CN117807750A - Method and equipment for determining welding process parameter information - Google Patents
Method and equipment for determining welding process parameter information Download PDFInfo
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Abstract
It is an object of the present application to provide a method and apparatus for determining welding process parameter information, the method comprising: determining parameter range information corresponding to welding process parameters; determining a fitness function matching the welding process parameters and the width of the heat affected zone; and determining target welding process parameter information from the parameter range information by utilizing the fitness function and combining a particle swarm algorithm based on the parameter range information. According to the method and the device, the welding process parameters which enable the welding quality to be optimal are searched through the fitness function matched with the welding process parameters and the width of the heat affected zone, and the corresponding parameter searching range is determined before searching is started, so that the calculation efficiency and accuracy are improved.
Description
Technical Field
The present application relates to the field of welding, and more particularly to a technique for determining welding process parameter information.
Background
In the automatic welding process, the welding quality is affected by various welding process parameters. For example, when the welding speed is too high, the weld bead may be narrowed, the penetration depth becomes shallow, the weld bead surplus height becomes low, and the like; when the welding current is increased, the welding seam can be widened, the penetration depth is deepened, the surplus height of the welding seam is increased, and the like; when the gas flow is insufficient, quality defects such as air holes and the like are very easy to generate. In order to ensure welding quality, enterprises often need to develop a large number of welding process experiments by a plurality of departments of combined process, production, quality and the like, polish and cut the welding seams and detect the welding quality condition through flaw detection or X-ray so as to finally determine a set of proper welding process parameters. Such welding process parameter determination often requires significant effort and resources, and the final determined welding process parameters are not necessarily optimal.
Disclosure of Invention
It is an object of the present application to provide a method and apparatus for determining welding process parameter information.
According to one aspect of the present application, there is provided a method for determining welding process parameter information, the method comprising:
determining parameter range information corresponding to welding process parameters;
determining a fitness function matching the welding process parameters and the width of the heat affected zone;
and determining target welding process parameter information from the parameter range information by utilizing the fitness function and combining a particle swarm algorithm based on the parameter range information.
According to one aspect of the present application there is provided a computer device for determining welding process parameter information comprising a memory, a processor and a computer program stored on the memory, characterised in that the processor executes the computer program to carry out the steps of any of the methods as described above.
According to one aspect of the present application there is provided a computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of any of the methods described above.
According to one aspect of the present application there is provided a computer program product comprising a computer program, characterized in that the computer program when executed by a processor implements the steps of any of the methods described above.
According to one aspect of the present application, there is provided an apparatus for determining welding process parameter information, the apparatus comprising:
the one-to-one module is used for determining parameter range information corresponding to welding process parameters;
the first module and the second module are used for determining a fitness function matched with the welding process parameters and the width of the heat affected zone;
and the three modules are used for determining target welding process parameter information from the parameter range information by utilizing the fitness function and combining a particle swarm algorithm based on the parameter range information.
Compared with the prior art, the method and the device have the advantages that parameter range information corresponding to welding process parameters is determined; determining a fitness function matching the welding process parameters and the width of the heat affected zone; and determining target welding process parameter information from the parameter range information by utilizing the fitness function and combining a particle swarm algorithm based on the parameter range information. The present application contemplates that during the actual welding process, the heat affected zone absorbs sufficient heat for a longer period of time, and the zone may undergo microstructural and performance changes, resulting in performance differences from the original parent material. These differences will make the heat affected zone the weakest part of the assembly, and thus the wider the heat affected zone, the poorer the corresponding weld quality and performance. According to the method and the device, the welding process parameters which enable the welding quality to be optimal are searched through the fitness function matched with the welding process parameters and the width of the heat affected zone, the corresponding parameter searching range can be further narrowed by utilizing an orthogonal test before searching, and the fitness function is determined by utilizing the neural network model, so that the problem that the fitness function is difficult to determine by a particle swarm algorithm is solved, and the calculation efficiency and accuracy of the algorithm are improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 illustrates a flow chart of a method for determining welding process parameter information according to one embodiment of the present application;
FIG. 2 illustrates an apparatus block diagram for determining welding process parameter information according to one embodiment of the present application;
FIG. 3 illustrates an exemplary system that may be used to implement various embodiments described herein.
