An intelligent parameter control method and system for wear-resistant coating cladding process optimization

Abstract

The application discloses an intelligent parameter control method and system for wear-resistant coating cladding process optimization, and relates to the technical field of material processing. The method comprises the following steps: establishing a database, marking the variable parameter values of each device, and calibrating the associated parameter items of each device to the connected devices; performing laser cladding operation according to the database; obtaining production requirement data, and adjusting the corresponding parameter values of the corresponding devices; when the corresponding parameter values of the corresponding devices change, the remaining parameter values of the corresponding devices and the multiple parameter values of the related devices are dynamically adjusted based on a dynamic regulation method, the application can continuously adjust the parameters of the devices according to the production requirement data in the production process until the production requirement data is met, and the parameter change data in the continuous adjustment process can be recorded, which facilitates direct calling when the same production requirement occurs subsequently, the dynamic adjustment method can be continuously optimized, and therefore the production efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of material processing, in particular to an intelligent parameter control method and system for optimizing a wear-resistant coating cladding process. BACKGROUND

[0002] Wear-resistant coating cladding is a technology for improving the wear resistance of a material surface by cladding a layer of wear-resistant material on the material surface through a specific process. Laser cladding is one of the commonly used types: it uses a high-energy-density laser as a heat source to cladding a layer of alloy material on the surface of a base material, so that the base material and the alloy material are metallurgically combined. Laser cladding has concentrated energy and a small heat-affected zone, and can accurately control the cladding area and thickness, so that a wear-resistant coating with dense structure and excellent performance can be prepared, and is commonly used for surface strengthening and repair of parts in the fields of aerospace, automobiles, molds, etc.

[0003] In the production of laser cladding, the parameters of the equipment need to be adjusted. For example, the Chinese invention with the patent publication number CN108873700A provides a laser cladding closed-loop control method based on stereoscopic vision, which includes installing a CCD connected with the control system and moving synchronously on the opposite sides of the laser, calibrating the two CCDs, monitoring the molten pool with the two CCDs at the same time, comparing the obtained data with the standard shape of the preset cladding forming layer by the control system, obtaining the width error and height error, taking the width error and / or height error as the input of the fuzzy controller, and taking the change amount of the controlled variable as the output, i.e. obtaining the adjustment change amount of the laser power or defocusing amount, so as to realize accurate adjustment of the cladding precision and quality. The two CCDs can obtain the molten pool change image in real time when the laser is cladding, and then the control system compares the image with the preset standard molten pool forming layer shape, and adjusts the laser according to the error, so that the current molten pool always keeps consistent with the preset forming layer, realizes real-time control of the cladding process, and improves the cladding precision and quality.

[0004] The laser cladding process involves the adjustment and optimization of multiple parameters. The above method needs to input the production parameters into the system in advance, and the system will automatically adjust the laser power according to the real-time feedback data during the production process to achieve the best cladding effect. However, in actual production, due to various factors, the production demand may change, and in this case, the parameters of the corresponding equipment need to be adjusted, and the parameters of the related equipment also need to be adjusted adaptively. The above method cannot adaptively adjust the parameters of each equipment, and the existing parameter adjustment needs to rely on a large amount of experimental data records to adjust to a specific parameter value. It cannot be adjusted step by step during the production process and select the optimal parameter value, and the production efficiency is low. SUMMARY

[0005] The application aims to provide an intelligent parameter control method and system for wear-resistant coating cladding process optimization to solve the problems in the background art.

[0006] To achieve the above-mentioned purpose, the application provides the following technical solutions: an intelligent parameter control method for wear-resistant coating cladding process optimization, the method comprising:

[0007] establishing a database, marking the variable parameter values of each device, and calibrating the associated parameter items of each device to its connected devices, and performing laser cladding operations according to the database;

[0008] obtaining production requirement data, and adjusting the corresponding parameter values of the corresponding device according to the preliminary adjustment method;

[0009] When the corresponding parameter values of the corresponding device change, the remaining parameter values of the corresponding device and the multiple parameter values of the related devices are dynamically adjusted based on the dynamic control method, the parameters of the device are continuously adjusted according to the production requirement data in the production process until the production requirement data is met, and the parameter variation data in the continuous adjustment process can be recorded, which facilitates direct calling when the same production requirement data occurs subsequently, and the dynamic control method can be continuously optimized to improve production efficiency;

[0010] The dynamic control method comprises:

[0011] S1: adjusting the remaining variable parameter values of the corresponding device, and obtaining feedback data information, and adjusting the variable parameter values according to the marking adjustment method;

[0012] S2: obtaining the changed parameter items of the corresponding device, and comparing the associated parameter items to determine the related devices that need to be adjusted;

[0013] S3: changing the variable parameter items of the related devices determined in the S2 step, and obtaining feedback data information, and adjusting the variable parameter values according to the marking adjustment method;

[0014] S4: taking the device whose parameter is changed in the S3 step as a new corresponding device object, repeating the S2 and S3 steps until there is no new corresponding device object;

[0015] After the parameter adjustment is completed, the stability of the current data is evaluated by a stability evaluation method;

[0016] After the stability evaluation, the stable running parameters of the device under the requirement are recorded and input into the database; when the production requirement data appears again, the stable running parameters of the device under the requirement are directly called.

