Intelligent shot blasting machine shot blasting control system

Abstract

The invention discloses an intelligent control system for shot blasting of a shot blasting machine, relates to the technical field of current control, is used for solving the problems of poor shot blasting effect and the like caused by constant rolling frequency of a roller and constant discharging speed of shot materials, and comprises the steps of providing various methods for determining workpiece models, collecting workpiece data and shot material data, and performing shot blasting. The influence degree of the crawler current on the blasting head is analyzed through a KNN algorithm according to the workpiece data and the shot material data, if the influence degree is high, the current threshold values of the crawler and the blasting head are dynamically adjusted at the same time through a self-adaptive exploration-utilization strategy, and whether the current reaches a set value or not is detected through the current threshold values; the shot flow and the workpiece flow are debugged when a set value is reached, the optimal flow matching is searched according to a genetic algorithm, the shot blasting effect is detected, shot blasting is continued if the shot blasting effect is good, and shot data are adjusted to improve the shot formula if the shot blasting effect does not reach the expectation, so that the problems of poor shot blasting effect and the like caused by constant rolling frequency and constant discharging speed are solved; the shot blasting efficiency is improved, and the shot blasting effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of current control, and more specifically, to an intelligent shot blasting control system for a shot blasting machine. Background Art

[0002] With the improvement of online shot blasting technology, its application scope has gradually expanded. Online shot blasting technology first puts forward higher requirements on the reliability and durability of shot blasting equipment, and also faces the environment of changing types and quantities of workpieces on the production line. Previously, a fixed large shot blasting volume that meets the highest output was generally used to meet production requirements, but at the same time it brought huge consumption of shot materials, waste of electricity, and increased equipment damage. Calculated based on a casting production line with an annual output of 25,000 tons, the intelligent shot blasting control system of this patent can be used. The shot material consumption can be less than 5Kg / ton of castings, which saves 1.25 million yuan in shot material costs per year compared with ordinary shot blasting control systems, and the electricity and equipment maintenance costs are significantly reduced, and the equipment durability is significantly improved. In 2023, my country's annual casting output will be 51.9 million tons, accounting for more than 60% of the global total output. The promotion and application of this system will have huge market opportunities.

[0003] The prior art has the following deficiencies:

[0004] In the past, when shot blasting machines were blasting, the drum rolling frequency and shot discharging speed were constant, which would lead to problems such as wasting shot materials or poor shot blasting effects if the shot blasting machines could not be used according to the actual situation. Summary of the invention

[0005] In order to overcome the above defects of the prior art, an embodiment of the present invention provides an intelligent shot blasting control system for a shot blasting machine, which solves the problems raised in the above background technology by analyzing the local current influence of the shot blasting machine and dynamically adjusting the shot blasting flow rate.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An intelligent shot blasting control system for a shot blasting machine includes: a workpiece parameter module, a production process module, a shot blasting parameter module and a feedback control module, and the modules are signal-connected;

[0008] The workpiece parameter module is used to determine the workpiece model and send the workpiece model to the production process module;

[0009] The production process module is used to receive the workpiece model, collect the workpiece data and shot material data according to the workpiece model and transfer them to the shot blasting parameter module;

[0010] The shot blasting parameter module receives workpiece data and shot material data, analyzes the influence of the crawler current on the shot blasting head, selects different current threshold adjustment methods for the crawler and shot blasting head according to the influence, and transmits the current threshold to the feedback control module after adjustment;

[0011] The feedback control module is used to determine the shot blasting requirements of the shot blasting equipment, detect the real-time current of the crawler and the blasting head and compare it with the received crawler current threshold and blasting head current threshold, adjust the workpiece flow and shot flow and find the best flow match, and compare the expected shot blasting effect to determine whether to adjust the shot formula.

[0012] In a preferred embodiment, the workpiece parameter module is used to input, store the workpiece model and workpiece data and call them to other modules through the HMI human-machine interface, PLC network transmission or machine vision to identify the workpiece model.

[0013] In a preferred embodiment, the workpiece data includes the number of shot blasting heads of the shot blasting machine and the track shot blasting rate; the shot material data includes the shot material formula and the smoothness of each shot material in the formula.

