Intelligent air compressor control system and method for laser cutting

Abstract

The invention relates to the field of laser cutting, in particular to an air compressor intelligent control system and method for laser cutting, and the system comprises a cleanliness detection module which is used for detecting the cleanliness of air in real time and feeding detection data back to an AI control device; the environment monitoring module is used for monitoring environment temperature, humidity and atmospheric pressure and feeding back monitoring data to the AI control device; the state monitoring module is used for monitoring the motor temperature, the output air pressure and the flow of the air compressor in real time; the AI control device obtains laser cutting machining parameters input by a user, calculates the optimal working rotating speed and the optimal cutting air pressure of the air compressor in combination with received data, controls operation of the air compressor through the frequency converter according to the optimal working rotating speed and the optimal cutting air pressure, and adjusts the output air pressure of the air compressor through the air pressure adjusting valve. And the air compressor can better adjust the laser equipment to accurately adjust the gas pressure.

Description

Technical Field

[0001] This application relates to the technical field of laser cutting control, and particularly relates to an intelligent control system and method for an air compressor used in laser cutting. Background Art

[0002] With the wide application of laser cutting technology in the industrial manufacturing field, the stability and cutting quality of laser cutting equipment have become key factors affecting production efficiency and product quality. As an important auxiliary equipment for laser cutting equipment, the quality, pressure, and flow rate of the compressed air output by the air compressor directly affect the accuracy and efficiency of laser cutting.

[0003] Traditional air compressor control systems usually adopt simple on-off control or fixed parameter control, and cannot be dynamically adjusted according to actual processing requirements and environmental conditions. This control method has the following deficiencies: The gas quality is unstable, and it is impossible to monitor and control the cleanliness of compressed air in real time, resulting in possible influence on cutting quality due to gas impurities during the cutting process. The air compressor operates in a non-optimal working state, resulting in increased energy consumption and rising operating costs. It is impossible to quickly adjust the working state of the air compressor according to environmental changes, affecting the response speed and processing efficiency of the cutting equipment. In harsh environments such as high temperature and high humidity, the air compressor is prone to damage due to overheating or excessive humidity, lacking an intelligent protection mechanism. Summary of the Invention

[0004] In view of the problem that traditional air compressor control systems usually adopt simple on-off control or fixed parameter control and cannot be dynamically adjusted according to actual processing requirements and environmental conditions, the present invention provides an intelligent control system and method for an air compressor used in laser cutting.

[0005] In a first aspect, the technical solution of the present invention provides an intelligent control system for an air compressor used in laser cutting, including an AI control device, and a cleanliness detection module, an environment monitoring module, and a status monitoring module respectively connected to the AI control device; The cleanliness detection module is used to detect the cleanliness of air in real time and feed the detection data back to the AI control device; The environment monitoring module is used to monitor the values of environmental parameters and feed the monitoring data back to the AI control device; the environmental parameters include environmental temperature, humidity, and atmospheric pressure; The status monitoring module is used to monitor the operating status of the air compressor in real time, including motor temperature, output air pressure, and flow rate; and feed the monitoring data back to the AI control device; The AI control device obtains the values of the laser cutting processing parameters input by the user, and combines the data fed back by the cleanliness monitoring device, the environmental monitoring module, and the status monitoring module to calculate the optimal operating speed and the optimal cutting air pressure of the air compressor. According to the optimal operating speed and the optimal cutting air pressure, the operating state of the air compressor is controlled through a frequency converter, and the output air pressure of the air compressor is adjusted through an air pressure regulating valve.

[0006] Preferably, as a technical solution of the present invention, the cleanliness detection module adopts a gas particle analyzer; The gas particle analyzer is used to detect the quantity and particle size distribution of suspended particles in the air in real time, and the detection data is fed back to the AI control device through the communication line of the gas particle analyzer; The environmental monitoring module includes a temperature sensor, a humidity sensor, and a barometric pressure detector, which are respectively used for monitoring the environmental temperature, humidity, and atmospheric pressure, and transmitting the monitoring data to the AI control device; The status monitoring module includes a motor temperature sensor, an outlet air pressure sensor and an outlet air flow sensor installed on the air outlet pipeline of the air compressor, which are respectively used for monitoring the temperature of the motor, the outlet air pressure, and the outlet air flow in real time, and sending the monitoring data to the AI control device.

