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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (3): 441-447    DOI: 10.3785/j.issn.1008-973X.2021.03.003
    
Quality prediction and process parameter optimization method for machining parts
Yong YU1,2(),Jing-yuan XUE1,Sheng DAI1,Qiang-wei BAO1,Gang ZHAO1,3
1. School of Mechanics and Automation, Beihang University, Beijing 100191, China
2. Beijing Engineering Technological Research Center of High-Efficient and Green CNC Machining Process, Beijing 100191, China
3. Key Laboratory of Aeronautics Smart Manufacturing, Beihang University, Beijing 100191, China
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Abstract  

A novel method based on machine learning algorithms was proposed to realize the quality prediction and the process parameter optimization, in order to reuse the process information and the inspection information of machining parts effectively. A model-based definition (MBD) model which was integrated with process information and inspection information was treated as input. Process and inspection parameter extraction based on the MBD model was developed and the corresponding structured data set was established through the secondary development of three-dimensional modeling software. Several classifiers in machine learning were used to construct the quality prediction model based on process parameters and quality classification labels. Combining the information gain algorithm, after sorting all process parameters, the process parameter that had the greatest impact on quality was selected. Quality prediction and process parameter optimization tool set was developed to realize the optimization of the selected parameter by using the gradient boost decision tree algorithm. The validity and the reliability of the proposed method were verified by the milling experiment data provided by an aviation company. Results show that the proposed method can realize the quality prediction and process parameter optimization of machining parts effectively.

Key words： model-based definition (MBD)      machining parts      process information      inspection information      quality prediction      process parameter optimization      machine learning     
Received: 31 October 2019      Published: 25 April 2021
CLC:  TP 391  
Fund:  工信部相关专项科研技术研究资助项目（**-2017-*-**）
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Yong YU
Jing-yuan XUE
Sheng DAI
Qiang-wei BAO
Gang ZHAO
Cite this article:

Yong YU,Jing-yuan XUE,Sheng DAI,Qiang-wei BAO,Gang ZHAO. Quality prediction and process parameter optimization method for machining parts. Journal of ZheJiang University (Engineering Science), 2021, 55(3): 441-447.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.03.003     OR     http://www.zjujournals.com/eng/Y2021/V55/I3/441


机加零件质量预测与工艺参数优化方法

为了有效利用机加零件工艺信息和检测信息，提出基于机器学习算法的质量预测与工艺参数优化方法. 以集成工艺信息和检测信息的基于模型定义（MBD）模型为输入，通过对三维建模软件的二次开发实现参数提取，并建立结构化数据集. 利用多种机器学习分类器构建基于工艺参数与质量分类标签的质量预测模型. 结合信息增益算法对所有工艺参数进行优先级排序，筛选出对质量影响最大的工艺参数；开发质量预测与工艺参数优化工具集，利用梯度提升树模型优化对质量影响最大的工艺参数. 以某航空企业提供的铣削实验数据验证所提出方法的有效性和可靠性. 验证结果表明，该方法能够较好地实现机加零件的质量预测和工艺参数优化.


关键词： 基于模型定义（MBD）,  机加零件,  工艺信息,  检测信息,  质量预测,  工艺参数优化,  机器学习 
i 工艺参数1 工艺参数2 $\cdots$ 工艺参数 $n$ 零件质量标签
0 $x_1^{(0)}$ $x_2^{(0)}$ $\cdots $ $x_n^{(0)}$ ${L^{(0)}}$
1 $x_1^{(1)}$ $x_2^{(1)}$ $\cdots $ $x_n^{(1)}$ ${L^{(1)}}$
$\vdots $ $\vdots$ $\vdots$ $ $ $\vdots$ $\vdots$
m $x_1^{(m)}$ $x_2^{(m)}$ $\cdots $ $x_n^{(m)}$ ${L^{(m)}}$
Tab.1 Structured data format for part quality prediction
Fig.1 Part quality predict process
i 工艺参数1 $\cdots $ 工艺参数 $n$ 零件质量标签 工艺参数 $o$
0 $x_1^{(0)}$ $\cdots $ $x_n^{(0)}$ ${L^{(0)}}$ $x_o^{(0)}$
1 $x_1^{(1)}$ $\cdots $ $x_n^{(1)}$ ${L^{(1)}}$ $x_o^{(1)}$
$\vdots $ $\vdots $ $\vdots$ $\vdots$ $\vdots$ $\vdots$
m $x_1^{(m)}$ $\cdots $ $x_n^{(m)}$ ${L^{(m)}}$ $x_o^{(m)}$
Tab.2 Structured data format for process parameter optimization
Fig.2 Systematic framework for quality prediction and process parameter optimization of machining parts
工艺参数 信息增益
径向切深 0.022 702 294
轴向切深 0.141 402 794
主轴转速 0.240 937 382
Tab.3 Calculation results of information gain
Fig.3 Structure data format for inspection information
Fig.4 Structure data format for processing information
Fig.5 MBD model integrated with processing and inspection information
Fig.6 Extraction process of processing and inspection information
Fig.7 User interface of toolset for quality prediction and process parameter optimization of parts
模型名称 十折交叉验证平均准确率
LR 0.658 0
SVM 0.813 2
XGBoost 0.828 8
GBDT+LR 0.863 1
Tab.4 Accuracy of selected four classifiers
数据
编号 		径向
切深 		轴向
切深 		主轴
转速 		表面粗
糙度 		原始质量
标签 		预测零件
质量
0 4.2 2.2 13500 3.62 0 0
1 4.2 2.2 14000 3.23 0 0
2 4.2 2.2 14500 3.14 1 1
3 4.2 2.2 15000 2.95 1 1
4 4.2 2.2 15500 3.01 1 1
Tab.5 Part quality predict result
Fig.8 Optimization results of processing parameters
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