The same or similar reference numbers in the drawings refer to the same or similar parts.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
In one typical configuration of the present application, the terminal, the devices of the services network, and the trusted party each include one or more processors (e.g., central processing units (Central Processing Unit, CPU)), input/output interfaces, network interfaces, and memory.
The Memory may include non-volatile Memory in a computer readable medium, random access Memory (RandomAccess Memory, RAM) and/or non-volatile Memory, etc., such as Read Only Memory (ROM) or Flash Memory (Flash Memory). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase-Change Memory (PCM), programmable Random Access Memory (Programmable Random Access Memory, PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (Dynamic Random Access Memory, DRAM), other types of Random Access Memory (RAM), read-Only Memory (ROM), electrically erasable programmable read-Only Memory (EEPROM), flash Memory or other Memory technology, read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), digital versatile disks (Digital Versatile Disc, DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device.
The device referred to in the present application includes, but is not limited to, a user device, a network device, or a device formed by integrating a user device and a network device through a network. The user equipment includes, but is not limited to, any mobile electronic product which can perform man-machine interaction with a user (for example, perform man-machine interaction through a touch pad), such as a smart phone, a tablet computer and the like, and the mobile electronic product can adopt any operating system, such as an Android operating system, an iOS operating system and the like. The network device includes an electronic device capable of automatically performing numerical calculation and information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic device (Programmable Logic Device, PLD), a field programmable gate array (Field Programmable gateway array, FPGA), a digital signal processor (Digital Signal Processor, DSP), an embedded device, and the like. The network device includes, but is not limited to, a computer, a network host, a single network server, a plurality of network server sets, or a cloud of servers; here, the Cloud is composed of a large number of computers or network servers based on Cloud Computing (Cloud Computing), which is a kind of distributed Computing, a virtual supercomputer composed of a group of loosely coupled computer sets. Including but not limited to the internet, wide area networks, metropolitan area networks, local area networks, VPN networks, wireless Ad Hoc networks (Ad Hoc networks), and the like. Preferably, the device may be a program running on the user device, the network device, or a device formed by integrating the user device and the network device, the touch terminal, or the network device and the touch terminal through a network.
Of course, those skilled in the art will appreciate that the above-described devices are merely examples, and that other devices now known or hereafter may be present as appropriate for use in the present application, are intended to be within the scope of the present application and are incorporated herein by reference.
In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
FIG. 1 illustrates a flow chart of a method for determining welding process parameter information, according to one embodiment of the present application, the method comprising: step S11, step S12, and step S13. In step S11, the apparatus 1 determines parameter range information corresponding to the welding process parameters; in step S12, the apparatus 1 determines a fitness function matching the welding process parameters and the heat affected zone width; in step S13, the apparatus 1 determines target welding process parameter information from the parameter range information using the fitness function in combination with a particle swarm algorithm based on the parameter range information.
In step S11, the apparatus 1 determines parameter range information corresponding to the welding process parameters. In some embodiments, the device 1 includes, but is not limited to, a user device, a network device, e.g., a tablet, a computer, a server, having information processing or computing capabilities. In some embodiments, the welding process parameters include, but are not limited to, welding current, welding voltage, gas flow, welding speed, and/or wire feed speed. Referring to the following table 1, the parameter range information is a value range corresponding to the welding process parameter. The parameter range information corresponding to the welding process parameters can be preset based on historical welding experience, or can be determined through a welding test. The target welding process parameter information with the best welding quality falls into the parameter range information. It should be understood by those skilled in the art that the welding process parameters and the corresponding parameter ranges shown in the following table are only examples, and that other welding process parameters and parameter ranges may be available in the present application, and are also included in the scope of the present application and are incorporated herein by reference.
TABLE 1 exemplary welding Process parameters and parameter Range information Table
| Sequence number | Welding process parameters | Parameter range information |
| 1 | Welding current | 240-260(A) |
| 2 | Welding voltage | 20-27(V) |
| 3 | Flow rate of gas | 17-18(L/min) |
| 4 | Welding speed | 4-4.9(mm/s) |
| 5 | Wire feed speed | 4-5.2(m/min) |
In some embodiments, the step S11 includes: the device 1 determines the parameter range information corresponding to the welding process parameters by using an orthogonal test method based on the original parameter range information corresponding to the welding process parameters. In some embodiments, the original parameter range information includes a range of values corresponding to the welding process parameters preset based on welding experience. The value range is usually larger, and if the value range is directly used for determining the target welding process parameter information in the subsequent step, the calculation efficiency is often reduced, and the accuracy of the result is often affected. Therefore, the influence degree of the welding process parameters on welding quality can be determined within the minimum test times by using the orthogonal test method, so that the value range corresponding to the welding process parameters is optimized, and the optimal parameter range information is determined.
