CN117350598B - Gear steel process control method and system - Google Patents
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- 239000001301 oxygen Substances 0.000 claims description 22
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical group [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 21
- 239000002893 slag Substances 0.000 claims description 20
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Abstract
The invention provides a process control method and a process control system for gear steel, which relate to the technical field of intelligent control, and are used for screening and analyzing continuous casting process indexes of the gear steel to obtain preset continuous casting variables, randomly acquiring preset continuous casting variable parameters and taking the preset continuous casting variable parameters as initial solutions, carrying out initial solution expansion based on preset expansion rules, analyzing an optimal solution of a screening target of the initial expansion solution, and carrying out gear steel processing control to obtain a gear steel intermediate piece; the method comprises the steps of reading a preset grinding scheme to perform grinding control on the gear steel middleware to obtain target gear steel, solving the technical problems that when gear steel machining control is performed in the prior art, the process is not intelligent enough in control parameter configuration level treatment, and the process defects exist to limit the production machining quality due to insufficient analysis depth and precision, performing process segmentation, configuring an adaptability optimization mode based on quality requirements aiming at continuous casting and grinding, performing process optimization by combining an algorithm, guaranteeing the accuracy and the preference of an optimizing result, and improving the machining quality qualification degree.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to a process control method and system for gear steel.
Background
With the development of the mechanical manufacturing industry, the production quality requirement on gear steel is higher and higher, and the current traditional process needs to be optimized to optimize and control the processing based on quality standards. At present, the conventional process control method is mainly based on preconfigured processing control parameters, and is used for carrying out targeted control adjustment in combination with application requirements, so that the relative control influence is ignored, and the global processing effect is limited.
When the prior art is used for processing and controlling the gear steel, the processing is not intelligent enough in the control parameter configuration layer, and the analysis depth and the precision are insufficient, so that the production and processing quality is limited due to the existence of process flaws.
Disclosure of Invention
The application provides a process control method and a process control system for gear steel, which are used for solving the technical problems that when gear steel processing control is carried out in the prior art, processing is not intelligent enough on a control parameter configuration level, analysis depth and precision are insufficient, and production and processing quality are limited due to process flaws.
In view of the above, the present application provides a method and a system for controlling a process of gear steel.
In a first aspect, the present application provides a method of process control for gear steel, the method comprising:
screening and analyzing continuous casting process indexes of the gear steel to obtain preset continuous casting variables, wherein the preset continuous casting variables comprise terminal temperature, terminal carbon content, refining slag alkalinity and ladle molten steel superheat degree;
randomly acquiring preset continuous casting variable parameters of the preset continuous casting variable, wherein the preset continuous casting variable parameters comprise an endpoint temperature value, an endpoint carbon content value, a refining slag alkalinity value and a ladle molten steel superheat value, and taking the preset continuous casting variable parameters as initial solutions;
expanding the initial solution based on a preset expansion rule to obtain an initial expansion solution, and screening to obtain a target optimal solution after analyzing the initial expansion solution, wherein the target optimal solution is used for processing and controlling the gear steel to obtain a gear steel middleware;
and reading a preset grinding scheme, and performing grinding control on the gear steel intermediate piece based on the preset grinding scheme to obtain the target gear steel.
In a second aspect, the present application provides a process control system for gear steel, the system comprising:
the index screening analysis module is used for screening and analyzing the continuous casting process index of the gear steel to obtain a preset continuous casting variable, wherein the preset continuous casting variable comprises terminal temperature, terminal carbon content, refining slag alkalinity and ladle molten steel superheat degree;
the parameter acquisition module is used for randomly acquiring preset continuous casting variable parameters of the preset continuous casting variable, wherein the preset continuous casting variable parameters comprise an end point temperature value, an end point carbon content value, a refining slag alkalinity value and a ladle molten steel superheat value, and the preset continuous casting variable parameters are used as initial solutions;
the expansion optimizing module is used for expanding the initial solution based on a preset expansion rule to obtain an initial expansion solution, analyzing the initial expansion solution and screening to obtain a target optimal solution, wherein the target optimal solution is used for processing and controlling the gear steel to obtain a gear steel middleware;
and the processing control module is used for reading a preset grinding scheme and carrying out grinding control on the gear steel intermediate piece based on the preset grinding scheme to obtain the target gear steel.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the process control method for the gear steel, provided by the embodiment of the application, continuous casting process indexes of the gear steel are screened and analyzed to obtain preset continuous casting variables including terminal temperature, terminal carbon content, refining slag alkalinity and ladle molten steel superheat degree; randomly acquiring preset continuous casting variable parameters of the preset continuous casting variable and taking the preset continuous casting variable parameters as initial solutions, wherein the initial solutions comprise an end point temperature value, an end point carbon content value, a refining slag alkalinity value and a ladle molten steel superheat value, expanding the initial solutions based on preset expansion rules to obtain initial expansion solutions, analyzing the initial expansion solutions, and screening target optimal solutions for processing and controlling the gear steel to obtain gear steel middleware; the gear steel intermediate piece is subjected to grinding control by reading a preset grinding scheme, so that target gear steel is obtained, the technical problems that production and processing quality is limited due to the fact that processing is not intelligent enough in a control parameter configuration layer and analysis depth and precision are insufficient in the prior art when gear steel processing control is performed are solved, process segmentation is performed, an adaptability optimization mode is configured based on quality requirements aiming at continuous casting and grinding, process optimization is performed by combining an algorithm, accuracy and preference of an optimizing result are guaranteed, and processing quality qualification degree is improved.
