An optimization system for electrode cap dressing

Abstract

The application discloses an optimization system for electrode cap grinding, and relates to the technical field of mechanical manufacturing.The user sets the grinding target of the electrode cap through a target setting module; a blade information acquisition module acquires blade information; a blade judgment module receives the blade information, acquires the blade utilization according to the blade information, acquires the blade utilization threshold, and compares the blade utilization threshold with the blade utilization; when the blade utilization is greater than or equal to the blade utilization threshold, the blade utilization is sent to an instruction generation module; the instruction generation module receives the grinding target and the blade utilization, acquires the grinding parameters according to the grinding target, the blade and a parameter prediction model, and generates the grinding instruction according to the grinding parameters; and the execution module receives the grinding instruction, grinds the electrode cap according to the grinding instruction; the grinding parameters are set according to multiple factors, the accuracy of the grinding is improved, the adjustment parameters of the grinding parameters are reduced, and the grinding efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of mechanical manufacturing, and relates to electrode cap grinding optimization technology, in particular to an optimization system for electrode cap grinding. BACKGROUND

[0002] The electrode cap needs to be ground after being used for a certain number of times. The number of grinding times is an experience parameter setting, which is too much or too little. In fact, the blade for grinding has a life limit after being used for many times. If it is not replaced in time, it will affect the quality and the number of grinding. Many electrode caps and blades are replaced before reaching the service life, resulting in waste. Not only is the electrode cap as a consumable wasted, but also the product welding quality is affected, which is easy to cause batch quality accidents.

[0003] The existing patent (CN109048024B) discloses a grinding method of a welding electrode cap. Its features include the following steps: S1, a user sets a preset condition; S2, a detection judgment module generates a judgment instruction, if the judgment instruction is detected, step S3 is entered; S3, according to the number of times of blade grinding, the detection judgment module sends a blade replacement instruction, if the judgment instruction is generated, step S4 is executed; S4, according to the number of times of blade grinding determined by the judgment module, the reaction is sent to the setting module, and the corresponding blade grinding parameter is selected by the setting module; S5, the grinding system calls the execution module to grind the welding gun; S6, after the grinding is completed, the execution module detects the grinding amount of the welding gun, and displays the detection result on the display screen connected with the grinding system.

[0004] However, the existing technology has the following problems: adjusting the grinding parameter according to the number of times of blade grinding will lead to inaccurate grinding effect, and multiple adjustments of the grinding parameter will lead to low grinding efficiency. Therefore, an optimization system for electrode cap grinding is proposed. SUMMARY

[0005] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes an optimization system for electrode cap grinding, which solves the problem that adjusting the grinding parameter according to the number of times of blade grinding will lead to inaccurate grinding effect, and multiple adjustments of the grinding parameter will lead to low grinding efficiency.

[0006] To achieve the above-mentioned purpose, according to the first aspect of the embodiment of the present application, an optimization system for electrode cap grinding is proposed, which comprises a target setting module, a blade information acquisition module, a blade judgment module, an instruction generation module and an execution module.

[0007] The target setting module is used for the user to set the grinding target of the electrode cap; and the grinding target is sent to the instruction generation module.

[0008] The blade information acquisition module is configured to acquire blade information, and send the blade information to the blade judgment module.

[0009] The blade judgment module is configured to receive the blade information, and acquire a blade utilization rate according to the blade information.

[0010] The blade judgment module is configured to acquire a blade utilization rate threshold, and compare the blade utilization rate threshold with the blade utilization rate.

[0011] When the blade utilization rate is greater than or equal to the blade utilization rate threshold, the blade judgment module is configured to send the blade utilization rate to the instruction generation module.

[0012] When the blade utilization rate is less than the blade utilization rate threshold, the blade judgment module is configured to generate blade replacement information, and send the blade replacement information to a smart terminal of a worker to remind the worker to replace the blade.

[0013] The instruction generation module is configured to receive the grinding target and the blade utilization rate, acquire a grinding parameter according to the grinding target and the blade and a parameter prediction model, and send a grinding instruction generated according to the grinding parameter to the execution module, wherein the parameter prediction model is established based on an artificial intelligence model.