[0007] The gas particle analyzer is used to accurately detect the quantity and particle size distribution of suspended particles in the compressed air in real time. When the AI control device determines that the gas cleanliness is unqualified according to the detection data, it timely controls the air compressor to stop. This effectively avoids the damage to the laser cutting equipment caused by unclean compressed air, prolongs the service life of the equipment, reduces the equipment maintenance cost, and ensures the high-quality operation of laser cutting.

[0008] The status monitoring module sets a motor temperature sensor, an outlet air pressure sensor and an outlet air flow sensor of the air compressor to monitor the operating state of the air compressor in real time and comprehensively. It provides accurate data support for the AI control device, so as to timely adjust the operating parameters of the air compressor, ensure the stable operation of the air compressor, and at the same time provide a data basis for the maintenance of the equipment, which helps to discover potential fault hazards in advance.

[0009] Preferably, as a technical solution of the present invention, the system further includes a cooling device, a refrigerated dryer, and a desiccant dryer connected to the AI control device; The AI control device is configured to: During the operation of the air compressor, when the environmental temperature is higher than the set first environmental temperature threshold, control the cooling device to start; When the environmental humidity is higher than the set humidity threshold, control to lower the dew point of the refrigerated dryer and shorten the A-B tower exchange time, the drainage cycle of the gas storage tank, and the drainage cycle of the filter of the desiccant dryer; When the ambient temperature is higher than the first ambient temperature threshold and the temperature of the air compressor motor is higher than the set value, a high-temperature alarm is triggered and the air compressor is controlled to stop.

[0010] When the ambient temperature is too high, the cooling device is started. When the humidity is high, the operating parameters of equipment such as the refrigerant dryer and desiccant dryer are adjusted, and high-temperature alarm and shutdown protection are carried out in extreme cases. This effectively ensures the stable operation of the air compressor under different environmental conditions, improves the environmental adaptability of the equipment, and ensures that the quality of the compressed air is not disturbed by environmental factors.

[0011] As a preference of the technical solution of the present invention, the AI control device is further configured to: When it is determined that the gas particle analyzer detects that the gas cleanliness is unqualified, the air compressor is controlled to stop and an alarm signal is issued.

[0012] As a preference of the technical solution of the present invention, the AI control device is further configured to: When the air compressor is started for the first time, the AI control device receives the temperature, humidity, and atmospheric pressure data collected by the environmental monitoring module, calculates the maximum speed of the air compressor according to the received data, and controls the air compressor to work at the maximum speed to quickly load the pressure of the air storage tank to the rated pressure.

[0013] When the air compressor is started for the first time, the temperature sensor, humidity sensor, and air pressure detector of the environmental monitoring module work together to transmit the collected data to the AI control device for in-depth calculation, obtain the maximum speed of the air compressor and control it to work at this speed, which can quickly load the pressure of the air storage tank to the rated pressure. This process greatly shortens the start-up time of the air compressor, improves the production preparation efficiency, and enables the laser cutting equipment to be put into work faster.

[0014] As a preference of the technical solution of the present invention, the AI control device includes a control panel, an AI algorithm processing module, and a PLC control module; The control panel is connected to the AI algorithm processing module, the AI algorithm processing module is connected to the PLC control module, and the PLC control module is connected to the air compressor through a frequency converter; The user inputs the values of the laser cutting processing parameters to the AI algorithm processing module through the control panel; The AI algorithm processing module is configured to: Obtain the values of the target processing material properties, cutting thickness, cutting speed, nozzle type, and focus position parameters input through the control panel, and calculate the optimal working speed and optimal cutting air pressure of the air compressor in combination with the data fed back by the cleanliness monitoring device, environmental monitoring module, and status monitoring module; The PLC control module is configured to: According to the optimal operating speed and the optimal cutting air pressure, control the operation of the air compressor through a frequency converter, and adjust the output air pressure through an air pressure regulating valve.