[0017] Preferably, the stability evaluation method comprises:

[0018] L1: Obtain the use data of the device, and divide the use data into several groups according to time periods;

[0019] L2: Calculate the stability of each device in different groups by a stability calculation method to obtain a preliminary stability evaluation;

[0020] L3: Compare the preliminary stability evaluation results of different devices in the same group. When the same group of a plurality of consecutive groups of different devices all pass the preliminary stability evaluation, it is judged that the current data passes the stability evaluation, and the situation that the device is affected by external factors in the time period of the group can be excluded, thereby improving the accuracy of the stability evaluation. When the same group of a plurality of consecutive groups of different devices does not pass the preliminary stability evaluation, it is judged that the current data does not pass the stability evaluation;

[0021] L4: When the stability evaluation fails, select the corresponding device from the group with the largest number of devices that fail the preliminary stability evaluation, adjust the variable parameter value by a dynamic control method, and then repeat the stability evaluation method after the adjustment of the device parameters in the target group that fail the preliminary stability evaluation is completed, until the current data passes the stability evaluation and the data is recorded. The adjusted parameters can be continuously optimized, the corresponding device is preferentially selected from the group with the largest number of devices that fail the preliminary stability evaluation for adjustment, the unstable parameters can be covered as much as possible, the adjustment frequency is reduced, and the adjustment speed is further improved.

[0022] Preferably, the stability calculation method comprises:

[0023] B1: Collect and organize the use data of the device, and the use data includes energy consumption data, start-stop data, maintenance data and consumable data. The percentages a, b, c and d of the energy consumption data, start-stop data, maintenance data and consumable data are obtained by big data.

[0024] B2: Calculate the preliminary stability value according to the formula:

[0025]

[0026]

[0027] wherein is the preliminary stability value, , , , a, b, c and d are weight coefficients, respectively, is a target device constant, and the value of the target device constant can represent the difference between the stability of the target device of the company and the stability of the device in the big data, the number of times of passing the preliminary stability evaluation before, the preliminary stability value of the i+1th time of passing the preliminary stability evaluation before, the preliminary stability value of the ith time of passing the preliminary stability evaluation before, the difference between and is a weight coefficient set by a technician, is a big data evaluation value representing the recognition of big data to the stability of the equipment;

[0028] B3: when ≥ 0.5, it is determined that the equipment passes the preliminary stability evaluation, otherwise, it is determined that the equipment fails to pass the preliminary stability evaluation, the preliminary stability value of the equipment passing the preliminary stability evaluation before can be brought into the calculation of the preliminary stability evaluation this time, so that the historical stability of the equipment is used as a reference for comparison, the accuracy of the preliminary stability evaluation is improved, and the standard of the preliminary stability evaluation of the target equipment can be improved or reduced according to the actual situation, the dependence on big data is reduced, the preliminary stability evaluation is combined with the situation of the equipment itself, and the preliminary stability evaluation has more practical value.

[0029] Preferably, the use data further includes an environmental protection coefficient H, the value of H is 1 when the equipment meets the environmental protection requirements, and the value of H is 0 when the equipment does not meet the environmental protection requirements, and the calculation formula of the preliminary stability value is:

[0030]

[0031]

[0032] wherein is the preliminary stability value, , , , are weight coefficients of a, b, c and d respectively, is a local equipment constant, is the number of times of passing the preliminary stability evaluation before, the preliminary stability value of the i+1th time of passing the preliminary stability evaluation before, the preliminary stability value of the ith time of passing the preliminary stability evaluation before, the difference between and is a weight coefficient set by a technician, is a big data evaluation value representing the recognition of big data to the stability of the equipment, the environmental coefficient H is taken into the calculation of the preliminary stability value, and is used as a prerequisite for the preliminary stability evaluation of the existing data, which is beneficial to avoid environmental risks and meet the requirements of laws and regulations.

[0033] Preferably, in the adjustment process of the dynamic regulation method, the feedback data of the equipment with the specific parameter as the non-correlation parameter item is obtained, if the feedback data is abnormal, the specific parameter is re-calibrated as the correlation parameter item of the corresponding equipment and the equipment, the parameter change of the equipment with the correlation parameter item of the corresponding equipment in the adjustment process is recorded, when the parameter does not change, the correlation parameter item is removed, the database information is updated, the correlation parameter item calibration accuracy is continuously improved, so as to optimize the adjustment speed of the dynamic regulation method to the variable parameter value.

