An electrode cap state optimization method, device, equipment and storage medium

Abstract

The present application discloses a method, device, equipment and storage medium for optimizing the state of an electrode cap, which determines the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap. The dynamic resistance of the electrode cap can accurately reflect the surface state of the electrode cap, and the dynamic resistance of the electrode cap is easy to obtain. Therefore, the surface state of the electrode cap can be conveniently, quickly and accurately determined according to the dynamic resistance of the electrode cap. At the same time, the optimization of the electrode cap can be effectively guided according to the dynamic resistance of the electrode cap, so as to achieve the optimization of the electrode cap grinding process parameters.

Description

Technical Field

[0001] The present application relates to the technical field of resistance spot welding, and particularly to a method, device, equipment and storage medium for optimizing the state of an electrode cap. Background Art

[0002] At present, with the development of the automotive industry, the proportion of high-strength steel and ultra-high-strength steel in automotive body steel plates is increasing. Therefore, medium-frequency DC technology is widely used in the resistance spot welding of automotive body steel plates, and dynamic resistance adaptive technology is mostly used in the welding machine control technology. During welding, the upper and lower electrode caps of the spot welder clamp and energize to melt the contact points of the two workpieces to form solder joints, realizing the welding of the workpieces. The end face state of the electrode cap directly affects the quality of the solder joint and is a key factor affecting the welding quality.

[0003] The electrode cap includes three states: the state of a new electrode cap, the state before grinding, and the state after grinding. The electrode cap is put into use in the state of a new electrode cap. The heat generated during welding will cause oxidation of the end face of the electrode cap, forming metal compounds such as zinc brass, resulting in an increase in the resistance of the electrode cap. At the same time, under the action of welding pressure and heat, the end face of the electrode cap will be upset and its size will increase. This state is the state before grinding of the electrode cap. If not dealt with in time after the above situation occurs, it will seriously lead to poor solder joint quality. Therefore, in the actual welding process, it is necessary to grind the electrode cap to remove the end face oxide and restore the end face size, so that the end face of the electrode cap returns to the original state, that is, the state after grinding of the electrode cap. The closer the state after grinding of the electrode cap is to the state of the new electrode cap, the better the welding quality.

[0004] In the prior art, the methods for checking the state of the electrode cap include indirectly inferring the state of the electrode cap by detecting the state of the solder joint, or inferring the state of the electrode cap during use by checking the state of the electrode cap with exhausted life, and also determining the state of the electrode cap by checking the electrode cap-related equipment such as grinding equipment and welding tongs. However, the above inspection methods cannot quickly determine the cause of the problem with the electrode cap, nor can they accurately determine whether the unground electrode cap needs to be ground, and how the grinding effect is after grinding the electrode cap and whether improvement is needed. And all the existing methods for checking the state of the electrode cap require manual operation, resulting in a large consumption of human resources.

[0005] Therefore, how to conveniently and quickly evaluate the state of the electrode cap before and after grinding is a technical problem to be solved. Summary of the Invention

[0006] The main purpose of the present application is to provide a method, device, equipment and storage medium for optimizing the state of an electrode cap, aiming to solve the technical problem in the related art that the current state and grinding state of the electrode cap cannot be accurately evaluated.

[0007] In a first aspect, the present application provides a method for optimizing the state of an electrode cap, and the method includes the following steps:

[0008] Determine the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap.

[0009] In some embodiments, the step of determining the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap specifically includes the following steps:

[0010] Obtain the dynamic resistance of the electrode cap during dry welding in the states of a new electrode cap, before grinding, and after grinding.

[0011] Determine the dynamic resistance of the electrode cap during dry welding in the state of a new electrode cap as the base resistance.

[0012] Obtain a first resistance change parameter based on the difference between the dynamic resistance in the state before grinding and the base resistance.

[0013] Obtain a second resistance change parameter based on the difference between the dynamic resistance in the state after grinding and the base resistance.

[0014] Obtain a third resistance change parameter based on the difference between the dynamic resistance in the state before grinding and the dynamic resistance in the state after grinding.

[0015] Determine the surface state and optimization direction of the electrode cap according to the base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter.

