Method and system for computer-implemented design and / or lifetime optimization of machine elements of a metallurgical plant

Abstract

The invention relates to a method for the computer-implemented design and / or lifetime optimization of machine elements of a metallurgical plant, comprising the following process steps: Calculating and / or providing at least an initial data set comprising constructive characteristics of the machine element based on an assumed load and expected service life of the machine element in operational use, Recording the actual load and / or load history of the machine element in the metallurgical plant in the form of application-related measurement data and information from the operational use of the machine element, and The measurement data is used to calculate at least one second modified data set with constructive characteristics, optimized taking into account the installation situation of the machine element and / or the application-related measurement data.

Description

[0001] The invention relates to a method and system for computer-implemented design and / or lifetime optimization of machine elements of a metallurgical plant.

[0002] Machine elements in rolling mill drive trains, in particular, are subject to wear. Their wear is due to numerous continuous and discontinuous, regular and sporadic, preventable and unavoidable phenomena associated with the rolling process. Examples include maximum torques during strip threading and release, alternating strip gripping and slipping, and also the continuous loading resulting from the deformation of the rolled stock. When a critical level of wear is finally reached, the machine element must be replaced. For some machine elements, such as rollers or rolls, or gear spindles or gear spindle sleeves of roll drives, repair or overhaul can extend the service life of the machine element while maintaining or improving performance.

[0003] When manufacturing and designing machine components, the designer generally has no information about the causes of the continuous reduction in functionality due to wear. Often, the exact load on the machine component during operational use, and in particular the load history and current condition of the component, are unknown or only insufficiently known, making targeted improvement or further development of the product or machine component impossible.

[0004] The invention is therefore based on the object of providing a method and a system for the computer-implemented design and / or service life optimization of machine elements of a metallurgical plant, which takes this disadvantage into account.

[0005] The problem is solved by a method according to patent claim 1 and by a system having the features of patent claim 12.

[0006] Advantageous embodiments of the invention emerge from the subclaims.

[0007] According to a first aspect of the invention, a method for computer-implemented design and / or service life optimization of machine elements of a metallurgical plant is provided, which method comprises calculating and / or providing at least one first data set, comprising design characteristics of the machine element based on an assumed load and service life expectancy of the machine element in operational use, recording the actual load and / or load history of the machine element in the metallurgical plant in the form of application-related measurement data and information from the operational use of the machine element, and feeding back and using the measurement data to calculate at least one second modified data set with design characteristics that are optimized taking into account the installation situation of the machine element and / or the application-related measurement data.

[0008] The method provided according to the invention can be summarized as providing a quasi-dynamic design and / or service life optimization of machine elements of a metallurgical plant by retrieving and appropriately using information and measurement data relating to the machine element from operational use. Using the measurement data from operational use, the design characteristics of the machine element are adapted and optimized according to the invention. This can also be done repeatedly and iteratively, for example, with respect to a specific machine element.

[0009] The method according to the invention relates both to the computer-implemented design of the machine element in the sense of new production and to the service life optimization of the machine element in the sense of repair, maintenance or restoration.

[0010] Computer-implemented in the sense of the present invention means that the method is carried out at least partially automatically with the aid of computer hardware and software.

[0011] The manufacture of machine elements subject to high mechanical stress typically comprises numerous steps up to the production of the raw part, e.g., melting, casting, forming, heat treatment, and finally to the production of the finished machine element, e.g., by machining with a geometrically defined cutting edge, machining with a geometrically undefined cutting edge, welding, heat treatment. For all steps, the specified parameters (target values), which often characterize the quality of the machine element (e.g., geometry and surface finish), and the actually achieved values ​​(actual values) can be documented in detail, and the generally extensive, often continuously occurring measured values ​​(process parameters such as forces, moments, or feed rates) can be recorded and stored.

[0012] These data contain information or can be converted into information using suitable methods that show a direct influence of various variables on the load and properties of the machine element during later operation and reveal interactions and correlations that were previously unknown and undiscovered.

[0013] The exact geometry and surface shape influence, for example, the load occurring during operation, the actual material composition and the heat treatment condition influence, for example, the permissible load or load capacity.