[0014] In a preferred embodiment, the shot blasting parameter module receives workpiece data and shot material data, and uses the KNN algorithm to analyze the influence of the track current on the shot blasting head based on the workpiece data and the shot material data. If the influence is low, the track current threshold is adjusted and determined, and then the shot blasting head current threshold is adjusted; if the influence is high, an adaptive exploration-utilization strategy is used to adjust the current thresholds of the track and the shot blasting head at the same time.

[0015] In a preferred embodiment, the specific steps of analyzing the influence of track current on the shot blasting head by the KNN algorithm in the shot blasting parameter module are as follows:

[0016] Set feature group: The shot blasting parameter module uses the workpiece data and shot material data in multiple historical projects as feature groups to calculate the influence coefficient;

[0017] Similarity screening: set similarity thresholds to classify feature groups into similarities;

[0018] Classification judgment: Use the workpiece data and shot material data in the project to calculate the influence coefficient and classify it, and judge the influence of crawler current on the blasting head according to the category.

[0019] In a preferred embodiment, the shot blasting parameter module uses an adaptive exploration-utilization strategy to adjust the current threshold of the crawler and the blasting head at the same time. The specific steps are as follows:

[0020] Set the exploration probability: When the current of the crawler or shot blasting head changes in real time, the current change during the current change period will be compared according to the exploration probability, and the current with the best shot blasting effect will be selected;

[0021] Set the probability frequency: When the track current changes, explore according to the probability frequency; set the exploration limit to stop the exploration behavior.

[0022] In a preferred embodiment, the comparison rules of the real-time current of the crawler and the throwing head with the crawler current threshold and the throwing head current threshold in the feedback control module are as follows:

[0023] Rule 1: If the real-time current is lower than the current threshold, increase the current input and continue to compare the real-time current with the current threshold;

[0024] Rule 2: When the current input reaches the maximum value and the real-time current still does not reach the current threshold, the current threshold is adjusted to the real-time current;

[0025] Rule 3: When the real-time current reaches the current threshold, the crawler motor frequency is adjusted to control the workpiece flow, and the shot flow is adjusted to control the shot flow.

[0026] In a preferred embodiment, the feedback control module randomly adjusts the workpiece flow rate and the shot flow rate when the real-time current reaches the current threshold, and records them at different time points and merges them into a workpiece flow data set and a shot flow data set, respectively, and uses a genetic algorithm to find the best flow match. The specific steps are as follows:

[0027] Coding and initial population: randomly select a data from the workpiece flow data set and the shot flow data set and encode it into a chromosome;

[0028] Fitness evaluation: The fitness value of each chromosome is calculated by using the shot blasting speed formula to integrate the data in the chromosome;

[0029] Selection operation: Use the roulette wheel selection method to select offspring as new parents;

[0030] Crossover operation: randomly exchange data in chromosomes of different parents;

[0031] Mutation operation: randomly select data of different offspring chromosomes for adjustment;

[0032] Iteration and termination conditions: Set the termination conditions according to the shot blasting requirements of the equipment to obtain the best flow matching combination.

[0033] In a preferred embodiment, the feedback control module sets different termination conditions when searching for the best flow rate match using the genetic algorithm, and determines whether to adjust the shot formula according to the termination conditions.

[0034] The technical effects and advantages of the intelligent shot blasting control system of the shot blasting machine of the present invention are as follows:

[0035] The present invention preferentially determines the workpiece model, collects workpiece data and shot material data, comprehensively analyzes the influence of the track current of the shot blasting machine on the current of the blasting head by combining the workpiece data and the shot material data, selects different component current threshold setting methods according to the influence, detects whether the current reaches the set value after the component current is set, adjusts the shot material flow rate of the blasting head and the track motor frequency after reaching the set value, thereby controlling the shot material flow rate and the workpiece flow rate, and checks the shot blasting effect by matching the workpiece flow rate with a suitable shot material flow rate, adjusts the shot material data if the preset expected effect is not achieved, and continues the shot blasting until the preset expected effect is achieved until the workpiece model is changed, thereby improving the shot blasting effect and the shot blasting efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention is a schematic diagram of a control structure of shot blasting of an intelligent shot blasting machine.

