Real-Time Supervision of Annealing Process in Stainless Steel Production Lines

Transcription

1 Journal of Metallurgical Engineering (ME) Volume 3 Issue 1, January 2014 doi: /me Real-Time Supervision of Annealing Process in Stainless Steel Production Lines Carlos G. Spínola *1, J.M.Cañero-Nieto 2, C.J.Galvez-Fernandez 3, J.M. Bonelo 4 *1,2,3 Department of Electronics, University of Málaga Bulevar Louis Pasteur 35, Málaga, Spain 4 Acerinox Europa S.A.U. Factoría del Campo de Gibraltar, Los Barrios (Cádiz), Spain *1 cspinola@uma.es; 2 jmcanero@ctima.uma.es; 3 carlosgalvezfernandez@gmail.com; 4 josemaria.bonelo@acerinox.com Abstract Annealing is an important process in the production of stainless steel coils. This paper presents a system and its supervision in production lines. The model is based on continuous data of line speed, temperatures throughout the annealing furnace, and the detection of the start of each coil entering the furnace. Annealing functions have been proposed whose integration along the space-time trajectory provides a powerful supervision tool for quality control engineers. This model has been checked and adjusted with continuous data of magnetic remanence in the case of ferritic stainless steel. These areas must be identified, cut and discarded before processing the sheet in the following lines to reduce production costs and increase productivity. Keywords Magnetic Remanence; Process Models; Production Systems; Quality Control; Steel Industry; Supervisory Control Introduction Stainless steel as a material with high added value and increasing importance is manufactured and delivered mainly as cold rolled sheets of specific thickness according to its intended use [Erdem and Taptik, 2005]. The desired thickness is achieved in the hot and cold rolling mills, where the steel sheets are treated by extrusion, stretching and rolling. These processes deform the crystal metallic structure of steel, leaving it brittle and breakable [Quach et al., 2009]. Subsequently, steel sheets are usually processed in annealing lines, where it is heated in order to recover their metallurgical and mechanical properties. The annealing process is performed in a tunnel furnace several tens of meters long, where the stainless sheet is heated as it is conveyed through the furnace (see FIG. 1). The quality of the final product depends very much on the processing conditions, such as furnace temperature and the time the material is within the furnace [Carvalho et al., 2006]. Changes in these conditions could produce a bad annealing on specific areas of the sheet that are not easy to detect. FIG. 1 AN ANNEALING FURNACE The production units are stainless steel sheets in coils hundred or thousand meter length. Many coils can be annealed in a day; therefore, it is difficult to supervise them all. Moreover, if the line speed or the temperature in the furnace vary due to operational events, it is difficult to determine which parts of the affected coils have not been well annealed. Several research works have been proposed for modelling control and optimization of the continuous annealing furnace [Li et al., 2006; Tian et al., 2004; Yoshitani and Hasegawa, 1998]. Nevertheless, an accurate model to simulate the complex thermal reactions is very hard to obtain due to the number of parameters the model depends on [Li et al., 2006]. In addition to this, the proposed models do not consider the impact on the quality of the steel when the processing conditions deviate from the ideal ones. The main contribution of this work is a model to supervise the quality of the manufactured steel sheets. 1