[0034] Preferably, the marking adjustment method is:

[0035] N1: The variable parameter value is positively or negatively correlated with the corresponding parameter value, and real-time feedback data is obtained, the parameter change direction is judged according to the change of the feedback data;

[0036] N2: The parameter change direction is recorded, and the target parameter value and the corresponding parameter value are marked as positive correlation, negative correlation and non-correlation;

[0037] N3: When the same variable parameter value and corresponding parameter value are used as adjustment targets again, the adjustment speed can be improved according to the marking in N2.

[0038] Preferably, the preliminary adjustment method is:

[0039] E1: A demand classification menu containing various demand categories is established for technicians to select, the equipment is matched with each category in the demand classification set, and the adjustable parameter value of each equipment is matched with each category in the demand classification set;

[0040] E2: According to the demand classification selected by the technician, the corresponding variable parameter value of the corresponding equipment and the target equipment is selected;

[0041] E3: A secondary quantization menu is established, and the technicians can quantize the demand categories of the previous level;

[0042] E4: The selected adjustable parameter value is continuously adjusted according to the change amount set in E3, and the result feedback data is obtained, when the feedback data matches the quantization value of the demand category, the adjustment of the adjustable parameter value is stopped, the production demand data is classified and quantized, the adjustment target is clear, and the adjustment efficiency is improved.

[0043] Preferably, the demand classification menu can be edited, when the result feedback data is abnormal in the adjustment process, the existing demand classification menu item is marked and the technician is notified to correct, which can timely detect itself and prevent errors when the technician selects the demand category, and can continuously optimize the matching degree between the demand category and the equipment and the demand category and the variable parameter value.

[0044] Compared with the prior art, the present application has the beneficial effects of:

[0045] By producing demand data, adjusting the corresponding parameter value according to the production demand data, dynamically adjusting the remaining parameter values of the corresponding equipment and the parameter values of the related equipment based on the dynamic regulation method, continuously adjusting the parameters of the equipment according to the production demand data in the production process until the production demand data is met, and the parameter variation data in the continuous adjustment process can be recorded, facilitating direct calling when the same production demand data occurs subsequently, and continuously optimizing the dynamic regulation method, thereby improving the production efficiency;

[0046] At the same time, after the parameter adjustment is completed, the current data is evaluated for stability by the stability evaluation method, and the stable operation parameters of the equipment under the demand are recorded and entered into the database after the stability evaluation; when the production demand data appears again, the stable operation parameters of the equipment under the demand are directly called to further improve the parameter adjustment efficiency and thereby improve the production efficiency;

[0047] Moreover, during the preliminary stability evaluation, the preliminary stability value evaluated previously can be brought into the calculation of the present preliminary stability evaluation, so that the historical stability of the equipment is used as a comparison reference to improve the accuracy of the preliminary stability evaluation, and the standard of the preliminary stability evaluation of the target equipment can be improved or reduced according to the actual situation, reducing the dependence on big data, combining with the situation of the equipment itself, so that the preliminary stability evaluation has more practical value. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The flowchart of the intelligent parameter control method of the present application;

[0049] Figure 2 The flowchart of the preliminary adjustment method of the present application;

[0050] Figure 3 The flowchart of the dynamic regulation method of the present application;

[0051] Figure 4 The flowchart of the stability calculation method (the equipment has an environmental protection coefficient) of the present application;

[0052] Figure 5 The structure diagram of the stability calculation method (the equipment does not have an environmental protection coefficient) of the present application. DETAILED DESCRIPTION

[0053] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0054] In the laser cladding operation, when the production demand changes, for example, the production speed is improved, the detection standard is improved, etc., it is necessary to adjust the parameters of the corresponding equipment to ensure that the production demand is met. The present application provides an intelligent adjustment method for equipment parameters when the production demand changes. According to the change of the production demand, the parameters of each equipment on the production line are adjusted in matching manner to ensure the continuous production, thereby improving the production efficiency.

[0055] As shown in Figures 1-5 The present application provides a technical solution: an intelligent parameter control method for wear-resistant coating cladding process optimization, comprising:

[0056] A database is established, the variable parameter values of each equipment are marked, and the associated parameter items of each equipment to its connected equipment are calibrated, and the laser cladding operation is carried out according to the database;

[0057] It should be noted that the variable parameter values of each equipment refer to the parameter items that can be changed during the use of the equipment, which can be summarized from the instruction manual of the equipment and the multiple trial runs of the equipment. In addition, the associated parameter items refer to a parameter of a target equipment, when the parameter changes, the upstream and downstream connected equipment of the target equipment also need to adjust the parameter. At this time, the parameter can be determined as the associated parameter item of the target equipment and the connected equipment. The associated parameter items can be summarized from the instruction manual of the two equipment and the multiple trial run data of the equipment.