[0015] Preferably, as a technical solution of the present invention, the AI algorithm processing module is further configured to: Match in the process database according to the values of the processing parameters input by the user and the values of the monitored environmental parameters; If there is a historical record in the process database that exactly matches the current processing parameters and environmental parameters, directly call the optimal air pressure parameters in this record; If there is no exactly matching record, select the closest reference air pressure from the process database through interpolation or approximation algorithms, and dynamically adjust it in combination with the values of the processing parameters and the environmental parameters to generate the optimal cutting air pressure.

[0016] When performing parameter matching in the process library, different strategies are adopted for the presence or absence of exactly matching records to obtain the optimal cutting air pressure. This flexible algorithm mechanism can make full use of the historical data in the process library, combine the current actual processing parameters and environmental parameters, provide more accurate air pressure parameters for different laser cutting tasks, and improve the stability and consistency of the cutting quality.

[0017] The AI algorithm processing module calculates the optimal operating speed and cutting air pressure according to the processing parameters and environmental parameters input by the user, and the PLC control module accurately controls the operation of the air compressor accordingly. This control architecture with clear division of labor realizes the precise adjustment of the operating parameters of the air compressor, improves the accuracy and response speed of control, and can better meet the strict requirements of laser cutting for the output parameters of the air compressor.

[0018] Preferably, as a technical solution of the present invention, the PLC control module is configured to: During the cutting process, real-time receive the monitoring data of the outlet air pressure sensor; If there is a deviation between the actual air pressure and the optimal cutting air pressure, dynamically adjust the air compressor speed or the opening of the air pressure regulating valve to maintain constant-pressure air supply.

[0019] According to the deviation between the actual air pressure feedback by the outlet air pressure sensor and the optimal cutting air pressure, timely adjust the operating speed of the air compressor. This enables the gas pressure entering the laser head to always remain in the optimal state during the laser cutting process, effectively ensuring the stability of the cutting process, reducing cutting defects caused by air pressure fluctuations, and further improving the quality of laser cutting.

[0020] Secondly, the technical solution of the present invention also provides an intelligent control method for an air compressor used in laser cutting, including: The environmental monitoring module collects the environmental temperature, humidity and atmospheric pressure in real time, the status monitoring module obtains the motor temperature, outlet pressure and flow rate of the air compressor, and the cleanliness detection module detects the cleanliness data of the compressed air in real time; Receive the values of the processing parameters input by the user. The processing parameters include material properties, cutting thickness, cutting speed, nozzle type and focal position; Calculate the optimal operating speed and the optimal cutting air pressure according to the values of the processing parameters, the monitoring data of the environmental monitoring module and the detection data of the cleanliness detection module; Adjust the speed of the air compressor through the frequency converter, and control the output air pressure through the air pressure regulating valve to dynamically match the actual air pressure with the optimal cutting air pressure.

[0021] By periodically collecting various data, calculating the optimal outlet pressure and the speed of the air compressor in combination with the parameters input by the user, and dynamically adjusting the speed of the air compressor motor through the frequency converter to achieve constant pressure air supply. At the same time, compare the feedback information with the target information in real time to dynamically adjust the gas pressure. The entire control process forms a closed loop, which can adjust the operating state of the air compressor in real time and accurately according to the gas consumption requirements of the laser cutting equipment, ensure that the laser cutting equipment always works at the optimal processing air pressure, and greatly improve the efficiency and quality of laser cutting.

[0022] As an optimization of the technical solution of the present invention, the steps of calculating the optimal operating speed and the optimal cutting air pressure according to the values of the processing parameters, the monitoring data of the environmental monitoring module and the detection data of the cleanliness detection module include: Match according to the values of the processing parameters input by the user and the values of the monitored environmental parameters in the process database; If there is a historical record in the process database that completely matches the current processing parameters and environmental parameters, directly call the optimal air pressure parameter in this record; If there is no completely matching record, select the closest reference air pressure from the process database through interpolation or approximation algorithm, and dynamically adjust it in combination with the values of the processing parameters and environmental parameters to generate the optimal cutting air pressure.

