【導(dǎo)讀】料流體管理系統(tǒng)與PLC自動控制系統(tǒng)聯(lián)系起來。在生產(chǎn)過程中以及預(yù)測最終屬性，這種。在線模型采用有限元時域微分法去計算溫度的變化和相的轉(zhuǎn)變。因?yàn)椴煌摬牡倪B續(xù)冷。為新鋼材的生產(chǎn)和優(yōu)化提供直接的指導(dǎo)。卻過程中，斯太爾摩生產(chǎn)線中也需要更可靠預(yù)測控制技術(shù)。如圖1，斯太爾摩冷卻控制系統(tǒng)，1000度的線材快速通過幾個冷水槽，到達(dá)吐絲機(jī)，以重疊的方式存儲在存放容器中。它的冷卻速率靠打開一下一系列的風(fēng)扇。鋼材最終的機(jī)械屬性主要取決于在相轉(zhuǎn)換前的鋼材化學(xué)組成和冷卻速度。型尚沒有人研究。這很有利于生產(chǎn)產(chǎn)品的穩(wěn)定性。目前，這種在線模型已經(jīng)應(yīng)用在三個斯太爾摩生產(chǎn)線，并取得滿意效果。在斯太爾摩生產(chǎn)線中，由于無線長鋼條高速移動，軸向熱傳導(dǎo)可以忽略。初始溫度，這些和現(xiàn)實(shí)非常接近。隨著極限抗拉強(qiáng)度系統(tǒng)的發(fā)展，使預(yù)測線材的微觀結(jié)構(gòu)成為可能。準(zhǔn)確性和穩(wěn)定性系統(tǒng)的兩個基本要求。輸出結(jié)果應(yīng)當(dāng)正確便于指導(dǎo)生產(chǎn)。SCCS可以以300毫秒為周期正常運(yùn)行。

　　

【正文】 s adopted to predict its UTS, pare with real measurement, then modify its related coef?cients based on the multiregression result (seeFig. 8). . Advantage of the online model in the production It needs to point out that variation of UTS during mass production is inevitable as many factors such as steel position and cooling rate cannot maintain unchanged during whole procession. Reasonable online prediction can help to alert workers to pay attention to some key factors such as temperatures at various point and cooling rates. At present, the online SCCS model has installed in one Stelmor production for more than one year, which shows great advantage of model during production. Before installation of CSSC model, pre?xed policy was adopted for every steel during production, workers couldn’t directly observe the phase transformation range and tempera tures at various point, sometime, phase transformation could shift outside the controlled area, which could cause serious quality problem, such as ?nal microstructure to be martensite or coarsen pearlite. Workers can do nothing only after ?nal checking. After installation of the model, workers can directly observe the production process from userfriendly interface of the model, adjust the opening volume of various fans according to requirement, control the temperatures at various points in the reasonable range, and make phase transformation occur in the exact area. According to statistics from this production line, rate of ?nal steel product with ?ne pearlite with grade or below has been increased from % to % due to installation of the CSSC model, at the same time, cases of quality argument has greatly reduced to give users enough con?dence in its ?nal product. 4. Conclusions An unique online quality prediction system for the Stelmor controlled cooling line was developed, following conclusions can be obtained based on the working experience: 1. The prehensive model the for Stelmor controlled cooling line consists three parts: the thermal model is obtained by FDTD method, phase change model is obtained by solving Avrami equation based on the experimental CCT data, and mechanical property model is physical model based on regressing the production data, three parts are coupled internally. 2. This online model is open to get the production information during production, which can municate with the material ?ow management system and Program Logic Control System (PLCs) automatically through local work. 3. No model is perfect and de?nitely accurate, so selfadapted function was designed for this online model to adjust the predicted temperature along the production line based on online pyrometers and ?nal properties based on ?nal checking. Present results prove that it is possible to predict the ?nal mechanical properties with the help of this online model with standard deviation MPa, which meet the requirement. After repeated modi?cation, this online model has been put into production for more than 2 years, which can greatly reduce the la bor work and improve the stability of product quality. References [1] S. Jaiswal, . Mclvor, Microalloyed high carbon steel rod, Ironmaking and Steelmaking 16 (1) (1989) 49–54. [2] . Mclvor, Microalloyed low carbon steel rod, Ironmaking and Steelmaking 16 (1) (1989) 55–62. [3] Prakash K. Agarwal, . Brimabe, Mathematical model of heat ?ow and austenite–pearlite transformation in eutectoid carbon steel rods for wire, Metallurgical Transactions B 12B (1981) 121–133. [4] . Morales, . Lop233。z, . Olivares, Mathematical simulation of Stelmorprocess, Ironmaking and Steelmaking 18 (2) (1991) 128–138. [5] . Morales, . Lop233。z, . Olivares, Heat transfer analysis during water spray cooling of steel rods, ISIJ International 30 (1) (1990) 48–57. [6] Ettore Anelli, Application of mathematical modelling to hot rolling and controlled cooling of wire rods and bars, ISIJ International 32 (3) (1992)440–449. [7] . Hawbolt, B. Chau, . Brimabe, Kiics of austenite–ferrite and austenite–pearlite transformations in a 1025 carbon steel, Metallurgical Transactions A 16A (1985) 565–578. [8] S. Denis, S. J246。str246。m, A. Simon, Coupled temperature, stress, phase transformation calculation model numerical illustration of the internal stresses evolution during cooling of a eutectoid carbon steel cylinder, Metallurgical Transactions A 18A (1987) 1203–1212. [9] J. Iyer, . Brimabe, . Hawbolt, Prediction of the structure and mechanical properties of controlcooled eutectoid steel rods, in: Conf. on Mechanical Working and Steel Processing, Chicago 1984, vol. XXII, ISS/AIME, Penn, 1985, p. 47. [10] A. Kumar, C. McCulloch, . Hawbolt, . Samarasekera, Modelling thermal and microstructural evolution on runout table of hot strip mill, Materials Science and Technology 7 (1991) 1211–1223. [11] M. Militzer, . Hawbolt, . Meadowcroft, Microstructural model for hot strip rolling of highstrength lowalloy steels, Metallurgical and Materials Transactions A 31A (2021) 1247–1259.