Abstract:
In a method for determining the rolling force in a roll stand for rolling metallic material which is to be rolled, the rolling force is determined by means of at least one neural network. According to the invention, the neural network is trained using, in particular measured, values for the rolling force under different operating conditions with a view to improving the determination of the rolling force. The neural network is advantageously trained using values for the rolling force and values for the different operating conditions for roll stands of different rolling trains.

Description:
This is a continuation of copending application Ser. No. PCT/DE00/00887 filed Mar. 22, 2000, PCT Publication WO 00/56477, which claims the priority of DE 199 13 126.0, filed Mar. 23, 1999 and DE 199 30 124.7 filed Jun. 30, 1999. 
    
    
     FIELD OF THE INVENTION 
     The invention relates to a method and a device for determining the rolling force in a roll stand for rolling metallic material to be rolled, the rolling force being determined by means of at least one neural network which is trained by using measured values for the rolling force under different operating conditions with a view to improving the determination of the rolling force. 
     BACKGROUND OF THE INVENTION 
     It is known to determine the rolling force by means of a rolling-force model which has a neural network. DE 197 27 821 A1 describes a method and a device for controlling and/or presetting a roll stand or a rolling train as a function of at least the rolling force. The rolling force is determined by means of a rolling-force model, and at least one neural network. To improve the quality of the rolling-force model, the neural network is trained while a rolling train which it is intended to control is operating. In this context, the term quality of model is to be understood as meaning the deviation between the actual rolling force and the rolling force which is determined by means of the rolling-force model. To achieve a high quality of model, this deviation should be as low as possible across the entire range of metals which are to be rolled by means of the roll stand. 
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to increase the accuracy of the determination of the rolling force in a roll stand for rolling metallic material which is to be rolled compared to the known method. Accordingly, the rolling force in a roll stand for rolling metallic material to be rolled is determined by means of at least one neural network which is trained, using measured values for the rolling force under different operating conditions, and thereby improving the determination of the rolling force. The neural network is preferably trained using values for the rolling force and values for the different operating conditions for roll stands of different rolling trains. 
     The term operating conditions is to be understood as meaning, for example, the width of the metallic material which is to be rolled by means of the roll stand before it enters the roll stand, the thickness of the metallic material to be rolled before it enters the roll stand, the relative reduction in thickness of the metallic material which is rolled in the roll stand, the temperature of the metallic material to be rolled when it enters the roll stand, the tension in the metallic material which is to be rolled upstream of the roll stand, the tension in the metallic rolled material downstream of the roll stand, the radius of the working rollers of the roll stand, and the levels of iron, carbon, silicon, manganese, phosphorus, sulfur, aluminum, copper, molybdenum, titanium, nickel, vanadium, niobium, nitrogen, boron and/or tin in the metallic material to be rolled and, if appropriate, the modulus of elasticity of the rollers. 
     Although in known methods for determining the rolling force in a roll stand using a neural network, the rolling force is specially matched to the roll stand by training, the present invention leads to a more precise determination of the rolling force for the roll stand across the entire range of metals which are to be rolled by means of the roll stand. 
     In a preferred embodiment of the present invention, the neural network is trained using values for the rolling force and values for the different operating conditions for at least one roll stand from a roughing train and at least one roll stand from a finishing train. 
     Other preferred embodiments of the present invention are disclosed herein below, the first of which is wherein the neural network is trained using values for the rolling force and values for the different operating conditions for roll stands of rolling trains of different rolling mills. This embodiment leads to particularly accurate determination of the rolling force in a roll stand, even though roll stands of different rolling mills, i.e. different plants, are used for training. 
     In the next embodiment, the neural network is trained using values for the rolling force and values for the different operating conditions for roll stands of rolling trains of at least five different rolling mills. In this embodiment, the accuracy of determination of the rolling force can be increased even further. 
     In the next embodiment, the neural network is used to determine a correction value for correcting (e.g., by multiplication) a value for the rolling force which is determined by means of an analytical rolling-force model. The linking of analytical models and neural networks is disclosed, for example, in DE 43 38 607 A1, DE 43 38 608 A1 and DE 43 38 615 A1. In particular, a structure as described in DE 43 38 607 A1 has proven particularly advantageous in connection with linking by multiplication. 