In some embodiments, determining the parameter range information corresponding to the welding process parameter using the orthogonal test method based on the original parameter range information corresponding to the welding process parameter includes: the equipment 1 acquires corresponding first welding process parameter information and first heat affected zone width information corresponding to the first welding process parameter information by using an orthogonal test method based on original parameter range information corresponding to the welding process parameter; and optimizing the original parameter range information based on the first welding process parameter information and the first heat affected zone width information, and determining the parameter range information. In some embodiments, the first welding process parameter information is a plurality of value combinations corresponding to welding process parameters. The welding process parameters include, but are not limited to, welding current, welding voltage, gas flow, welding speed, and/or wire feed speed. For example, welding current 240A, welding voltage 20V, gas flow 15L/min, welding speed 4mm/s, wire feed speed 4m/min may correspond to one combination of values in the first welding process parameter information. The first welding process parameter information is a combination of values of welding process parameters determined from the original parameter range information based on the horizontal number information. The level number information may be determined based on a sensitivity level of the heat affected zone width variation relative to the welding process parameter value variation. The higher the sensitivity is, the smaller the corresponding welding process parameter value change step length is, and the higher the level number is. Table 2 below shows an example of partial value combinations included in the first welding process parameter information.
TABLE 2 first welding process parameter information exemplary Table
In some embodiments, the apparatus 1 obtains corresponding first heat affected zone width information from performing a corresponding welding test based on the first welding process parameter information. The first heat affected zone width information comprises the heat affected zone width corresponding to each value combination in the first welding process parameter information. The apparatus 1 may determine corresponding preferred welding process parameter information from the first welding process parameter information based on the first heat affected zone width information. For example, a welding test is performed based on each welding process parameter value combination in the first welding process parameter information, and the width of the heat affected zone when welding is performed under the corresponding welding process parameter value combination is detected, so that the welding process parameter value combination with the smallest width of the heat affected zone can be used as the preferred welding process parameter information. The device 1 optimizes the original parameter range information based on the preferred welding process parameter information to determine corresponding parameter range information. For example, a relationship of the welding process parameter to the heat affected zone width may be determined based on the first welding process parameter information and the first heat affected zone width information. And expanding each parameter towards an optimization direction on the basis of the optimized welding process parameter information based on the relation between the welding process parameter and the heat affected zone width, and determining optimal parameter range information. For another example, the apparatus 1 may sort the value combinations of the various welding process parameters in the first welding process parameter information according to the first heat affected zone width information in order of the corresponding heat affected zone width from small to large. Based on the combination of values of a plurality of welding process parameters which are sequenced in the front, corresponding parameter range information is determined.
In step S12, the device 1 determines a fitness function that matches the welding process parameters and the heat affected zone width. In some embodiments, the heat affected zone (HeatAffected Zone, HAZ) width is the width of the region where microstructure and performance changes occur over a range of areas adjacent to both sides of the weld under the influence of a high temperature heat source when welding. The smaller the heat affected zone width, the better the corresponding weld quality and performance. Therefore, the association relation between the welding process parameters and the width of the heat affected zone can be established, and the fitness function is constructed by utilizing the association relation between the welding process parameters and the width of the heat affected zone, so that the width of the heat affected zone can be utilized to find target welding process parameter information which can optimize welding quality. Here, the present embodiment is not limited to the execution sequence of the step S11 and the step S12, and the step S11 may be executed before the step S12, may be executed after the step S12, or may be executed simultaneously.