[0037] Figure 2 The present invention is a flow chart of a control system for shot blasting of an intelligent shot blasting machine. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The present invention preferentially determines the workpiece model, collects workpiece data and shot material data, comprehensively analyzes the influence of the track current of the shot blasting machine on the current of the blasting head by combining the workpiece data and the shot material data, selects different component current threshold setting methods according to the influence, detects whether the current reaches the set value after the component current is set, adjusts the shot material flow rate of the blasting head and the track motor frequency after reaching the set value, thereby controlling the shot material flow rate and the workpiece flow rate, and checks the shot blasting effect by matching the workpiece flow rate with a suitable shot material flow rate, adjusts the shot material data if the preset expected effect is not achieved, and continues the shot blasting until the preset expected effect is achieved until the workpiece model is changed, thereby improving the shot blasting effect and the shot blasting efficiency.

[0040] Examples, such as Figure 1 As shown, an intelligent shot blasting control system for a shot blasting machine includes: a workpiece parameter module, a production process module, a shot blasting parameter module and a feedback control module, and signals are connected between the modules.

[0041] The functions of each module are as follows:

[0042] The workpiece parameter module is used to determine the workpiece model. When determining the workpiece model, the workpiece parameter module provides multiple confirmation methods to the user end. Method 1, the pre-process information such as shaping, pouring, cooling, and conveying of the shot blasting machine is transmitted through the PLC network, and the workpiece parameter module reads the workpiece model and parameters in the PLC program memory; Method 2, by providing an HMI interface, the user can manually enter the workpiece model and parameters; Method 3, the workpiece image is identified through machine vision technology for processing and comparison to obtain the workpiece model and parameters. After the workpiece parameter module determines the workpiece model, it sends the workpiece model to the production process module.

[0043] The production process module is used to receive the workpiece model, collect the workpiece data and shot material data according to the workpiece model and transmit them to the shot blasting parameter module. The workpiece data includes the number of shot blasting heads and the crawler shot blasting rate; the shot material data includes the shot material formula and the smoothness of each shot material in the formula.

[0044] The shot blasting parameter module receives workpiece data and shot material data, and uses the KNN algorithm to analyze the influence of the track current on the shot blasting head based on the workpiece data and the shot material data. If the influence is low, the track current threshold is adjusted and determined, and then the shot blasting head current threshold is adjusted; if the influence is high, the adaptive exploration-utilization strategy is used to adjust the current thresholds of the track and the shot blasting head at the same time, and the adjusted track current threshold and shot blasting head current threshold are transmitted to the feedback control module.

[0045] The feedback control module is used to determine the shot blasting requirements of the shot blasting equipment, detect the real-time current of the crawler and the blasting head and compare it with the received crawler current threshold and the blasting head current threshold. According to the comparison results, the crawler motor frequency and the blasting head shot are adjusted to change the workpiece flow and the shot flow. The feedback control module uses a genetic algorithm to best match the workpiece flow and the shot flow for different types of shot blasting equipment, compares the matched shot blasting effect with the expected shot blasting effect, and chooses to continue shot blasting or adjust the shot formula based on the comparison.

[0046] It should be noted that, in the above system, the workpiece parameter module is used to confirm the workpiece so that the production process module can collect corresponding data, and the production process module transmits the collected data to the subsequent module for analysis and processing, wherein the workpiece parameter module determines that the workpiece model is provided in the prior art, and the workpiece data and shot material data can be obtained by accessing the shot blasting machine database. There are many types of shot materials, and their main different characteristics are particle size and smoothness. In this example, the smoothness of each shot material in the shot material formula is collected as shot material data. The smoother the shot material surface, the easier it is to be pushed to the track by the blasting head, and the lower the influence of the track current on the blasting head; the more blasting heads there are, the greater the current that needs to be allocated to the blasting heads, and the more easily it is affected by the track current; the faster the track shot peening rate, the faster the engine speed, and the higher the track current is required to maintain stability, and the greater the influence of the track current on the blasting head.

[0047] The workpiece parameter module is used to input and store workpiece models and fixed workpiece information, call the current production workpiece model, and share information with other modules; the workpiece parameter module mainly inputs, stores, and calls workpiece models and information through HMI human-machine interface devices, and can also be transmitted through the PLC network of front-end equipment such as molding, pouring, cooling, and conveying, or through machine vision to identify workpiece models.

[0048] The production process module detects and records the dynamic process information of the workpieces produced at the molding, pouring, cooling, shot blasting, conveying and other stations for quality control and traceability purposes; among them, the shot blasting workpiece input, output quantity, shot blasting quality and other information can be introduced by the feedback control module and the shot blasting formula parameters can be automatically corrected.

[0049] Before selecting the adjustment method, the shot blasting parameter module uses the KNN algorithm to analyze the influence of the crawler current on the shot blasting head. The specific steps are as follows:

[0050] The production process module accesses the shot blasting machine database to obtain multiple groups of different workpiece data and shot material data in historical projects and passes them into the shot blasting parameter module. The shot blasting parameter module takes the workpiece data and shot material data in each historical project as a feature group, that is, the number of blasting heads, crawler shot peening rate and shot material surface smoothness in each historical project as a feature group, normalizes the data in the feature group, and marks the normalized results as a, b and c respectively. The influence coefficient is calculated according to the actual influence of the crawler on the blasting head. The formula can be: s=a+bc, where s is the influence coefficient, a is the normalized result of the number of shots, b is the normalized result of the crawler shot peening rate, and c is the normalized result of the shot material.

[0051] It should be explained that there is no unique method for normalizing the data in the feature group. The number of blasting heads in the feature group can be normalized by dividing the number of blasting heads by the maximum number of blasting heads in historical projects. The same is true for the track shot peening rate and the surface smoothness of the shot. Different shot formulas have different shot surface smoothness. The weighted average of the proportion of shot types in the shot formula and the smoothness of the corresponding shot types can be used as the shot surface smoothness.

[0052] The influence coefficient calculated for each feature group is similarly screened, and a similarity threshold is set according to the influence coefficient, and each feature group is similarly classified. The similarity threshold can be set according to the percentile method, and the influence coefficients are arranged from small to large to form an influence coefficient data set. N% is set as the screening condition, and the similarity threshold is set for the influence coefficient data set with the N% screening condition. For example, if 40% is used as the screening condition, the influence coefficient of more than 40% of the data in the influence coefficient data set is used as the similarity threshold. The influence coefficient calculated for each feature group is compared with the similarity threshold. When the influence coefficient exceeds the similarity threshold, its feature group is marked as a high-impact feature group; when the influence coefficient is lower than the similarity threshold, its feature group is marked as a low-impact feature group.

[0053] The influence coefficient is calculated by collecting the workpiece data and shot material data in the current project and marking them. If the workpiece data and shot material data in the current project are marked as a high-impact feature group, it is judged that the influence of the track current on the blasting head is high; if the workpiece data and shot material data in the current project are marked as a low-impact feature group, it is judged that the influence of the track current on the blasting head is low.

[0054] It should be noted that the calculation of the influence coefficient and the setting of the similarity threshold can be set by yourself as long as it conforms to the actual situation. The larger the influence coefficient, the greater the influence of the track current on the throwing head. The KNN algorithm is a similarity classification method. The prediction results are classified by similarity and can be used to analyze the influence of the track current on the throwing head.

[0055] When the shot blasting parameter module determines that the influence of the track current on the blasting head is low, the track current at different time points is collected through the historical data of the shot blasting machine and merged into a track current data set. The median of the data in the track current data set is selected, and the workpiece data and shot material data in the project at the time point corresponding to the median are used to calculate the influence coefficient. The workpiece data and shot material data in the current project are used to calculate the influence coefficient. The track current threshold is set according to the influence coefficient, that is, the track current threshold is set according to the influence of the track on the blasting head. The setting formula can be: where y new is the track current threshold, k 2 Calculate the influence coefficient, k, for the workpiece data and shot data in the current project 1 Calculate the influence coefficient for the workpiece data and shot material data in the project at the median corresponding time point, y old is the track current at the corresponding time point.

[0056] The current threshold of the throwing head can be set to the difference between the total charge in the project and the charge required for the set track current, divided by the number of throwing heads.

[0057] When the shot blasting parameter module determines that the influence of the crawler current on the shot blasting head is low or high, the adaptive exploration-utilization strategy is used to dynamically adjust the current thresholds of the crawler and shot blasting head. The specific steps are as follows:

[0058] Set the exploration probability: Set the exploration probability mark to t, set the exploration probability occurrence time point during the shot blasting machine operation, and there is a 1-t probability of selecting the current optimal action at the exploration probability occurrence time point. That is, when the track current of the shot blasting machine changes slightly due to instability during operation, the shot blasting effect will be evaluated at the exploration probability occurrence time point. If the shot blasting effect is better than the effect before the track current changes, the initial track current threshold will be adjusted according to the slight change difference. For example, if the track current is reduced by 2A, the initial track current threshold will be reduced by 2A as the adjusted track current threshold.

[0059] Set the probability frequency: Setting the probability frequency means setting multiple exploration probability time points during the operation of the shot blasting machine. When the track current changes, an exploration behavior occurs according to the probability frequency. For example, if the probability frequency is set to 0.5 seconds, there is a probability of selection every 0.5 seconds.

[0060] Set the exploration limit: The current threshold of the shot blasting head is dynamically adjusted in the same way. The exploration limit is set so that the sum of the track current threshold and the shot blasting head current threshold is lower than the total input current of the shot blasting machine. That is, when the sum of the track current threshold and the shot blasting head current threshold exceeds the total input current of the shot blasting machine, exploration behavior will only occur when the track current or the shot blasting head current decreases.

[0061] It should be noted that the shot blasting parameter module uses the collected data to analyze the influence of the track current on the shot blasting head current, and selects different modes to determine the current thresholds of the track and the shot blasting head according to the influence, so that the shot blasting machine can work after reaching the current threshold, thereby improving its working stability. The adaptive exploration-utilization strategy is a method of making decisions in an uncertain environment. It attempts to find a balance between exploring new possibilities and utilizing known information. Among them, the exploration probability, probability occurrence frequency, and exploration limit can be adjusted through data analysis and experiments based on actual conditions. The current optimal action is selected, that is, the shot blasting effect is best under the current current. The exploration behavior is the behavior of selecting the current optimal action.

[0062] The shot blasting parameter module can set the shot blasting formula and parameters according to different workpiece models and parameter storage, and call and execute the online workpiece shot blasting formula parameters; the shot blasting parameters mainly include the shot blasting workpiece load, the shot blasting amount of each shot blasting machine, the shot blasting time, etc.; the shot blasting workpiece load is mainly fed back by the crawler running current, the shot blasting amount is mainly adjusted by the shot blasting current feedback of each shot blasting machine, and the shot blasting time is mainly adjusted by the crawler speed feedback. The crawler running current, the shot blasting current of each shot blasting machine, the crawler speed, etc. are the main shot blasting formula parameters. The shot blasting formula can use different crawler current thresholds, each shot blasting machine current threshold, crawler speed frequency, etc. to set different levels of formula parameters, and can also be set through related numerical continuous curves.

[0063] The feedback control module compares the detected real-time current of the crawler with the crawler current threshold, and the real-time current of the throwing head with the throwing head threshold using the following rules:

[0064] Rule 1: If the corresponding real-time current is lower than the corresponding current threshold, the current input is increased and the real-time current is continuously compared with the current threshold;

[0065] Rule 2: When the current input reaches the maximum value and the real-time current still does not reach the current threshold, the corresponding current threshold is adjusted to the current corresponding real-time current;

[0066] Rule 3: When the corresponding real-time current reaches the corresponding current threshold, the crawler motor frequency is adjusted to control the workpiece flow, and the shot flow is adjusted to control the shot flow.

[0067] It should be noted that the input current control rule can be adjusted according to actual conditions, and the above is only an example.

[0068] The feedback control module accesses the engineering database to confirm the shot blasting requirements of the equipment. The shot blasting requirements are divided into two types: high-requirement shot blasting, that is, the shot blasting equipment is not easily worn by the shot, and high-speed shot blasting can be performed; low-requirement shot blasting, that is, the shot blasting equipment is easily worn by the shot, and moderate-speed shot blasting is required.

[0069] After the corresponding real-time current reaches the corresponding current threshold, the feedback control module selects a period of time as the analysis time, and sets multiple time points during the analysis time to randomly adjust the workpiece flow and shot flow for test recording under the condition of meeting the current requirements of the corresponding parts. The corresponding part current requirements are that the real-time current of the track reaches the track current threshold and the real-time current of the blasting head reaches the blasting head current threshold. The recorded workpiece flow is merged into the workpiece flow data set, and the recorded shot flow is merged into the shot flow data set.

[0070] The genetic algorithm is used to find the best match between the workpiece flow data set and the shot flow data set. The specific steps are as follows:

[0071] Coding and initial population: Randomly select a data in the workpiece flow data set and the shot flow data set and encode it into a chromosome. For example, [A, B] represents a randomly selected workpiece flow data A and a randomly selected shot flow data B. Randomly generate N chromosomes as the initial population.

[0072] Fitness evaluation: The shot blasting speed of each chromosome is calculated using the shot blasting speed formula: shot blasting speed = (workpiece flow rate * shot material flow rate) / shot blasting area, where the shot blasting area can be accessed in the engineering database, and the calculated shot blasting speed is used as the fitness value of the corresponding chromosome.

[0073] Selection operation: Use the roulette wheel selection method to select the better chromosome individuals to enter the next generation population. The roulette wheel selection is to sum the fitness values ​​of all chromosomes to obtain the total fitness value, and take the result of the calculation of the fitness value of each chromosome and the total fitness value as the probability of the corresponding chromosome being selected. The chromosomes are screened by randomly surviving a value between 0 and 1 as the rand value, that is, if the probability of the first chromosome being selected among all chromosomes exceeds the rand value, the corresponding chromosome will be used as the next generation parent individual, and multiple next generation parent individuals will be obtained by repeating it multiple times.

[0074] Crossover operation: Randomly select two chromosome individuals, exchange their position genes, and generate new individuals. For example, if the two chromosomes are [A1, B1] and [A2, B2], their B genes can be exchanged to obtain [A1, B2] and [A2, B1].

[0075] Mutation operation: Randomly select chromosome individuals and set the probability p to mutate the gene. For example, set the probability p to 0.05, that is, each offspring has a 0.05 probability of gene mutation. There is a chromosome [A3, B3], and this chromosome mutates to [A3, B4]. Set the mutation range for the mutated B gene. The mutation range can be set to a random survival value between the maximum data and the minimum data in the shot flow data set.

[0076] Iteration and termination conditions: Set the termination conditions according to the shot blasting requirements of the equipment. Two termination conditions can be set:

[0077] Condition 1: The fitness value of each iterated chromosome is calculated and compared with the preset shot-peening requirement threshold. The iteration is stopped when the preset shot-peening requirement threshold is reached.

[0078] Condition 2: Set the number of iterations to N, and stop iterating when the number of iterations exceeds N.

[0079] It should be noted that the number of iterations can be set according to actual conditions. For example, the number of iterations is set to N=100 times. The shot blasting requirements of the equipment are set by professionals in this field and will not be described here.

[0080] The feedback control module determines the shot blasting effect through the termination conditions. The specific steps are as follows:

[0081] When the iteration is stopped with condition 1, it is judged that the shot blasting effect is achieved, and the A and B genes of the corresponding chromosomes are retained, that is, the workpiece flow rate and shot material flow rate corresponding to the A and B genes are retained, and shot blasting is continued with the workpiece flow rate and shot material flow rate corresponding to the A and B genes until the workpiece model is changed.

[0082] When the iteration is stopped under condition 2, it is judged that the shot blasting effect has not been achieved, and the shot material ratio is adjusted to recalculate the shot material surface smoothness.

[0083] The feedback control module executes the shot blasting process according to the shot blasting formula parameters, and manually or automatically corrects the shot blasting formula parameters according to the shot blasting results; during shot blasting, the crawler current is detected according to different workpieces to reflect the load of the shot blasting workpiece; the current of each blasting head is detected and adjusted through the shot valve, and the current of each blasting head is controlled according to the formula parameter requirements; the crawler speed frequency is adjusted according to the formula parameters; after shot blasting, the shot blasting formula parameters are manually or automatically fed back and corrected by outputting information such as the number of workpieces and the shot blasting quality, combined with the online workpiece information entering the shot blasting machine.

[0084] It should be noted that the feedback control module matches the shot flow rate and the workpiece flow rate, determines the shot blasting effect, and adjusts the shot parameters according to the shot blasting effect. In this example, if the shot blasting machine does not achieve the shot blasting effect, the proportion of high-smoothness shot in the shot formula can be increased to adjust the surface smoothness of the shot and improve the shot blasting efficiency until the shot blasting effect is achieved.

[0085] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0086] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0087] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0088] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0089] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent shot blasting machine shot blasting control system, characterized in that: include: Workpiece parameter module, production process module, shot blasting parameter module and feedback control module, and signal connection between each module; The workpiece parameter module is used to determine the workpiece model and send the workpiece model to the production process module; The production process module is used to receive the workpiece model, collect the workpiece data and shot material data according to the workpiece model and transfer them to the shot blasting parameter module; The shot blasting parameter module receives workpiece data and shot material data, analyzes the influence of the crawler current on the shot blasting head, selects different current threshold adjustment methods for the crawler and shot blasting head according to the influence, and transmits the current threshold to the feedback control module after adjustment; The feedback control module is used to determine the shot blasting requirements of the shot blasting equipment, detect the real-time current of the crawler and the blasting head and compare it with the received crawler current threshold and blasting head current threshold, adjust the workpiece flow and shot flow and find the best flow match, and compare the expected shot blasting effect to determine whether to adjust the shot formula.