[0014] The data, preferably after plausibility checks and validation and conversion into relevant information, can be used to evaluate the performance of the machine element, taking into account the structural design on the one hand and the assumed operating load on the other, and to quantitatively determine important related static parameters such as the expected service life. This improves the accuracy and quality of load prediction during design compared to the standard approach, which usually only considers a few target values ​​from the manufacturing process.

[0015] It is also possible, for example, in the context of a parameter study, to determine how a variation in key manufacturing process parameters affects the static load parameters. In this context, static parameters are those that are not compared and adjusted with the actual load and therefore do not change.

[0016] In contrast, dynamic parameters take into account the real loads that deviate from the forecast or that can only be imprecisely represented by the assumptions made during design, and possibly also the load history, which leads to, for example, only a remaining service life remaining instead of the calculated service life.

[0017] The invention also aims to record the subsequent operation of the machine element within a metallurgical plant as comprehensively and seamlessly as possible.

[0018] The recording and evaluation of the measurement data using suitable methods and models and the comparison with the parameters and measurement data from the production of the machine element allow the conversion of the static parameters from the production phase into dynamic and current parameters that are valid for the respective operation and thus the specific application.

[0019] For example, actual service lives and current remaining service lives can be calculated, but also limit values ​​regarding the load on the machine elements that must be observed during production can be adapted to the actual condition of the machine element and the actual course of the process and can also be exceeded in a defined manner if the associated and always known reduction in safety and / or service life is acceptable.

[0020] Conversely, particularly important, expensive or difficult to procure or replace machine elements can be relieved to a certain extent to the detriment of other machine elements if precise information is available on the currently occurring and currently permissible load.

[0021] The method according to the invention preferably comprises changing the load and / or service life expected for calculating and / or providing the design parameters in the event of a detected deviation between the assumed load and an actual load derived from the measured data and / or from information obtained regarding the actual installation situation of the machine element, and calculating and / or specifying optimized design parameters taking the actual load into account. These optimized parameters were also referred to above as dynamic parameters or characteristic values.

[0022] It is therefore possible to adjust important process variables so that the load on the machine element corresponds to the prediction determined during the design process, or to deliberately increase or decrease it if, for example, this would relieve the load on machine elements or allow process-related or product-related goals to be achieved. In this case, precise knowledge of the actual values ​​or specified or calculated parameters based on the assumed load, as well as comparison with the measured values ​​from operational use, and especially the deviation between target values ​​and actual values, is of great importance.

[0023] The individual process steps are preferably carried out in the order in which they are listed and can be repeated if necessary in order to achieve an iterative optimization of the machine element.

[0024] Preferably, the measurement data are plausibility checked and / or validated on the basis of physical models and / or artificial intelligence.

[0025] The method may further comprise the step of contour optimization of the machine element based on the recorded actual load and / or load history of the machine element during operational use.

[0026] The plausibility check and / or validation of the measurement data and / or the contour optimization process step are preferably carried out using procedures based on machine learning methods.

[0027] The calculation and / or specification of modified design parameters can also be carried out using procedures based on machine learning methods.

[0028] The method according to the invention includes the possibility of making changes to the machine element by recording comprehensive data and converting it into relevant information, thereby increasing its performance and expanding its functionality. Linking data from production and operation with data characterizing the current state of the machine element can allow not only a more precise design of the machine elements but also an optimization of the structural design.

[0029] For example, if contour optimization is performed based on a single measurement, the stresses acting on the machine element are reduced. An example of such a measurement is the determination of wear-related material removal in the area of ​​a gear spindle sleeve that is in direct contact with a roll neck. As a result of the measurement and subsequent evaluation and assessment of the underlying mechanism, the geometry of a functional surface of the gear spindle sleeve can be changed, for example. The complexity of the process increases if, instead, several or even numerous measurements are evaluated together using suitable methods, and several or even numerous measures for machine element optimization are derived from them using suitable methods.