[0016] In some embodiments, the step of determining the surface state and optimization direction of the electrode cap according to the base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of the electrode cap specifically includes the following steps:

[0017] Input the base resistance, the first resistance change parameter, the second resistance change parameter, and the third change parameter of the electrode cap into the electrode cap state scoring model to obtain a first state prediction score, a second state prediction score, and a third state prediction score output by the electrode cap state scoring model.

[0018] Determine the surface state and optimization direction of the electrode cap according to the first state prediction score, the second state prediction score, and the third state prediction score.

[0019] Wherein, the electrode cap state scoring model is trained by using the base resistance, the first resistance change parameter, the second resistance change parameter, the third resistance change parameter of multiple electrode caps, and the actual state scores corresponding to each resistance change parameter.

[0020] Among them, the first state prediction score, the second state prediction score, and the third state prediction score correspond to the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of the electrode cap in sequence.

[0021] In some embodiments, determining the surface state of the electrode cap according to the first state prediction score, the second state prediction score, and the third state prediction score specifically includes the following steps:

[0022] If the first state prediction score is less than the preset first state prediction score threshold, it is determined that the surface state of the electrode cap before grinding is unqualified;

[0023] If the second state prediction score is less than the preset second state prediction score threshold, it is determined that the surface state of the electrode cap after grinding is unqualified;

[0024] If the third state prediction score is less than the preset third state prediction score threshold, it is determined that the grinding degree of the surface of the electrode cap is unqualified.

[0025] In some embodiments, determining the optimization direction of the electrode cap according to the first state prediction score, the second state prediction score, and the third state prediction score specifically includes the following steps:

[0026] When the surface state of the electrode cap before grinding is unqualified, adjust the number of weldable times of the electrode cap to be measured according to the first state prediction score;

[0027] When the surface state of the electrode cap after grinding is unqualified, adjust the grinding pressure and grinding duration of the electrode cap to be measured according to the second state prediction score;

[0028] When the grinding degree of the surface of the electrode cap is unqualified, adjust the grinding degree of the surface of the electrode cap to be measured according to the third state prediction score.

[0029] In some embodiments, building an electrode cap state scoring model specifically includes the following steps:

[0030] Obtain the base resistance and the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of multiple electrode caps, and score each resistance change parameter to obtain the actual state scores corresponding to the resistance change parameters of multiple electrode caps;

[0031] Use the base resistance and resistance change parameters of a part of the obtained electrode caps as the input of the training set of the original electrode cap state scoring model, and use the actual state scores corresponding to the resistance change parameters of this part as the output of the training set of the original electrode cap state scoring model, and train the original electrode cap state scoring model;

[0032] Input the obtained base resistance and resistance change parameters of another part of the electrode cap into the trained electrode cap state scoring model, and obtain the corresponding state prediction score output by the trained electrode cap state scoring model;

[0033] When the error probability between the state prediction score output by the trained electrode cap state scoring model and the corresponding actual state score is less than the preset probability, it is determined that the electrode cap state scoring model is successfully built.

[0034] In some embodiments, the actual state score decreases as the corresponding resistance change parameter increases.

[0035] In a second aspect, the present application also provides an electrode cap state optimization device, which includes:

[0036] An electrode cap state optimization module, which is used to determine the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap.

[0037] In a third aspect, the present application also provides a computer device, which includes a processor, a memory, and a computer program stored on the memory and executable by the processor. When the computer program is executed by the processor, the steps of the electrode cap state optimization method as described above are implemented.

[0038] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the electrode cap state optimization method as described above are implemented.

[0039] The present application provides an electrode cap state optimization method, device, equipment and storage medium, which determine the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap. The dynamic resistance of the electrode cap can accurately reflect the surface state of the electrode cap, and the dynamic resistance of the electrode cap is easy to obtain. Therefore, the surface state of the electrode cap can be conveniently, quickly and accurately determined according to the dynamic resistance of the electrode cap. At the same time, the optimization of the electrode cap can be effectively guided according to the dynamic resistance of the electrode cap, so as to achieve the optimization of the electrode cap grinding process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a schematic flow chart of an electrode cap state optimization method provided by an embodiment of the present application;

[0042] Figure 2 Schematic diagram of the method for obtaining the dynamic resistance of the electrode cap

[0043] Figure 3 Schematic block diagram of the electrode cap status scoring model;

[0044] Figure 4 Schematic flow chart of the electrode cap optimization method;

[0045] Figure 5 Schematic block diagram of an electrode cap optimization device provided by an embodiment of the present application;

[0046] Figure 6 Schematic block diagram of the structure of a computer device related to an embodiment of the present application.

