基于SMOTE-IKPCA-SeNet深度迁移学习的小批量生产质量预测研究

Quality Prediction for Small-batch Production Based on SMOTE-IKPCA-SeNet Deep Transfer Learning

随着智能制造技术的发展和客户个性化需求的增加,多品种小批量生产方式逐渐成为制造业的主流。面向大批量生产、以统计过程控制为核心的质量管理方式并不适用于小批量生产。针对复杂生产过程存在参数多、非线性和交互作用的问题,提出利用深度迁移学习的方式将历史生产数据作为源域迁移至小样本目标产品数据进行质量预测。首先,通过合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)和改进的核主成分分析(improved kernel principal component analysis,IKPCA)算法筛选源域和目标域的可迁移特征,这不仅兼顾了特征重要性和可迁移性,还减少了“负迁移”,提高了模型泛化能力;然后,采用结合通道注意力机制的卷积神经网络SeNet构建基于深度迁移学习的质量预测模型。仿真结果表明,随着目标域样本的增加,所提方法的预测准确性明显优于广泛采用的支持向量机建模方法。同时,所提可迁移特征筛选方法显著提高了深度迁移学习的质量预测效果,为复杂的小批量生产过程质量保证提供了新方法。

With the development of intelligent manufacturing technology and the growing demand for personalization,multi-variety and small-batch production has gradually become the mainstream in the manufacturing industry.In this condition,traditional quality management methods which focus on large-batch production and statistical process control are not suitable for small-batch production.In complex production processes,it also exists challenges such as numerous parameters,non-linearity and interactions.To this end,a deep transfer learning method is adopted to predict the quality of target products using small sample data transferred from massive historical production data.First,by using the synthetic minority over-sampling technique(SMOTE)algorithm and an improved kernel principal component analysis(KPCA)algorithm,transferable features from both the source and target domains are selected,balancing feature importance and transferability.It also mitigates negative transfer issues and enhances the generalization capability of the model.Then,a quality prediction model based on deep transfer learning is built using a convolutional neural network,i.e.,SeNet,which incorporates a channel attention mechanism.Simulation results demonstrate that as the number of target domain samples increases,the proposed method is significantly superior to prediction accuracy compared with the widely adopted support vector machine modeling method.Additionally,the proposed selection method of transferable features significantly enhances the quality prediction performance of deep transfer learning,providing a novel approach to ensuring the quality of complex small-batch production processes.