目的 针对模切机模切压力难以在线直接测量的问题,构建一种基于结构微变形的间接测量方法,实现模切压力的准确获取与实时预测,从而提高模切过程的稳定性和纸板成型质量。方法 以平压平模切机为研究对象,基于应变片传感器和压力胶片,提取不同加载压力下的上平台压力与微应变数据对应数据,并引入BP神经网络与PSO（粒子群优化）算法进行非线性建模与参数优化,从而建立模切压力与上平台微应变的关联模型。结果 上平台微应变与模切压力之间呈显著非线性关系,传统线性方法难以准确表征。构建的PSO-BP模型在训练集和测试集上均表现出良好的预测性能,模型拟合精度明显优于单一传统模型,预测误差较小,能够有效反映不同压力工况下的变化规律。结论 该模型能够准确建立模切压力与上平台微应变之间的映射关系,结果表明通过测量结构变形实现模切压力的间接获取是可行且有效的。
Abstract
To address the challenge of direct online measurement of die-cutting pressure in die-cutting machines, the work aims to propose an indirect measurement method based on structural micro-deformation analysis, enabling accurate pressure acquisition and real-time prediction to enhance process stability and cardboard forming quality. With flat-bed die-cutting machines as research subjects, strain gauge sensors and pressure films were employed to extract upper platform pressure and micro-strain data under varying loading conditions. A backpropagation (BP) neural network combined with particle swarm optimization (PSO) algorithm was applied for nonlinear modeling and parameter optimization, establishing a correlation model between die-cutting pressure and upper platform micro-strain. Significant nonlinear relationships were observed between upper platform micro-strain and die-cutting pressure, which could not be accurately characterized by traditional linear methods. The developed PSO-BP model demonstrated superior predictive performance across both training and test datasets, showing markedly better fitting accuracy compared to conventional single-model approaches with smaller prediction errors that effectively reflected pressure variation patterns under different operating conditions. This model successfully establishes a precise mapping relationship between die-cutting pressure and upper platform micro-strain, validating the feasibility and effectiveness of indirect pressure measurement through structural deformation monitoring.
关键词
模切机 /
有限元分析 /
上平台变形 /
BP神经网络 /
PSO优化
Key words
die-cutting machine /
finite element analysis /
upper platform deformation /
BP neural network /
PSO optimization
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基金
北京市教育委员会-市自然科学基金联合资助项目（KZ202210015019）
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