[0030] Using suitable methods and special calculation procedures based on physical models and / or artificial intelligence, the influence of process variables and system settings on the machine element load during operation, which is calculated as part of the design of the machine element, can be verified and specified. For this purpose, in addition to the variables such as rolling force or temperature that are directly measured or determined using process models, for example, a rolling mill, a pass schedule model, or the profile, contour, and flatness model, additional variables are preferably measured or determined that allow direct conclusions to be drawn about the load on the machine element. This can be, for example, a current measurement of the geometry and / or surface or a local measurement of forces or stresses.With additional measurements that, for example, allow direct information about wear or an indirect calculation of the remaining service life, the relationship between the above-mentioned influencing factors and the associated load on the one hand, and the condition of machine elements and safety or service life on the other hand, can also be determined using methods and calculation procedures based on physical models and / or artificial intelligence. These two types of measurements also improve the quality of the design and enable the verification of important assumptions and the establishment of new correlations.

[0031] The relationship between characteristic quantities and parameters during the manufacture of the machine element and its subsequent load during operation, which is often not known to the same extent as the relationship between characteristic quantities and parameters during operation and the load during operation, can also be analyzed using methods and calculation procedures based on physical models and / or artificial intelligence and verified analogously to the procedure described above.

[0032] Examples of such artificial intelligences include neural networks, adaptive algorithms, evolutionary algorithms, genetic algorithms, and the like. State-of-the-art examples include: Bayesian belief networks, decision trees, hidden Markov models, case-based reasoning, k-nearest neighbors, self-organizing maps, instance-based learning, support vector machines, artificial neural networks (ANNs), recurrent neural networks (RNNs), deep neural networks (DNNs), or convolutional neural networks (CNNs). Any conceivable combination of such approaches can also be used.

[0033] The comprehensive data situation and the evaluation of the data using, for example, analytical and numerical methods allow process optimization with regard to the machine element, but to the same extent also with regard to the process, e.g. stability, yield, use of alloying elements and energy, use of input materials, and product, e.g. properties, quality, reproducibility. This means that plant limits and process-related limit values ​​can be adjusted if the currently permissible load on the machine element is known. This can give the plant operator more flexibility in production or improve product quality. This can be the case, for example, in a hot rolling mill if larger (or smaller) pass reductions and / or higher or lower temperatures are used, thereby saving alloying elements or achieving better product properties.

[0034] Preferably, the characteristic data or parameters are selected from a group of data comprising material characteristics and / or material composition, surface quality, as well as the geometric dimensions and contours of the machine element.

[0035] Preferably, the measurement data from the operational use of the machine element are selected from a group of data comprising the tribological characteristics of the machine element in the specific installation situation, temperatures, torques, speeds and static loads.

[0036] Preferably, the measurement data are fed back at least partially from a process control and guidance system of a steelworks plant or a continuous casting plant or an annealing furnace or a hot-dip galvanizing line or a rolling mill or a strip treatment plant or a forging plant or a casting-rolling plant into a device for the computer-aided design of machine elements and used to optimize the characteristics or parameters of the machine element.

[0037] Preferably, the machine element is selected from a group of machine elements comprising the rolls of a rolling mill, roller table rollers of a rolling mill, linear guides of roller tables, rolling stands, and edging stands as well as drive shafts and gear parts and toothed spindles of rolling stands

[0038] The invention further comprises a system for the design and / or service life optimization of machine elements of a metallurgical plant, comprising at least one device for the computer-aided design of machine elements and means for measuring, recording and storing operating data from the operational use of the machine element, wherein the system is configured to carry out the method described above.

[0039] The invention is explained below using an embodiment and with reference to the accompanying drawings.

[0040] They show: Fig. 1 a perspective view of a finishing train of a rolling mill as a metallurgical plant, Fig. 2 a perspective view of the drive side of a rolling stand with parts of the drive as machine elements to be designed according to the method, Fig. 3a a cross-section through a gear spindle sleeve as a machine element and Fig. 3b a perspective longitudinal section through the gear spindle sleeve.

[0041] The invention is described below based on a further development of gear spindle sleeves 1 as machine elements of drive trains for rolling stands F1-Fn of a finishing train 2 of a hot rolling mill for steel. The finishing train 2 comprises, in a known manner, a plurality of individual rolling stands F1-FN, which are part of the hot rolling mill as a metallurgical plant.

