During the batch assembly analysis of linear axis of machine tool, assembly quality evaluation is crucial to reduce assembly quality fluctuations and improve efficiency. This study presented a data-driven modeling approach for evaluating assembly quality of linear axis based on normalized mutual information and random sampling with replacement (NMI-RSWR) variable selection method, synthetic minority over-sampling technique (SMOTE), and genetic algorithm (GA)-optimized multi-class support vector machine (SVM). First, a variable selection method named NMI-RSWR was proposed to select key assembly parameters which affected assembly quality of linear axis. Then, a hybrid method based on SMOTE and GA-optimized multi-class SVM was presented to construct assembly quality evaluation model. In this method, Class imbalance problem was solved by using SMOTE, and parameters optimization problem was solved by using GA. Finally, the assembly-related data from the batch assembly of x-axis of a three-axis vertical machining center were collected to validate the proposed method. The results indicate that the proposed NMI-RSWR approach has capacity for selecting the highly related assembly parameters with assembly quality of linear axis, and the proposed data-driven modeling approach is effective for assembly quality evaluation of linear axis.

在机床线性轴的批装配分析中，装配质量评估对于减少装配质量波动和提高效率至关重要。这项研究提出了一种数据驱动的建模方法，该方法基于标准化的互信息和替换随机抽样（NMI-RSWR）变量选择方法，合成少数过采样技术（SMOTE）和遗传算法（GA）评估线性轴的装配质量）优化的多类支持向量机（SVM）。首先，提出了一种变量选择方法NMI-RSWR，以选择影响线性轴装配质量的关键装配参数。然后，提出了一种基于SMOTE和遗传算法优化的多类支持向量机的混合方法，以构建装配质量评估模型。在这种方法中，通过使用SMOTE解决了类不平衡问题，利用遗传算法解决了参数优化问题。最后，从三轴立式加工中心的x轴的批装配中收集与装配相关的数据，以验证所提出的方法。结果表明，所提出的NMI-RSWR方法具有选择与线性轴装配质量高度相关的装配参数的能力，并且该数据驱动的建模方法对于线性轴装配质量的评估是有效的。