基于機器學習的產品質量在線智能監控方法

徐鋼; 黎敏; 呂志民; 徐金梧

doi:10.13374/j.issn2095-9389.2021.06.22.001

基于機器學習的產品質量在線智能監控方法

doi: 10.13374/j.issn2095-9389.2021.06.22.001

徐鋼^{1, 2},
黎敏¹,
呂志民¹,
徐金梧^1, ,

1.
北京科技大學鋼鐵共性技術協同創新中心，北京 100083
2.
蘇州寶聯重工有限公司，蘇州 215131

基金項目: 國家自然科學基金資助項目（51004013, 51204018）

詳細信息

通訊作者:
E-mail: jwxu@ustb.edu.cn

中圖分類號: TP274
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- 文章訪問數: 850
- HTML全文瀏覽量: 350
- PDF下載量: 72
- 被引次數: 0
出版歷程
- 收稿日期: 2021-06-22
- 網絡出版日期: 2021-10-15
- 刊出日期: 2022-04-02

Online intelligent product quality monitoring method based on machine learning

XU Gang^{1, 2},
LI Min¹,
Lü Zhi-min¹,
XU Jin-wu^{1
, ,}

1.
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
2.
Suzhou Baolian Heavy Industry Co., Suzhou 215131, China

More Information

Corresponding author: E-mail: jwxu@ustb.edu.cn

摘要

摘要: 為了提高產品質量的穩定性和可靠性，利用機器學習方法實現產品質量在線監控、在線優化和在線預設定，是鋼鐵企業目前亟待解決的關鍵技術。針對企業需求，提出基于軟超球體算法的產品質量異常在線識別和異常原因診斷方法、基于流形學習的工藝參數在線優化方法和基于多變量統計過程控制的工藝規范制定方法。通過將上述方法進行系統集成，并利用工業互聯網技術和大數據分析方法，研發了產品質量在線智能監控系統。目前該系統已在鋼鐵企業十余條生產線上推廣應用，質量在線判定的準確率達到99.2%，在線檢測時間不到0.1 s。
- 大數據分析 /
- 機器學習 /
- 質量在線監控 /
- 質量在線優化 /
- 產品質量設計
Abstract: In recent years, Chinese iron and steel enterprises have mainly adopted the “sampling after the event” method to inspect the product quality before it leaves the factory. Due to the inability to achieve quality inspection for all products, customers often claim and return defective products, leading to major economic losses in steel enterprises. To improve the stability and reliability of product quality, the use of machine learning methods to realize the online monitoring, optimization, and preset of product quality is the key technology to be solved in iron and steel enterprises. Therefore, the online identification and diagnosis of abnormal product quality based on the soft hypersphere, online optimization of the process parameters based on manifold learning and process specification formulation based on the multivariate statistical process control were proposed. In this study, integrated methods of online monitoring, diagnosis, and optimization of product quality were proposed in which the abnormal point of the product quality by the soft hypersphere method, based on the support vector data description, was identified online, and the process parameters were diagnosed through the contribution chart. Optimizing in real time, abnormal process parameters via a local projective transformation of neighbor points was then achieved. The process parameter setting model based on manifold learning by multiclass neighborhoods to extract the manifold of process parameters was established. Meanwhile, the process specification model, based on the maximum inner rectangle of the soft hypersphere, was established to obtain an effective control interval of the process parameters. Through system integration with the proposed methods and using industrial internet technology and big data analysis methods, the system of intelligent online monitoring of product quality has been successfully developed. At present, the system has been applied to more than ten production lines in iron and steel enterprises. The accuracy rate of online quality determination is 99.2%, and the online detection time is less than 0.1 s.
- big data analysis /
- machine learning /
- online monitoring of product quality /
- online optimization of quality /
- product quality design

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圖 1 最小封閉超球體示意圖

Figure 1. Minimum hypersphere diagram

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圖 2 線性超橢球將異常點判為正常點

Figure 2. Abnormal samples misjudged as normal samples in the linear hypersphere

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圖 3 樣本點從原始空間映射到特征空間

Figure 3. Samples mapped from the original space into the feature space

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圖 4 圖４流形學習示意圖

Figure 4. Manifold learning diagram

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圖 9 時效溫度與快冷溫度的上、下限

Figure 9. Up and low limits of the aging temperature and fast-cooling temperature

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圖 5 訓練集的控制限R²(a)和在線監測結果(b)

Figure 5. Control limit R² (a) of the training set and online monitoring result (b)

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圖 6 工藝參數(參數序號在表1中)的貢獻圖.(a)第25樣本;(b)第57樣本

Figure 6. Contribution chart of parameters: (a) sample No. 25; (b) sample No. 57 (serial numbers of the parameters are listed in Table 1)

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圖 7 工藝參數與屈服強度的主流形。(a)碳含量流形;(b)錳含量流形;(c)精軋入口溫度流形;(d)均熱溫度流形

Figure 7. Main manifold between the process parameters and yield strength: (a) manifold of C; (b) manifold of Mn; (c) manifold of the entry temperature of finish rolling; (d) manifold of the soaking temperature

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圖 8 熱處理工序的工藝參數上、下限.(a)均熱溫度與快冷溫度;(b)均熱溫設與時效溫度;(c)均熱溫度與緩冷溫度;(d)時效溫度與緩冷溫度