2 Journal of Metallurgical Engineering (ME) Volume 3 Issue 1, January 2014 This model detects in real time bad annealed areas making use of the available real time production signals and the ideal annealing setting established by quality control engineering. This model has been tested in a real stainless steel production line and validated regarding the magnetic remanence, an indicator of the metallurgical properties of steel. The outline of this paper is structured as following. The Annealing Process describes the annealing process of stainless steel as well as the magnetic remanence. Next, a space-time model describing the production conditions during the annealing process is proposed in Temperature Space-Time Model. Then, a supervision model for the steel quality is presented in Annealing Model. This model is validated regarding magnetic remanence in Validating of Annealing Model. Finally, Results and Conclusions presents a summary and our main conclusions. The Annealing Process A continuous annealing production line is a highly efficient heat treatment process for cold-rolled sheets in steel works and consists of several steps, including annealing, soaking, and cooling. Among these steps, the annealing is typically that has higher influence on the productivity and quality of the steel [Li et al., 2006]. The annealing process requires exposing the steel to high temperatures during a specific time in order to recover their metallurgical and mechanical properties, as well to reduce the hardness, yield strength, and tensile strength of the stainless steel [Bailey and Hirsch, 1962]. The temperature from which the grain microstructure of the steel is re-crystallized is called the annealing temperature T r, typically between 900 and 1100, depending on the carbon content and steel grade as well as thickness and width of the coils. If the material is exposed to temperatures higher than T r or during a time longer than required, an excess of annealing appears, otherwise, a defect of annealing is achieved. In both cases, the steel properties are not totally recovered, and therefore the quality of the final product is considerably reduced or even scrap. The annealing is performed in a tunnel furnace composed of several heating zones. Each zone has a set of independent gas burners and temperature sensors for their upper and lower part. As can be seen in Fig. 2, steel sheets are inserted in the entrance and conveyed through the furnace at a specific speed. The dimension of these sheets is from 1 to 10 mm thickness, 1000 to 1500 mm width, and several thousand meters length. The tail of a coil is welded with the head of the following coil in order to create a continuous process. FIG. 2 SCHEMATIC GRAPHICAL REPRESENTATION OF THE FURNACE ZONES. Tu n (t) AND Tl n (t) ARE THE TEMPERATURE IN THE UMPTHEENTH UPPER AND LOWER ZONE n Since the steel is treated in a continuous production line, the quality of the final product depends greatly on the operating conditions and the furnace characteristic [Carvalho et al., 2006]. In fact, the temperature in each furnace zone and the time the material stays inside it are the critical parameters that assure a good annealing. Time ranges from 0.5 to 5 minutes and temperature between 900 and These values are defined by the factory engineers for each sheet thickness, width and steel grade. Operational events and incidents during the production can affect the line speed or the temperature within the furnace. This modification of the production conditions could cause several areas of the sheet to be poorly annealed. The general supervision of the steel quality is normally done by performing metallurgical tests of several samples taken along the coil. Nevertheless, this procedure is highly costly and can be inept at detecting badly annealed areas, due to the number of samples required for each sheet and the amount of coils produced per day. Alternative procedures for detecting the quality of steel in real-time can be found in literature [Michel et al., 1979; Allison et al., 1992]; Mitra et al., 2003; Gfrerer and Erlangen, 1983]. These industrial measurement devices analyze residual magnetization that remains in the steel, after the material is magnetized by an external field. This residual magnetization, called magnetic remanence, has been proposed as an indicator of good annealing for ferritic steels as it depends on the level of the re-crystallized structure achieved during the annealing process. Nevertheless, although this magnitude is quite significant, it can not be used with non-ferromagnetic materials, like austenitic steels. Austenitic steel is widely produced stainless steels and exhibit superior corrosion resistance and a greater added value than ferritic ones [Charles, 2008]. 2