[0023] As an optimization of the technical solution of the present invention, the method further includes: When the temperature sensor detects that the environmental temperature is higher than the set first environmental temperature threshold, the AI control device controls the cooling device to start; when the environmental humidity is higher than the set humidity threshold, the AI control device controls to lower the dew point of the refrigerant dryer, control to shorten the A-B tower exchange time of the desiccant dryer, shorten the drainage cycle of the automatic drain valve at the bottom of the air storage tank, shorten the action cycle of the solenoid valve at the bottom of the filter and increase the drainage duration each time; when the environmental temperature is higher than the first environmental temperature threshold and the temperature of the air compressor motor is higher than the set value, the AI control device gives a high temperature alarm and performs air compressor shutdown protection; When the gas cleanliness is judged to be unqualified according to the monitoring data of the gas particle analyzer, the air compressor is controlled to stop.

[0024] The control strategies and protection mechanisms set for factors such as environmental temperature, humidity, and gas cleanliness further improve the entire intelligent control system. While ensuring the stable operation of the air compressor and providing high-quality compressed air, it also ensures the safe and reliable operation of the equipment under various complex environmental conditions, improving the overall stability and reliability of the system.

[0025] As can be seen from the above technical solutions, the present application has the following advantages: By setting up an AI control device to integrate the data of the cleanliness detection module, environmental monitoring module, and air compressor status monitoring module, and combining the laser cutting processing parameters input by the user, intelligent start-stop control of the air compressor and dynamic adjustment of operating parameters are realized. This integrated and intelligent control method can accurately match the real-time requirements of compressed air pressure and flow during the laser cutting process, greatly improving the stability and quality of laser cutting, reducing manual intervention, and enhancing production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the present application, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a block diagram of the system according to the embodiment of the present invention.

[0028] Figure 2 It is a system structure diagram according to the embodiment of the present invention.

[0029] Figure 3 It is a schematic diagram of the position of the environmental monitoring module in the embodiment of the present invention.

[0030] Figure 4 It is a schematic flow chart of the method provided by an embodiment of the present invention.

[0031] Figure 5 It is a flow chart of the method provided by another embodiment of the present invention.

[0032] Among them, 1 - AI control device, 2 - frequency converter, 3 - outlet pipeline, 4 - communication line of outlet air pressure sensor, 5 - outlet air pressure sensor, 6 - gas particle analyzer, 7 - communication line of gas particle analyzer, 8 - environmental monitoring module, 9 - temperature sensor, 10 - humidity sensor, 11 - air pressure detector. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] To make the application purpose, features, and advantages of this application more obvious and understandable, specific embodiments and accompanying drawings will be used below to clearly and completely describe the technical solutions protected by this application. Obviously, the embodiments described below are only a part of the embodiments of this application, rather than all embodiments. Based on the embodiments in this patent, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this patent.

[0034] As Figure 1 shown, an intelligent control system for an air compressor for laser cutting provided by an embodiment of the present invention includes an AI control device and a cleanliness detection module, an environmental monitoring module, and a status monitoring module that are respectively connected to the AI control device; The cleanliness detection module is used to detect the cleanliness of the air in real time and feed back the detection data to the AI control device; The environmental monitoring module is used to monitor the environmental temperature, humidity, and atmospheric pressure, and feed back the monitoring data to the AI control device; The status monitoring module is used to monitor the motor temperature, output air pressure, and flow rate of the air compressor in real time; The AI control device obtains the laser cutting processing parameters input by the user, combines the data sent by the cleanliness monitoring device, the environmental monitoring module, and the status monitoring module, calculates the optimal operating speed and the optimal cutting air pressure of the air compressor, and controls the operation of the air compressor through a frequency converter according to the optimal operating speed and the optimal cutting air pressure, and adjusts the output air pressure of the air compressor through a pneumatic regulating valve.

[0035] As Figure 2 、 Figure 3 shown, in an embodiment of the present invention, the cleanliness detection module adopts a gas particle analyzer 6; The gas particle analyzer 6 detects the number and particle size distribution of suspended particles in the air in real time, and feeds back the detection data to the AI control device 1 through the gas particle analyzer communication line 7; The environmental monitoring module 8 includes a temperature sensor 9, a humidity sensor 10, and a barometric pressure detector 11, which are respectively used to monitor the environmental temperature, humidity, and atmospheric pressure, and transmit the monitoring data to the AI control device; The barometric pressure detector 11 detects the atmospheric environmental pressure in real time, and the AI control device reads the atmospheric pressure data and theoretically calculates the real-time air density to adjust the speed of the air compressor.