[0058] Production demand data is obtained, and the corresponding parameter values of the corresponding equipment are adjusted according to the preliminary adjustment method;

[0059] When the corresponding parameter values of the corresponding equipment change, the remaining parameter values of the corresponding equipment and the multiple parameter values of the related equipment are dynamically adjusted based on the dynamic control method. In the production process, the parameters of the equipment are continuously adjusted according to the production demand data until the production demand data is met. The parameter variation data in the continuous adjustment process can be recorded, which facilitates direct calling when the same production demand data occurs in the subsequent production, and the dynamic adjustment method can be continuously optimized, thereby improving the production efficiency;

[0060] After the parameter adjustment is completed, the stability of the current data is evaluated by the stability evaluation method;

[0061] The stable operation parameters of the equipment under the demand are recorded after stability evaluation and entered into the database; when the production demand data reappears, the stable operation parameters of the equipment under the demand are directly called.

[0062] By adjusting the corresponding parameter values according to the production demand data, dynamically adjusting the remaining parameter values of the corresponding equipment and the parameter values of the related equipment based on the dynamic control method, continuously adjusting the parameters of the equipment according to the production demand data in the production process until the production demand data is met, and recording the parameter variation data in the continuous adjustment process, the same production demand data can be directly called in the subsequent production, the dynamic control method can be continuously optimized, and the production efficiency can be improved.

[0063] Reference Figure 2 The preliminary adjustment method is as follows:

[0064] E1: Establish a demand classification menu containing various demand categories for technicians to select, match the equipment with each category in the demand classification set, and match the adjustable parameter values of each equipment with each category in the demand classification set;

[0065] It should be noted that the demand categories can be developed according to the demands frequently encountered in actual production, such as production speed, detection intensity, cleaning intensity, cladding thickness, etc. The matching of demand categories and equipment and parameters can be summarized from multiple channels such as the instruction manual of the equipment, the data of the equipment manufacturer, and the multiple trial data of the equipment, and arranged by technicians, for example, the cleaning intensity is matched with the cleaning equipment, and matched with the cleaning agent usage and water pressure intensity of the cleaning equipment.

[0066] E2: According to the demand classification selected by the technician, select the corresponding adjustable parameter values of the corresponding equipment and the target equipment;

[0067] E3: Establish a secondary quantification menu, and technicians can quantize the demand categories of the previous level;

[0068] E4: Continuously adjust the selected adjustable parameter values according to the change amount set in E3, and obtain result feedback data; when the feedback data matches the quantized value of the demand category, stop adjusting the adjustable parameter values, classify and quantize the production demand data, and clearly define the adjustment target to improve the adjustment efficiency.

[0069] It should be noted that in the process of continuously adjusting the selected adjustable parameter value, the dynamic control method is also being carried out, and it is not necessary to wait for the preliminary adjustment method to end before proceeding. The result feedback data is collected by various sensors installed on the equipment, and in actual use, it is not limited to the sensors on the corresponding equipment. For example, in the process of adjusting the cladding thickness parameter, the result feedback data can be obtained by collecting images and calculating the thickness according to the CCD camera in the laser scanning equipment, or by obtaining the result feedback data according to the detection results of the ray detection equipment.

[0070] Further, the demand classification menu can be edited. When the result feedback data is abnormal during the adjustment process, the current demand classification menu item is marked and the technician is notified to correct it. This can timely detect errors when the technician selects the demand category and continuously optimize the matching degree between the demand category and the equipment and the demand category and the variable parameter value.

[0071] It should be noted that the result feedback abnormality can be set by the technician. For example, in actual use, the laser power of the laser scanning equipment is set to 800W-1200W, and the technician sets the abnormal range to 800W-850W and 1000W-1200W. When other equipment is adjusted during the process, the laser power of the laser scanning equipment enters the abnormal range, and the result feedback data is determined to be abnormal. The technician is notified in time to correct it through the alarm light, buzzer or send remote information, etc. so that the preliminary adjustment method can be continuously optimized to improve the matching degree between the demand category and the equipment and the demand category and the variable parameter value.

[0072] Reference Figure 1 The dynamic control method is as follows:

[0073] S1: Adjust the remaining variable parameter values of the corresponding equipment and obtain feedback data information. The variable parameter values are adjusted according to the marking adjustment method. The corresponding equipment is the equipment selected in the preliminary adjustment method, and the feedback data information is obtained by various sensors installed on the equipment;

[0074] S2: Obtain the changed parameter item of the corresponding equipment, compare the associated parameter items to determine the related equipment that needs to be adjusted;

[0075] S3: Change the variable parameter item of the related equipment determined in step S2 and obtain the feedback data information. The variable parameter values are adjusted according to the marking adjustment method;

[0076] S4: Take the equipment whose parameter is changed in step S3 as a new corresponding equipment object, repeat steps S2 and S3 until there is no new corresponding equipment object;

[0077] The parameters of the device can be continuously adjusted until the production requirement data is met, and the parameter change data during the continuous adjustment process can be recorded, facilitating direct calling when the same production requirement data is generated subsequently, and the dynamic adjustment method can be continuously optimized to improve production efficiency.