2. According to claim 1, the intelligent shot blasting machine shot blasting control system is characterized in that: The workpiece parameter module recognizes the workpiece model through the HMI human-machine interface, PLC network transmission or machine vision, and is used to input, store the workpiece model and workpiece data and call them to other modules.

3. According to claim 1, the intelligent shot blasting machine shot blasting control system is characterized in that: The workpiece data includes the number of shot blasting heads of the shot blasting machine and the crawler shot blasting rate; the shot material data includes the shot material formula and the smoothness of each shot material in the formula.

4. An intelligent shot blasting control system according to any one of claims 1 or 3, characterized in that: The shot blasting parameter module receives workpiece data and shot material data, and uses the KNN algorithm to analyze the influence of the track current on the shot blasting head based on the workpiece data and the shot material data. If the influence is low, the track current threshold is adjusted and determined, and then the shot blasting head current threshold is adjusted; if the influence is high, the adaptive exploration-utilization strategy is used to adjust the current thresholds of the track and the shot blasting head at the same time.

5. An intelligent shot blasting control system according to any one of claims 1 or 4, characterized in that: The specific steps of the KNN algorithm in the shot blasting parameter module to analyze the influence of track current on the shot blasting head are as follows: Set feature group: The shot blasting parameter module uses the workpiece data and shot material data in multiple historical projects as feature groups to calculate the influence coefficient; Similarity screening: set similarity thresholds to classify feature groups into similarities; Classification judgment: Use the workpiece data and shot material data in the project to calculate the influence coefficient and classify it, and judge the influence of crawler current on the blasting head according to the category.

6. According to claim 4, the intelligent shot blasting machine shot blasting control system is characterized in that: The shot blasting parameter module uses an adaptive exploration-utilization strategy to adjust the current threshold of the crawler and the shot blasting head at the same time. The specific steps are as follows: Set the exploration probability: When the current of the crawler or shot blasting head changes in real time, the current change during the current change period will be compared according to the exploration probability, and the current with the best shot blasting effect will be selected; Set the probability frequency: When the track current changes, explore according to the probability frequency; set the exploration limit to stop the exploration behavior.

7. The intelligent shot blasting control system of claim 1 is characterized in that: The comparison rules of the real-time current of the crawler and the throwing head with the crawler current threshold and the throwing head current threshold in the feedback control module are as follows: Rule 1: If the real-time current is lower than the current threshold, increase the current input and continue to compare the real-time current with the current threshold; Rule 2: When the current input reaches the maximum value and the real-time current still does not reach the current threshold, the current threshold is adjusted to the real-time current; Rule 3: When the real-time current reaches the current threshold, the crawler motor frequency is adjusted to control the workpiece flow, and the shot flow is adjusted to control the shot flow.

8. An intelligent shot blasting control system according to any one of claims 1 or 7, characterized in that: When the real-time current reaches the current threshold, the feedback control module randomly adjusts the workpiece flow rate and the shot flow rate and records them at different time points to merge them into the workpiece flow data set and the shot flow data set, and uses the genetic algorithm to find the best flow match. The specific steps are as follows: Coding and initial population: randomly select a data from the workpiece flow data set and the shot flow data set and encode it into a chromosome; the chromosome is a data combination package randomly selected from the workpiece flow data set and the shot flow data set; Fitness evaluation: The fitness value of each chromosome is calculated by using the shot blasting speed formula to integrate the data in the chromosome; Selection operation: Use the roulette wheel selection method to select offspring as new parents; Crossover operation: randomly exchange data in chromosomes of different parents; Mutation operation: randomly select data of different offspring chromosomes for adjustment; Iteration and termination conditions: Set the termination conditions according to the shot blasting requirements of the equipment to obtain the best flow matching combination.

9. An intelligent shot blasting control system according to any one of claims 1 or 8, characterized in that: The feedback control module sets different termination conditions when searching for the best flow rate match using the genetic algorithm, and determines whether to adjust the shot formula according to the termination conditions.

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