In some embodiments, the step S12 includes: the equipment 1 constructs a heat affected zone width prediction model based on second welding process parameter information and second heat affected zone width information corresponding to the second welding process parameter information; the fitness function is determined based on the heat affected zone width prediction model. In some embodiments, the second welding process parameter information and the second heat affected zone width information are obtained from an actual weld. The second welding process parameter information comprises a plurality of value combinations corresponding to the welding process parameters. The second heat affected zone width information includes heat affected zone widths detected by welding at various combinations of values. In some embodiments, in order to improve the data utilization rate and ensure the coverage (representativeness) of the second welding process parameter information and the corresponding second heat affected zone width information, and improve the validity of the fitness function constructed, step S11 may be performed first, where the first welding process parameter information and the first heat affected zone width information in the orthogonal test are used as the second welding process parameter information and the second heat affected zone width information, so as to construct the fitness function. In some embodiments, the apparatus 1 may construct the heat affected zone width prediction model using a corresponding neural network model. The heat affected zone width prediction model may output corresponding heat affected zone width information based on the corresponding welding process parameter information. So that the merits of the welding process parameter information can be evaluated based on the heat affected zone width information. The neural network model includes, but is not limited to, a feed forward neural network model, such as a BP (Back Propagation) neural network model. In some embodiments, the apparatus 1 may directly use the heat affected zone width prediction model as the fitness function, where the heat affected zone width prediction information output by the heat affected zone width prediction model is the fitness information output by the fitness function. The apparatus 1 may process the heat affected zone width prediction model as a fitness function, and may, for example, take the result of the output as the inverse number or the reciprocal number. It should be understood by those skilled in the art that the foregoing methods of treating the heat affected zone width prediction model are merely examples, and that other methods of treating that may be present or may occur in the future are intended to be included within the scope of the present application and are incorporated herein by reference.
In some embodiments, the step S12 further includes: the device 1 updates the heat affected zone width prediction model based on third welding process parameter information and third heat affected zone width information corresponding to the third welding process parameter information. In some embodiments, the third welding process parameter information includes a plurality of value combinations corresponding to the welding process parameters. The third heat affected zone width information includes heat affected zone widths detected by welding at various combinations of values. In some embodiments, the first welding process parameter information and the first heat affected zone width information in the orthogonal test may also be used as the third welding process parameter information and the third heat affected zone width information. For example, a part of the data in the orthogonal test is used as the second welding process parameter information and the second heat affected zone width information, and another part of the data is used as the third welding process parameter information and the third heat affected zone width information. In some embodiments, the apparatus 1 inputs the third welding process parameter information into a heat affected zone width prediction model to obtain corresponding heat affected zone width prediction information. And optimizing the heat affected zone width prediction model based on the heat affected zone width prediction information and the third heat affected zone width information, so that the output result of the heat affected zone width prediction model is more accurate.
In some embodiments, the updating the heat affected zone width prediction model based on the third welding process parameter information and the third heat affected zone width information corresponding to the third welding process parameter information includes: the equipment 1 determines corresponding error information based on the heat affected zone width prediction model, the third welding process parameter information and the third heat affected zone width information; and updating the heat affected zone width prediction model based on the error information. In some embodiments, the apparatus 1 inputs the third welding process parameter information into a heat affected zone width prediction model to obtain corresponding heat affected zone width prediction information. The apparatus 1 determines corresponding error information based on the heat affected zone width prediction information and the third heat affected zone width information. The error information includes a mean square error between the heat affected zone width prediction information and the third heat affected zone width information. The apparatus 1 evaluates the prediction effect of the heat affected zone width prediction model based on the error information, thereby performing heat affected zone width prediction model update until the number of iterations is satisfied or the number of times the determined error information is smaller than a threshold satisfies a corresponding number of times threshold.
In step S13, the apparatus 1 determines target welding process parameter information from the parameter range information using the fitness function in combination with a particle swarm algorithm based on the parameter range information. In some embodiments, the target welding process parameter information includes, but is not limited to, welding current information, welding voltage information, gas flow information, welding speed information, and/or wire feed speed information. And the fitness information of the target welding process parameter information determined by the fitness function is optimal in various parameter value combinations corresponding to the parameter range information, namely the welding quality corresponding to the target welding process parameter information is the best.
