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机器学习在深冲钢质量自动判级中的应用

徐钢 黎敏 徐金梧

徐钢, 黎敏, 徐金梧. 机器学习在深冲钢质量自动判级中的应用[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.05.08.002
引用本文: 徐钢, 黎敏, 徐金梧. 机器学习在深冲钢质量自动判级中的应用[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.05.08.002
XU Gang, LI Min, XU Jin-wu. Application of machine learning in automatic discrimination of product quality of deep drawn steel[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.05.08.002
Citation: XU Gang, LI Min, XU Jin-wu. Application of machine learning in automatic discrimination of product quality of deep drawn steel[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.05.08.002

机器学习在深冲钢质量自动判级中的应用

doi: 10.13374/j.issn2095-9389.2021.05.08.002
基金项目: “十三五”国家科技支撑计划资助项目(2015BAF30B01)
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    E-mail: watermoon999@126.com

  • 中图分类号: TP274

Application of machine learning in automatic discrimination of product quality of deep drawn steel

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  • 摘要: 在流程工业中,生产过程需根据客户对产品质量要求进行判级,以满足客户提出的产品质量需求。目前,企业主要采用“事后”抽检方式,但因无法对所有产品实现在线自动判级,常发生索赔和退货,导致我国钢铁企业每年近100亿元损失。为了实现产品质量在线自动判级,提出基于高维数据非线性同等缩放与核简支集类边界确定相结合的质量在线智能判级方法。首先,将高维的工艺参数通过非线性同等缩放算法变换成低维的数据集,并对缩放后数据集进行聚类,分析工艺参数的类分布特征。然后,根据分类后样本的质量指标值分布,采用核简支集类边界算法来确定不同产品质量级别的类边界。最后,依据已确定的类边界,通过质量指标预测实现产品在线判级。通过深冲钢(IF钢)应用实例,证实该方法在训练阶段的在线自动判级准确率达到97.2%,测试阶段的准确率为96%。

     

  • 图  1  利用简支集确定类边界的例子

    Figure  1.  Example of determining class boundaries using reduced sets

    图  2  产品质量自动判级流程

    Figure  2.  Workflow for automatic discrimination of product quality

    图  3  核参数取不同值时累积误差分布图

    Figure  3.  Accumulated error with different Kernel parameters

    图  4  线性PCA方法(a)和非线性多维同等缩放方法(b)降维后的工艺参数聚类图

    Figure  4.  Parameter distribution after reduction using PCA (a) and MDPS (b)

    图  5  标记样本的质量指标分布图

    Figure  5.  Quality index distribution of labeled samples

    图  6  三种钢种的质量指标的上下限

    Figure  6.  Up and down limits of quality indexes for three steel types

    表  1  主要工艺参数名称及统计值

    Table  1.   Major process parameters and statistics

    Parameter nameMaxMinMeanVariance
    Mass fraction of C / %0.00280.00110.00180.0004
    Mass fraction of Mn / %0.1600.090.12630.0154
    Mass fraction of P / %0.0140.0070.00990.0019
    Mass fraction of S / %0.01390.00610.007660.0019
    Exit temperature of heating furnace / °C1277.301247.101263.045.998
    Entry temperature of finish
    rolling / °C
    1083.941014.031039.089.804
    Exit temperature of finish
    rolling / °C
    928.46898.68917.174.167
    Coiling temperature / °C755.40654.45711.7041.358
    Cold-rolled reduction ratio / %82.9065.5080.494.139
    Heating temperature/ °C854.27786.96821.9112.498
    Soaking temperature / °C854.97789.66824.2712.352
    Fast-cooling exit temperature / °C455.73299.84431.1324.296
    Aging exit temperature / °C394.12287.12374.5212.299
    Slow-cooling exit temperature / °C676.39605.97641.6111.280
    下载: 导出CSV

    表  2  汽车钢性能指标的行标/企业内标

    Table  2.   Industry/internal standard of performance index of interstitial-free steel

    TypeYield strength/MPaTensile strength/MPaElongation/%Plastic strain ratio
    DC04210/(135‒160)(270‒350)/
    (260‒350)
    38/(40‒44)1.7/2.1
    DC05180/(125‒150)(270‒330)/
    (250‒330)
    40/(43‒46)2.0/2.2
    DC06170/(120‒140)(270‒330)/
    (250‒330)
    41/(45‒48)2.1/2.4
    下载: 导出CSV

    表  3  三种方法工艺参数类中心/类内方差数据

    Table  3.   Class center/ mean square error of quality indexes using three methods