[0036] The calculation formula is as follows:

[0037] ρ represents the environmental atmospheric density (kg / m³); P 1 represents the actual atmospheric pressure (Mpa); P 2 represents the standard atmospheric pressure (Mpa); T represents the standard ambient temperature.

[0038] If the calculated density of the air compressor is lower than 0.8 kg / m³, the load is too light, and the frequency converter overclocks by 15% to increase the motor speed. Even when the speed of a traditional air compressor is at its maximum, due to the overly thin air density, the maximum operating power of the whole machine may be half of the rated power.

[0039] The status monitoring module includes a motor temperature sensor, an outlet air pressure sensor 5 installed on the outlet air pipeline 3 of the air compressor, and an outlet air flow sensor, which are respectively used to monitor the temperature of the motor, the outlet air pressure, and the outlet air flow in real time, and send the monitoring data to the AI control device.

[0040] In the embodiment of the present invention, a gas particle analyzer is used to accurately detect the number and particle size distribution of suspended particles in the compressed air in real time. When the AI control device determines that the gas cleanliness is unqualified according to the detection data, it timely controls the air compressor to stop. This effectively avoids the damage to the laser cutting equipment caused by unclean compressed air, extends the service life of the equipment, reduces the equipment maintenance cost, and ensures the high-quality operation of laser cutting.

[0041] Generally, if it is read that the dust particle size in the gas > 0.01 μm and the oil content > 0.003 ppm, the air compressor directly stops. Here, an infrared spectrum analysis or a capacitive humidity sensor is used to monitor the oil residue and humidity level (such as the dew point value) in the compressed air.

[0042] The status monitoring module comprehensively monitors the operating status of the air compressor in real time by setting a motor temperature sensor, an outlet air pressure sensor, and an outlet air flow sensor of the air compressor. It provides accurate data support for the AI control device to timely adjust the operating parameters of the air compressor, ensure the stable operation of the air compressor, and at the same time provides a data basis for the maintenance of the equipment, which helps to discover potential fault hazards in advance.

[0043] In the embodiment of the present invention, the system further includes a cooling device, a refrigerated dryer, and a desiccant dryer connected to the AI control device; The AI control device is configured as: During the operation of the air compressor, when the temperature sensor detects that the ambient temperature is higher than the set first ambient temperature threshold, it controls the cooling device to start; When the ambient humidity is higher than the set humidity threshold, it controls to lower the dew point of the refrigerated dryer and shorten the A-B tower exchange time of the desiccant dryer, the drainage cycle of the air storage tank, and the drainage cycle of the filter; When the ambient temperature is higher than the first ambient temperature threshold and the temperature of the air compressor motor is higher than the set value, a high-temperature alarm is triggered and the air compressor is controlled to stop. When the ambient temperature is too high, the cooling device is started. When the humidity is high, the operating parameters of equipment such as the refrigerated dryer and the adsorption dryer are adjusted, and high-temperature alarm and shutdown protection are carried out in extreme cases. This effectively ensures the stable operation of the air compressor under different environmental conditions, improves the environmental adaptability of the equipment, and ensures that the quality of the compressed air is not interfered by environmental factors.

[0044] The AI control device is also configured to: When the gas particle analyzer detects that the gas cleanliness does not meet the standard, the air compressor is controlled to stop and an alarm signal is issued.

[0045] When the air compressor is started for the first time, the environmental monitoring module is received to collect temperature, humidity, and atmospheric pressure data. The maximum rotational speed of the air compressor is calculated based on the received data, and the air compressor is controlled to operate at the maximum rotational speed so that the air compressor quickly loads the pressure of the air storage tank to the rated pressure.

