基于XGBoost的车身尺寸装配质量智能预测模型

针对汽车多级制造系统中传统机器学习方法处理多元数据样本时间久、精度低等问题，提出一种基于XGboost的车身尺寸装配质量智能预测模型，解决多级制造系统的车身装配精准预测控制问题。首先，通过对车身多级装配过程的分析，对数据样本进行预处理，建立基于Spearman系数的不同特征要素的绝对相关性矩阵；其次，对生产流程的相关数据实时采集、清洗及挖掘分析，提出数据分析流程与数据处理框架，建立基于XGBoost的车身尺寸装配质量智能预测模型，并通过对模型性能的有效评估实现对车身尺寸装配的精准控制；最后，仿真实例对比分析表明，基于XGboost的质量智能预测模型能精准地解决多级制造系统中的车身装配质量控制问题。

An Intelligent Prediction Model of Body Size Assembly Quality Based on XGBoost Algorithm

Aiming at the problems of long processing time and low accuracy of the traditional machine learning method in the multi-level manufacturing system of the automobile, an intelligent prediction model of body size and assembly quality based on XGBoost is proposed to solve the problem of accurate predictive control of the multi-level manufacturing system body assembly. Firstly, the multi-level assembly process of the car body is analyzed, the data samples processed, and the absolute correlation matrix of different feature elements established based on Spearman coefficients. Secondly, through a real-time collection, cleaning and mining analysis of relevant data in the production process, the data analysis process and data processing framework are proposed, an intelligent prediction model of body size and assembly quality based on XGBoost established, and the effective evaluation of model performance conducted to ensure accurate control of body size assembly. Finally, an example shows that the XGBoost quality intelligent prediction model can solve the problem of car body assembly quality control in multi-level manufacturing systems.