Figure 8. Up and low limits of the process parameters in the heat treatment: (a) soaking and fast-cooling temperature; (b) soaking and aging temperature; (c) soaking and slow-cooling temperature; (d) aging and slow-cooling temperature

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圖 10 σ取不同值時,軟超球體的邊界

Figure 10. Border of the soft hypersphere with different σ values

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表 1 關鍵工藝參數、質量指標及統計量

Table 1. Key process parameters, quality indexes, and statistics

Parameters			Max	Min	Mean
Process parameters	No.1	Mass fraction of C / %	0.0027	0.0011	0.0017
	No.2	Mass fraction of Mn / %	0.160	0.100	0.126
	No.3	Mass fraction of P / %	0.014	0.007	0.010
	No.4	Mass fraction of S / %	0.0139	0.0024	0.0077
	No.5	Exit temperature of heating furnace / °C	1277.3	1247.1	1263.04
	No.6	Entry temperature of finish rolling / °C	1083.9	1014.0	1039.08
	No.7	Exit temperature of finish rolling / °C	928.5	898.7	917.17
	No.8	Coiling temperature / °C	755.4	654.5	711.70
	No.9	Soaking temperature / °C	854.9	789.7	824.27
	No.10	Fast-cooling exit temperature / °C	455.7	378.8	431.13
	No.11	Aging exit temperature / °C	394.1	345.1	374.52
	No.12	Slow-cooling exit temperature / °C	676.4	606.0	641.61
Quality indexes	No.1	Tensile strength / MPa	308.0	276.0	290.1
	No.2	Yield strength / MPa	125.0	160.0	139.4
	No.3	Elongation / %	40.5	50.5	45.1
	No.4	Plastic strain ratio	2.10	3.5	2.85

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表 2 屈服強度的質量設計

Table 2. Quality design of the yield strength

Process parameter		Mass fraction of C / %	Mass fraction of Mn / %	Mass fraction of P / %	Mass fraction of S / %	Exit temperature of heating furnace / °C	Entry temperature of finish rolling / °C	Exit temperature of finish rolling / °C	Coiling temperature / °C	Soaking temperature / °C	Fast-cooling exit temperature / °C	Aging exit temperature / °C	Slow-cooling exit temperature / °C
Yield strength	130 MPa	0.0015–0.00185	0.105–0.125	0.008–0.012	0.006–0.0095	1261–1267	1030–1040	914–920	725–740	827–843	420–450	375–390	625–645
	140 MPa	0.0016–0.0019	0.125–0.135	0.008–0.012	0.006–0.0095	1259–1266	1030–1040	914–920	725–740	817–835	420–450	370–385	630–650
	150 MPa	0.0016–0.002	0.135–0.155	0.008–0.012	0.006–0.0095	1257–1265	1040–1050	914–920	650–660	810–827	420–450	365–380	635–655
	160 MPa	0.0016–0.0021	0.155–0.170	0.008–0.012	0.006–0.0095	1256–1264	1040–1050	914–920	650–660	806–826	420–450	360–375	640–660

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表 3 第25號樣本點的工藝參數調整值

Table 3. Adjustment of process parameters for sample No. 25

Process parameter	Exit temperature of heating furnace / °C	Entry temperature of finish rolling / °C	Exit temperature of finish rolling / °C	Coiling temperature / °C	Soaking temperature / °C	Fast-cooling exit temperature / °C	Aging exit temperature / °C	Slow-cooling exit temperature / °C
Original value	1247.5	1036.9	917.2	740	840.1	445.1	389.4	664.3
Adjustment	11.2	6.3	3.3	?36.0	?12.4	?6.3	?5.3	?19.7
Real value	1258.7	1043.2	920.5	704	827.7	438.8	384.1	644.6

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表 4 熱處理工藝參數的相關系數

Table 4. Correlation coefficient of the process parameters in the heat treatment

Correlation coefficient	Soaking temperature	Fast-cooling temperature	Aging temperature	Slow-cooling temperature
Soaking temperature	1.0	0.12	0.10	0.10
Fast-cooling temperature	—	1.0	0.72	0.61
Aging temperature	—	—	1.0	0.43
Slow-cooling temperature	—	—	—	1.0

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表 5 工藝參數的預設值

Table 5. Preinstalling values of process parameters

Process parameter	Mass fraction of C / %	Mass fraction of Mn / %	Mass fraction of P / %	Mass fraction of S / %	Exit temperature of heating furnace / °C	Entry temperature of finish rolling / °C	Exit temperature of finish rolling / °C	Coiling temperature / °C	Soaking temperature / °C	Fast-cooling exit temperature / °C	Aging exit temperature / °C	Slow-cooling exit temperature / °C
Soft hypersphere	0.0024– 0.0018	0.150– 0.100	0.0115– 0.0065	0.0118– 0.0048	1273–1255	1055–1030	908–924	740–685	839–814	451–398	392–357	662–620
Max–min	0.0027– 0.0011	0.160– 0.10	0.014– 0.007	0.0139– 0.0024	1277–1247	1084–1014	928–899	755–654	855–790	456–379	394–345	674–606
6σ	0.0026– 0.0008	0.1759– 0.0799	0.0156– 0.0048	0.0133– 0.0025	1280–1246	1067–1013	926–907	834–582	857–793	489–376	407–345	675–609

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