Prediction Method of Draw Resistance in Handmade Cigar Embryos Based on Neural Network
The suction resistance of cigar wrappers,serving as a core metric in the design and manufacturing of cigars,is influenced by numerous factors exhibiting complex nonlinear characteristics.Traditional empirical models,derived from extensive practical experience,often struggle to provide accurate quantitative guidance for the design and production processes of cigars.To rapidly and conveniently determine the suction resistance of full-leaf cigar wrappers,a multi-layer perception neural network prediction model was established,utilizing nine key parameters from the cigar rolling process as the input layer.Upon validation,the prediction model demonstrated a sum of squared errors of 4.908 and a relative error of 0.572.Notably,within the suction resistance range of(300-500)Pa,the model's predictions aligned closely with actual measurements.Additionally,neural network models were developed by incorporating two cigar rolling techniques used in production,and subsequent production validation confirmed that the differences between the model's predictions and actual values were insignificant.This successful implementation enables rapid prediction of wrapper suction resistance in production scenarios.
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