     In a further preferred embodiment, a stand-specific correction value for correcting the value for the rolling force which is determined by means of the analytical rolling-force model is determined by means of a stand network. The stand network is in the form of a neural network, as a function of physical properties of the metallic material which is to be rolled (e.g. the width of the metallic material which is to be rolled by means of the roll stand before it enters the roll stand, the thickness of the metallic material which is to be rolled before it enters the roll stand, the relative reduction in thickness of the metallic rolled material in the roll stand, the temperature of the metallic material which is to be rolled when it enters the roll stand, the tension in the metallic material which is to be rolled upstream of the roll stand, the tension in the metallic rolled material downstream of the roll stand and the radius of the working rollers of the roll stand, and of physical properties of the roll stand (e.g. the modulus of elasticity of the rollers). 
     A further advantageous embodiment of the present invention utilizes a chemistry-specific correction value for correction of the value for the rolling force. The rolling force is determined by means of the analytical rolling-force model. The correction value is determined by means of a chemistry network. The chemistry network is in the form of a neural network, as a function of chemical properties of the metallic material which is to be rolled (e.g. the levels of iron, carbon, silicon, manganese, phosphorus, sulfur, aluminum, chromium, copper, molybdenum, titanium, nickel, vanadium, niobium, nitrogen, boron and/or tin in a metallic material to be rolled). 
     This division into a stand-specific correction value and a chemistry-specific correction value, in conjunction with the invention, leads to a further improved accuracy in the determination of the rolling force. Further, under certain operating conditions of the roll stand, the accuracy of determination of the rolling force is significantly improved if, in combination with the invention, a microstructure-specific correction value for correction of the value for the rolling force which is determined by means of the analytical rolling-force model is determined by means of a microstructure network, which is in the form of a neural network, as a function of chemical properties of the metallic material which is to be rolled and the temperature of the metallic material which is to be rolled. In this context, chemical properties are, for example, the levels of iron, carbon, silicon, manganese, phosphorus, sulfur, aluminum, chromium, copper, molybdenum, titanium, nickel, vanadium, niobium, nitrogen, boron and/or tin in the metallic material which is to be rolled. This applies in particular if a distinction is drawn between a stand-specific correction value, a chemistry-specific correction value and a microstructure-specific correction value. 
     In yet another preferred embodiment of the present invention, the correction value of at least one neural network is multiplied by a confidence value. The confidence value forms a statistical measure for the reliability of the correction value. The confidence value is advantageously between 0 and 1. 
     The invention is used to particularly good effect for the presetting of a rolling train. In this case, the rolling force which is to be expected when rolling the metallic material to be rolled is determined in advance and is used to preset the rolling train, i.e. for example to adjust the roll nips on the roll stands, before the metallic material to be rolled enters the rolling train. Finally, in a very practical embodiment of the present invention, the rolling force is determined by means of at least two neural networks, the output values of which are linked. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Further advantages and details are apparent from the following detailed description of exemplary embodiments taken in context with the drawings. The exemplary embodiments relate to the rolling of steel. If suitably adapted, it is also possible to roll other metals, for example aluminum. 
     FIG. 1 shows a rolling-force model; 
     FIG. 2 shows a first advantageous rolling-force model; 
     FIG. 3 shows another advantageous rolling-force model; 
     FIG. 4 shows a correction for linking neural networks; 
     FIG. 5 shows a weighting structure for a neural network; 
     FIG. 6 shows a learning method for a neural network; 
     FIG. 7 shows a modification to the learning method shown in FIG. 6; 
     FIG. 8 shows a correction for a rolling-force model; and 
     FIG. 9 shows online adaptation of a correction in accordance with FIG.  8 . 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1 shows a rolling-force model  50  for determining the rolling force F H  in a roll stand. The rolling-force model  50  has an analytical model  1  and a neural network  2 . Input variables X M  and, optionally, a correction value k are input into the analytical rolling-force model  1 . The input variables X M  of the analytical rolling-force model  1  are, in a preferred embodiment, the modulus of elasticity of the rollers of the roll stand, the width of the steel which is to be rolled by means of the roll stand before it enters the roll stand, the thickness of the steel before it enters the roll stand, the relative reduction in thickness of the steel in the roll stand, the temperature of the steel when it enters the roll stand, the tension in the steel upstream of the roll stand, the tension in the steel downstream of the roll stand and the radius of the working rollers of the roll stand. The analytical rolling-force model  1  supplies, as output variable, an approximate value F M  for the rolling force, which is multiplied by the correction value k by means of a multiplier  6  and thereby supplies a value F H  for the rolling force, which is the output variable of the rolling-force model  50 . 