    Quality indexesClass1(DC06)Class2(DC05)Class3(DC04)
    Yield strength/MPaCPA134.85/8.04141.65/7.63147.30/6.26
    KCPA127.53/5.55138.83/7.77143.37/8.26
    MDPS127.53/5.55135.62/6.03146.81/6.45
    Tensile strength/MPaCPA287.5/6.67290.01/8.20295.40/8.16
    KCPA280.2/6.30292.10/7.45291.58/7.48
    MDPS280.2/6.30289.71/5.64293.7/8.31
    Elongation/%CPA45.73/1.9444.88/2.3244.26/2.52
    KCPA46.45/1.7744.69/2.1845.13/2.36
    MDPS46.45/1.7745.54/1.9944.44/2.42
    Plastic strain ratioCPA2.93/0.2002.83/0.2182.74/0.200
    KCPA3.02/0.1482.87/0.1942.81/0.231
    MDPS3.02/0.1482.91/0.1952.76/0.200
    下载: 导出CSV

    表  4  不同判级和性能预测方法的计算结果

    Table  4.   Calculating results of discrimination and predicting quality index using different methods

    MethodsAccuracy/%Standard deviation
    Elongation/%Yield strength/
    MPa
    Tensile strength/MPaPlastic strain ratio
    BP91.51.95.094.540.2
    LSTM93.51.053.993.030.1
    KPLS90.71.785.355.570.16
    PLS82.11.945.775.380.19
    KNN90.12.056.285.780.19
    Synthesis96
    下载: 导出CSV
  • [1] Chai T Y. Industrial process control systems: Research status and development direction. Sci Sin (Informationis), 2016, 46(8): 1003

    柴天佑. 工业过程控制系统研究现状与发展方向. 中国科学:信息科学, 2016, 46(8):1003
    [2] Gehrmann C, Gunnarsson M. A digital twin based industrial automation and control system security architecture. IEEE Trans Ind Inform, 2020, 16(1): 669 doi: 10.1109/TII.2019.2938885
    [3] China Electronics Standardization Institute. Hargrove grindability index of the coal blended [J/OL]. Website Online (2007-03-02) [2022-02-22]. http://www.cesi.cn/201703/2251.html

    中国电子技术标准化研究院. 信息物理系统白皮书(2017) [J/OL]. 网络在线 (2007-03-02) [2022-02-22]. http://www.cesi.cn/201703/2251.html
    [4] Wang L H, Törngren M, Onori M. Current status and advancement of cyber-physical systems in manufacturing. J Manuf Syst, 2015, 37: 517 doi: 10.1016/j.jmsy.2015.04.008
    [5] Zhang C X, Cheng L L, Wang X D. Research on architecture of intelligent manufacturing based on cyber-physical system. Comput Sci, 2013, 40(Supple 1): 37

    张彩霞, 程良伦, 王向东. 基于信息物理融合系统的智能制造架构研究. 计算机科学, 2013, 40(S1): 37
    [6] Xu G. Application Research of CPS in On-Line Quality Control to Metallurgical Products [Dissertation]. Beijing: University of Science and Technology Beijing, 2019

    徐钢. CPS在冶金产品质量在线管控中应用研究[学位论文]. 北京: 北京科技大学, 2019
    [7] Michniewicz J, Reinhart G. Cyber-Physical-Robotics - Modelling of modular robot cells for automated planning and execution of assembly tasks. Mechatronics, 2016, 34: 170 doi: 10.1016/j.mechatronics.2015.04.012
    [8] Penas O, Plateaux R, Patalano S, et al. Multi-scale approach from mechatronic to Cyber-Physical Systems for the design of manufacturing systems. Comput Ind, 2017, 86: 52 doi: 10.1016/j.compind.2016.12.001
    [9] Pirvu B C, Zamfirescu C B, Gorecky D. Engineering insights from an anthropocentric cyber-physical system: A case study for an assembly station. Mechatronics, 2016, 34: 147 doi: 10.1016/j.mechatronics.2015.08.010
    [10] Morgan J, O’Donnell G E. Multi-sensor process analysis and performance characterisation in CNC turning—a cyber physical system approach. Int J Adv Manuf Technol, 2017, 92(1-4): 855 doi: 10.1007/s00170-017-0113-8
    [11] Tao F, Cheng J F, Qi Q L. IIHub: an industrial Internet-of-things hub toward smart manufacturing based on cyber-physical system. IEEE Trans Ind Inform, 2018, 14(5): 2271 doi: 10.1109/TII.2017.2759178
    [12] Gehrmann C, Gunnarsson M. A digital twin based industrial automation and control system security architecture. IEEE Trans Ind Inform, 2020, 16(1): 669 doi: 10.1109/TII.2019.2938885
    [13] Wang J J, Ye L K, Gao R X, et al. Digital Twin for rotating machinery fault diagnosis in smart manufacturing. Int J Prod Res, 2019, 57(12): 3920 doi: 10.1080/00207543.2018.1552032
    [14] Knapp G L, Mukherjee T, Zuback J S, et al. Building blocks for a digital twin of additive manufacturing. Acta Mater, 2017, 135: 390 doi: 10.1016/j.actamat.2017.06.039
    [15] Barricelli B R, Casiraghi E, Fogli D. A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 2019, 7: 167653 doi: 10.1109/ACCESS.2019.2953499
    [16] Lin Z Q, Lai X M, Jin S, et al. Digital methods of complex product manufacturing precision control and its development trend. J Mech Eng, 2013, 49(6): 103 doi: 10.3901/JME.2013.06.103