[0078] Furthermore, in the adjustment process of the dynamic regulation method, the variable parameter value being adjusted is marked as a specific parameter, feedback data of the device with the specific parameter as a non-associated parameter item is obtained, if the feedback data is abnormal, the specific parameter is re-designated as an associated parameter item of the corresponding device and the device, the parameter change of the device with the associated parameter item of the corresponding device in multiple adjustment processes is recorded, when the parameter does not change, the associated parameter item is removed, and the database information is updated, and the accuracy of the associated parameter item designation is continuously improved, so that the adjustment speed of the dynamic regulation method to the variable parameter value is optimized.

[0079] It should be noted that the above feedback data is obtained by a plurality of sensors installed on the device with the specific parameter as a non-associated parameter item, and the abnormal range is set by a technician, when the data obtained on the sensor reaches the abnormal range, it is determined that the feedback data is abnormal, which indicates that the specific parameter can affect the normal operation of the device, i.e. the specific parameter can be recorded as an associated parameter item of the corresponding device and the device, and the specific parameter is adjusted, when the specific parameter is adjusted for multiple times (the number of adjustments can be set by a technician), the device with the associated parameter item of the corresponding device (i.e. the related device) does not change, which indicates that the adjustment of the associated parameter item does not affect the normal operation of the related device, i.e. the associated parameter item can be deleted. This way can continuously optimize the associated parameters and improve the accuracy of the associated parameter items, which can clearly define the adjustment target and improve the adjustment speed for subsequent adjustment.

[0080] Furthermore, the marking adjustment method is:

[0081] N1: adjust the variable parameter value and the corresponding parameter value in positive correlation or negative correlation, and obtain real-time feedback data, and determine the parameter change direction according to the change of the feedback data;

[0082] N2: record the parameter change direction, and mark the target parameter value and the corresponding parameter value as positive correlation, negative correlation and non-correlation;

[0083] N3: when the same variable parameter value and the corresponding parameter value are used as adjustment targets again, the adjustment speed can be improved according to the mark in N2.

[0084] When adjusting the corresponding parameter value, the variable parameter value is adjusted in a random direction, at which time feedback data can be obtained through sensors on the device, and according to the change of the feedback data, the change relationship (positive correlation, negative correlation or no correlation) of the variable parameter value with the corresponding parameter value can be determined and marked, and the next adjustment can be directly adjusted according to the recorded change relationship, thereby improving the adjustment speed.

[0085] Reference Figure 3 The stability evaluation method is as follows:

[0086] L1: Obtain the use data of the device, and divide the use data into several groups according to time periods; the length of the time period and the number of groups can be set by the technician;

[0087] L2: For each device, the stability of different groups is calculated separately by the stability calculation method to obtain a preliminary stability evaluation;

[0088] L3: Compare the preliminary stability evaluation results of the same group of different devices. When the same group of a plurality of consecutive groups (the number is set by the technician) of different devices all pass the preliminary stability evaluation, it is judged that the current data passes the stability evaluation, which can exclude the case that the device is affected by external factors in a certain group period, so that the device cannot pass the stability evaluation, thereby improving the accuracy of the stability evaluation. When the same group of a plurality of consecutive groups of different devices does not pass the preliminary stability evaluation, it is judged that the current data does not pass the stability evaluation;

[0089] L4: When the stability evaluation fails, the corresponding device is selected from the group with the largest number of devices that fail the preliminary stability evaluation, and the variable parameter value is adjusted by the dynamic control method. After the adjustment of the device parameters of the target group that do not pass the preliminary stability evaluation is completed, the stability evaluation method is repeated again until the current data passes the stability evaluation and the data is recorded. The adjusted parameters can be continuously optimized, the corresponding device is preferentially selected from the group with the largest number of devices that fail the preliminary stability evaluation for adjustment, which can cover as many unstable parameters as possible, reduce the number of adjustments, and further improve the adjustment speed.

[0090] It should be noted that, for the convenience of understanding, the simulation data is set as follows:

[0091] The devices are marked as X1, X2, X3 and X4 respectively;

[0092] The time periods are marked as Y1, Y2, Y3, Y4, Y5, Y6, Y7 and Y8;

[0093] The number of consecutive groups that pass the stability evaluation is set to 3;

[0094] The preliminary stability evaluation is as shown in Table 1 (where "Yes" represents passing the preliminary stability evaluation, and "No" represents failing the preliminary stability evaluation):

[0095] Table 1

[0096]

[0097] From Table 1, it can be found that in the four consecutive time periods Y4 to Y7, devices X1, X2, X3 and X4 all passed the preliminary stability evaluation, and the number of consecutive groups exceeded 3 groups, that is, the current data passed the stability evaluation. In the Y2 time period, the use data groups of devices X2, X3 and X4 did not pass the preliminary stability evaluation, but this may be due to external factors or human factors. Therefore, this stability evaluation method can avoid the instability of a single time period caused by external factors or human factors, and ensure the accuracy of the stability evaluation.

