基于SVM算法的纸张生产质量自动化测试

Automated Testing of Paper Production Quality Based on SVM Algorithm

由于外界环境污染、操作不当、设备污损等多重因素,纸张生产时会产生性能与表观等层面质量问题,从而影响纸张使用效果,降低经济效益。可见,纸张生产质量检测是造纸过程中至关重要的环节。而传统纸张生产质量人工检测方法实施效果不甚理想,效率也相对较低。对此,亟需引进自动化检测方法,从而改善检测效率,稳定检测水平,减少人工消耗。基于此,通过近红外光谱采集预处理试样,基于SVM算法(支持向量机)搭建模型并选择合适的惩罚系数与函数参数,以此实现纸张生产质量自动化检测。此算法不仅可减少模型参数数量,降低训练样本数量要求,适合小批量纸张生产质量检测,而且检测效率与正确率较高,模型性能表现优异。

Due to multiple factors such as external environmental pollution,improper operation,and equipment fouling,quality issues such as performance and appearance may occur during paper production,which can affect the effectiveness of paper use and reduce economic benefits.It can be seen that paper production quality inspection is a crucial link in the papermaking process.However,the real-time effect of traditional manual paper production quality inspection methods is not ideal and the efficiency is relatively low.Therefore,it is urgent to introduce automated inspection methods to improve inspection efficiency,stabilize inspection levels,and reduce manual consumption.This article collects preprocessed data through near-infrared spectroscopy,builds a model based on SVM algorithm,and selects appropriate penalty coefficients and function parameters to achieve automated detection of paper production quality.This algorithm not only reduces the number of model parameters and training sample requirements,but is also suitable for quality inspection in small batch paper production.Moreover,it has high detection efficiency and accuracy,and excellent comprehensive performance.