[0014] The instruction generation module is configured to receive the grinding target and the blade utilization rate, acquire a grinding parameter according to the grinding target and the blade and a parameter prediction model, and send a grinding instruction generated according to the grinding parameter to the execution module, wherein the parameter prediction model is established based on an artificial intelligence model.

[0015] The execution module is configured to receive the grinding instruction, and grind the electrode cap according to the grinding instruction.

[0016] Preferably, the grinding target includes a cutting amount and a smoothness.

[0017] Preferably, the blade information includes a use frequency of the blade, a blade height, a use time, and a wear degree.

[0018] Preferably, the blade judgment module acquires the blade utilization rate according to the blade information, including the following steps.

[0019] The blade judgment module receives the blade information, and marks the use frequency, the blade height, the use time, and the wear degree as n, h, t, and m, respectively.

[0020] The blade judgment module acquires the blade utilization rate by a calculation formula, and marks the blade utilization rate as S.

[0021] The calculation formula of the blade utilization rate is as follows:

[0022]

[0023] wherein, h 初The initial height of the blade.

[0024] Preferably, the brand new blade does not need to calculate the blade utilization rate, and the blade utilization rate is 1 by default.

[0025] Preferably, the instruction generation module obtains the grinding parameters according to the grinding target and the blade and the parameter prediction model, including the following steps:

[0026] The instruction generation module receives the grinding target and the blade utilization rate, and combines the grinding target and the blade utilization rate to generate original data;

[0027] Obtain the parameter prediction model from the instruction generation module;

[0028] The original data is input into the parameter prediction model to obtain the grinding parameters; wherein the grinding parameters include grinding time and grinding pressure;

[0029] The instruction generation module generates a grinding instruction according to the grinding parameters, and sends the grinding instruction to the execution module.

[0030] Preferably, the parameter prediction model is established based on an artificial intelligence model, including the following steps:

[0031] Obtain standard training data from the instruction generation module;

[0032] The artificial intelligence model is trained by the standard training data, and the trained artificial intelligence model is marked as a safety detection model.

[0033] Preferably, the target setting module is in communication and / or electrical connection with the instruction generation module;

[0034] The blade information acquisition module is in communication and / or electrical connection with the blade judgment module;

[0035] The blade judgment module is in communication and / or electrical connection with the instruction generation module;

[0036] The instruction generation module is in communication and / or electrical connection with the execution module.

[0037] Compared with the prior art, the beneficial effects of the present application are:

[0038] This invention allows users to set grinding targets for electrode caps via a target setting module, which then sends these targets to an instruction generation module. A blade information acquisition module obtains blade information and sends it to a blade judgment module. The blade judgment module receives the blade information, calculates the blade utilization rate, obtains a blade utilization rate threshold, and compares it with the actual blade utilization rate. When the blade utilization rate is greater than or equal to the threshold, it sends the blade utilization rate to the instruction generation module. When the blade utilization rate is less than the threshold, the blade judgment module generates blade replacement information and sends it to the worker's smart terminal, reminding them to replace the blade. The instruction generation module receives the grinding target and blade utilization rate, and based on the target, blade, and parameter prediction model, obtains grinding parameters. It then generates grinding instructions based on these parameters and sends them to an execution module. The execution module receives the grinding instructions and grinds the electrode cap accordingly. By setting grinding parameters based on multiple factors, the accuracy of grinding is improved, and the adjustment parameters are reduced, thus increasing grinding efficiency. Attached Figure Description

[0039] Fig. 1 This is a schematic diagram of the present invention;

[0040] Fig. 2 This is a flowchart of the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figs. 1-2 As shown, an optimization system for electrode cap grinding includes a target setting module, a blade information acquisition module, a blade judgment module, an instruction generation module, and an execution module; the modules interact with each other based on digital signals.

[0043] The target setting module is used by the user to set the grinding target of the electrode cap; wherein, the grinding target includes cutting amount and smoothness;

[0044] The grinding target is then sent to the instruction generation module.

[0045] The blade information acquisition module is configured to acquire blade information; wherein the blade information comprises the number of times of use of the blade, the height of the blade, the time of use, and the degree of wear; it should be further explained that the degree of wear is assigned in the form of a ten-point scale in this embodiment, with one point representing almost zero wear and ten points representing severe wear;

[0046] The blade information is sent to the blade judgment module.