[0047] The implementation, functional features and advantages of the purpose of the present application will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0048] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0049] The flow charts shown in the accompanying drawings are only illustrative, and do not necessarily include all the contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, combined or partially merged, so the actual execution order may be changed according to the actual situation.

[0050] The embodiments of the present application provide an electrode cap status optimization method, device, equipment and storage medium. Among them, the electrode cap optimization method can be applied to a computer device, and the computer device can be an electronic device such as a notebook computer or a desktop computer.

[0051] Next, some embodiments of the present application will be described in detail in conjunction with the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0052] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of an electrode cap status optimization method provided by an embodiment of the present application.

[0053] As Figure 1 shown, the method includes step S101: determining the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap.

[0054] It should be noted that the state of the electrode cap has a direct impact on the welding quality of the electrode cap and is a key factor related to quality in the electric welding technology. The welding quality of the electrode cap is the best in the state of a new electrode cap. After the electrode cap is used, when it is in the state before grinding, the surface is severely upset and the surface oxidation becomes thicker, which will cause the deterioration of the welding spot quality. After the electrode cap is ground, the closer the surface state of the electrode cap is to the surface state of the new electrode cap, the better the welding quality; the farther the surface state of the electrode cap is from the surface state of the new electrode cap, the worse the welding quality.

[0055] As Figure 2 shown, the intermediate frequency adaptive controller is used to control the spot welding machine for spot welding, and collect the secondary current I and secondary voltage U of the electrode cap during the welding process. The dynamic resistance is determined according to the secondary current I and secondary voltage U. Therefore, the dynamic resistance change of the electrode cap can be monitored by collecting the secondary voltage and secondary current of the electrode cap through the intermediate frequency adaptive controller. The dynamic resistance of the electrode cap during the welding process changes with the change of the surface state of the electrode cap. Therefore, the surface state of the electrode cap can be determined through the dynamic resistance of the electrode cap. After determining the surface state of the electrode cap, the electrode cap can be further optimized according to its surface state.

[0056] Specifically, determining the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap specifically includes the following steps:

[0057] Obtain the dynamic resistance of the electrode cap during open circuit welding in the state of a new electrode cap, the state before grinding, and the state after grinding; determine the dynamic resistance of the electrode cap during open circuit welding in the state of a new electrode cap as the base resistance; obtain the first resistance change parameter according to the difference between the dynamic resistance in the state before grinding and the base resistance; obtain the second resistance change parameter according to the difference between the dynamic resistance in the state after grinding and the base resistance; obtain the third resistance change parameter according to the difference between the dynamic resistance in the state before grinding and the dynamic resistance in the state after grinding; determine the surface state and optimization direction of the electrode cap according to the base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter.

[0058] In some embodiments, obtaining the dynamic resistance of the electrode cap during dry welding in the state of a new electrode cap specifically includes dry welding once immediately after the new electrode cap is installed on the welding machine. When the welding machine performs dry welding once, the electrode cap will continuously discharge for a period of time. The duration of performing dry welding once in this embodiment is 30 milliseconds. Therefore, the continuous waveform of the dynamic resistance within 30 milliseconds is currently obtained. The dynamic resistance can be sampled at intervals within the 30 - millisecond interval to obtain a set number of dynamic resistances, and the average value of the set number of dynamic resistances is calculated. The calculated average value is used as the dynamic resistance of the electrode cap in the state of the new electrode cap, and finally the base resistance is determined, denoted as Rx. For example, if the dry - welding duration is 30 milliseconds and a dynamic resistance is sampled every 10 milliseconds, then a total of three are sampled. The average value of the three sampled dynamic resistances is calculated and used as the dynamic resistance of the electrode cap in the state of the new electrode cap. Perform dry welding once before grinding the electrode cap, and the dynamic resistance of the electrode cap in the state before grinding can be determined according to the above method, denoted as Rq. Perform dry welding once after grinding the electrode cap, and the dynamic resistance of the electrode cap in the state after grinding can be determined according to the above method, denoted as Rh.