3 Journal of Metallurgical Engineering (ME) Volume 3 Issue 1, January Temperature Space-Time Model A graphical space-time model for the annealing process is presented in this section. Temperatures inside the furnace, the line speed, and the sheet thickness and length are synthesized in order to represent the process. A typical annealing furnace consists of several zones with its own burner and temperature sensors. Therefore, the furnace can be considered as a set of thermal generators that provide a homogeneous temperature in each zone. In addition, since temperature can vary independently in each zone over time, furnace temperature can be represented by a space surface that depends on time t and location x inside of the furnace T(x, t). FIG. 3 presents an example of the temperature evolution in the furnace zones. It can be seen that a decrease in temperature took place in all zones around 18:20. FIG. 3 GRAPHICAL REPRESENTATION OF TEMPERATURE EVOLUTION OVER TIME POSITION INSIDE OF THE ANNEALING FURNACE The main disadvantage of this representation is that it does not point out the impact on the steel quality when production conditions are modified, as well as which sheet regions have been affected. This graphical representation could be improved if the real line speed and strip length are also taken into account in the model. Let us consider a coil as a set of finite elements of length l, characterized by their distance to the coil head. As the instant a sheet head enters the furnace, the line speed v(t) and temperature in any furnace zone at any time are known, it is possible to track each coil element on its way inside the furnace. Besides, the amount of heat received by each element can be computed. FIG. 4 shows the temperatures in each furnace zone and the line speed versus time. It reveals that there has been an anomaly in line speed at 18:16. As can be seen in detail, this coil element has been heated in the first zone at more than Later, it has been exposed to a temperature below 1175 in the third, fourth and fifth zone, which could indicate a lack of annealing. FIG. 4 ZONE TEMPERATURES EVOLUTION IN ANNEALING FURNACE These graphical representations help quality control staff to supervise the annealing process for a specific region of the sheet. However, they are interested in supervising each sheet as a whole. Therefore, the model should be enhanced to calculate a new value, which should be representative of the annealing process. The processing of all production information and its compact representation is not a trivial task. It must be bear in mind that a coil could be thousands of meters length, and tens of them are produced every day in different production lines. In the next section, an annealing model is proposed for summarizing all data related to temperature, line speed, position in the coil, etc., into a representative value. Annealing Model So far, a space-time model to describe the production conditions during the annealing process has been presented. In this section, two functions for synthesizing all annealing information into a single representative value are proposed. The first formula computes the effective thermal energy received by the steel sheet within the annealing furnace: t out Ann 1 (d) = f 1 T t, x(t) dt (1) t in Where d is a sheet element, t in and t out are the time when d enters and exits from the furnace respectively, f 1 is the compensated temperature function (see FIG. 5 (a), and T t, x(t) is temperature at time t and position x(t) along the track of the element d inside the furnace. 3

4 Journal of Metallurgical Engineering (ME) Volume 3 Issue 1, January 2014 proportionally considered. The impact of heating the steel with a temperature higher than T a could be measured by the parameter m. The factory engineers set the line speed and furnace temperature for each coil according to the steel grade and sheet thickness, therefore fixing the time the sheet has to be inside of the furnace at the annealing temperature. For this reason, the second formula is more suitable and easy to interpret by production engineers. A comparison of both formulas can be found in [Spinola et al., 2008]. From this point to the end of this study, only equation (2) is considered and is referred to as the annealing model. FIG. 5 ANNEALING FUNCTIONS: (a) THE COMPENSATED TEMPERATURE AND (b) THE COMPENSATED ANNEALING TIME Let T r be the annealing temperature set. Function f 1 (T) is 0 when T < T r, since at this temperature the steel is heated, but the grain structure is not recrystallized. On the contrary, when T T r function f 1 is equal to T, depicting a slope = 1, as annealing takes place above this specific temperature. In (1), the physical dimension is temperature by time ( s), and it is related with the amount of effective thermal energy, devoted to annealing, received by the coil element d. One disadvantage of equation (1) is that it depends greatly on the critical value T r. But such a key value is not so precise and temperatures just below T r also affect the metal internal structure to a certain extent. Nevertheless, the main reason is that the physical dimension of equation (1), s, is not a measurement that can be easily interpreted by production engineers, which rely more on furnace temperature and the time the material has to be inside it. For these reasons, an alternative formula was proposed: t out Ann 2 (d) = f 2 T t, x(t) dt (2) t in Where f 2 is the compensated annealing time function (see FIG. 5 (b)). The defined interval for f 2, [T m, T a ] contains the critical value T r and parameter m should be zero or slightly above. Function f 2 has a different physical meaning. It represents the compensated time an element is within the furnace at temperature T a. The time the element is exposed to a temperature below T m is not considered at all, but the time the coil is heated with a temperature from T m to T a is Once the annealing model is computed for every element d of a coil, a representative set of values of the coil annealing is obtained. According to this information, bad annealing areas can be easily detected. In the next section, the annealing model is validated with respect to the magnetic remanence. Validating of Annealing Model In this section, the annealing model is put to work and its results compared with the magnetic remanence in two real production cases. FIG. 6 shows a real ferritic steel sheet processed in one of the factory annealing lines. FIG. 6 RELATIONSHIP BETWEEN Ann 2 AND MAGNETIC REMANENCE. PARAMETERS USED T a = 966, T m = 870, m = 0 This sheet is 1300 meters length. The figure is divided into two graphs: in the upper graph the production conditions are represented, i.e. average temperature and line speed for every sheet element. In the lower graph, the magnetic remanence, the ideal annealing time for that coil (Norm) and the values given by the annealing model (Ann 2 ) are presented. The annealing model has been computed with an 4