     The correction value k is determined by means of the neural network  2  as a function of its input variables X NN . The input variables X NN  of the neural network  2  comprise, in one exemplary embodiment, the width of the steel which is to be rolled by means of the roll stand before it enters the roll stand, the thickness of the steel before it enters the roll stand, the relative reduction in thickness of the steel in the roll stand, the temperature of the steel when it enters the roll stand, the tension in the steel upstream of the roll stand, the tension in the steel downstream of the roll stand, the radius of the working rollers of the roll stand, the circumferential speed of the working rollers of the roll stand, the level of carbon and preferably the level of silicon in the steel. Furthermore, it is preferred if the input variables X NN  of the neural network  2  also comprise the levels of manganese, phosphorus, sulfur, aluminum, chromium, copper, molybdenum, titanium, nickel, vanadium, niobium, nitrogen, boron and/or tin in the steel. 
     FIG. 2 shows a particularly preferred embodiment for a rolling-force model. This rolling-force model, which is denoted by reference numeral  51 , like the rolling-force model  50  shown in FIG. 1, has an analytical rolling-force model  1  and a multiplier  6 . By contrast, the neural network  2  shown in FIG. 1 is replaced by three neural networks  3 ,  4 ,  5 , the outputs k α , k β  and k γ  which are added to form the correction factor k. 
     Reference numeral  3  denotes a neural network which is referred to as the chemistry network and determines a chemistry-specific correction value k α  as a function of its input variables X α . The input variables X α  advantageously comprise the level of carbon in steel and advantageously the level of silicon in the steel. Furthermore, it is advantageous if the input variables X α  of the neural network  2  also comprise the levels of manganese, phosphorus, sulfur, aluminum, chromium, copper, molybdenum, titanium, nickel, vanadium, niobium, nitrogen, boron and/or tin in the steel. 
     Reference numeral  4  denotes a neural network which is referred to as the microstructure network and determines a microstructure-specific correction value k β  as a function of its input variables X α . The input variables X β  advantageously comprise the temperature of the steel when it enters the roll stand and the levels of carbon in the steel, and also, preferably the level of silicon in the steel. Furthermore, it is advantageous if the input variables X β  of the neural network  2  also comprise the levels of manganese, phosphorus, sulfur, aluminum, chromium, copper, molybdenum, titanium, nickel, vanadium, niobium, nitrogen, boron and/or tin in the steel. Reference numeral  5  denotes a neural network which is referred to as the stand network, which calculates a stand-specific correction value K γ  as a function of its input variables X γ . The input variables X γ  advantageously comprise the width of the steel which is to be rolled by means of the roll stand before it enters the roll stand, the thickness of the steel before it enters the roll stand, the relative reduction in thickness of the steel in the roll stand, the temperature of the steel when it enters the roll stand, the tension in the steel upstream of the roll stand, the tension in the steel downstream of the roll stand and the radius of the working rollers of the roll stand. 
     FIG. 3 shows a further preferred embodiment for a rolling-force model. This rolling-force model, which is denoted by reference numeral  52 , also has an analytical rolling-force model  1  and a multiplier  6 . According to this exemplary embodiment, the chemistry network  3  of the rolling-force model  51  is replaced, in the rolling-force model  52 , by a chemistry correction block  10 . The microstructure network  4  of the rolling-force model  51  is replaced, in the rolling-force model  52 , by a microstructure correction block  11 . The stand network  5  of the rolling-force model  51  is replaced, in the rolling-force model  52 , by a stand correction block  12 . Output variables from the chemistry correction block, the microstructure correction block and the stand correction block are denoted by A, B and C. The sum of these output variables forms the correction value k. 
     FIG. 4 shows a correction block  20 , the input variables of which are the variables X NN  and the output variable of which is the correction value k. The correction block  20  has neural networks  21 ,  22 ,  23  and a linking block  24 . Output variables of the neural networks are correction values k 21 , k 22  and k 23 , which are linked by means of the linking block  24  to form the correction value k. For this purpose, the linking block  24  advantageously forms the mean of the correction values k 21 , k 22 , and k 23  and outputs this mean as correction value k. The neural networks  21 ,  22 ,  23  have the same functionality. However, they are neural networks of different structures, i.e. neural networks with a different number of nodes and/or neural networks which undergo different training methods. 