[0059] It should be noted that the first resistance change parameter is obtained based on the difference between the dynamic resistance of the electrode cap in the state before grinding and the base resistance. The first resistance change parameter is Rq - Rx, which can reflect the severity of surface upsetting and the severity of oxidation and thickening of the electrode cap in the state before grinding compared with the state of the new electrode cap. The second resistance change parameter is obtained based on the difference between the dynamic resistance of the electrode cap in the state after grinding and the base resistance. The second resistance change parameter is Rh - Rx, which can reflect the surface - state gap between the state of the electrode cap after grinding and the state of the new electrode cap, thus reflecting the state of the electrode cap after grinding. The third resistance change parameter is obtained based on the difference between the dynamic resistance of the state before grinding and the dynamic resistance of the state after grinding. The third resistance change parameter is Rq - Rh, which can reflect the change degree of the electrode cap between the state before grinding and the state after grinding, thus reflecting the grinding degree of the electrode cap.

[0060] As a preferred implementation manner, by scoring the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter, the quantification of each resistance change parameter can be achieved, so as to accurately determine the specific state of the electrode cap before and after grinding according to the score.

[0061] In the embodiment of the present application, scoring of the resistance parameters is achieved by using the neural - network training method.

[0062] First, build an electrode - cap state scoring model, such as Figure 3As shown in the figure. In the electrode cap state scoring model, let the input layer be the vector x, and the output of the first hidden layer can be obtained as f(w1x + b1), where w1 is the weight (also called the connection coefficient), b1 is the bias, and the function f is

[0063] The setting from the hidden layer to the output layer of this electrode cap state scoring model is a multi-class logistic regression, and its output layer is softmax(w m X + b m ), where X represents the output f(w1x + b1) of the hidden layer, w m is the weight of the m-th hidden layer, and b m is the bias of the m-th hidden layer.

[0064] Combined according to the above three-layer formula, the output layer can be obtained as:

[0065] f(x) = softmax(b (m) + w (m) (s(b (m-1) + w (m-1) (...s(b (1) + w (1) x)))))

[0066] Among them, the operating principle of this electrode cap state scoring model is: randomly initialize all parameters, perform iterative training, and continuously calculate the gradient and update the parameters until a certain condition is met (when the error is small enough and the number of iterations is large enough) to obtain the final output.

[0067] It should be noted that the electrode cap state scoring model is trained through the basic resistance, the first resistance change parameter, the second resistance change parameter, the third resistance change parameter of multiple electrode caps, and the actual state scores corresponding to each resistance change parameter.

[0068] Specifically, obtain the basic resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of multiple electrode caps, and score each resistance change parameter to obtain the actual state scores corresponding to the resistance change parameters of multiple electrode caps.

[0069] It should be noted that the actual state scores corresponding to the resistance change parameters of multiple electrode caps are obtained by manual experience scoring according to the actual state of the electrode caps. The surface state of the electrode cap can be analyzed manually, and then combined with each resistance change parameter, the actual state scores of the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter can be obtained respectively. When scoring, the principle that the greater the difference between the surface state of the electrode cap and the surface state of the new electrode cap and the greater the resistance change parameter, the smaller the actual state score can be followed for scoring.

[0070] Further, a part of the obtained base resistance and resistance change parameters of the electrode caps are used as the input of the training set of the original electrode cap state scoring model, and the actual state scores corresponding to the part of the resistance change parameters are used as the output of the training set of the original electrode cap state scoring model to train the original electrode cap state scoring model;

[0071] The base resistance and resistance change parameters of another part of the obtained electrode caps are input into the trained electrode cap state scoring model to obtain the corresponding state prediction scores output by the trained electrode cap state scoring model;

[0072] When the error probability between the state prediction score output by the trained electrode cap state scoring model and the corresponding actual state score is less than the preset probability, it is determined that the electrode cap state scoring model is built.