In some embodiments, the step S13 includes: step S131 (not shown), the apparatus 1 determining a plurality of candidate welding process parameter information based on the parameter range information; step S132 (not shown), the apparatus 1 determines fitness information corresponding to each of the plurality of candidate welding process parameter information by using the fitness function; step S133 (not shown), the apparatus 1 updates first historical optimal welding process parameter information corresponding to the candidate welding process parameter information and second historical optimal welding process parameter information corresponding to the plurality of candidate welding process parameter information based on the fitness information corresponding to each candidate welding process parameter information; step S134 (not shown), if the corresponding termination condition is satisfied, the apparatus 1 determines the target welding process parameter information based on the second historical optimal welding process parameter information; otherwise, the plurality of candidate welding process parameter information is updated, and steps S132-S134 are repeated.
In some embodiments, the apparatus 1 determines randomly selected parameter values from the parameter range information as the candidate welding process parameter information including, but not limited to, candidate welding current information, candidate welding voltage information, candidate gas flow information, candidate welding speed information, and/or candidate wire feed speed information. The candidate welding technological parameter information corresponds to a combination of welding technological parameters. In some embodiments, the apparatus 1 further initializes updated parameter information corresponding to each candidate welding process parameter information for determining an iteration distance and direction corresponding to the candidate welding process parameter information in a subsequent iteration. The device 1 may randomly determine the updated parameter information corresponding to each candidate welding process parameter information within a preset updated parameter range, may determine the updated parameter information corresponding to each candidate welding process parameter information by using normal distribution, and may perform setting of the updated parameter information corresponding to each candidate welding process parameter information based on existing experience.
In some embodiments, taking the heat affected zone width prediction model as the fitness function directly as an example, the apparatus 1 determines heat affected zone width prediction information corresponding to each candidate welding process parameter information by using the heat affected zone width prediction model, and uses the heat affected zone width prediction information as fitness information corresponding to each candidate welding process parameter information. And comparing the adaptability information corresponding to the candidate welding process parameter information with the adaptability information of the first historical optimal welding process parameter information corresponding to the candidate welding process parameter information for each candidate welding process parameter information. If the fitness information corresponding to the candidate welding process parameter information is smaller than the fitness information corresponding to the first historical optimal welding process parameter information, taking the current values of the parameters of the candidate welding process parameter information as new first historical optimal welding process parameter information; otherwise, maintaining the current first historical optimal welding process parameter information. The first historical optimal welding process parameter information is a parameter value combination with the minimum fitness information in the previous parameter value combination corresponding to the candidate welding process parameter information. The apparatus 1 also compares the fitness information corresponding to the candidate welding process parameter information with fitness information corresponding to the second historical optimal welding process parameter information. If the fitness information corresponding to the candidate welding process parameter information is smaller than the fitness information corresponding to the second historical optimal welding process parameter information, taking the current values of the parameters of the candidate welding process parameter information as new second historical optimal welding process parameter information; otherwise, maintaining the current second historical optimal welding process parameter information. The second historical optimal welding process parameter information is a parameter value combination with the minimum adaptability information in parameter value combinations corresponding to all candidate welding process parameter information. In some embodiments, the apparatus 1 updates the second historical optimal welding process parameter information through an update iteration of the plurality of candidate welding process parameter information, and stops the iteration when a termination condition is satisfied, outputting the second historical optimal welding process parameter information as the target welding process parameter information.