[0047] The blade judgment module is configured to receive the blade information and acquire the utilization rate of the blade according to the blade information;

[0048] The blade judgment module acquires a threshold value of the utilization rate of the blade and compares the threshold value with the utilization rate of the blade;

[0049] When the utilization rate of the blade is greater than or equal to the threshold value, the utilization rate of the blade is sent to the instruction generation module.

[0050] When the utilization rate of the blade is less than the threshold value, the blade judgment module generates blade replacement information and sends the blade replacement information to the intelligent terminal of the worker to remind the worker to replace the blade.

[0051] In this embodiment, the blade judgment module acquires the utilization rate of the blade according to the blade information, including the following steps:

[0052] The blade judgment module receives the blade information and marks the number of times of use, the height of the blade, the time of use, and the degree of wear as n, h, t, and m respectively.

[0053] The blade judgment module acquires the utilization rate of the blade through a calculation formula and marks the utilization rate of the blade as S.

[0054] The calculation formula of the utilization rate of the blade is:

[0055]

[0056] It should be further explained that a brand-new blade does not need to calculate the utilization rate of the blade, and its utilization rate is 1 by default.

[0057] Wherein, h 初 is the initial height of the blade; it should be further explained that the initial height of the blade is known data.

[0058] The blade judgment module sets a threshold value of the utilization rate of the blade and compares the threshold value with the utilization rate of the blade.

[0059] When the utilization rate of the blade is greater than or equal to the threshold value, the utilization rate of the blade is sent to the instruction generation module.

[0060] When the blade utilization rate is less than the blade utilization rate threshold, the blade judgment module generates blade replacement information and sends the blade replacement information to the intelligent terminal of the worker to remind the worker to replace the blade.

[0061] In this embodiment, the intelligent terminal includes smart phones and computers and other smart devices.

[0062] The instruction generation module is configured to receive the grinding target and the blade utilization rate, and obtain grinding parameters according to the grinding target, the blade, and a parameter prediction model; wherein the parameter prediction model is established based on an artificial intelligence model.

[0063] And generate a grinding instruction according to the grinding parameters and send the grinding instruction to the execution module.

[0064] In this embodiment, the instruction generation module obtains grinding parameters according to the grinding target, the blade, and a parameter prediction model, including the following steps:

[0065] The instruction generation module receives the grinding target and the blade utilization rate, and combines the grinding target and the blade utilization rate to generate raw data.

[0066] Obtain a parameter prediction model from the instruction generation module; wherein the parameter prediction model is established based on an artificial intelligence model.

[0067] Input the raw data into the parameter prediction model to obtain grinding parameters; wherein the grinding parameters include grinding time and grinding pressure.

[0068] The instruction generation module generates a grinding instruction according to the grinding parameters and sends the grinding instruction to the execution module.

[0069] In this embodiment, the parameter prediction model is established based on an artificial intelligence model, including the following steps:

[0070] Obtain standard training data from the instruction generation module.

[0071] Train the artificial intelligence model through the standard training data, and mark the trained artificial intelligence model as a safety detection model.

[0072] In this embodiment, the standard training data includes a plurality of sets of input data and corresponding grinding parameters, and the input data and the raw data have consistent content attributes; it can be understood that the input data and the raw data both include selected grinding targets and blade utilization rates, but the numerical values of the data are different.

[0073] In the embodiment, the artificial intelligence model includes a deep convolutional neural network model or an RBF neural network model, or other models with strong nonlinear fitting capability.

[0074] The execution module is configured to receive the dressing instruction and dress the electrode cap according to the dressing instruction.

[0075] In the embodiment, the target setting module is in communication and / or electrical connection with the instruction generation module;

[0076] The blade information acquisition module is in communication and / or electrical connection with the blade judgment module;

[0077] The blade judgment module is in communication and / or electrical connection with the instruction generation module;

[0078] The instruction generation module is in communication and / or electrical connection with the execution module.

[0079] The above formulas are all dimensionless values calculated, and the formulas are obtained by software simulation of a large amount of data to obtain a formula closest to the real situation, and the preset parameters and the preset threshold in the formula are set by the person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0080] Working principle of the application:

[0081] The target setting module sets the dressing target of the electrode cap, and sends the dressing target to the instruction generation module.