[0073] Exemplarily, the base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of at least 100 electrode caps are obtained, as well as the actual state scores corresponding to each resistance change parameter. Two-thirds of the electrode cap base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter among the one hundred electrode caps are used as the input of the training set, and the corresponding actual state scores of these electrode cap resistance change parameters are used as the output of the training set to train the original electrode cap state scoring model to obtain a trained electrode cap state scoring model that meets the on-site actual generation environment and requirements. The base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of the remaining one-third of the electrode caps are used as the input of the test set to the trained electrode cap state scoring model, and the corresponding state prediction scores output by the trained electrode cap state scoring model. The state prediction scores output by the model are compared with the corresponding actual state scores, and the new grinding data is continuously iteratively improved as the training set. Finally, when the error probability between the state prediction score and the corresponding actual state score is less than the preset probability, or when the result accuracy rate reaches more than 90%, it is determined that the scoring prediction model training is completed.

[0074] Further, after the electrode cap state scoring model is built, the base resistance Rx of the electrode cap, the first resistance change parameter Rq - Rx, the second resistance change parameter Rh - Rx, and the third change parameter Rq - Rh are input into the electrode cap state scoring model to obtain the first state prediction score, the second state prediction score, and the third state prediction score output by the electrode cap state scoring model; the surface state and the optimization direction of the electrode cap are determined according to the first state prediction score, the second state prediction score, and the third state prediction score.

[0075] Among them, the first state prediction score, the second state prediction score, and the third state prediction score correspond to the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of the electrode cap in sequence.

[0076] Further, determining the surface state of the electrode cap according to the state prediction score of the electrode cap specifically includes the following steps:

[0077] If the first state prediction score is less than the preset first state prediction score threshold, it is determined that the surface state of the electrode cap before grinding is unqualified. If the second state prediction score is less than the preset second state prediction score threshold, it is determined that the surface state of the electrode cap after grinding is unqualified. If the third state prediction score is less than the preset third state prediction score threshold, it is determined that the grinding degree of the surface of the electrode cap is unqualified.

[0078] Further, determining the optimization direction of the electrode cap according to the state prediction score of the electrode cap specifically includes the following steps:

[0079] When the surface state of the electrode cap before grinding is unqualified, adjust the number of weldable points of the electrode cap to be measured according to the first state prediction score. When the surface state of the electrode cap after grinding is unqualified, adjust the grinding pressure and grinding duration of the electrode cap to be measured according to the second state prediction score. When the grinding degree of the surface of the electrode cap is unqualified, adjust the grinding degree of the surface of the electrode cap to be measured according to the third state prediction score.

[0080] It should be noted that the change parameter of the first resistance is the difference between the dynamic resistance of the electrode cap before grinding and the base resistance, which can reflect the surface state of the electrode cap before grinding. The first state prediction score reflects whether the welding parameters are reasonable and whether the number of weldable points is set reasonably within a grinding cycle. If the score is low, it means that the state of the electrode cap before grinding is poor, because the welding parameters are abnormal or too large, or the number of weldable points is too large, resulting in a poor end face state of the electrode cap. When this score is lower than a certain threshold, relevant process parameters need to be adjusted to bring the state of the electrode cap back to the grindable range and the score back above the threshold.

[0081] The second resistance change parameter is the difference between the dynamic resistance of the electrode cap after grinding and the base resistance, which can reflect the surface state of the electrode cap after grinding. The second state prediction score reflects whether the grinding process is reasonable. When this score is low, it is caused by factors such as abnormal grinding cutters, too small grinding pressure, or too short grinding duration. When this score is lower than a certain threshold, parameters such as grinding time and grinding pressure need to be adjusted to bring the state of the electrode cap back to the original state and the score back above the threshold.

[0082] The third resistance change parameter is the difference between the dynamic resistance of the electrode cap after dressing and the base resistance. The third state prediction score reflects the change in the resistance value of the electrode cap before and after dressing. When the dressing is insufficient or excessive, this value will change, which reminds us to check the dressing process to avoid the loss of the electrode cap's life caused by over-dressing of the electrode cap.

[0083] In a preferred embodiment, as Figure 4 shown, input the base resistance of the input electrode cap, the first resistance change parameter, the second resistance change parameter, and the first resistance change parameter into the electrode cap state scoring model to obtain the first state prediction score, the second state prediction score, and the third state prediction score output by the model. According to the obtained prediction scores, respectively judge whether the surface state of the electrode cap before dressing, the surface state of the electrode cap after dressing, and the surface state of the electrode cap are qualified. If the surface state of the electrode cap is qualified, store the base resistance of the electrode cap, each resistance change parameter, and the above state prediction scores; if it is unqualified, send the electrode cap state unqualified information to the operator through the APP, email, or other sensory information. After the operator obtains the information, they will make a manual confirmation and optimize the electrode cap state as needed. After the optimization is completed, the state prediction score will be obtained again based on the dynamic resistance of the electrode cap and compared with the previous state prediction score. According to the comparison result, optimize the electrode cap state scoring model to improve the accuracy of the state prediction score output by the electrode cap state scoring model.