Drawings
FIG. 1 is a schematic flow chart of a process control method for gear steel;
FIG. 2 is a schematic diagram of a process for obtaining an optimal solution of a gear steel in the process control method;
FIG. 3 is a schematic diagram of a process control method for gear steel with a predetermined grinding scheme read flow;
fig. 4 is a schematic structural diagram of a process control system for gear steel.
Reference numerals illustrate: index screening analysis module 11, parameter acquisition module 12, expansion optimizing module 13, processing control module 14.
Detailed Description
The gear steel continuous casting process index is screened and analyzed to obtain a preset continuous casting variable, preset continuous casting variable parameters are randomly obtained and used as initial solutions, initial solution expansion is carried out based on preset expansion rules, an initial expansion solution screening target optimal solution is analyzed, and gear steel processing control is carried out to obtain a gear steel intermediate piece; and reading a preset grinding scheme to perform grinding control on the gear steel intermediate piece to obtain target gear steel, so as to solve the technical problems that when the gear steel is processed and controlled in the prior art, the processing is not intelligent enough in a control parameter configuration layer, the analysis depth and the precision are insufficient, and the production and processing quality is limited due to technological flaws.
Example 1
As shown in fig. 1, the present application provides a process control method for gear steel, the method comprising:
step S100: screening and analyzing continuous casting process indexes of the gear steel to obtain preset continuous casting variables, wherein the preset continuous casting variables comprise terminal temperature, terminal carbon content, refining slag alkalinity and ladle molten steel superheat degree;
specifically, with the development of the mechanical manufacturing industry, the production quality requirement on gear steel is higher and higher, the current traditional process is required to be optimized, so that the optimized machining control is performed based on quality standards.
Specifically, in the processing process of the continuous casting technology of the gear steel, the endpoint temperature, the endpoint carbon content, the alkalinity of the refining slag and the superheat degree of the ladle molten steel are the control key points of production processing, and are the importance control indexes of processing quality. Specifically, in order to strictly control the content of harmful elements, such as C, P, in the main components of the gear steel, the end temperature needs to be controlled between 1600 and 1620 ℃ so as to ensure that the content of the components reaches the standard; the reduction of the carbon content can lead to the rapid increase of the oxygen content, and the corresponding consumption of the required deoxidizer is synchronously increased, so that the excessive inclusion content in the processed gear steel causes the subsequent refining burden, and the temperature of the final carbon content is controlled to be 0.08-0.14%; the refining process is used for carrying out diffusion deoxidation to separate useless inclusions, the alkalinity of the refining slag is controlled to be 3.5-4.5, and the inclusions in the steel can be adsorbed with high efficiency on the basis of ensuring good fluidity; in the casting control process, the superheat degree of the ladle molten steel is controlled to be 20-30 ℃, and meanwhile, the fluctuation range of the superheat degree of the ladle molten steel is required to be reduced in the processing process, so that the quality stability of a processed product is ensured. And taking the end point temperature, the end point carbon content, the alkalinity of the refining slag and the superheat degree of the ladle molten steel as the preset continuous casting variables, wherein the preset continuous casting variables are the basis for carrying out optimization processing control analysis.
Step S200: randomly acquiring preset continuous casting variable parameters of the preset continuous casting variable, wherein the preset continuous casting variable parameters comprise an endpoint temperature value, an endpoint carbon content value, a refining slag alkalinity value and a ladle molten steel superheat value, and taking the preset continuous casting variable parameters as initial solutions;
specifically, the control range of the predetermined continuous casting variable is used as a limiting standard, random numerical extraction is performed in the corresponding control range for the end point temperature, the end point carbon content, the refining slag alkalinity and the ladle molten steel superheat degree respectively, the end point temperature value, the end point carbon content value, the refining slag alkalinity value and the ladle molten steel superheat degree value are determined, for example, a random temperature value in the control range of 20-30 ℃ of the ladle molten steel superheat degree is extracted as the ladle molten steel superheat degree, and the random temperature value is used as the predetermined continuous casting variable parameter. And setting the preset continuous casting variable as the initial solution, namely the initial parameter for expansion optimization.
Step S300: expanding the initial solution based on a preset expansion rule to obtain an initial expansion solution, and screening to obtain a target optimal solution after analyzing the initial expansion solution, wherein the target optimal solution is used for processing and controlling the gear steel to obtain a gear steel middleware;
further, as shown in fig. 2, the step S300 of the present application further includes:
step S310: constructing an evaluation index set of the processing quality of the gear steel intermediate piece, wherein the evaluation index set comprises oxygen content, hardenability bandwidth, purity and grain size;
step S320: taking the evaluation index set as a preset optimization target and storing the preset optimization target into the preset expansion rule, wherein the preset optimization target refers to the machining quality of the gear steel intermediate piece;
step S330: sequentially carrying out weight assignment on each index in the preset optimization target to obtain a preset index coefficient, wherein the preset index coefficient comprises an oxygen content coefficient, a hardenability bandwidth coefficient, a purity coefficient and a grain size coefficient;
step S340: and carrying out optimization analysis on the initial expansion solution according to the preset index coefficient and the preset optimization target to obtain the target optimal solution.
Further, the step S340 of the present application further includes:
step S341: constructing an adaptability function according to the preset index coefficient and the preset optimization target, wherein the adaptability function is as follows:
；
wherein the saidRefers to the ith solution in the initial extended solution, the +.>Means said->Is said +.>、/>、/>、/>Respectively the oxygen content, the hardenability bandwidth, the purity, the grain size, the +.>Respectively the oxygen content, the hardenability bandwidth, the purity and the grain size, wherein N is N solutions in total in the initial extended solution, and the->Refers to the +.>Performing solution;
step S342: and screening the initial expansion solution according to the fitness function to obtain the target optimal solution.
Further, before the initial extended solution is filtered according to the fitness function to obtain the target optimal solution, step S342 of the present application further includes:
step S3421: obtaining N preset fitness of the N solutions in the initial extended solution according to the fitness function;
step S3422: comparing the N preset fitness degrees and determining the maximum fitness degree and the minimum fitness degree;
step S3423: extracting a preset solution set expansion interval in the preset expansion rule, wherein the preset solution set expansion interval comprises a maximum expansion interval and a minimum expansion interval;
step S3424: constructing a solution set expansion constraint function according to the maximum fitness, the minimum fitness, the maximum expansion interval and the minimum expansion interval;
step S3425: traversing the N preset fitness according to the solution set expansion constraint function, and obtaining N groups of solution set expansion results;
step S3426: and taking the N groups of solution set expansion results and the N solutions as the initial expansion solutions.
Further, step S3424 of the present application further includes:
the solution set expansion constraint function is as follows:
；
wherein the saidMeans +.f among the N solutions>An extended solution number generated by the solution, said +.>Refers to the i-th solution +.>Is>Fitness of the extended solution, said +.>Means said maximum fitness, said +.>Means said minimum fitness, said ++>Means said maximum expansion interval, said +.>Means the minimum extension interval, the +.>Is directed downward rounding.
Specifically, the preset expansion rule is used as a solution set expansion limit, the initial solution is expanded by combining an invasive weed optimization algorithm, the expansion quantity of the solution sets is measured by carrying out fitness calculation, a plurality of expansion sets to be optimized and screened are determined to be used as the initial expansion solution, fitness calculation is carried out on the initial expansion solution, an expansion solution corresponding to the maximum fitness is determined to be used as the target optimal solution, machining control of the gear steel is carried out based on the target optimal solution, and the gear steel middleware is obtained.
Specifically, a plurality of evaluation indexes for measuring the processing quality of the gear steel intermediate piece are determined, wherein the evaluation indexes comprise the oxygen content, the hardenability bandwidth, the purity and the grain size, for example, the lower the total oxygen content in the gear steel is for the same tooth surface contact stress, the cycle number can be effectively improved; the hardness change range measured by the width of the hardenability refers to a certain distance from the quenching end face, for example, 32-38HRCJ5, the hardness range which is 5mm away from the end quenching face is 32-38HRC, the smaller the width of the hardenability is, the more stable the material is quenched, the more uniform the hardness is, the smaller the deformation is, the more uniform the composition of the steel is, the width of the hardenability of the steel is controlled, the hardenability is ensured, the quenching deformation of the gear steel is improved, the quality and the service life of the gear steel can be measured based on the multiple evaluation indexes, and the oxygen content, the hardenability width, the purity and the grain size are integrated to be used as the evaluation index set. And taking the evaluation index set as a standard for measuring the machining quality of the gear steel middleware, namely, taking the evaluation index set as the preset optimization target and storing the evaluation index set into the preset expansion rule, wherein the preset expansion rule is used for performing expansion execution control of the initial solution. Further, the weights of the indexes in the predetermined optimization targets are assigned, for example, the indexes can be configured based on the importance degree of the indexes on the quality of the gear steel middleware, the higher the importance degree is, the higher the weight value is, the oxygen content coefficient, the hardenability bandwidth coefficient, the purity coefficient and the grain size coefficient are obtained, and as the predetermined index coefficient, preferably, the predetermined index coefficient is provided with a sign mark for measuring the optimization direction of the indexes, for example, the oxygen content mark is negative, which indicates that the oxygen content is decreased to be the optimization direction.
And further expanding the initial solution, and performing optimization analysis on the initial expanded solution based on the preset index coefficient and the preset optimization target. Specifically, the initial solution is initially expanded based on the predetermined expansion rule, that is, the predetermined expansion rule is used as expansion limit, for example, with respect to oxygen content, relevant parameters in the initial solution are determined, random adjustment of parameters within an allowable range is performed, a set of control parameters including the adjusted predetermined continuous casting variable is determined, expansion of the initial solution is performed N times by analogy, the N solutions are obtained, and fitness calculation of the N solutions is performed based on the fitness function.
Specifically, the fitness function is as follows:
；
wherein the saidRefers to the ith solution in the initial extended solution, the +.>Means said->Is said +.>、/>、/>、/>Respectively the oxygen content, the hardenability bandwidth, the purity, the grain size, the +.>Respectively the oxygen content, the hardenability bandwidth, the purity and the grain size, wherein N is N solutions in total in the initial extended solution, and the->Refers to the +.>It should be understood that the above parameters can be determined based on the earlier steps in this embodiment. Substituting the N solutions into the fitness function, directly calculating to determine the fitness corresponding to each solution, wherein the fitness is used for measuring the re-expansion quantity of the solutions, and the higher the fitness is, the more superior the solution is, and taking the solution as a baseThe more the number of basic re-expansion solutions is, the preference of the expansion solutions is ensured.
Further, the N preset fitness degrees are checked, and the maximum fitness degree and the minimum fitness degree are determined. Determining a solution set number expansion boundary based on the maximum fitness and the minimum fitness, exemplarily, randomly determining a solution set expansion number based on the minimum fitness as the minimum expansion interval, and adding the solution set expansion number corresponding to the maximum fitness as the maximum expansion interval, wherein the solution set expansion number corresponding to the maximum fitness is calculated as the solution set expansion limiting condition with the maximum expansion interval and the minimum expansion interval as the solution set expansion limiting condition with equal ratio increment of the corresponding solution set expansion number with increment of the fitness as the minimum expansion interval.
And extracting the maximum expansion interval and the minimum expansion interval based on the preset expansion rule, and constructing the solution set expansion constraint function by combining the maximum fitness and the minimum fitness. The solution set expansion constraint function is as follows:
；
wherein the saidMeans +.f among the N solutions>An extended solution number generated by the solution, said +.>Refers to the i-th solution +.>Is>Fitness of the extended solution, said +.>Refers to the maximumFitness, said->Means said minimum fitness, said ++>Means said maximum expansion interval, said +.>Means the minimum extension interval, the +.>The above parameters are all obtained by the pre-analysis in this embodiment, and are known parameters. Based on the N preset fitness corresponding to the N solutions, respectively inputting the N preset fitness to the expansion constraint function for carrying out solution set expansion quantity calculation, and determining the expansion solution quantity matched with the expansion Jie Shi fitness. And (3) based on the result, re-expanding the corresponding solutions, wherein the specific expansion mode is consistent with the N solutions, and the N groups of solution set expansion results, namely the N expansion solution sets corresponding to the N solutions, are determined. Integrating the N groups of solution set expansion results with the N solutions to serve as the initial expansion solutions. The initial extended solution is a determined target solution set to be optimized.
Further, performing fitness calculation on the N groups of solution set expansion results based on the fitness function, determining N groups of fitness sets, performing correction including fitness based on the N groups of fitness sets and the N preset fitness sets, determining the maximum fitness, and matching and determining an expansion solution corresponding to the maximum fitness, wherein the target optimal solution is an optimal parameter for guaranteeing processing quality, which is determined by optimizing, and comprises the corresponding endpoint temperature, endpoint carbon content, refining slag alkalinity and ladle molten steel superheat degree respectively, and performing processing control on the gear steel based on the target optimal solution, so that a gear steel intermediate is obtained, and the processing quality of the gear intermediate can be guaranteed maximally.
Step S400: and reading a preset grinding scheme, and performing grinding control on the gear steel intermediate piece based on the preset grinding scheme to obtain the target gear steel.
Further, as shown in fig. 3, the step S400 of reading the predetermined grinding scheme further includes:
step S410: constructing a grinder control feature set, wherein the grinder control feature set comprises a grinding wheel linear speed, a grinding wheel radial feeding speed and a grinding wheel granularity;
step S420: taking the gear steel rotating speed as a gear steel grinding control characteristic, and collecting the gear steel rotating speed with the grinding machine control characteristic to obtain a characteristic to be optimized;
step S430: randomly extracting any group of control characteristic parameters in the characteristics to be optimized, and adding the control characteristic parameters to the preset grinding scheme;
step S440: reading a preset neighborhood scheme, and determining a first neighborhood of the control characteristic parameters based on the preset neighborhood scheme, wherein the first neighborhood comprises a plurality of groups of neighborhood control characteristic parameters;
step S450: comparing the multiple groups of neighborhood control characteristic parameters based on the preset characteristic parameter evaluation constraint and determining an optimal neighborhood control characteristic parameter;
step S460: comparing the optimal neighborhood control characteristic parameter with the control characteristic parameter;
step S470: and if the optimal neighborhood control characteristic parameter is better than the control characteristic parameter, replacing the control characteristic parameter with the optimal neighborhood control characteristic parameter, and adding the optimal neighborhood control characteristic parameter to the preset grinding scheme.
Further, the step S450 of comparing the plurality of sets of neighborhood control parameters based on the predetermined feature parameter evaluation constraint and determining the optimal neighborhood control feature parameter further includes:
step S451: reading a gear steel surface of the gear steel;
wherein the gear steel face comprises a first face, a second face and a third face;
step S452: the roughness of the first surface, the second surface and the third surface is calculated in a weighting mode, and the comprehensive roughness of the gear steel is obtained;
step S453: and taking the gear steel comprehensive roughness as the preset characteristic parameter to evaluate constraint.
Further, after comparing the optimal neighborhood control feature parameter with the control feature parameter, step S470 of the present application further includes:
step S471: if the control characteristic parameter is better than the optimal neighborhood control characteristic parameter, determining a second neighborhood of the control characteristic parameter based on the preset neighborhood scheme;
step S472: and carrying out iterative optimization on the control characteristic parameters based on the second neighborhood until a preset iterative threshold is reached.
Specifically, grinding control optimization is performed, processing control parameter extraction is performed on a digital grinder, wherein the processing control parameter extraction comprises the linear speed of the grinding wheel, the radial feeding speed of the grinding wheel and the granularity of the grinding wheel, and the characteristics to be controlled of grinding processing are integrally executed and used as the grinding machine control characteristic set. And determining the rotating speed of the gear steel in the grinding process, and combining the grinding machine control feature set to determine the feature to be optimized, wherein the feature to be optimized is the grinding feature to be configured for finishing the grinding process, as the gear steel grinding control feature. Based on the features to be optimized, specific feature control parameter configuration of each feature is randomly carried out, for example, the radial feeding speed of the grinding wheel is configured to be 20mm/r, and the specific feature control parameter configuration is used as the control feature parameter. The control characteristic parameter is added to the predetermined grinding scheme as a scheme to be processed.
Further, the control characteristic parameters are randomly adjusted in a controllable range, a plurality of groups of neighborhood control characteristic parameters are derived, the neighborhood control characteristic parameters are used as the preset neighborhood scheme, the preset neighborhood scheme is read, and the space covering the preset neighborhood scheme is used as the first neighborhood. And performing correction and optimization on the preset neighborhood scheme in the first neighborhood and the control characteristic parameters.
And further acquiring the preset characteristic parameter evaluation constraint, specifically, reading the first face, the second face and the third face of the gear steel, wherein the first face, the second face and the third face are surfaces, such as double-sided surfaces and tooth surfaces, of the gear steel, used for performing grinding processing, and serve as the gear steel surfaces. The first face, the second face, and the third face are weighted, for example, the gear steel face is weighted based on an application importance level, for example, a tooth face weight is higher than a double-sided weight. And determining the roughness of the first surface, the second surface and the third surface according to the quality requirement, carrying out the roughness weighted summation of the gear steel surfaces, taking the gear steel comprehensive roughness as the gear steel comprehensive roughness, taking the gear steel comprehensive roughness as the preset characteristic parameter evaluation constraint, and carrying out optimizing and limiting of the control characteristic parameters.
And respectively carrying out gear steel comprehensive roughness calculation on the plurality of groups of neighborhood control characteristic parameters, and determining a plurality of characteristic parameter evaluation results in the same specific calculation mode. And combining the preset characteristic parameter evaluation constraint, carrying out difference calculation on the characteristic parameter evaluation results and the preset characteristic parameter evaluation constraint, checking the difference calculation result, and determining a neighborhood control characteristic parameter corresponding to the minimum difference as the optimal neighborhood control characteristic parameter.
Further, the optimal neighborhood control parameters and the control characteristic parameters are checked, if the optimal neighborhood control parameters are superior to the control characteristic parameters, the optimal neighborhood control parameters are used for replacing the control characteristic parameters and are added to the preset grinding scheme to serve as current scheme execution configuration parameters. If the control characteristic parameters are better than the optimal neighborhood control characteristic parameters, respectively carrying out random adjustment on the preset neighborhood schemes by a preset number, determining a space containing a complete adjustment scheme as the second neighborhood, carrying out gear steel comprehensive roughness calculation on each scheme in the second neighborhood, obtaining a calculation result, carrying out difference calculation and correction on the calculation result and the preset characteristic parameter evaluation constraint, determining neighborhood control characteristic parameters in the second neighborhood corresponding to the minimum difference, further carrying out correction on the neighborhood control characteristic parameters, determining the optimal control characteristic parameters, repeating the steps to carry out optimizing replacement until the preset iteration threshold is reached, for example, the maximum iteration number is met, taking the control characteristic parameters replaced by the preset grinding scheme which is currently determined as the pre-control scheme, namely, carrying out grinding control on the gear steel intermediate, and obtaining the target gear steel, wherein the multi-dimensional quality index of the target gear steel is better than that of a traditional process product.
Example 2
Based on the same inventive concept as the process control method of a gear steel in the foregoing embodiments, as shown in fig. 4, the present application provides a process control system of a gear steel, the system comprising:
the index screening analysis module 11 is used for screening and analyzing indexes of the continuous casting process of the gear steel to obtain preset continuous casting variables, wherein the preset continuous casting variables comprise end point temperature, end point carbon content, refining slag alkalinity and ladle molten steel superheat degree;
a parameter obtaining module 12, where the parameter obtaining module 12 is configured to randomly obtain a predetermined continuous casting variable parameter of the predetermined continuous casting variable, where the predetermined continuous casting variable parameter includes an endpoint temperature value, an endpoint carbon content value, a refining slag alkalinity value, and a ladle molten steel superheat value, and take the predetermined continuous casting variable parameter as an initial solution;
the expansion optimizing module 13 is used for expanding the initial solution based on a preset expansion rule to obtain an initial expansion solution, analyzing the initial expansion solution and then screening to obtain a target optimal solution, wherein the target optimal solution is used for processing and controlling the gear steel to obtain a gear steel middleware;
and the machining control module 14 is used for reading a preset grinding scheme, and performing grinding control on the gear steel intermediate piece based on the preset grinding scheme to obtain the target gear steel.
Further, the system further comprises:
the index set building module is used for building an evaluation index set of the processing quality of the gear steel intermediate piece, wherein the evaluation index set comprises oxygen content, hardenability bandwidth, purity and grain size;
the preset optimization target storage module is used for taking the evaluation index set as a preset optimization target and storing the evaluation index set into the preset expansion rule, wherein the preset optimization target refers to the machining quality of the gear steel intermediate piece;
the preset index coefficient acquisition module is used for sequentially carrying out weight assignment on each index in the preset optimization target to obtain a preset index coefficient, wherein the preset index coefficient comprises an oxygen content coefficient, a hardenability bandwidth coefficient, a purity coefficient and a grain size coefficient;
and the optimization analysis module is used for carrying out optimization analysis on the initial extended solution according to the preset index coefficient and the preset optimization target to obtain the target optimal solution.
Further, the system further comprises:
the function construction module is used for constructing a fitness function according to the preset index coefficient and the preset optimization target, wherein the fitness function is as follows:
；
wherein the saidRefers to the ith solution in the initial extended solution, the +.>Means said->Is said +.>、/>、/>、/>Respectively the oxygen content, the hardenability bandwidth, the purity, the grain size, the +.>Respectively the oxygen content, the hardenability bandwidth, the purity and the grain size, wherein N is N solutions in total in the initial extended solution, and the->Refers to the +.>Performing solution;
and the initial expansion solution screening module is used for screening the initial expansion solution according to the fitness function to obtain the target optimal solution.
Further, the system further comprises:
the fitness obtaining module is used for obtaining N preset fitness of the N solutions in the initial extended solution according to the fitness function;
the fitness comparison module is used for comparing the N preset fitness degrees and determining the maximum fitness degree and the minimum fitness degree;
the interval extraction module is used for extracting a preset solution set expansion interval in the preset expansion rule, wherein the preset solution set expansion interval comprises a maximum expansion interval and a minimum expansion interval;
the solution set expansion constraint function construction module is used for constructing a solution set expansion constraint function according to the maximum fitness, the minimum fitness, the maximum expansion interval and the minimum expansion interval;
the expansion result acquisition module is used for traversing the N preset fitness according to the solution set expansion constraint function and obtaining N groups of solution set expansion results;
and the initial expansion solution acquisition module is used for taking the N groups of solution set expansion results and the N solutions as the initial expansion solutions.
Further, the system further comprises:
the solution set expansion constraint function acquisition module is used for the solution set expansion constraint functions as follows:
；
wherein the saidMeans +.f among the N solutions>An extended solution number generated by the solution, said +.>Refers to the i-th solution +.>Is>Fitness of the extended solution, said +.>Means said maximum fitness, said +.>Means said minimum fitness, said ++>Means said maximum expansion interval, said +.>Means the minimum extension interval, the +.>Is directed downward rounding.
Further, the system further comprises:
the device comprises a feature set building module, a grinding machine control feature set generation module and a grinding machine control feature set generation module, wherein the grinding machine control feature set comprises a grinding wheel linear speed, a grinding wheel radial feeding speed and a grinding wheel granularity;
the feature acquisition module to be optimized is used for taking the gear steel rotating speed as a gear steel grinding control feature, and collecting the gear steel rotating speed with the grinding machine control feature to obtain the feature to be optimized;
the control characteristic parameter adding module is used for randomly extracting any group of control characteristic parameters in the characteristics to be optimized and adding the control characteristic parameters to the preset grinding scheme;
the first neighborhood acquisition module is used for reading a preset neighborhood scheme and determining a first neighborhood of the control characteristic parameters based on the preset neighborhood scheme, wherein the first neighborhood comprises a plurality of groups of neighborhood control characteristic parameters;
the optimal neighborhood control characteristic parameter determining module is used for comparing the multiple groups of neighborhood control characteristic parameters based on preset characteristic parameter evaluation constraints and determining optimal neighborhood control characteristic parameters;
the parameter comparison module is used for comparing the optimal neighborhood control characteristic parameters with the control characteristic parameters;
and the parameter replacement module is used for replacing the control characteristic parameters with the optimal neighborhood control characteristic parameters and adding the optimal neighborhood control characteristic parameters to the preset grinding scheme if the optimal neighborhood control characteristic parameters are better than the control characteristic parameters.
Further, the system further comprises:
the second neighborhood determining module is used for determining a second neighborhood of the control characteristic parameter based on the preset neighborhood scheme if the control characteristic parameter is better than the optimal neighborhood control characteristic parameter;
and the parameter iteration optimization module is used for carrying out iteration optimization on the control characteristic parameters based on the second neighborhood until a preset iteration threshold is reached.
Further, the system further comprises:
the gear steel surface reading module is used for reading the gear steel surface of the gear steel;
wherein the gear steel face comprises a first face, a second face and a third face;
the roughness calculation module is used for weighting and calculating the roughness of the first surface, the second surface and the third surface to obtain the comprehensive roughness of the gear steel;
and the predetermined characteristic parameter evaluation constraint determining module is used for taking the gear steel comprehensive roughness as the predetermined characteristic parameter evaluation constraint.
The foregoing detailed description of a process control method for gear steel will be apparent to those skilled in the art, and the process control method and system for gear steel in this embodiment may be implemented in a device disclosed in the embodiments, and thus the description is relatively simple, and the relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A process control method for gear steel, comprising:
screening and analyzing continuous casting process indexes of the gear steel to obtain preset continuous casting variables, wherein the preset continuous casting variables comprise terminal temperature, terminal carbon content, refining slag alkalinity and ladle molten steel superheat degree;
randomly acquiring preset continuous casting variable parameters of the preset continuous casting variable, wherein the preset continuous casting variable parameters comprise an endpoint temperature value, an endpoint carbon content value, a refining slag alkalinity value and a ladle molten steel superheat value, and taking the preset continuous casting variable parameters as initial solutions;
expanding the initial solution based on a preset expansion rule to obtain an initial expansion solution, and screening to obtain a target optimal solution after analyzing the initial expansion solution, wherein the target optimal solution is used for processing and controlling the gear steel to obtain a gear steel middleware;
reading a preset grinding scheme, and performing grinding control on the gear steel intermediate piece based on the preset grinding scheme to obtain target gear steel;
the expanding the initial solution based on a preset expansion rule to obtain an initial expansion solution, and screening the initial expansion solution to obtain a target optimal solution after analyzing the initial expansion solution, comprising the following steps:
constructing an evaluation index set of the processing quality of the gear steel intermediate piece, wherein the evaluation index set comprises oxygen content, hardenability bandwidth, purity and grain size;
taking the evaluation index set as a preset optimization target and storing the preset optimization target into the preset expansion rule, wherein the preset optimization target refers to the machining quality of the gear steel intermediate piece;
sequentially carrying out weight assignment on each index in the preset optimization target to obtain a preset index coefficient, wherein the preset index coefficient comprises an oxygen content coefficient, a hardenability bandwidth coefficient, a purity coefficient and a grain size coefficient;
carrying out optimization analysis on the initial expansion solution according to the preset index coefficient and the preset optimization target to obtain the target optimal solution;
the optimizing analysis is carried out on the initial extended solution according to the preset index coefficient and the preset optimizing target to obtain the target optimal solution, and the optimizing method comprises the following steps:
constructing an adaptability function according to the preset index coefficient and the preset optimization target, wherein the adaptability function is as follows:
λ a +λ b +λ c +λ d ＝1
wherein the X is i Refers to the ith solution in the initial extended solution, the F (X i ) Refers to the X i The f (a), f (b), f (c), f (d) refer to the oxygen content, the hardenability bandwidth, the purity, the grain size, and the lambda, respectively a 、λ b 、λ c 、λ d The weight coefficients of the oxygen content, the hardenability bandwidth, the purity and the grain size are respectively referred to, N refers to N solutions in total in the initial extended solution, and N refers to an nth solution in the N solutions;
and screening the initial expansion solution according to the fitness function to obtain the target optimal solution.
2. The process control method according to claim 1, wherein before said screening said initial extended solution according to said fitness function to obtain said target optimal solution, comprising:
obtaining N preset fitness of the N solutions in the initial extended solution according to the fitness function;
comparing the N preset fitness degrees and determining the maximum fitness degree and the minimum fitness degree;
extracting a preset solution set expansion interval in the preset expansion rule, wherein the preset solution set expansion interval comprises a maximum expansion interval and a minimum expansion interval;
constructing a solution set expansion constraint function according to the maximum fitness, the minimum fitness, the maximum expansion interval and the minimum expansion interval;
traversing the N preset fitness according to the solution set expansion constraint function, and obtaining N groups of solution set expansion results;
and taking the N groups of solution set expansion results and the N solutions as the initial expansion solutions.
3. The process control method of claim 2, wherein the solution set extended constraint function is as follows:
wherein the Q is j Refers to the expansion solution number generated by the jth solution in the N solutions, the F (X) (i，j) ) Refers to the ith solution X in the initial extended solution i The fitness of the jth extended solution of F max Refers to the maximum fitness, the F min Refers to the minimum fitness, the Q max Refers to the maximum expansion interval, the Q min The minimum extension interval is referred to, and the floor is a downward rounding.
4. The process control method according to claim 1, wherein the reading of the predetermined grinding schedule includes:
constructing a grinder control feature set, wherein the grinder control feature set comprises a grinding wheel linear speed, a grinding wheel radial feeding speed and a grinding wheel granularity;
taking the gear steel rotating speed as a gear steel grinding control characteristic, and collecting the gear steel rotating speed with the grinding machine control characteristic to obtain a characteristic to be optimized;
randomly extracting any group of control characteristic parameters in the characteristics to be optimized, and adding the control characteristic parameters to the preset grinding scheme;
reading a preset neighborhood scheme, and determining a first neighborhood of the control characteristic parameters based on the preset neighborhood scheme, wherein the first neighborhood comprises a plurality of groups of neighborhood control characteristic parameters;
comparing the multiple groups of neighborhood control characteristic parameters based on the preset characteristic parameter evaluation constraint and determining an optimal neighborhood control characteristic parameter;
comparing the optimal neighborhood control characteristic parameter with the control characteristic parameter;
and if the optimal neighborhood control characteristic parameter is better than the control characteristic parameter, replacing the control characteristic parameter with the optimal neighborhood control characteristic parameter, and adding the optimal neighborhood control characteristic parameter to the preset grinding scheme.
5. The process control method of claim 4, further comprising, after said comparing said optimal neighborhood control signature to said control signature:
if the control characteristic parameter is better than the optimal neighborhood control characteristic parameter, determining a second neighborhood of the control characteristic parameter based on the preset neighborhood scheme;
and carrying out iterative optimization on the control characteristic parameters based on the second neighborhood until a preset iterative threshold is reached.
6. The process control method of claim 5, wherein comparing the plurality of sets of neighborhood control parameters based on predetermined feature parameter evaluation constraints and determining optimal neighborhood control feature parameters, further comprises:
reading a gear steel surface of the gear steel;
wherein the gear steel face comprises a first face, a second face and a third face;
the roughness of the first surface, the second surface and the third surface is calculated in a weighting mode, and the comprehensive roughness of the gear steel is obtained;
and taking the gear steel comprehensive roughness as the preset characteristic parameter to evaluate constraint.
7. A process control system for gear steel for performing the method of any one of claims 1-6, the system comprising:
the index screening analysis module is used for screening and analyzing the continuous casting process index of the gear steel to obtain a preset continuous casting variable, wherein the preset continuous casting variable comprises terminal temperature, terminal carbon content, refining slag alkalinity and ladle molten steel superheat degree;
the parameter acquisition module is used for randomly acquiring preset continuous casting variable parameters of the preset continuous casting variable, wherein the preset continuous casting variable parameters comprise an end point temperature value, an end point carbon content value, a refining slag alkalinity value and a ladle molten steel superheat value, and the preset continuous casting variable parameters are used as initial solutions;
the expansion optimizing module is used for expanding the initial solution based on a preset expansion rule to obtain an initial expansion solution, analyzing the initial expansion solution and screening to obtain a target optimal solution, wherein the target optimal solution is used for processing and controlling the gear steel to obtain a gear steel middleware;
and the processing control module is used for reading a preset grinding scheme and carrying out grinding control on the gear steel intermediate piece based on the preset grinding scheme to obtain the target gear steel.
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