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基于DA-XGboost算法的复杂机械产品质量预测 被引量:1

Quality Prediction of Complex Mechanical Products Based on DA-XGboost Algorithm
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摘要 针对复杂机械产品质量特性数据集的高维度、小样本、数据不平衡特点,建立DA-XGboost模型解决复杂机械产品质量损失问题。首先,针对复杂机械产品关键质量特性,提出复杂机械产品质量预测流程,采用蜻蜓算法(Dragonfly Algorithm,DA)对该类产品质量预测特征进行选择,降低其质量特性数据集的维度;其次,建立XGboost模型,处理复杂机械产品质量数据集不平衡问题,降低数据不平衡对最终预测结果的影响;最后,通过数值实例验证,DA-XGboost算法能够针对影响质量的关键因素进行有效预测,提高工艺产量,降低单位生产成本。 According to the characteristic of high dimensionality,small sample size and the imbalance of data set of quality characteristic of complicated mechanical products,this paper establishes DA-XGboost model to solve the quality loss caused by the unqualified complex mechanical products.Firstly,through the identification and analysis of the key quality characteristics of complex mechanical products,the construction process of the quality prediction model of complex mechanical products is established,and the dragonfly algorithm(DA)are used to reduce the dimension of the quality characteristics data set of complex mechanical products.Secondly,the XGboost model is built to handle the imbalance of complicated mechanical product quality data set and reduce the effect of data imbalance on the ultimate prognostication results.Finally,through numerical examples to verify the DA-XGboost algorithm to predict the quality of these key factors will help to improve the process output and reduce the unit production cost.
作者 董海 田赛 DONG Hai;TIAN Sai(School of Applied Technology,Shenyang University,Shenyang 110044,China;School of Mechanical Engineering,Shenyang University,Shenyang 110044,China)
出处 《组合机床与自动化加工技术》 北大核心 2021年第3期53-56,共4页 Modular Machine Tool & Automatic Manufacturing Technique
基金 国家自然科学基金“具有柔性分组决策的吊机集成调度优化理论与方法研究”(71672117) 辽宁省重点研发计划“基于云制造的高端装备制造产品协同设计与控制应用研究”(2019JH8/10200024)。
关键词 复杂机械产品 蜻蜓算法 XGboost 质量预测 complex mechanical products dragonfly algorithm XGboost quality prediction
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