[0082] The blade information acquisition module acquires the blade information, and sends the blade information to the blade judgment module.

[0083] The blade judgment module receives the blade information, acquires the blade utilization rate according to the blade information, acquires the blade utilization rate threshold, and compares the blade utilization rate threshold with the blade utilization rate; when the blade utilization rate is greater than or equal to the blade utilization rate threshold, the blade utilization rate is sent to the instruction generation module; when the blade utilization rate is less than the blade utilization rate threshold, the blade judgment module generates the blade replacement information, and sends the blade replacement information to the intelligent terminal of the worker to remind the worker to replace the blade;

[0084] The instruction generation module receives the dressing target and the blade utilization rate, acquires the dressing parameters according to the dressing target and the blade and the parameter prediction model, generates the dressing instruction according to the dressing parameters, and sends the dressing instruction to the execution module;

[0085] The execution module receives the dressing instruction, and dresses the electrode cap according to the dressing instruction.

[0086] The above examples are only used to illustrate the technical method of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present application.

Claims

1. An optimization system for electrode cap dressing, characterized by, The target setting module is used for a user to set a grinding target of an electrode cap, and sends the grinding target to the instruction generation module. The blade information acquisition module is used for acquiring blade information. The blade information is sent to the blade judgment module. The blade judgment module is used for receiving the blade information, and acquiring a blade utilization rate according to the blade information. A blade utilization rate threshold is acquired, and the blade utilization rate threshold is compared with the blade utilization rate. When the blade utilization rate is greater than or equal to the blade utilization rate threshold, the blade utilization rate is sent to the instruction generation module. When the blade utilization rate is less than the blade utilization rate threshold, the blade judgment module generates blade replacement information, and sends the blade replacement information to a smart terminal of a worker to remind the worker to replace the blade. The instruction generation module is used for receiving the grinding target and the blade utilization rate, acquiring a grinding parameter according to the grinding target and the blade and a parameter prediction model, wherein the parameter prediction model is established based on an artificial intelligence model, and generating a grinding instruction according to the grinding parameter, and sending the grinding instruction to the execution module. The execution module is used for receiving the grinding instruction, and grinding the electrode cap according to the grinding instruction. The blade information includes a use frequency of the blade, a blade height, a use time and a wear degree. The blade judgment module acquires the blade utilization rate according to the blade information, including the following steps. The blade judgment module receives the blade information, and marks the use frequency, the blade height, the use time and the wear degree as n, h, t and m respectively. The blade judgment module acquires the blade utilization rate through a calculation formula, and marks the blade utilization rate as S. The calculation formula of the blade utilization rate is: The grinding target includes a cutting amount and smoothness. A brand new blade does not need to calculate the blade utilization rate, and the blade utilization rate of the brand new blade is 1 by default. ； where h 初 is the initial height of the blade.

2. An optimized system for electrode cap dressing according to claim 1, wherein, The instruction generation module acquires the grinding parameter according to the grinding target and the blade and the parameter prediction model, including the following steps.

3. The optimized system for electrode cap dressing according to claim 1, wherein, The instruction generation module receives the grinding target and the blade utilization rate, and combines the grinding target and the blade utilization rate to generate original data.

4. The optimized system for electrode cap dressing according to claim 1, wherein, The parameter prediction model is acquired from the instruction generation module. The original data is input into the parameter prediction model to acquire the grinding parameter, wherein the grinding parameter includes a grinding time and a grinding pressure. The instruction generation module generates the grinding instruction according to the grinding parameter, and sends the grinding instruction to the execution module. The parameter prediction model is established based on the artificial intelligence model, including the following steps. Standard training data is acquired from the instruction generation module.

5. The optimized system for electrode cap dressing according to claim 2, wherein, The artificial intelligence model is trained through the standard training data, and the trained artificial intelligence model is marked as a safety detection model. The target setting module and the instruction generation module are in communication and / or electrical connection. ​ 6. The optimized system for electrode cap dressing according to claim 1, wherein, ​ The blade information acquisition module is in communication and / or electrical connection with the blade judgment module; The blade judgment module is in communication and / or electrical connection with the instruction generation module; The instruction generation module is in communication and / or electrical connection with the execution module.

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