[0046] In some embodiments, the AI control device includes a control panel, an AI algorithm processing module, and a PLC control module; The control panel is connected to the AI algorithm processing module, the AI algorithm processing module is connected to the PLC control module, and the PLC control module is connected to the air compressor through a frequency converter; The user inputs laser cutting processing parameters to the AI algorithm processing module through the control panel; The AI algorithm processing module is configured to: Obtain the target processing material properties, cutting thickness, cutting speed, nozzle type, and focus position parameters input through the control panel, combine the monitoring data of the environmental monitoring module, the gas cleanliness data, and the air compressor status data, and calculate the optimal operating speed and the optimal cutting air pressure of the air compressor; The PLC control module is configured to: According to the optimal operating speed and the optimal cutting air pressure, control the operation of the air compressor through the frequency converter, and adjust the output air pressure through the air pressure regulating valve.

[0047] The AI algorithm processing module is also configured to: Match according to the processing parameters and environmental parameters input by the user in the process database; If there is a historical record in the process database that exactly matches the current processing parameters and environmental parameters, directly call the optimal air pressure parameters in this record; If there is no exactly matching record, select the closest reference air pressure from the process database through interpolation or approximation algorithms, and perform dynamic adjustment in combination with the processing parameters and environmental parameters to generate the optimal cutting air pressure.

[0048] When performing parameter matching in the process library, different strategies are adopted for the presence or absence of fully matching records to obtain the optimal cutting air pressure. This flexible algorithm mechanism can make full use of the historical data in the process library, combined with the current actual processing parameters and environmental parameters, to provide more accurate air pressure parameters for different laser cutting tasks, improving the stability and consistency of cutting quality.

[0049] The AI algorithm processing module calculates the optimal working speed and cutting air pressure according to the processing parameters and environmental parameters input by the user, and the PLC control module accurately controls the operation of the air compressor accordingly. This control architecture with clear division of labor realizes the precise adjustment of the operating parameters of the air compressor, improves the accuracy and response speed of control, and can better meet the strict requirements of laser cutting for the output parameters of the air compressor.

[0050] The PLC control module is configured to: During the cutting process, it receives the monitoring data of the outlet air pressure sensor in real time; If there is a deviation between the actual air pressure and the optimal cutting air pressure, it dynamically adjusts the speed of the air compressor or the opening of the air pressure regulating valve to maintain constant pressure air supply.

[0051] According to the deviation between the actual air pressure feedback by the outlet air pressure sensor and the optimal cutting air pressure, it timely adjusts the working speed of the air compressor. This enables the gas pressure entering the laser head to always remain in the optimal state during the laser cutting process, effectively ensuring the stability of the cutting process, reducing cutting defects caused by air pressure fluctuations, and further improving the quality of laser cutting.

[0052] The specific working process is as follows: First, the gas pressure in the initial state of the gas source power system is 0 Mpa. The air compressor is started with one key through the AI control device. The environmental monitoring module quickly collects the temperature, humidity, and atmospheric pressure, and calculates the maximum speed of the air compressor through the AI control device, enabling the air compressor to quickly load the pressure of the air storage tank to the rated pressure.

[0053] The cleanliness of high-pressure gas is crucial for high-power laser equipment. If the air cleanliness is detected to be unqualified, the lower protective mirror of the laser head will be ablated, contaminating the entire cavity of the laser head. When the gas cleanliness is detected to be unqualified, the air compressor is shut down for inspection and processing.

[0054] By inputting the material properties, cutting thickness, cutting speed, nozzle type, and focal position through the control panel, the optimal outlet air pressure is calculated. At the same time, according to the actual air pressure feedback inside the laser head, the outlet air pressure of the air compressor is adjusted in real time, and a pressure curve is plotted to obtain the optimal speed. When the air compressor continuously generates gas, the speed of the air compressor is adjusted to make the air output equal to the air consumption, achieving the final balance.

[0055] The air compressor controlled by the AI control device can achieve remote monitoring, and the status of each air compressor can be read in real time on the mobile terminal, including data such as the temperature of the equipment, real-time pressure, real-time flow rate, motor speed, and frequency. The system can monitor the maintenance timeliness of each air compressor in real time and can selectively remotely turn off and turn on the equipment.

[0056] During the laser cutting process, if it is necessary to pause the processing, the load air compressor / screw stops running. When the customer reselects the cutting thickness, the AI control device recalculates the speed of the air compressor. After the user continues the processing, the air production volume is made equal to the gas consumption volume to reach a new stable state. If a pause in processing occurs during the laser cutting process, the AI control device stops the air compressor to reduce the air production volume. If a speed reduction phenomenon occurs during the processing, the air compressor supplies gas at the original air production volume.