    林忠钦, 来新民, 金隼, 等. 复杂产品制造精度控制的数字化方法及其发展趋势. 机械工程学报, 2013, 49(6):103 doi: 10.3901/JME.2013.06.103
    [17] Xu G, Zhang X T, Li M, et al. Online monitoring and control method of product quality based on embedded cyber-physical system models. J Mech Eng, 2017, 53(12): 94 doi: 10.3901/JME.2017.12.094

    徐钢, 张晓彤, 黎敏, 等. 基于嵌入式CPS模型的产品质量在线管控方法. 机械工程学报, 2017, 53(12):94 doi: 10.3901/JME.2017.12.094
    [18] Xu G, Zhang X T, Li M, et al. A method of establishing process specifications in process industry based on statistical process control. J Mech Eng, 2019, 55(8): 208 doi: 10.3901/JME.2019.08.208

    徐钢, 张晓彤, 黎敏, 等. 基于统计过程控制的流程工业工艺规范制定方法. 机械工程学报, 2019, 55(8):208 doi: 10.3901/JME.2019.08.208
    [19] Xu G, Li M, Xu J W. Application research of on-line quality control method to metallurgical products // 2019 IEEE International Conference on Industrial Engineering and Engineering Management. Macao, 2019: 390
    [20] Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res, 2020, 50: 71 doi: 10.1146/annurev-matsci-070218-010015
    [21] Arróyave R, McDowell D L. Systems approaches to materials design: Past, present, and future. Annu Rev Mater Res, 2019, 49: 103 doi: 10.1146/annurev-matsci-070218-125955
    [22] Rosenbrock C W, Gubaev K, Shapeev A V, et al. Machine-learned interatomic potentials for alloys and alloy phase diagrams. Npj Comput Mater, 2021, 7: 24 doi: 10.1038/s41524-020-00477-2
    [23] Fan Y S, Yang X G, Shi D Q, et al. Quantitative mapping of service process-microstructural degradation-property deterioration for a Ni-based superalloy based on chord length distribution imaging process. Mater Des, 2021, 203: 109561 doi: 10.1016/j.matdes.2021.109561
    [24] Hart G L W ", Mueller T, Toher C, et al. Machine learning for alloys. Nat Rev Mater, 2021, 6(8): 730 doi: 10.1038/s41578-021-00340-w
    [25] Kondo R, Yamakawa S, Masuoka Y, et al. Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics. Acta Mater, 2017, 141: 29 doi: 10.1016/j.actamat.2017.09.004
    [26] Wang C, Shi D Q, Li S L. A study on establishing a microstructure-related hardness model with precipitate segmentation using deep learning method. Materials, 2020, 13(5): 1256 doi: 10.3390/ma13051256
    [27] Kipf T, Welling M. Semi-supervised classification with graph convolutional networks.https://doi.org/10.48550/arXiv.1609.02907
    [28] Cao N D, Kipf T. MolGAN: An implicit generative model for small molecular graphs.https://doi.org/10.48550/arXiv.1805.11973
    [29] Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. Cambridge: Cambridge University Press, 2004
    [30] Cutkosky A, Orabona F. Black-box reductions for parameter-free online learning in Banach spaces.https://doi.org/10.48550/arXiv.1802.06293
    [31] Mesbah A. Stochastic model predictive control: An overview and perspectives for future research. IEEE Control Syst Mag, 2016, 36(6): 30 doi: 10.1109/MCS.2016.2602087
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  • 收稿日期:  2021-05-08
  • 网络出版日期:  2021-10-15

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