[0042] Such a finishing train 2 can, for example, comprise up to fourteen rolling stands F1-Fn, each of which is driven by drives 3 via geared spindles 4 as drive shafts. The geared spindles 4 are each laterally connected to a gear 5 via geared spindle sleeves 1 and are part of the drive train for the rolling stands F1-Fn. On the drive side of the rolling stands F1-FN, an upper work roll 6 and a lower work roll 7 are connected to the respective geared spindle 4 via a work roll journal 8 with a geared spindle sleeve 1, wherein the geared spindle 4 engages with a corresponding gearing of the geared spindle sleeve 1 via a circumferential toothing 9. On the side of the geared spindle sleeve 1 opposite the toothing 9, the sleeve is provided with a cross-sectional profile complementary to the work roll journal 8.

[0043] Process variables relevant to the load on the gear spindle sleeve 1 and changing over the course of the finishing train 2 include the speed n and the torque M, which can change, for example, from the rolling stands F1 to F7 as follows: Walzgerüst F1 F2 F3 F4 F5 F6 F7 n [Upm] 21 46 81 114 148 179 196 M [kNm] 2430 1460 770 340 250 150 70

[0044] Due to the reduction in thickness of the rolled stock, the speed of the work rolls increases significantly from an upstream roll stand F1 to a downstream roll stand F7, as can be seen from the table above. Accordingly, the torque M decreases.

[0045] The load on the gear spindle sleeve 1 during operational use also depends on the installation situation, for example, depending on whether the gear spindle sleeve 1 is connected to the gear spindle 4 on the stand side or the gearbox side, or whether the latter is arranged in the drive train of an upper work roll 6 or a lower work roll 7. The corresponding data, which can be provided in the form of measured data, and the information on which stand F1 to F7 of the finishing train 2 the respective gear spindle sleeve 1 is arranged in, are important for optimizing the service life of the gear spindle sleeve. Using simple sensors, the position and direction of rotation of the various gear spindle sleeves 1 in the finishing train 2 can be determined.An evaluation of the measurement data supplied by the sensors also provides information as to whether the respective gear spindle sleeve 1 is always used in the same position or whether it is installed and operated at a different position within the rolling stand F1-Fn or in another rolling stand F1-Fn.

[0046] On the one hand, key load data for the gear spindle sleeve, such as rpm, speed, rolling force, and rolling torque, can be estimated. On the other hand, rpm and speed can also be measured using additional sensors. Alternatively or additionally, the rolling force and torque values ​​can be obtained from the automation systems or a higher-level process control system of the rolling mill or from corresponding databases.

[0047] The measured or otherwise determined values ​​are assigned to each individual gear spindle sleeve 1 for the entire operating period. This creates an individual load history for each gear spindle sleeve 1 as a load collective with all available and relevant measurement data as a function of time.

[0048] The load collective is used as the basis for the design.

[0049] Gear spindle sleeves 1 that are always used in the same location must be able to withstand the loads occurring there permanently or for a defined period of time. Gear spindle sleeves 1 that are exposed to high forces and moments can be designed differently than those that operate at low loads. Gear spindle sleeves 1 that operate at high speeds can be designed differently than slow-running gear spindle sleeves.

[0050] For gear spindle sleeves 1, which are used at both positions within at least one stand, it is taken into account that the stress due to the change in the direction of rotation is of a changing rather than a pulsating nature and that the permissible load is therefore reduced with the design remaining unchanged.

[0051] According to the invention, the most important design criteria, guidelines, and algorithms are stored in appropriate software based on machine learning methods. This software is trained accordingly so that the individual design of the machine element, in particular the gear spindle sleeve 1, is automated and runs without intervention by a designer.

[0052] The load collective can be used in a next and further process step as a basis for an individual further development of the gear spindle sleeve 1 that is optimally tailored to the respective application.

[0053] Gear spindle sleeves 1 that are always used in the same location must be able to withstand the loads occurring there permanently or for a defined period of time. Gear spindle sleeves 1 that are exposed to high forces and moments are dimensioned larger using this process. These can be equipped with a further developed and thus improved geometry of the gearing 9 and / or the roll neck mount.

[0054] Gear spindle sleeves 1 that are operated at high speeds can have a different design and be manufactured from different materials and using different production methods than slow-running gear spindle sleeves 1.

