithm of Multi output Workshop Based on ATT-CNN-TCN

Abstract

In the view of the existing workshop process quality prediction method for the process parameters related timing information mining is not sufficient, existing research does not consider the contribution of different characteristics to the prediction target difference, this paper proposes the fusion of attention mechanism, convolutional neural network and time convolutional network. The attention module adaptively allocates weight information to the input features, convolutional neural network module to deeply mine the correlation information between process parameters was used, extracts the temporal information between process sequences with time convolutional neural learning, and finally superposition the full connection network mapping to obtain the workshop process quality prediction value. After example verification, the experimental results show that the constructed model is better than other process quality prediction models in the prediction accuracy, stability and network structure.