[0084] This application obtains the dynamic resistance of the electrode cap during open circuit welding in the new electrode cap state, the state before dressing, and the state after dressing, determines the resistance change parameters between each state, and uses an AI analysis tool (electrode cap state scoring model) for data analysis, so as to confirm various states of the electrode cap dressing, and further puts forward the requirements and optimization directions for optimizing the electrode cap dressing procedure, and finally realizes the optimization of the electrode cap dressing procedure. This application has low development cost and is easy to use, can meet the needs of on-line monitoring and rapid adjustment of the electrode cap state, thus effectively reducing the risk of batch quality accidents caused by electrode cap end face quality problems, improving the solder joint quality assurance level, and extending the life of the electrode cap. The voltage and current measurement equipment required for the equipment is installed inside the welding tongs, is basically not affected by the spot welding environment, and has strong anti-environmental interference ability. At the same time, the single-sampling data is small, which is convenient for storage and later analysis. The optimization and confirmation of the dressing procedure contribute to the improvement of the accuracy of the monitoring model judgment. The present invention is applicable to applications such as spot welding and projection welding of automotive white bodies, and has the advantages of low development cost, easy to use, support for on-line monitoring, convenient data storage, can be used for AI development, iterative upgrade, and indicating the optimization direction of process parameters.

[0085] Please refer to Figure 5 , Figure 5Schematic block diagram of an electrode cap state optimization device provided by an embodiment of the present application.

[0086] As Figure 5 shown, the device includes an electrode cap state optimization module. The electrode cap state optimization module is used to:

[0087] Determine the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap.

[0088] Wherein, the electrode cap state optimization module is further used to:

[0089] Obtain the dynamic resistance of the electrode cap during open circuit welding in the new electrode cap state, the state before grinding, and the state after grinding;

[0090] Determine the dynamic resistance of the electrode cap during open circuit welding in the new electrode cap state as the base resistance;

[0091] Obtain the first resistance change parameter according to the difference between the dynamic resistance in the state before grinding and the base resistance;

[0092] Obtain the second resistance change parameter according to the difference between the dynamic resistance in the state after grinding and the base resistance;

[0093] Obtain the third resistance change parameter according to the difference between the dynamic resistance in the state before grinding and the dynamic resistance in the state after grinding;

[0094] Determine the surface state and optimization direction of the electrode cap according to the base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter.

[0095] Wherein, the electrode cap state optimization module is further used to:

[0096] Input the base resistance, the first resistance change parameter, the second resistance change parameter, and the third change parameter of the electrode cap into the electrode cap state scoring model to obtain the first state prediction score, the second state prediction score, and the third state prediction score output by the electrode cap state scoring model;

[0097] Determine the surface state and optimization direction of the electrode cap according to the first state prediction score, the second state prediction score, and the third state prediction score;

[0098] Wherein, the electrode cap state scoring model is trained by the base resistance, the first resistance change parameter, the second resistance change parameter, the third resistance change parameter of multiple electrode caps, and the actual state scores corresponding to each resistance change parameter;

[0099] Wherein, the first state prediction score, the second state prediction score, and the third state prediction score correspond to the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of the electrode cap in sequence.

[0100] Among them, the electrode cap state optimization module is further configured to:

[0101] If the first state prediction score is less than a preset first state prediction score threshold, it is determined that the surface state of the electrode cap before grinding is unqualified;

[0102] If the second state prediction score is less than a preset second state prediction score threshold, it is determined that the surface state of the electrode cap after grinding is unqualified;

[0103] If the third state prediction score is less than a preset third state prediction score threshold, it is determined that the grinding degree of the surface of the electrode cap is unqualified.