[0098] Example One:

[0099] As shown in Figure 4 , when performing preliminary stability evaluation, when the device does not affect the environmental factors, the stability calculation method is:

[0100] B1: Collect and organize the use data of the device, including energy consumption data, start-stop data, maintenance data and consumable data, and obtain the percentage scores a, b, c and d of the energy consumption data, start-stop data, maintenance data and consumable data through big data;

[0101] It should be noted that the percentage scores a, b, c and d of the energy consumption data, start-stop data, maintenance data and consumable data can be first integrated through device manufacturer-provided device parameters, network platform-collected data, data of the same type of company, and data purchased from professional third-party data providers, and the percentage scores a, b, c and d of the energy consumption data, start-stop data, maintenance data and consumable data of the target device of the company are obtained through percentage grading method calculation, wherein the percentage grading method is an existing calculation method, which is not described here.

[0102] B2: Calculate the preliminary stability value again according to the formula:

[0103]

[0104]

[0105] wherein is the preliminary stability value, , , , weight coefficient of a, b, c, d respectively, a constant of target device, whose value can represent the gap between the stability of target device and the stability of the same type of device in big data, the number of times of passing the preliminary stability evaluation, the preliminary stability value of the i+1th time of passing the preliminary stability evaluation, the preliminary stability value of the ith time of passing the preliminary stability evaluation, the difference between and weight coefficient of the difference, set by the technical personnel, the big data evaluation value;

[0106] It should be noted that, , , , are set by the technical personnel in combination with the actual situation of the company, and , , , the sum of which is 1, for example, set as 0.2, set as 0.5, set as 0.2, and set as 0.1, which means that the company attaches the most importance to start-stop data, relatively emphasizes energy consumption data, and attaches the least importance to maintenance data and consumable data.

[0107] In addition, according to the data recorded in the multiple trial operation of the equipment installation, when the equipment is running at a stable level that the company can accept (which may be affected by factors other than the company's equipment), the value of is obtained by calculating the running data at that time, and then the value of is subtracted by 0.5 to obtain the value of , the larger the value of , the larger the gap between the running stability of the company's equipment and the running stability of the equipment recorded in the big data (which may be affected by negative factors other than the company's equipment, such as poor power supply stability of the local power supply bureau, etc.), when is negative, it means that the running stability of the company's equipment is better than the running stability of the equipment recorded in the big data (which may be affected by positive factors other than the company's equipment, such as the company's technical personnel being more skilled, etc.).

[0108] B3: when When the value is ≥0.5, the device is judged to have passed the preliminary stability assessment, otherwise it has failed the preliminary stability assessment. The preliminary stability value that has passed the stability assessment before can be brought into the calculation of this preliminary stability assessment, so that the historical stability of the device can be used as a comparative reference to improve the accuracy of the preliminary stability assessment. In addition, the standard of the preliminary stability assessment of the target device can be increased or decreased according to the actual situation, reducing the dependence on big data. Combined with the device's own situation, the preliminary stability assessment has more practical value.

[0109] It should be noted that for ease of understanding, the simulation data is set as follows:

[0110] Will Set to 0.2, Set to 0.5, Set to 0.2, Set to 0.1;

[0111] Set a to 50%, b to 40%, c to 60%, and d to 40%;

[0112] Will Set to 0.06;

[0113] Will Set to 5;

[0114] The initial stability values ​​of the first five preliminary stability assessments were 0.52, 0.54, 0.53, 0.58, and 0.58, respectively. All are set to 1, indicating that the importance of each difference is the same.

[0115] Substitute into the calculation formula:

[0116]

[0117]

[0118] get The value is 0.46, The value is 0.50. The value is equal to 0.5, so the current data are judged to have passed the preliminary stability assessment.

[0119] It should be noted that A positive number indicates the stability of the device during installation and testing. Compared with the stability that the device should have in the big data, it is poor, indicating that the device is affected by external factors. Since the initial stability values ​​of the first five preliminary stability assessments were 0.52, 0.54, 0.53, 0.58, and 0.58, respectively, the formula is calculated. The value of the part is 0.06, and it can be seen that the preliminary stability value of the first five passes of the device through the preliminary stability evaluation shows an upward trend, which represents that the influence of external factors on the device is decreasing, so when calculating the value, subtracting 0.06 can improve the preliminary stability evaluation standard of the device, so that when the preliminary stability evaluation of the device is carried out, the evaluation standard can be continuously optimized according to the previous historical data, fully considering the gap between the device and the devices in the big data, reducing the dependence on big data, combining the situation of the device itself, and making the preliminary stability evaluation more practical.

[0120] Example two:

[0121] As shown in the preliminary stability evaluation, when the device can affect environmental factors: Figure 5

[0122] The use data also includes an environmental coefficient H, when the device meets the environmental protection requirements, the value of H is 1, and when the device does not meet the environmental protection requirements, the value of H is 0, and the calculation formula of the preliminary stability value is:

[0123]

[0124]

[0125] Among them is the preliminary stability value, , , , a, b, c, d are weight coefficients, is a local device constant, is the number of times through the preliminary stability evaluation, is the preliminary stability value of the i+1 times through the preliminary stability evaluation, is the preliminary stability value of the i times through the preliminary stability evaluation, is the difference between and , the weight coefficient is set by the technical personnel, is the big data evaluation value, which represents the recognition of the big data to the stability of the device, and the environmental coefficient H is taken into account in the calculation of the preliminary stability value, and as a prerequisite for the preliminary stability evaluation of the current data, it is beneficial to avoid environmental risks and meet the requirements of laws and regulations.