[0055] According to the invention, gear spindle sleeves 1, which are used in various rolling stands F1-Fn, are specifically optimized for their heterogeneous and complex loading scenario.

[0056] For gear spindle sleeves 1 that are used only at one position within a rolling stand F1-Fn, a pulsating load can be assumed instead of a fluctuating load. Most importantly, an asymmetrical tooth shape can be selected for the gearing 9. This results in a restriction to one direction of rotation, but higher torques can then be transmitted in this direction. If higher torques are not required, a smaller and more economical size can be selected.

[0057] Particularly for gear spindle sleeves 1 used in various rolling stands F1-Fn, but also for all other gear spindle sleeves, the data-supported load analysis may reveal that a technically optimal or economically favorable design or construction is difficult to achieve due to the loading situation or the loading history. The software can then make alternative suggestions for modified operation or implement them automatically. This could mean, on the one hand, that the rolling stand in which the gear spindle sleeve 1 is currently located is relieved of the load with regard to the critical parameters, or, on the other hand, that the gear spindle sleeve 1 is used in a specific rolling stand F1-Fn but not in another.

[0058] Furthermore, the procedure can include a computer-aided analysis of all load histories and all measures, derivation and manual or automated application of design principles, if necessary taking customer specifications into account as additional boundary conditions. List of reference symbols 1 gear spindle sleeve 2 finishing line 3 drives 4 toothed spindles 5 gearboxes 6 upper working roll 7 lower working roll 8 work roll necks 9 Gearing F1-Fn rolling stands n speed M torque

Claims

[1] Method for the computer-implemented design and / or lifetime optimization of machine elements of a metallurgical plant comprising the following process steps: Calculating and / or providing at least one first data set comprising design characteristics of the machine element, based on an assumed load and service life expectancy of the machine element in an operational application, Recording the actual load and / or load history of the machine element in the metallurgical plant in the form of application-related measurement data and information from the operational use of the machine element, and Trace back and use the measurement data to calculate at least a second modified data set with design characteristics that are optimized taking into account the installation situation of the machine element and / or the application-related measurement data. [2] Method according to claim 1, comprising changing the assumed load and / or expected service life in the event of a detected deviation between the assumed load and an actual load derived from the measured data and / or information and calculating and / or specifying optimized design characteristics taking into account the actual load. [3] Method according to one of claims 1 or 2, characterized by that the procedural steps are carried out in the order in which they are listed and, if necessary, repeated iteratively. [4] Method according to one of claims 1 to 3, characterized by that the measurement data is plausible and / or validated. [5] Method according to one of claims 1 to 4, further comprising the method step of contour optimization of the machine element based on the recorded actual load and / or load history of the machine element in operational use. [6] Method according to one of claims 4 or 5, characterized by that the plausibility check and / or validation of the measurement data and / or the contour optimization process step is carried out using procedures based on machine learning methods. [7] Method according to one of claims 1 to 6, characterized by that the calculation and / or specification of modified design parameters is carried out using procedures based on machine learning methods. [8] Method according to one of claims 1 to 7, characterized by that the characteristic data are selected from a group of data comprising material characteristics and / or material composition, surface quality, as well as the geometric dimensions and contours of the machine element. [9] Method according to one of claims 1 to 8, characterized bythat the measurement data are selected from a group of data comprising the tribological characteristics of the machine element in the specific installation situation, temperatures, torques, speeds and static loads. [10] Method according to one of claims 1 to 9, characterized by that the measurement data are fed back, at least in part, from a process control and guidance system of a steelworks plant, a continuous casting plant, an annealing furnace, a hot-dip galvanising line, a rolling mill or a strip treatment plant, a forging plant or a casting-rolling plant into a device for the computer-aided design of machine elements. [11] Method according to one of claims 1 to 9, characterized bythat the machine element is selected from a group of machine elements comprising rolls of a rolling mill, roller table rollers of a rolling mill, linear guides of roller tables, rolling stands, and edging stands as well as drive shafts and gear parts and toothed spindles of rolling stands. [12] System for the design and / or service life optimization of machine elements of a metallurgical plant comprising at least one device for the computer-aided design of machine elements and means for measuring, recording and storing operating data from the operational use of the machine element, wherein the system is designed to carry out the method according to one of claims 1 to 11.

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