In some embodiments, the updating the plurality of candidate welding process parameter information comprises: the apparatus 1 updates the plurality of candidate welding process parameter information based on the first historical optimal welding process parameter information and the second historical optimal welding process parameter information. In some embodiments, the apparatus 1 may update updated parameter information corresponding to each candidate welding process parameter information based on the first historical optimal welding process parameter information corresponding to each candidate welding process parameter information and the second historical optimal welding process parameter information. And updating the candidate welding process parameter information based on the updated parameter information corresponding to the candidate welding process parameter information. For example, for an i-th candidate welding process parameter information of the plurality of candidate welding process parameter information, the parameter information v is updated at k+1 iterations i (k+1)＝ω(k)v i (k)+φ 1 c 1 (p i (k)-x i (k))+φ 2 c 2 (g i (k)-x i (k) Where ω (k) is an inertial weight factor, the value of which is typically set to decrease with increasing iteration number, and the update of ω (k) may be set to linearly decrease, non-linearly decrease, or dynamic weight, etc.; c 1 ＝rand(0,a 1 )、c 2 ＝rand(0,a 2 ) Wherein a is 1 、a 2 Is a preset constant; phi (phi) 1 、φ 2 Is a preset constant; p is p i (k) The method comprises the steps of obtaining the first historical optimal welding process parameter information corresponding to the ith candidate welding process parameter information in the kth iteration; g i (k) Optimal welding process parameter information for a second history in a kth iteration; x is x i (k) Is candidate welding process parameter information corresponding to the ith candidate welding process parameter information in the kth iteration. Updated parameter information v at k+1 iterations based on the ith candidate welding process parameter information i (k+1) the candidate welding process parameter information x corresponding to the ith candidate welding process parameter information at k+1 iterations may be determined i (k+1)＝x i (k)+v i (k+1). In some embodiments, for each updated candidate welding process parameter information, the apparatus 1 may also determine whether it satisfies the respective constraint, and if so, retain the candidate welding process parameter information; otherwise, the candidate welding process parameter information is removed. For example, judging whether the candidate welding process parameter information is still in the corresponding parameter range information, if so, reserving the candidate welding process parameter information; otherwise, deleting the candidate welding process parameter information. The apparatus 1 updates the corresponding first historical optimal welding process parameter information and the second historical optimal welding process parameter information based on the retained candidate welding process parameter information.
In some embodiments, the termination condition comprises: the update times of the candidate welding process parameter information meet corresponding quantity threshold values; and the second historical optimal welding process parameter information meets the corresponding ending condition. For example, a number threshold value of the corresponding number of updates is preset in the calculation, and when the number threshold value is not satisfied, the apparatus 1 may determine a difference value of fitness information corresponding to the second historical optimal welding process parameter information in each update and the second historical optimal welding process parameter information determined in the previous update, and record once when the difference value of fitness information is smaller than the threshold value. The end condition includes that the number of times the difference value of the fitness information of the recorded two-time updated second historical optimal welding process parameter information is smaller than the threshold value meets the corresponding number of times threshold value. Therefore, when the number of updating times does not meet the number threshold and the target welding process parameter information is found, iteration can be finished in advance, calculation efficiency is improved, and waste of calculation resources is avoided.
Fig. 2 shows a block diagram of an apparatus for determining welding process parameter information according to one embodiment of the present application, the apparatus 1 comprising a one-to-one module 11, a two-to-two module 12 and a three-to-three module 13. The one-to-one module 11 determines parameter range information corresponding to welding process parameters; the two modules 12 determine a fitness function that matches the welding process parameters and the heat affected zone width; the three modules 13 determine target welding process parameter information from the parameter range information using the fitness function based on the parameter range information. Here, the specific embodiments of the one-to-one module 11, the two-to-one module 12 and the three-to-one module 13 shown in fig. 2 are the same as or similar to the specific embodiments of the foregoing step S11, the step S12 and the step S13, respectively, so that the detailed description is omitted and the specific embodiments are incorporated herein by reference.
In some embodiments, the tri-module 13 includes a tri-unit 131 (not shown), a tri-unit 132 (not shown), a tri-unit 133 (not shown), and a tri-unit 134 (not shown). The unit 131 determines a plurality of candidate welding process parameter information based on the parameter range information; the one-three-two unit 132 determines fitness information corresponding to each candidate welding process parameter information in the plurality of candidate welding process parameter information by using the fitness function; the one-three-unit 133 updates first historical optimal welding process parameter information and second historical optimal welding process parameter information based on the fitness information corresponding to each candidate welding process parameter information, wherein the first historical optimal welding process parameter information corresponds to the candidate welding process parameter information, and the second historical optimal welding process parameter information corresponds to the plurality of candidate welding process parameter information; if the one-three-four unit 134 meets the corresponding termination condition, determining the target welding process parameter information based on the second historical optimal welding process parameter information; otherwise, the candidate welding process parameter information is updated and the execution of one-three-two unit 132, one-three unit 133, one-three-four unit 134 is repeated. Here, the embodiments of the one-three-one unit 131, one-three-two unit 132, one-three-unit 133 and one-three-four-unit 134 are the same as or similar to the embodiments of the steps S131, S132, S133 and S134, respectively, so that the detailed description is omitted herein and the description is omitted.
