Abstract
Roll-to-Roll (R2R) processing is a common processing method for flexible photoelectric film materials. Due to the physical properties of the materials, the change in the performance of the R2R processing equipment can easily cause deformation of the flexible film material, it is particularly important to predict the performance degradation of the processing equipment. Based on the accuracy and real-time requirements of performance degradation prediction, a PTS-FNN model for performance degradation prediction was proposed in this paper, which combines the Possibilistic C-Means (PCM) fuzzy clustering and Takagi–Sugeno Fuzzy Neural Network (TS-FNN). We also studied the PCM classification algorithm of input data of PTS-FNN model, the predecessor network of TS-FNN prediction model and the construction method of post-component network. Finally, the implementation process of PCM classification algorithm and TS-FNN prediction model were given. The R2R processing equipment health prediction experiment system was built and the PTS-FNN model experiment was carried out. The experimental results showed that the training time of PTS-FNN model was 50.37% less than the standard TS-FNN prediction model. The prediction accuracy increased by 5.48%, and the PTS-FNN had no error in the judgment of state 1 and state 4.
Highlights
Roll-to-Roll (R2R) processing technique is the most widespread processing method for a series of flexible thin film materials internationally, for it can maintain the enhancement its productivity while automatizing the mechanical equipment to the greatest extent
In order to verify the efficiency of the health prognosis PTSFNN model for R2R processing device this paper proposed, the R2R continuous manufacturing device shown in Figure 4 is introduced as the experiment platform, its principle of operation is shown as Figure 5
In order to verify the validity of the PTS-FNN model proposed, select 240 sets of the 320 sample sets to build the PTS-FNN model and the standard Takagi–Sugeno Fuzzy Neural Network (TS-FNN) [7] prognosis model, respectively, and repeat training both of the models 20 times each to establish the average time of model building and training
Summary
Roll-to-Roll (R2R) processing technique is the most widespread processing method for a series of flexible thin film materials internationally, for it can maintain the enhancement its productivity while automatizing the mechanical equipment to the greatest extent. Because the neural network modeling has the characteristics to deal with the nonlinear approximation problem well, the PCM clustering method can well divide the data with irregular boundaries. The following content will be binding the PCM clustering analyzing method and the Takagi–Sugeno Fuzzy Neural Network (TS-FNN) to discuss the health state prognosis modeling of the R2R processing device. E PCM algorithm can adjust the clustering center, radius, and number of clusters of the input space; rationally divide the ambiguity of the input data; and determine the membership function of the data points and the rule adaptability; this PCM algorithm achieves basic elimination of the low quality samples to participate in the antecedent networks calculation, and model training speed and accuracy can be greatly improved. In equation (1), i 1, 2, ···, c; j 1, 2, ···, p; k 1, 2, ···, n. en the target function of PCM: Consequent network Subnetwork 1 p11i
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