5 Journal of Metallurgical Engineering (ME) Volume 3 Issue 1, January element length ( l) equal to 50 meters and a parameter m equal to zero. At the moment, parameters T r, T m and T a have been set empirically and saved in look-up tables by the factory engineers depending on sheet thickness and steel grade. As can be seen in FIG. 6 the average speed of sheet elements between 500 and 750 meters suffered an average speed reduction while they were inside the furnace. In addition, the average temperature that they were exposed to was significantly increased due to this production incident. The Ann 2 function shows an excess of annealing located at meter 560th that could have affected the metallic structure of the steel. This is corroborated by the increasing measurements of magnetic remanence in this zone. Both the annealing excess and defect are manifested graphically as an increase above normal magnetic remanence values [Allison et al., 1992; Michel et al., 1979]. Ann 2 values in FIG. 6 are directly related to the desired annealing time (20 seconds) and very easy to interpret by production engineers. Some tolerance around the ideal value can be established, which will be determined by laboratory metallurgical tests. Quality inspectors can detect defects with this representation and analyze them to confirm which regions have not been well annealed. thermocouples used by the control system of the furnace are utilized. Although the most important result is that this function is directly valid for austenitic as well as ferritic steels, wich can be seen in FIG. 7. This annealing anomaly was also verified and reported by the on-line quality control staff and the zone was marked as scrap quality along 130 meters. In this case, only Ann 2 function is represented, because there is not any magnetic remanence data. In fact, Ann 2 function represents the deviation from the annealing parameters (time and temperature) set by the quality control engineers. The installation of this model in the factory has provided satisfactory results because it allows the inspection of the annealing process in a very efficient way. Furthermore, it would also allow the line operators to modify the production conditions according to the real data it can generate. Nevertheless, although flaws can be seen in graphical representation, the model does not automatically detect them as it does not assign a quality value for neither each element nor the entire coil. Additional work needs to be carry out in this field, and a supervised neural network is proposed for future investigations. The negative effect of furnace temperatures above certain limits should also be included in the annealing equations. ACKNOWLEDGMENT The authors wish to thank Acerinox Co. management for supporting this investigation and the Information Systems and Quality Control departments for the suggestions offered during this research. REFERENCES FIG. 7 ANNEALING ANOMALY FOR AUSTENITIC STAINLESS STEEL Results and Conclusions In this paper, a supervisory model of the annealing process has been presented. This system allows quality control staff to easily supervise a very complex production process since it summarizes all the generated information into a set of representative values along the coil length. Another advantage is that additional sensors are not required, as the existing Allison, S. G., Namkung, M., Yost, W. T., Cantrell, J. H. Magnetic remanence method and apparatus to test materials for embrittlement. US Patent , Bailey, J. E., Hirsch, P. B. The recrystallization process in some poly-crystalline metals. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences 267 (1962): Carvalho, S. R., Ong, T. H., Guimaraes, G. A mathematical and com-putational model of furnaces for continuous steel strip processing. Materials Processing Technology 5

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