FIG. 3 illustrates an exemplary system that may be used to implement various embodiments described herein; in some embodiments, as shown in fig. 3, system 300 can function as any of the devices of the various described embodiments. In some embodiments, system 300 can include one or more computer-readable media (e.g., system memory or NVM/storage 320) having instructions and one or more processors (e.g., processor(s) 305) coupled with the one or more computer-readable media and configured to execute the instructions to implement the modules to perform the actions described herein.
For one embodiment, the system control module 310 may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) 305 and/or any suitable device or component in communication with the system control module 310.
The system control module 310 may include a memory controller module 330 to provide an interface to the system memory 315. Memory controller module 330 may be a hardware module, a software module, and/or a firmware module.
The system memory 315 may be used, for example, to load and store data and/or instructions for the system 300. For one embodiment, system memory 315 may include any suitable volatile memory, such as, for example, a suitable DRAM. In some embodiments, the system memory 315 may comprise a double data rate type four synchronous dynamic random access memory (DDR 4 SDRAM).
For one embodiment, system control module 310 may include one or more input/output (I/O) controllers to provide an interface to NVM/storage 320 and communication interface(s) 325.
For example, NVM/storage 320 may be used to store data and/or instructions. NVM/storage 320 may include any suitable nonvolatile memory (e.g., flash memory) and/or may include any suitable nonvolatile storage device(s) (e.g., one or more Hard Disk Drives (HDDs), one or more Compact Disc (CD) drives, and/or one or more Digital Versatile Disc (DVD) drives).
NVM/storage 320 may include storage resources that are physically part of the device on which system 300 is installed or which may be accessed by the device without being part of the device. For example, NVM/storage 320 may be accessed over a network via communication interface(s) 325.
Communication interface(s) 325 may provide an interface for system 300 to communicate over one or more networks and/or with any other suitable device. The system 300 may wirelessly communicate with one or more components of a wireless network in accordance with any of one or more wireless network standards and/or protocols.
For one embodiment, at least one of the processor(s) 305 may be packaged together with logic of one or more controllers (e.g., memory controller module 330) of the system control module 310. For one embodiment, at least one of the processor(s) 305 may be packaged together with logic of one or more controllers of the system control module 310 to form a System In Package (SiP). For one embodiment, at least one of the processor(s) 305 may be integrated on the same die as logic of one or more controllers of the system control module 310. For one embodiment, at least one of the processor(s) 305 may be integrated on the same die with logic of one or more controllers of the system control module 310 to form a system on chip (SoC).
In various embodiments, the system 300 may be, but is not limited to being: a server, workstation, desktop computing device, or mobile computing device (e.g., laptop computing device, handheld computing device, tablet, netbook, etc.). In various embodiments, system 300 may have more or fewer components and/or different architectures. For example, in some embodiments, system 300 includes one or more cameras, keyboards, liquid Crystal Display (LCD) screens (including touch screen displays), non-volatile memory ports, multiple antennas, graphics chips, application Specific Integrated Circuits (ASICs), and speakers.
In addition to the methods and apparatus described in the above embodiments, the present application also provides a computer-readable storage medium storing computer code which, when executed, performs a method as described in any one of the preceding claims.
The present application also provides a computer program product which, when executed by a computer device, performs a method as claimed in any preceding claim.
The present application also provides a computer device comprising:
one or more processors;
a memory for storing one or more computer programs;
the one or more computer programs, when executed by the one or more processors, cause the one or more processors to implement the method of any preceding claim.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, using Application Specific Integrated Circuits (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions as described above. Likewise, the software programs of the present application (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. In addition, some steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
Furthermore, portions of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application by way of operation of the computer. Those skilled in the art will appreciate that the form of computer program instructions present in a computer readable medium includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the manner in which the computer program instructions are executed by a computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Herein, a computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by a computer.
Communication media includes media whereby a communication signal containing, for example, computer readable instructions, data structures, program modules, or other data, is transferred from one system to another. Communication media may include conductive transmission media such as electrical cables and wires (e.g., optical fibers, coaxial, etc.) and wireless (non-conductive transmission) media capable of transmitting energy waves, such as acoustic, electromagnetic, RF, microwave, and infrared. Computer readable instructions, data structures, program modules, or other data may be embodied as a modulated data signal, for example, in a wireless medium, such as a carrier wave or similar mechanism, such as that embodied as part of spread spectrum technology. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. The modulation may be analog, digital or hybrid modulation techniques.
By way of example, and not limitation, computer-readable storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include, but are not limited to, volatile memory, such as random access memory (RAM, DRAM, SRAM); and nonvolatile memory such as flash memory, various read only memory (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic/ferroelectric memory (MRAM, feRAM); and magnetic and optical storage devices (hard disk, tape, CD, DVD); or other now known media or later developed computer-readable information/data that can be stored for use by a computer system.
An embodiment according to the present application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to operate a method and/or a solution according to the embodiments of the present application as described above.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the apparatus claims can also be implemented by means of one unit or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Claims (10)
1. A method for determining welding process parameter information, wherein the method comprises:
determining parameter range information corresponding to welding process parameters;
determining a fitness function matching the welding process parameters and the width of the heat affected zone;
and determining target welding process parameter information from the parameter range information by utilizing the fitness function and combining a particle swarm algorithm based on the parameter range information.
2. The method of claim 1, wherein the determining parameter range information corresponding to the welding process parameter comprises:
and determining the parameter range information corresponding to the welding process parameters by using an orthogonal test method based on the original parameter range information corresponding to the welding process parameters.
3. The method of claim 2, wherein the determining the parameter range information corresponding to the welding process parameter using the orthogonal test method based on the original parameter range information corresponding to the welding process parameter comprises:
based on the original parameter range information corresponding to the welding process parameters, acquiring corresponding first welding process parameter information and first heat affected zone width information corresponding to the first welding process parameter information by using an orthogonal test method;
And optimizing the original parameter range information based on the first welding process parameter information and the first heat affected zone width information, and determining the parameter range information.
4. A method according to any one of claims 1 to 3, wherein the determining a fitness function that matches the welding process parameter and heat affected zone width comprises:
constructing a heat affected zone width prediction model based on second welding process parameter information and second heat affected zone width information corresponding to the second welding process parameter information;
the fitness function is determined based on the heat affected zone width prediction model.
5. The method of claim 4, wherein the determining a fitness function that matches the welding process parameter and a heat affected zone width further comprises:
and updating the heat affected zone width prediction model based on third welding process parameter information and third heat affected zone width information corresponding to the third welding process parameter information.
6. The method of claim 5, wherein the updating the heat affected zone width prediction model based on third welding process parameter information and third heat affected zone width information corresponding to the third welding process parameter information comprises:
Determining corresponding error information based on the heat affected zone width prediction model and the third welding process parameter information and the third heat affected zone width information;
and updating the heat affected zone width prediction model based on the error information.
7. The method of any of claims 1-6, wherein the determining target welding process parameter information from the parameter range information using the fitness function in combination with a particle swarm algorithm based on the parameter range information comprises:
determining a plurality of candidate welding process parameter information based on the parameter range information;
i, determining adaptability information corresponding to each candidate welding process parameter information in the plurality of candidate welding process parameter information by utilizing the adaptability function;
j updating first historical optimal welding process parameter information and second historical optimal welding process parameter information based on fitness information corresponding to each candidate welding process parameter information, wherein the first historical optimal welding process parameter information corresponds to the candidate welding process parameter information, and the second historical optimal welding process parameter information corresponds to the plurality of candidate welding process parameter information;
k, if the corresponding termination condition is met, determining the target welding process parameter information based on the second historical optimal welding process parameter information; otherwise, updating the plurality of candidate welding process parameter information, and repeating the steps i-k.
8. The method of claim 7, wherein the updating the plurality of candidate welding process parameter information comprises:
updating the plurality of candidate welding process parameter information based on the first historical optimal welding process parameter information and the second historical optimal welding process parameter information.
9. A computer device for determining welding process parameter information, comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to carry out the steps of the method according to any one of claims 1 to 8.
10. A computer readable storage medium having stored thereon a computer program/instruction which when executed by a processor performs the steps of the method according to any of claims 1 to 8.
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| CN202310682636.8A CN117807750A (en) | 2023-06-09 | 2023-06-09 | Method and equipment for determining welding process parameter information |
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| CN202310682636.8A CN117807750A (en) | 2023-06-09 | 2023-06-09 | Method and equipment for determining welding process parameter information |
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