华南理工大学学报(自然科学版) ›› 2022, Vol. 50 ›› Issue (6): 19-26.doi: 10.12141/j.issn.1000-565X.210664

所属专题: 2022年计算机科学与技术

• 计算机科学与技术 • 上一篇    下一篇

基于Stacking集成学习的注塑件尺寸预测方法

宋建王文龙李东梁家睿2   

  1. 1. 华南理工大学聚合物国家工程研究中心
    2. 华南理工大学广东省高分子先进制造技术及装备重点实验室
    3. 金发科技股份有限公司企业技术中心
  • 收稿日期:2021-10-18 修回日期:2021-12-12 出版日期:2022-06-25 发布日期:2021-12-31
  • 通信作者: 宋建 (1971-),男,高级工程师,主要从事工业装备智能控制研究 E-mail:songjian@ scut. edu. cn
  • 作者简介:宋建 (1971-),男,高级工程师,主要从事工业装备智能控制研究
  • 基金资助:
    高分子产品混合制造产线智能决策与管控技术

Injection molding part size prediction method based on Stacking integration learning

SONG Jian1  WANG Wenlong1  LI Dong2  LIANG Jiarui2   

  1. 1. Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing/
    Key Laboratory of Polymer Processing Engineering of the Ministry of Education,South China
    University of Technology,Guangzhou 510640,Guangdong,China; 2. Enterprise Technology Center,
    Kingfa Science and Technology Co. ,Ltd. ,Guangzhou 510663,Guangdong,China
  • Received:2021-10-18 Revised:2021-12-12 Online:2022-06-25 Published:2021-12-31
  • Contact: 宋建 (1971-),男,高级工程师,主要从事工业装备智能控制研究 E-mail:songjian@ scut. edu. cn
  • About author:宋建 (1971-),男,高级工程师,主要从事工业装备智能控制研究
  • Supported by:
    Supported by the National Key R&D Project of China (2019YFB1704900)

摘要: 机器学习算法能够处理高维和多变量数据,并在复杂和动态环境中提取数据中的隐藏关系,在注塑件尺寸预测中具有很好的应用前景。注塑件尺寸预测系统的性能取决于机器学习算法的选择,然而,传统的机器学习算法在实际应用中不能达到很好的预测效果。本文提出一种基于Stacking集成学习方法的融合模型,采用优化的特征选择方法,建立模型时通过对比不同的Stacking学习器组合方式,组合多种类型的学习器,从而得到预测性能最佳的模型。该模型在注塑件尺寸预测方面的性能较传统模型有了很大的提升,同时模型预测结果可根据特征解释回溯到实际生产中,为制造工艺和工序的优化提供决策指导。

关键词: 注塑件尺寸预测, 机器学习, 模型融合, Stacking集成学习

Abstract: Machine learning algorithms can handle high-dimensional and multivariate data and extract hidden relationships in data in complex and dynamic environments, which has a good application prospect in injection molding part size prediction. The performance of injection molding part size prediction system depends on the choice of machine learning algorithm, however, the traditional machine learning algorithm can not achieve good prediction effect in practical application. In this paper, a fusion model based on Stacking integrated learning method is proposed, and the optimal performance model is obtained by comparing different Stacking learner combinations and combining multiple types of learners. The performance of the model in injection molding parts size prediction is greatly improved compared with the traditional model, and the model prediction results can be explained back to the actual production according to the characteristics, providing decision guidance for the optimization of manufacturing processes and processes.

Key words: Injection part size prediction, machine learning, model fusion, Stacking integrated learning.

中图分类号: