An intelligent shot blasting control system for shot blasting machines

Abstract

The present invention discloses an intelligent shot blasting control system for a shot blasting machine, which relates to the field of current control technology and is used to improve problems such as poor shot blasting effects caused by constant drum rolling frequency and shot material discharge speed. The control system comprises providing multiple methods to determine a workpiece model, collecting workpiece data and shot material data, and analyzing the influence of a track current on a blasting head using a KNN algorithm based on the workpiece data and the shot material data. If the influence is high, an adaptive exploration-utilization strategy is used to dynamically adjust the current thresholds of the track and the blasting head simultaneously. The current threshold is used to detect whether the current reaches a set value. If the current reaches the set value, the shot material flow rate and the workpiece flow rate are debugged. The optimal flow rate match is found according to a genetic algorithm and the shot blasting effect is detected. If the shot blasting effect is good, the shot blasting is continued. If the shot blasting effect does not meet expectations, the shot material data is adjusted to improve the shot material formula, thereby solving problems such as poor shot blasting effects caused by constant rolling frequency and discharge speed, improving shot blasting efficiency, and improving shot blasting effects.

Description

Technical Field

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

[0002] With the advancement of online shot blasting technology, its scope of application has gradually expanded. Online shot blasting technology first places higher demands on the reliability and durability of shot blasting equipment. It also faces the challenge of varying workpiece types and quantities on the production line. Previously, a fixed, large shot blasting volume that met maximum production output was commonly used to meet production requirements, but this also resulted in significant shot consumption, wasted electricity, and increased equipment damage. Based on a 25,000-ton annual casting production line, using this patented intelligent shot blasting control system, shot consumption can be less than 5 kg per ton of casting, saving 1.25 million yuan in shot blasting costs per year compared to conventional shot blasting control systems. Electricity and equipment maintenance costs are significantly reduced, and equipment durability is significantly improved. In 2023, my country's annual casting production was expected to reach 51.9 million tons, accounting for over 60% of the global total. The promotion and application of this system presents enormous market opportunities.

[0003] The existing technology has the following deficiencies:

[0004] In the past, when shot blasting, the drum rolling frequency and shot discharging speed were constant, which would lead to problems such as wastage of shot according to actual conditions or poor shot blasting effect. Summary of the Invention

[0005] In order to overcome the above-mentioned 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-mentioned background technology by analyzing the influence of the local current 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 machine shot blasting control system 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 pass them into the shot blasting parameter module;

[0010] The shot blasting parameter module receives workpiece data and shot material data, analyzes the impact 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 impact, 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 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 recognition of 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 crawler 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 degree of 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 threshold of the track and the shot blasting head at the same time.

[0015] In a preferred embodiment, the KNN algorithm in the shot blasting parameter module analyzes the influence of the crawler current on the shot blasting head in the following specific steps:

[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 similar categories;

[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 the crawler current on the blasting head according to the category.

[0019] In a preferred embodiment, the shot blasting parameter module uses an adaptive exploration-exploitation 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 based on the exploration probability, and the current with the best shot blasting effect will be selected;

[0021] Set the probability occurrence frequency: When the track current changes, exploration is performed based on the probability occurrence 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: If the current input reaches the maximum value but the real-time current has not yet reached 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 blast head 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, records them at different time points and merges them into a workpiece flow data set and a shot flow data set, and uses a genetic algorithm to find the optimal flow rate 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 to 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 children as new parents;

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

[0031] Mutation operation: randomly select data from 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 optimal flow rate match using the genetic algorithm, and determines whether to adjust the shot formulation based on the termination conditions.

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

[0035] The present invention prioritizes determining the workpiece model, collects workpiece data and shot material data, comprehensively analyzes the influence of the shot blasting machine crawler current on the shot blasting head current with the workpiece data and 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 blasting head shot material flow and the crawler motor frequency after reaching the set value, thereby controlling the shot material flow and the workpiece flow, and checks the shot blasting effect according to the workpiece flow matching the appropriate shot material flow. If the preset expected effect is not achieved, the shot material data is adjusted. If the preset expected effect is achieved, the shot blasting is continued until the workpiece model is replaced, thereby improving the shot blasting effect and improving the shot blasting efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention is a schematic diagram of the control structure 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0039] The present invention prioritizes determining the workpiece model, collects workpiece data and shot material data, comprehensively analyzes the influence of the shot blasting machine crawler current on the shot blasting head current with the workpiece data and 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 blasting head shot material flow and the crawler motor frequency after reaching the set value, thereby controlling the shot material flow and the workpiece flow, and checks the shot blasting effect according to the workpiece flow matching the appropriate shot material flow. If the preset expected effect is not achieved, the shot material data is adjusted. If the preset expected effect is achieved, the shot blasting is continued until the workpiece model is replaced, thereby improving the shot blasting effect and improving 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 the modules are signal connected.

[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 of the shot blasting machine, such as shaping, pouring, cooling, and conveying, 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 by 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 pass them into 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 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 blasting head and compare it with the received crawler current threshold and 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 shot flow. The feedback control module uses a genetic algorithm to best match the workpiece flow and 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 will pass the collected data to the subsequent module for analysis and processing. The workpiece parameter module determines the workpiece model in the existing technology. 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 crawler by the blasting head, and the lower the influence of the crawler current on the blasting head; the more blasting heads there are, the greater the current that needs to be allocated to the blasting head, and the more easily it is affected by the crawler current; the faster the crawler shot blasting rate, the faster the engine speed, and the higher the crawler current is required to maintain stability, and the greater the influence of the crawler 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 workstations for quality control and traceability purposes; among them, the information such as the input and output quantity of the shot blasting workpieces and the shot blasting quality can be introduced by the feedback control module to automatically correct the shot blasting formula parameters.

[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 sets 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 blasting 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 result after the normalization of the number of shots, b is the result after the normalization of the crawler shot blasting rate, and c is the result after the normalization 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 it 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 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 surface smoothness of the shot.

[0052] The influence coefficients calculated for each feature group are screened for similarity, and a similarity threshold is set based on the influence coefficients to classify each feature group into similar categories. The similarity threshold can be set using the percentile method, arranging the influence coefficients from smallest to largest to form an influence coefficient dataset. N% is set as the screening condition, and a similarity threshold is set for the influence coefficient dataset using this N% screening condition. For example, if 40% is used as the screening condition, the influence coefficients exceeding 40% of the data in the influence coefficient dataset are used as the similarity threshold. The influence coefficient calculated for each feature group is compared with the similarity threshold. If the influence coefficient exceeds the similarity threshold, the feature group is marked as a high-impact feature group; if the influence coefficient is below the similarity threshold, the feature group is marked as a low-impact feature group.

[0053] By collecting the workpiece data and shot material data in the current project, the influence coefficient is calculated and marked. If the workpiece data and shot material data in the current project are marked as the 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 the 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 they are in line with 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 that uses similarity to classify the prediction results. It 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 influence coefficient is calculated using the workpiece data and shot material data in the project at the time point corresponding to the median. The influence coefficient is then calculated using the workpiece data and shot material data in the current project. The track current threshold is set according to the influence coefficient, that is, the track current threshold is set according to the influence degree of the track on the blasting head. The setting formula can be: where y new is the track current threshold, k2 is the influence coefficient of workpiece data and shot material data calculation in the current project, k1 is the influence coefficient of workpiece data and shot material data calculation 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 is set to the difference between the total charge in the project and the charge required by the set track current, divided by the number of throwing heads.

[0057] When the shot blasting parameter module determines whether the track current has a high or low impact on the shot blasting head, it uses an adaptive exploration-utilization strategy to dynamically adjust the current thresholds of the track 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 when the shot blasting machine is working, 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 occurrence time points during the shot blasting machine operation. 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 to the sum of the track current threshold and the shot blasting head current threshold being 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 crawler current on the blasting head current, and selects different modes to determine the current thresholds of the crawler and 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 shot blasting formulas and parameters according to different workpiece models and parameter storage, and call and execute online workpiece shot blasting formula parameters; shot blasting parameters mainly include shot blasting workpiece load, shot blasting amount of each shot blasting machine, 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 feedback of the shot blasting current of each shot blasting machine, and the shot blasting time is mainly adjusted by the feedback of the crawler speed. 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 be set with different levels of formula parameters using different crawler current thresholds, each shot blasting machine current threshold, crawler speed frequency, etc., or it can be set through a continuous curve of related values.

[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, increase the current input and continue to compare the real-time current with the current threshold;

[0065] Rule 2: If the current input reaches the maximum value but the real-time current has not yet reached the current threshold, the corresponding current threshold will be adjusted to the current 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 blasting head 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. Shot blasting requirements are generally 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. During the analysis time, multiple time points are set to randomly adjust the workpiece flow and shot flow under the condition of meeting the current requirements of the corresponding parts for test recording. 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 throwing head reaches the throwing 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 point from each of 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 point A and a randomly selected shot flow data point 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. The shot blasting area can be accessed in the engineering database. The calculated shot blasting speed is used as the fitness value of the corresponding chromosome.

[0073] Selection: Roulette wheel selection is used to select the best chromosome individuals for the next generation. Roulette wheel selection involves summing the fitness values of all chromosomes to obtain a total fitness value. The quotient of each chromosome's fitness value and the total fitness value is used as the calculated result to determine the probability of selection. Chromosomes are screened by randomly selecting a value between 0 and 1 as the rand value. If the probability of the first chromosome being selected exceeds the rand value, the corresponding chromosome is selected as the parent individual of the next generation. Repeat this process multiple times to obtain multiple parent individuals of the next generation.

[0074] Crossover operation: Randomly select two chromosome individuals, exchange their position genes, and produce 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 surviving 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-blasting requirement threshold. The iteration is stopped when the preset shot-blasting requirement threshold is reached.

[0078] Condition 2: Set the number of iterations to N and stop the iteration 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 in detail here.

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

[0081] When the iteration is stopped by 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 according to condition 2, it is judged that the shot blasting effect has not been achieved, and the shot material ratio is adjusted and the shot material surface smoothness is recalculated.

[0083] The feedback control module executes the shot blasting process according to the shot blasting formula parameters, and manually or automatically modifies 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 modified 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 through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0086] Those skilled 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 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 description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection 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 scope of protection 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 connections between modules; 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 pass them into 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 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. The intelligent shot blasting control system of claim 1, characterized in that: The workpiece parameter module is used to input and store workpiece models and workpiece data and call them to other modules through HMI human-machine interface, PLC network transmission or machine vision to identify the workpiece model.

3. The intelligent shot blasting machine shot blasting control system according to claim 1 is characterized in that: 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.

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-exploitation strategy is used to adjust the current thresholds of both 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 similar categories; 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 the crawler current on the blasting head according to the category.

6. The intelligent shot blasting control system of claim 4, characterized in that: The shot blasting parameter module uses the 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 based on the exploration probability, and the current with the best shot blasting effect will be selected; Set the probability occurrence frequency: When the track current changes, exploration is performed based on the probability occurrence frequency; set the exploration limit to stop the exploration behavior.

7. The intelligent shot blasting control system of claim 1, 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: If the current input reaches the maximum value but the real-time current has not yet reached 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 blast head 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, merging them into a workpiece flow data set and a shot flow data set. The genetic algorithm is used to find the optimal flow match. The specific steps are as follows: Coding and initial population: A data set is randomly selected from each of the workpiece flow data set and the shot flow data set and encoded 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 children as new parents; Crossover operation: randomly exchange data in different parent chromosomes; Mutation operation: randomly select data from 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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