[0126] It should be noted that the environmental coefficient H is obtained by sensor detection, first according to the local laws and regulations and the company's rules and regulations to query the emission standard of the device, and then according to the parameters of the purification system to set the parameters of various sensors at the emission detection position of the device. When detecting that the pollutants exceed the standard, the value of the environmental coefficient H is 0, otherwise it is 1.​​

[0127] It should be noted that for ease of understanding, the simulation data is set as follows:

[0128] The environmental protection coefficient H is 0;

[0129] Will Set to 0.2, Set to 0.5, Set to 0.2, Set to 0.1;

[0130] Set a to 60%, b to 70%, c to 50%, and d to 60%;

[0131] Will Set to 0.02;

[0132] Will Set to 5;

[0133] The initial stability values ​​of the first five preliminary stability assessments were 0.64, 0.68, 0.67, 0.66, and 0.64, respectively. All are set to 1, indicating that the importance attached to each difference is the same;

[0134] Substitute into the calculation formula:

[0135]

[0136]

[0137] get The value is 0.63, The value is 0. The value is less than 0.5, so the current data are judged to have passed the preliminary stability assessment.

[0138] Although The value is relatively large, which meets the recognition of big data on equipment stability. However, since the pollutant emissions of the equipment exceed the allowable standards of the company or local regulations, it is directly judged to fail the preliminary stability assessment. The environmental factor H is included in the calculation of the preliminary stability value and used as a prerequisite for the preliminary stability assessment of the current data, which is conducive to avoiding environmental risks and meeting the requirements of laws and regulations.

[0139] It should be noted that the implementation one and the embodiment two can be applied simultaneously in the process of dynamic adjustment, for example, the device X1 will discharge sewage during operation, the device X2, the device X3 and the device X4 will not have impact on the environment, that is, the calculation method of the embodiment two can be used on the device X1, the calculation method of the embodiment one can be used in the device X2, the device X3 and the device X4, and the preliminary stability evaluation is carried out separately.

[0140] Although the embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent parameter control for optimization of wear coating cladding process characterized by: The method comprises: Establishing a database, marking the variable parameter values of each device, and calibrating the associated parameter items of each device to its connected devices, and performing laser cladding operation according to the database; Obtaining production demand data, and adjusting the corresponding parameter values of the corresponding device according to the preliminary adjustment method; When the corresponding parameter values of the corresponding device change, the remaining parameter values of the corresponding device and the multiple parameter values of the related devices are dynamically adjusted based on the dynamic control method, and the parameters of the device are continuously adjusted according to the production demand data in the production process until the production demand data is met, and the parameter change data in the continuous adjustment process can be recorded to facilitate direct calling when the same production demand data occurs again, and the dynamic control method can be continuously optimized; The dynamic control method comprises: S1: adjusting the remaining variable parameter values of the corresponding device, and obtaining feedback data information, and adjusting the variable parameter values according to the marking adjustment method; S2: obtaining the changed parameter items of the corresponding device, and comparing the associated parameter items to determine the related devices that need to be adjusted; S3: changing the variable parameter items of the related devices determined in S2, and obtaining feedback data information, and adjusting the variable parameter values according to the marking adjustment method; S4: taking the device whose parameter is changed in S3 as a new corresponding device object, repeating S2 and S3 until there is no new corresponding device object; After parameter adjustment, the stability of the current data is evaluated by the stability evaluation method, and the stability evaluation method is used to avoid instability caused by external human factors in a single time period; After stability evaluation, the stable operation parameters of the device under the demand are recorded and input into the database; when the production demand data appears again, the stable operation parameters of the device under the demand are directly called; The marking adjustment method comprises: N1: positively or negatively adjusting the variable parameter values and the corresponding parameter values, and obtaining real-time feedback data, and judging the parameter change direction according to the change of the feedback data; N2: recording the parameter change direction, and marking the target parameter values and the corresponding parameter values as positive correlation, negative correlation and no correlation; N3: when the same variable parameter values and corresponding parameter values are taken as adjustment targets again, the adjustment is directly performed according to the marking in N2; The preliminary adjustment method comprises: E1: establishing a demand classification menu containing various demand categories, selecting by a technician, matching the device with each category in the demand classification set, and matching the adjustable parameter values of each device with each category in the demand classification set; E2: selecting the corresponding variable parameter values of the corresponding device and the target device according to the demand classification selected by the technician; E3: establishing a secondary quantification menu, and the technician quantifies the demand categories of the previous level; E4: continuously adjusting the selected adjustable parameter values according to the change amount set in E3, and obtaining result feedback data, stopping the adjustment of the adjustable parameter values when the feedback data matches the quantification value of the demand category, classifying and quantifying the production demand data, and clearly defining the adjustment target.

2. A method for intelligent parameter control for optimization of hardfacing cladding process as claimed in claim 1 wherein: The stability evaluation method comprises: L1: Obtain the use data of the equipment, and divide the use data into several groups according to time periods; L2: The stability calculation method is used to calculate the stability of each device in different groups respectively, and the preliminary stability evaluation is obtained; L3: Compare the preliminary stability evaluation results of different devices in the same group. When the same group of continuous several groups of different devices all pass the preliminary stability evaluation, it is judged that the current data passes the stability evaluation, which can exclude the case that the equipment is affected by external factors in the time period of several groups, and improve the accuracy of stability evaluation. When the same group of continuous several groups of different devices do not all pass the preliminary stability evaluation, it is judged that the current data does not pass the stability evaluation; L4: When the stability evaluation does not pass, select the corresponding device from the group with the most devices that do not pass the preliminary stability evaluation, adjust the variable parameter value by the dynamic control method, and then repeat the stability evaluation method until the current data passes the stability evaluation and records the data. The adjusted parameters can be optimized, the corresponding device in the group with the most devices that do not pass the preliminary stability evaluation is adjusted, the unstable parameters are covered as much as possible, the adjustment frequency is reduced, and the adjustment speed is further improved.

3. A method for intelligent parameter control for optimization of hardfacing cladding process as claimed in claim 2 wherein: The stability calculation method comprises: B1: Collect and arrange the use data of the equipment, which includes energy consumption data, start-stop data, maintenance data and consumable data. The percentages a, b, c and d of the energy consumption data, start-stop data, maintenance data and consumable data are obtained by big data; B2: Calculate the preliminary stability value according to the formula: wherein is a preliminary stability value, , , , are weight coefficients of a, b, c, d, respectively, is a target device constant, the value of which can represent the gap between the stability of the target device and the stability of the same type of device in the big data, is the number of times of passing the preliminary stability evaluation, is the preliminary stability value of the i+1th time of passing the preliminary stability evaluation, is the preliminary stability value of the i th time of passing the stability evaluation, is is the weight coefficient of the difference between , is a big data evaluation value, representing the recognition of the big data to the stability of the device; B3: When ≥0.5, then the device passes the preliminary stability evaluation, otherwise, the device fails the preliminary stability evaluation, the previous preliminary stability value passed the stability evaluation can be brought into the calculation of the current preliminary stability evaluation, thereby taking the historical stability of the device as a comparison reference, improving the accuracy of the preliminary stability evaluation, and the standard of the preliminary stability evaluation of the target device can be improved or reduced according to the actual situation, reducing the dependence on big data, combining the device itself, so that the preliminary stability evaluation has more practical value.

4. A method for intelligent parameter control for optimization of hardfacing cladding process as claimed in claim 3 wherein: The use data also includes an environmental protection coefficient H. When the equipment meets the environmental protection requirements, the value of H is 1; when the equipment does not meet the environmental protection requirements, the value of H is 0. The calculation formula of the preliminary stability value is: in is the initial stability value, 、 、 、 are the weight coefficients of a, b, c, and d respectively, is a local device constant, is the number of times the initial stability assessment was passed before, is the initial stability value of the first i+1 passes of the initial stability assessment, is the initial stable value of the stability assessment in the previous i times, for and The weight coefficient of the difference is set by the technicians. It is the big data evaluation value, representing the recognition of big data on the stability of the equipment. The environmental factor H is included in the calculation of the preliminary stability value and serves as the premise for the preliminary stability evaluation of the current data.

5. A method for intelligent parameter control for optimization of hardfacing cladding process as claimed in claim 1 wherein: In the adjustment process of the dynamic control method, the variable parameter value being adjusted is marked as a specific parameter, the feedback data of the equipment with the specific parameter as a non-associated parameter item is obtained, if the feedback data is abnormal, the specific parameter is re-marked as the associated parameter item of the corresponding equipment and the equipment, the parameter change of the equipment with the associated parameter item in the adjustment process is recorded, when the parameter does not change, the associated parameter item is removed, the database information is updated, and the accuracy of the associated parameter item marking is continuously improved, so as to optimize the adjustment speed of the dynamic control method to the variable parameter value.

6. A method for intelligent parameter control for optimization of hardfacing cladding process as claimed in claim 1 wherein: The demand classification menu can be edited. When the result feedback data is abnormal during the adjustment process, the current demand classification menu item is marked and the technical personnel are notified to correct, which can timely detect itself and prevent errors when the technical personnel select the demand type. Moreover, the matching degree between the demand type and the equipment and the matching degree between the demand type and the variable parameter value can be continuously optimized.

7. An intelligent parameter control system for wear coating cladding process optimization characterized in that: An intelligent parameter control method for optimizing a wear-resistant coating cladding process is used.

Images