AbstractWith the continuous improvement of workshop production process, the complexity of workshop production is also increasing, which aggravates the difficulty of workshop process parameter prediction. In view of the above problems, this paper proposes a process quality prediction method based on a hybrid model of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The data set features composed of massive process parameter data, quality parameter data and date information were analyzed for correlation. After filtered the features most relevant to the quality indicators of the target variables, the features were used as input to construct a continuous feature map according to the time sliding window. Firstly, CNN were used to extract the feature vector, which was constructed in the form of time series and used as the input data of LSTM network, and then LSTM network was used to predict the process quality. Using the proposed method to predict the production line data of a workshop in Southwest China. The experimental results show that the material moisture and environmental temperature and humidity have a great impact, so the data set is divided according to the season to verify the model. Compared with the traditional prediction method, the Random Forest model prediction method and the standard LSTM network prediction method, the prediction method proposed in this paper has higher prediction accuracy.KeywordsProcess manufacturingTemporal sequenceCNN_LSTMProcess quality prediction 

Process Quality Prediction Method Based on CNN_LSTM Hybrid Neural Network Model

AbstractIn order to improve the calculation speed in the simulation and analysis of the temperature field of the leaf moistening machine rotary roller and reveal the variation law of the environmental temperature and humidity in the roller, a reconstructed algorithm of the temperature field of the leaf moistening machine roller based on spatial interpolation was proposed. Firstly, the simulation model of the leaf moistening machine roller is established through local simplification, and the numerical simulation and analysis of the temperature field of the roller is carried out by the equivalent substitution method, based on which the grid of the alternative model is restored. Then the spatial interpolation algorithm is used to solve the grid node data of the target model, and the search domain division and clustering algorithm are combined to realize the rapid reconstruction of the temperature field in the process of simulation and analysis. The effectiveness of the method is verified by taking the roller of a tobacco factory’s loose moisture regain leaf machine as an example. This research provides a new idea for effectively revealing the change law of environmental temperature and humidity of closed roller equipment in cut tobacco production line.KeywordsCigarette processSimulation analysisTemperature fieldEquivalent substitutionReconstructed algorithm 

Nont Sarnrak

Papers

Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis