Copper alloy component optimization system and method

Abstract

The invention discloses a copper alloy component optimization system and method, and the method comprises the steps: screening out a target sub-performance prediction result sequence from all sub-performance prediction result sequences based on a preset screening condition, judging whether the number of the target sub-performance prediction result sequences is greater than a preset threshold value or not, and if the number is greater than the preset threshold value, carrying out the optimization of copper alloy components. If yes, the target sub-performance prediction result sequences are subtracted in pairs, the final target sub-performance prediction result sequence is determined according to the difference value result, and the copper alloy component with the optimal comprehensive performance index can be rapidly screened out.

Description

Technical Field

[0001] The present invention belongs to the technical field of copper alloy composition optimization, and in particular relates to a copper alloy composition optimization system and method. Background Art

[0002] The properties of copper alloys are significantly affected by their composition. Traditional alloy composition design relies on experience and trial and error methods, which is inefficient.

[0003] Machine learning methods, based on rich algorithms and powerful multi-dimensional modeling capabilities, can effectively promote material discovery, and therefore have attracted considerable interest from many materials researchers. At the same time, machine learning is superior to traditional modeling methods because it can mine internal relationships from given data. Therefore, data-driven materials science research has become an emerging field in materials research.

[0004] Although great progress has been made in the application of machine learning methods in copper alloy design, there are still some problems to be solved. Current machine learning applications of copper alloys are mainly focused on the optimization of heat treatment processes and the study of single properties, including stress corrosion cracking resistance, ultimate tensile strength and hardness. However, for copper alloys, due to the complex nonlinear relationship between preparation strategies and material properties, conventional machine learning modeling methods rarely consider microstructural information. In addition, the discovery of new alloying elements has always been a key factor in the development of high-performance copper alloys. Although machine learning algorithms have excellent learning and modeling prediction capabilities, these models cannot quickly screen out the best combination of performance indicators among multiple performance indicators. Summary of the invention

[0005] The present invention provides a copper alloy composition optimization system and method, which are used to solve the technical problem that it is impossible to quickly screen out the best performance index combination among multiple performance indexes.

[0006] In a first aspect, the present invention provides a copper alloy composition optimization system, the copper alloy composition optimization system comprising:

[0007] An acquisition module, used to acquire product demand information of copper alloys, wherein the product demand information includes components and performance indicators of the copper alloys;

[0008] A construction module, used to obtain the proportion and composition information of the corresponding alloy elements in a preset ingredient database based on the product demand information, and to construct at least one preset standard parameter table;

[0009] An output module, used to input the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result;

[0010] A sorting module, used to sort at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result;

[0011] A judgment module, used to filter out a target sub-performance prediction result sequence from each sub-performance prediction result sequence based on a preset screening condition, and to judge whether the number of the target sub-performance prediction result sequence is greater than a preset threshold;

[0012] The first determination module is used to perform pairwise subtraction of each target sub-performance prediction result sequence if it is greater than a preset threshold, determine the final target sub-performance prediction result sequence according to the difference results, and use a preset standard parameter table corresponding to the final target sub-performance prediction result sequence as the component composition of the copper alloy.

[0013] In a second aspect, the present invention provides a method for optimizing copper alloy composition, comprising:

[0014] Acquire product demand information of copper alloy, wherein the product demand information includes composition and performance index of copper alloy;

[0015] Based on the product demand information, obtaining the proportion and composition information of the corresponding alloy elements in a preset ingredient database, and constructing at least one preset standard parameter table;

[0016] Inputting the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result;

[0017] Sorting at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result;

[0018] Filtering out a target sub-performance prediction result sequence from each sub-performance prediction result sequence based on a preset filtering condition, and determining whether the number of the target sub-performance prediction result sequences is greater than a preset threshold;

[0019] If it is greater than a preset threshold, the target sub-performance prediction result sequences are subtracted from each other, and the final target sub-performance prediction result sequence is determined based on the difference results, and a preset standard parameter table corresponding to the final target sub-performance prediction result sequence is used as the composition of the copper alloy.

[0020] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the copper alloy composition optimization method of any embodiment of the present invention.

[0021] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the steps of the copper alloy composition optimization method of any embodiment of the present invention.

[0022] The copper alloy composition optimization system and method of the present application selects a target sub-performance prediction result sequence from each sub-performance prediction result sequence based on a preset screening condition, and determines whether the number of the target sub-performance prediction result sequences is greater than a preset threshold. If it is greater than the preset threshold, each target sub-performance prediction result sequence is subtracted from each other, and the final target sub-performance prediction result sequence is determined based on the difference result, so as to quickly select the copper alloy composition with the best comprehensive performance index. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A structural block diagram of a copper alloy composition optimization system provided by one embodiment of the present invention;

[0025] Figure 2 A flow chart of a copper alloy composition optimization method provided by one embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 , which shows a structural block diagram of a copper alloy composition optimization system of the present application.

[0029] like Figure 1 As shown, the copper alloy composition optimization system 200 includes:

[0030] An acquisition module 210 is used to acquire product demand information of copper alloys, wherein the product demand information includes components and performance indicators of copper alloys;

[0031] The construction module 220 is used to obtain the proportion and composition information of the corresponding alloy elements in the preset ingredient database based on the product demand information, and construct at least one preset standard parameter table.

[0032] Furthermore, the construction module 220 includes: a screening unit, which is used to screen out the composition information of the corresponding alloy elements in the preset product demand-element mapping table based on the product demand information, and to screen out the proportion information of the corresponding alloy elements in the ingredient database in combination with the preset performance-composition-proportion comparison table; a matching unit, which is used to match the composition information and proportion information of the alloy elements with the production database to obtain the production process, and to construct at least one preset standard parameter table that matches the copper alloy target product according to the standard production parameters under each production step in the production process and the standard matching parameters of the alloy elements.

[0033] The output module 230 is used to input the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result.

[0034] The sorting module 240 is used to sort at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result.

[0035] The judgment module 250 is used to filter out target sub-performance prediction result sequences from each sub-performance prediction result sequence based on a preset filtering condition, and to judge whether the number of the target sub-performance prediction result sequences is greater than a preset threshold.

[0036] Furthermore, the judgment module 250 includes: a comparison unit, which is used to obtain the performance threshold of each sub-performance prediction result, and take a sub-performance prediction result sequence in which the number of sub-performance prediction results greater than the corresponding performance threshold is greater than a preset number threshold as a target sub-performance prediction result sequence.

[0037] The first determination module 260 is used to perform pairwise subtraction of each target sub-performance prediction result sequence if it is greater than a preset threshold, determine the final target sub-performance prediction result sequence according to the difference results, and use a preset standard parameter table corresponding to the final target sub-performance prediction result sequence as the component composition of the copper alloy.

[0038] Furthermore, the first determination module 260 includes: a difference unit, used to differ a sub-performance prediction result in a certain target sub-performance prediction result sequence from another sub-performance prediction result in another target sub-performance prediction result sequence, wherein the certain sub-performance prediction result and the other sub-performance prediction result belong to the same performance indicator; an assignment unit, used to add one to the difference result of the certain target sub-performance prediction result sequence if the difference is greater than zero, otherwise add one to the other target sub-performance prediction result sequence; a comparison unit, used to compare the difference result of a certain target sub-performance prediction result sequence with the difference result of another target sub-performance prediction result sequence after all sub-performance prediction results are differed; and a selection unit, used to select a target sub-performance prediction result sequence with a larger difference result as the final target sub-performance prediction result sequence.

[0039] In a specific embodiment, the copper alloy composition optimization system further includes: a second determination module, which is used to directly use a preset standard parameter table corresponding to the target sub-performance prediction result sequence as the composition of the copper alloy if it is not greater than a preset threshold.

[0040] In summary, the copper alloy composition optimization system of the present application selects the target sub-performance prediction result sequence from each sub-performance prediction result sequence based on preset screening conditions, and determines whether the number of target sub-performance prediction result sequences is greater than a preset threshold. If it is greater than the preset threshold, each target sub-performance prediction result sequence is subtracted from each other, and the final target sub-performance prediction result sequence is determined according to the difference results, so as to quickly select the copper alloy composition with the best comprehensive performance index.

[0041] See also Figure 2 , which shows a flow chart of a copper alloy composition optimization method of the present application.

[0042] like Figure 2 As shown, the copper alloy composition optimization method specifically includes the following steps:

[0043] Step S101, obtaining product demand information of copper alloy, wherein the product demand information includes composition and performance index of copper alloy;

[0044] Step S102, based on the product demand information, obtaining the proportion and composition information of the corresponding alloy elements in a preset ingredient database, and constructing at least one preset standard parameter table;

[0045] Step S103, inputting the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result;

[0046] Step S104, sorting at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result;

[0047] Step S105, screening out target sub-performance prediction result sequences from each sub-performance prediction result sequence based on a preset screening condition, and determining whether the number of the target sub-performance prediction result sequences is greater than a preset threshold;

[0048] Step S106, if it is greater than the preset threshold, then the target sub-performance prediction result sequences are subtracted from each other, and the final target sub-performance prediction result sequence is determined according to the difference results, and a preset standard parameter table corresponding to the final target sub-performance prediction result sequence is used as the composition of the copper alloy.

[0049] In other embodiments, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the copper alloy composition optimization method in any of the above method embodiments;

[0050] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0051] Acquire product demand information of copper alloy, wherein the product demand information includes composition and performance index of copper alloy;

[0052] Based on the product demand information, obtaining the proportion and composition information of the corresponding alloy elements in a preset ingredient database, and constructing at least one preset standard parameter table;

[0053] Inputting the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result;

[0054] Sorting at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result;

[0055] Filtering out a target sub-performance prediction result sequence from each sub-performance prediction result sequence based on a preset filtering condition, and determining whether the number of the target sub-performance prediction result sequences is greater than a preset threshold;

[0056] If it is greater than a preset threshold, the target sub-performance prediction result sequences are subtracted from each other, and the final target sub-performance prediction result sequence is determined based on the difference results, and a preset standard parameter table corresponding to the final target sub-performance prediction result sequence is used as the composition of the copper alloy.

[0057] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the copper alloy composition optimization system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the copper alloy composition optimization system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0058] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 The example of the bus connection is taken as an example. The memory 320 is the computer-readable storage medium mentioned above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the copper alloy composition optimization method of the above method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the copper alloy composition optimization system. The output device 340 may include a display device such as a display screen.

[0059] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0060] As an embodiment, the electronic device is applied to a copper alloy composition optimization system and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0061] Acquire product demand information of copper alloy, wherein the product demand information includes composition and performance index of copper alloy;

[0062] Based on the product demand information, obtaining the proportion and composition information of the corresponding alloy elements in a preset ingredient database, and constructing at least one preset standard parameter table;

[0063] Inputting the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result;

[0064] Sorting at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result;

[0065] Filtering out a target sub-performance prediction result sequence from each sub-performance prediction result sequence based on a preset filtering condition, and determining whether the number of the target sub-performance prediction result sequences is greater than a preset threshold;

[0066] If it is greater than a preset threshold, the target sub-performance prediction result sequences are subtracted from each other, and the final target sub-performance prediction result sequence is determined based on the difference results, and a preset standard parameter table corresponding to the final target sub-performance prediction result sequence is used as the composition of the copper alloy.

[0067] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A copper alloy composition optimization system, characterized in that: The copper alloy composition optimization system comprises: An acquisition module, used to acquire product demand information of copper alloys, wherein the product demand information includes components and performance indicators of the copper alloys; A construction module, used to obtain the proportion and composition information of the corresponding alloy elements in a preset ingredient database based on the product demand information, and construct at least one preset standard parameter table; An output module, used to input the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result; A sorting module, used to sort at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result; A judgment module, used to filter out a target sub-performance prediction result sequence from each sub-performance prediction result sequence based on a preset screening condition, and to judge whether the number of the target sub-performance prediction result sequence is greater than a preset threshold; The first determination module is used to perform pairwise subtraction of each target sub-performance prediction result sequence if it is greater than a preset threshold, determine the final target sub-performance prediction result sequence according to the difference results, and use a preset standard parameter table corresponding to the final target sub-performance prediction result sequence as the component composition of the copper alloy.

2. A copper alloy composition optimization system according to claim 1, characterized in that: The building blocks include: A screening unit, configured to screen the composition information of the corresponding alloy elements in a preset product requirement-element mapping table based on the product requirement information, and screen the proportion information of the corresponding alloy elements in a proportioning database in combination with a preset performance-composition-proportion comparison table; A matching unit is used to match the composition information and ratio information of the alloy elements with a production database to obtain a production process, and to construct at least one preset standard parameter table that matches the copper alloy target product based on the standard production parameters under each production step in the production process and the standard matching parameters of the alloy elements.

3. A copper alloy composition optimization system according to claim 1, characterized in that: The copper alloy composition optimization system also includes: The second determination module is used to directly use a preset standard parameter table corresponding to the target sub-performance prediction result sequence as the component composition of the copper alloy if it is not greater than a preset threshold.

4. A copper alloy composition optimization system according to claim 1, characterized in that: The judging module comprises: The comparison unit is used to obtain the performance threshold of each sub-performance prediction result, and take a sub-performance prediction result sequence in which the number of sub-performance prediction results greater than the corresponding performance threshold is greater than a preset number threshold as a target sub-performance prediction result sequence.

5. The copper alloy composition optimization system according to claim 1, characterized in that: The first determining module comprises: A difference unit, used to differ a certain sub-performance prediction result in a certain target sub-performance prediction result sequence from another sub-performance prediction result in another target sub-performance prediction result sequence, wherein the certain sub-performance prediction result and the another sub-performance prediction result belong to the same performance indicator; An assignment unit, configured to increase the difference result of a certain target sub-performance prediction result sequence by one if the difference is greater than zero, and increase the difference result of another target sub-performance prediction result sequence by one otherwise; A comparison unit is used to compare the difference result of a certain target sub-performance prediction result sequence with the difference result of another target sub-performance prediction result sequence after the difference of all sub-performance prediction results is completed; The selection unit is used to select a target sub-performance prediction result sequence with a large difference result as the final target sub-performance prediction result sequence.

6. A method for optimizing copper alloy composition, characterized in that: include: Acquire product demand information of copper alloy, wherein the product demand information includes composition and performance index of copper alloy; Based on the product demand information, obtaining the proportion and composition information of the corresponding alloy elements in a preset ingredient database, and constructing at least one preset standard parameter table; Inputting the at least one preset standard parameter table into an alloy performance prediction model pre-constructed according to a machine learning algorithm, wherein the alloy performance prediction model uses the alloy composition range to be optimized as the search range and the performance index as the optimization target, and outputs a comprehensive performance prediction result corresponding to the at least one preset standard parameter table, wherein the comprehensive performance prediction result includes at least one sub-performance prediction result; Sorting at least one sub-performance prediction result in each comprehensive performance prediction result to obtain a sub-performance prediction result sequence associated with each comprehensive performance prediction result, wherein a sub-performance prediction result sequence only includes sub-performance prediction results of the same comprehensive performance prediction result; Filtering out a target sub-performance prediction result sequence from each sub-performance prediction result sequence based on a preset filtering condition, and determining whether the number of the target sub-performance prediction result sequences is greater than a preset threshold; If it is greater than a preset threshold, the target sub-performance prediction result sequences are subtracted from each other, and the final target sub-performance prediction result sequence is determined based on the difference results, and a preset standard parameter table corresponding to the final target sub-performance prediction result sequence is used as the composition of the copper alloy.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method of claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 6 is implemented.

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