[0104] Among them, the electrode cap state optimization module is further configured to:

[0105] When the surface state of the electrode cap before grinding is unqualified, adjust the number of weldable times of the electrode cap to be measured according to the first state prediction score;

[0106] When the surface state of the electrode cap after grinding is unqualified, adjust the grinding pressure and grinding duration of the electrode cap to be measured according to the second state prediction score;

[0107] When the grinding degree of the surface of the electrode cap is unqualified, adjust the grinding degree of the surface of the electrode cap to be measured according to the third state prediction score.

[0108] Among them, building an electrode cap state scoring model specifically includes the following steps:

[0109] Obtain the base resistance, first resistance change parameter, second resistance change parameter, and third resistance change parameter of multiple electrode caps, and score each resistance change parameter to obtain the actual state scores corresponding to the resistance change parameters of multiple electrode caps;

[0110] Take the base resistance and resistance change parameters of a part of the obtained electrode caps as the input of the training set of the original electrode cap state scoring model, and take the actual state scores corresponding to the resistance change parameters of this part as the output of the training set of the original electrode cap state scoring model, and train the original electrode cap state scoring model;

[0111] Input the base resistance and resistance change parameters of another part of the obtained electrode caps into the trained electrode cap state scoring model, and obtain the corresponding state prediction scores output by the trained electrode cap state scoring model;

[0112] When the error probability between the state prediction score output by the trained electrode cap state scoring model and the corresponding actual state score is less than the preset probability, it is determined that the electrode cap state scoring model is built.

[0113] Among them, the actual status score decreases as the corresponding resistance change parameter increases.

[0114] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described device and each module and unit can refer to the corresponding processes in the foregoing embodiments, and will not be elaborated herein.

[0115] The device provided in the foregoing embodiment can be implemented in the form of a computer program, and the computer program can run on a computer device as shown in Figure 6 shown.

[0116] Please refer to Figure 6 , Figure 6 , which is a schematic block diagram of the structure of a computer device provided by an embodiment of the present application. The computer device can be a notebook computer or a desktop computer.

[0117] As shown in Figure 6 shown, the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory can include a non-volatile storage medium and an internal memory.

[0118] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any electrode cap status optimization method.

[0119] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0120] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any electrode cap status optimization method.

[0121] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that the structure shown in Figure 6 is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0122] It should be understood that the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0123] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions, and the method implemented when the program instructions are executed may refer to the various embodiments of the present application.

[0124] Among them, the computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device.

[0125] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or system. Without more limitations, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article, or system including that element.

[0126] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments. The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An electrode cap state optimization method, characterized in that Including the following steps: Determine the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap; Among them, the step of determining the surface state and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap specifically includes the following steps: Obtain the dynamic resistance of the electrode cap during open welding in the new electrode cap state, the state before grinding, and the state after grinding; Determine that the dynamic resistance of the electrode cap during open welding in the new electrode cap state is the base resistance; Obtain the first resistance change parameter according to the difference between the dynamic resistance in the state before grinding and the base resistance; Obtain the second resistance change parameter according to the difference between the dynamic resistance in the state after grinding and the base resistance; Obtain the third resistance change parameter according to the difference between the dynamic resistance in the state before grinding and the dynamic resistance in the state after grinding; Determine the surface state and optimization direction of the electrode cap according to the base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter; Among them, the step of determining the surface state and optimization direction of the electrode cap according to the base resistance, the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of the electrode cap specifically includes the following steps: Input the base resistance, the first resistance change parameter, the second resistance change parameter, and the third change parameter of the electrode cap into the electrode cap state scoring model to obtain the first state prediction score, the second state prediction score, and the third state prediction score output by the electrode cap state scoring model; Determine the surface state and optimization direction of the electrode cap according to the first state prediction score, the second state prediction score, and the third state prediction score; Among them, the electrode cap state scoring model is trained by the base resistance, the first resistance change parameter, the second resistance change parameter, the third resistance change parameter of multiple electrode caps, and the actual state scores corresponding to each resistance change parameter; Among them, the first state prediction score, the second state prediction score, and the third state prediction score correspond to the first resistance change parameter, the second resistance change parameter, and the third resistance change parameter of the electrode cap in sequence; Among them, the step of determining the optimization direction of the electrode cap according to the first state prediction score, the second state prediction score, and the third state prediction score specifically includes the following steps: When the surface state of the electrode cap before grinding is unqualified, adjust the number of weldable times of the electrode cap to be measured according to the first state prediction score; When the surface state of the electrode cap after grinding is unqualified, adjust the grinding pressure and grinding duration of the electrode cap to be measured according to the second state prediction score; When the grinding degree of the electrode cap surface is unqualified, adjust the grinding degree of the electrode cap surface to be measured according to the third state prediction score.