[0057] After the laser processing is completed, the AI control device can control the air compressor to achieve one-key shutdown.

[0058] The operator will input parameters such as the target processing material properties (such as material hardness, material, etc.), cutting thickness, cutting speed, nozzle type, and focus position on the control panel of the system. Different material properties and cutting requirements have very different demands for air pressure and speed. Through the environmental monitoring module, data such as environmental temperature, humidity, and atmospheric pressure are obtained in real time. The status monitoring module monitors the status data such as the motor temperature, output air pressure, and flow rate of the air compressor in real time. The cleanliness detection module detects the cleanliness of the air. If the cleanliness does not meet the standard, it may affect the cutting quality and equipment life, and thus affect the calculation of the optimal parameters.

[0059] The AI algorithm processing module receives the above data and performs integration and analysis. According to the processing parameters and environmental parameters input by the user, it is matched in the process database. If there is a historical record in the process database that exactly matches the current processing parameters and environmental parameters, the best air pressure parameters in this record are directly called. These historical records are empirical data accumulated in actual production and have high reliability. If there is no exactly matching record, the closest reference air pressure is selected from the process database through interpolation or approximation algorithms. For example, when the current cutting thickness and environmental temperature are between two records in the database, the interpolation algorithm is used to calculate a relatively reasonable reference air pressure. Then, it is dynamically adjusted in combination with the current processing parameters and environmental parameters to generate the best cutting air pressure.

[0060] The AI algorithm processing module uses a pre-trained model and combines the optimal cutting air pressure calculated above to calculate the optimal operating speed of the air compressor. This model is constructed based on a neural network and trained with a large amount of experimental data and actual production cases, enabling it to learn the complex relationship between different parameter combinations and the optimal operating speed. An optimal speed that can both meet the cutting requirements and ensure the efficient and stable operation of the equipment is comprehensively calculated.

[0061] During the cutting process, the PLC control module receives the monitoring data from the outlet air pressure sensor in real time and compares the actual air pressure with the calculated optimal cutting air pressure. If there is a deviation between the actual air pressure and the optimal cutting air pressure, the PLC control module will dynamically adjust the speed of the air compressor or the opening degree of the air pressure regulating valve to maintain constant pressure air supply. For example, when the actual air pressure is lower than the optimal air pressure, the speed of the air compressor is appropriately increased or the opening degree of the air pressure regulating valve is increased; conversely, the speed is decreased or the valve opening degree is decreased. At the same time, these real-time adjustment data will be fed back to the AI algorithm processing module for further optimizing the model and subsequent parameter calculations, enabling the system to continuously adapt to different working conditions and improve the cutting quality and efficiency.

[0062] In the embodiment of the present invention, the specific process of calculating the optimal speed through constructing a model by a neural network is as follows: Collect a large amount of data related to the operation of the air compressor and laser cutting, including processing parameters input by users (target processing material properties, cutting thickness, cutting speed, nozzle type, focal position), environmental parameters (ambient temperature, humidity, atmospheric pressure), equipment status data (motor temperature, output air pressure, flow rate), and the corresponding optimal cutting air pressure and optimal operating speed. This data can be obtained from channels such as historical records in the actual production process and experimental test results.

[0063] Clean the collected data to remove outliers, duplicate data, and incorrect data. For example, if the cutting speed in a certain data point significantly exceeds the reasonable range, it is regarded as an outlier and removed.

[0064] Perform standardization processing on the cleaned data to make the data of different features have the same scale, avoiding affecting the model training effect due to too large differences in feature scales. Divide the standardized data into a training set, a validation set, and a test set, generally in the ratio of 70%, 15%, 15%. The training set is used for the model to learn the rules in the data, the validation set is used to adjust the model hyperparameters and evaluate the model performance, and the test set is used to finally evaluate the generalization ability of the model.