     The correction block  20  replaces the neural network  2  in FIG.  1 . Furthermore, it is in particular possible for the chemistry correction block  10 , the microstructure correction block  11  and the stand correction block  12  to be replaced in each case by one correction block  20 . In this case, for correction block  20  X NN  and k are to be replaced by X α  and A, X β  and B and X γ  and C. 
     FIG. 5 shows a weighting correction block  33 , the input variables of which are the variables X NN  and the output variable of which is the correction value k. The weighting correction block  33  has a neural network  30 , a regression model  31 , a weighting block  32  and a multiplier  34 . The output variable of the weighting block  32  is a confidence value W NN , which may adopt values between 0 and 1. The confidence value WNN forms a statistical measure of the reliability of a correction value k NN  output from the neural network  30 . The regression model  31  also outputs a correction value k M . The correction value k NN  determined by the neural network  30  is multiplied by the confidence value WNN by means of the multiplier  34 . The correction value k M  determined by means of the regression model  31  is added to the product of k NN  and WNN. This sum forms the correction value k. It is advantageous for the neural network  2  to be replaced by the weighting correction block  33 . 
     The chemistry correction block  10 , the microstructure correction block  11  and the stand correction block  12  can be replaced by a weighting correction block similar to weighting correction block  33 . In this case X NN  and k are to be replaced by X α  and k α , X β  and k β  and X γ  and k γ . The neural network  30  may be replaced by the correction block  20 . In this case, the correction value k in FIG. 4 replaces the correction value k NN  in FIG.  5 . 
     FIG. 6 shows a learning method for the neural network  2  from FIG.  1 . For this purpose, the value F H  for the rolling force which is determined by means of the rolling-force model  50  is subtracted from a measured value FT for the actual rolling force. The difference is passed to the learning algorithm  55 , which determines the parameters P of the new network  2 . This method can be extended in a corresponding way to the neural networks  2 ,  3 ,  4 ,  5 ,  21 ,  22 ,  23  and  30  if the rolling-force model  50  is added to or replaced in accordance with the explanations given above. 
     In FIG. 7, the learning method which has been described with reference to FIG. 6 is modified to the extent that the neural networks  3 ,  4 ,  5  and the function block  2  from FIG. 1 are shown. For this purpose, the value F H  for the rolling force which is determined by means of the rolling-force model  51  is subtracted from a measured value F T  for the actual rolling force. This difference is passed to the learning algorithm  55 , which determines new parameters P 3 , P 4 , P 5  for the neural networks  3 ,  4 ,  5 , or corresponding correction factors for the parameters of the neural networks  3 ,  4 ,  5 . Furthermore, the learning algorithm  55  determines a new value for the correction value k 2  or a correction value P 2  for correcting the correction value k 2 . Correspondingly, this procedure may be applied to the neural networks in the correction blocks  10 ,  11  and  12  and to the neural networks  21 ,  22 ,  23  and  30  in FIG. 3, FIG.  4  and FIG.  5 . 
     The data X M  in X NN , X α , X β , X γ , and F T  are used to train the neural networks  2 ,  3 ,  4 ,  5 ,  21 ,  22 ,  23  and  30 . It is advantageous if these data originate from different rolling mills. 
     It is possible for the stand network  5  to be trained further online after it has been installed in a plant. However, it is particularly advantageous for the rolling-force model  51  or  52 , after fitting in a plant, to be left unchanged with regard to its parameters, and to additionally provide for correction of the output F H  of the rolling-force model  51  or of the rolling-force model  52 . As shown in FIG. 8, the value F H  is multiplied, by means of a multiplier, by a correction value k on  which is stored, for example, in a memory  61 . As an alternative to multiplication, it is also possible to provide for some other form of operation, for example addition. The product of F H  and k on  is a corrected value F H,on  for the rolling force. 
     While the parameters of the rolling-force model  51  or  52  remain unchanged after installation in the plant, the correction factor k on  can be adapted online, as illustrated in detail in FIG.  9 . For this purpose, the difference between F H,on  and a measured value F T  for the actual rolling force is fed to a learning algorithm  62 . The learning algorithm  62  is used to determine a new correction value k on,new  which replaces the previous correction value k on .