2. The method for optimizing the state of the electrode cap according to claim 1, wherein The step of determining the surface state of the electrode cap according to the first state prediction score, the second state prediction score, and the third state prediction score specifically includes the following steps: If the first state prediction score is less than the preset first state prediction score threshold, it is determined that the surface state of the electrode cap before grinding is unqualified; If the second state prediction score is less than the preset second state prediction score threshold, it is determined that the surface state of the electrode cap after grinding is unqualified; If the third state prediction score is less than the preset third state prediction score threshold, it is determined that the grinding degree of the electrode cap surface is unqualified.

3. The method for optimizing the state of the electrode cap according to claim 1, characterized in that, Building the electrode cap state scoring model specifically includes the following steps: Obtain the base resistance and the first, second, and third resistance change parameters of multiple electrode caps, and score each resistance change parameter to obtain the actual status scores corresponding to the resistance change parameters of the multiple electrode caps; Use the base resistance and resistance change parameters of a part of the obtained electrode caps as the input of the training set of the original electrode cap status scoring model, and use the actual status scores corresponding to the resistance change parameters of this part as the output of the training set of the original electrode cap status scoring model to train the original electrode cap status scoring model; Input the base resistance and resistance change parameters of another part of the obtained electrode caps into the trained electrode cap status scoring model to obtain the corresponding status prediction scores output by the trained electrode cap status scoring model; When the error probability between the status prediction score output by the trained electrode cap status scoring model and the corresponding actual status score is less than the preset probability, determine that the electrode cap status scoring model is built.

4. The electrode cap status optimization method according to claim 3, characterized in that: The actual status score decreases as the corresponding resistance change parameter increases.

5. An electrode cap state optimization device, characterized in that, The device includes: An electrode cap status optimization module for determining the surface status and optimization direction of the electrode cap according to the dynamic resistance of the electrode cap; Wherein, the electrode cap status optimization module is further used for: Obtain the dynamic resistance of the electrode cap during open soldering in the new electrode cap status, the pre-grinding status, and the post-grinding status; Determine the dynamic resistance of the electrode cap during open soldering in the new electrode cap status as the base resistance; Obtain the first resistance change parameter according to the difference between the dynamic resistance in the pre-grinding status and the base resistance; Obtain the second resistance change parameter according to the difference between the dynamic resistance in the post-grinding status and the base resistance; Obtain the third resistance change parameter according to the difference between the dynamic resistance in the pre-grinding status and the dynamic resistance in the post-grinding status; Determine the surface status and optimization direction of the electrode cap according to the base resistance, the first, second, and third resistance change parameters; Wherein, the electrode cap status optimization module is further used for: Input the base resistance, the first, second, and third change parameters of the electrode cap into the electrode cap status scoring model to obtain the first, second, and third status prediction scores output by the electrode cap status scoring model; Determine the surface status and optimization direction of the electrode cap according to the first, second, and third status prediction scores; Wherein, the electrode cap status scoring model is trained by the base resistance, the first, second, and third resistance change parameters of multiple electrode caps, and the actual status scores corresponding to each resistance change parameter; Wherein, the first, second, and third status prediction scores correspond to the first, second, and third resistance change parameters of the electrode cap in sequence; Wherein, the electrode cap status optimization module is further used for: When the surface state of the electrode cap before grinding is unqualified, adjust the number of weldable times of the electrode cap to be measured according to the first state prediction score; When the surface state of the electrode cap after grinding is unqualified, adjust the grinding pressure and grinding duration of the electrode cap to be measured according to the second state prediction score; When the grinding degree of the electrode cap surface is unqualified, adjust the grinding degree of the electrode cap surface to be measured according to the third state prediction score.

6. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored on the memory and executable by the processor. When the computer program is executed by the processor, the steps of the electrode cap state optimization method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the electrode cap state optimization method according to any one of claims 1 to 4 are implemented.

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