Abstract:

The stability of cigarette ventilation rate is affected by multiple factors, such as laser drilling methods and equipment parameters. This makes it difficult for traditional methods to fully capture the complex interactions among these factors and fail to effectively adapt to the needs of modeling long-range dependencies, thus struggling to accurately control the precision of cigarette ventilation rate. To address this challenge, a laser drilling ventilation rate prediction model for the cigarette manufacturing process based on the adaptive multi-scale temporal attention network (AMTA-Net) was proposed. Firstly, to resolve the dimension mismatch between laser drilling process parameters and cigarette categories, a category feature embedding module for laser drilling was designed. By integrating one-hot encoding with a learnable embedding matrix, feature fusion of cigarette category information and drilling process data is achieved. Secondly, to capture the complex nonlinear relationship between hole morphology and cigarette ventilation rate, a multi-scale feature fusion module named ELAN-1D was proposed. This module adopts a two-layer convolutional architecture, realizing deep extraction of temporal features and effective capture of local-global information through convolution kernels with different dilation rates. Finally, to characterize the spatial heterogeneity and sequential correlation of hole morphology features, a sequential attention feature fusion module was constructed. Combining multi-scale sequential convolution with a cross-attention mechanism, this module implemented a “channel-sequence” dual-dimensional cross-logic modeling for the mapping relationship between drilling morphology and ventilation rate. Experimental results demonstrated that the mean squared error (MSE) of the proposed AMTA-Net for filter ventilation rate and total ventilation rate prediction reached as low as 6.799×10^-4 and 6.874×10^-4, respectively. Compared with baseline models, the proposed method reduced the MSE of filter ventilation rate and total ventilation rate by 28.96% and 20.61%, respectively, with the mean absolute error (MAE) decreased by 18.16% and 11.51%. In contrast to traditional models, the MSE and MAE of AMTA-Net were reduced by more than 15.64% and 8.05%, respectively. The method proposed provided robust model support for the precise regulation of laser drilling processes and for achieving cigarette tar reduction and harm mitigation.

Key words: cigarette rolling process, laser drilling, ventilation rate prediction, deep learning, Transformer