[0065] Construct a multi-layer perceptron (MLP) neural network model. The number of neurons in the input layer is the same as the number of input features. For example, if the input features include processing parameters, environmental parameters, equipment status data, and the optimal cutting air pressure, a total ofn For each feature, the input layer has n neurons. The output layer has 1 neuron for outputting the optimal operating speed. The number of hidden layers and neurons can be adjusted according to the actual situation. Generally, 2 - 3 hidden layers can be tried first, and the number of neurons in each hidden layer can be set to 1 - 2 times the number of neurons in the input layer. The ReLU (Rectified Linear Unit) activation function is used in the hidden layer, and its formula is f ( x ) = max(0, x ), which can increase the non - linear expression ability of the model. No activation function is used in the output layer because the optimal operating speed is a continuous value, belonging to a regression problem.

[0066] Select the mean squared error (MSE) as the loss function, and the formula is , where is the true optimal operating speed, is the optimal operating speed predicted by the model, and N is the number of samples.

[0067] Use the Adam optimization algorithm to update the parameters of the model to minimize the loss function.

[0068] Input the training set data into the model, calculate the predicted values of the model through forward propagation, then calculate the loss between the predicted values and the true values according to the loss function, calculate the gradient through the backpropagation algorithm, and finally use the Adam optimization algorithm to update the parameters of the model. Repeat this process multiple times until the loss function converges or reaches the preset number of training epochs.

[0069] Use the validation set data to evaluate the trained model, and use the test set data to finally verify the tuned model to ensure that the model has good generalization ability on unseen data. If the verification results meet the requirements, it can be considered that the model training is successful.

[0070] Input the current processing parameters (target processing material properties, cutting thickness, cutting speed, nozzle type, focus position), environmental parameters (ambient temperature, humidity, atmospheric pressure), equipment status data (motor temperature, output air pressure, flow rate), and the calculated optimal cutting air pressure into the trained neural network model. Calculate the predicted value of the model through forward propagation, that is, the optimal operating speed of the air compressor. Output the optimal operating speed predicted by the model for use by the PLC control module to control the operation of the air compressor through the frequency converter.

[0071] As Figure 4 shown, the embodiment of the present invention also provides an intelligent control method for an air compressor used in laser cutting, including: S1: Collect the ambient temperature, humidity, and atmospheric pressure in real time through the environmental monitoring module, obtain the motor temperature, outlet pressure, and flow rate of the air compressor through the status monitoring module, and detect the cleanliness data of the compressed air in real time through the cleanliness detection module; S2: Receive the values of the processing parameters input by the user. The processing parameters include material properties, cutting thickness, cutting speed, nozzle type, and focal position; S3: Calculate the optimal operating speed and the optimal cutting air pressure based on the values of the processing parameters, the monitoring data of the environmental monitoring module, and the detection data of the cleanliness detection module; S4: Adjust the speed of the air compressor through the frequency converter, and control the output air pressure through the air pressure regulating valve to dynamically match the actual air pressure with the optimal cutting air pressure.

[0072] By periodically collecting various types of data, calculating the optimal outlet air pressure and the speed of the air compressor in combination with the parameters input by the user, and dynamically adjusting the speed of the air compressor motor through the frequency converter to achieve constant pressure air supply. At the same time, compare the feedback information with the target information in real time to dynamically adjust the gas pressure. The entire control process forms a closed loop, which can adjust the operating state of the air compressor in real time and accurately according to the gas consumption requirements of the laser cutting equipment, ensure that the laser cutting equipment always operates at the optimal processing air pressure, and greatly improve the efficiency and quality of laser cutting.

[0073] In the embodiment of the present invention, the steps of calculating the optimal operating speed and the optimal cutting air pressure according to the processing parameters, environmental data, and cleanliness data include: Match according to the processing parameters and environmental parameters input by the user in the process database; If there is a historical record in the process database that completely matches the current processing parameters and environmental parameters, directly call the optimal air pressure parameter in this record; If there is no completely matching record, select the closest reference air pressure from the process database through interpolation or approximation algorithm, and perform dynamic adjustment in combination with the processing parameters and environmental parameters to generate the optimal cutting air pressure.

[0074] It should be noted that when the temperature sensor detects that the ambient temperature is higher than the set first ambient temperature threshold, the AI control device controls the cooling device to start; when the ambient humidity is higher than the set humidity threshold, the AI control device controls to lower the dew point of the cold dryer, control to shorten the A-B tower exchange time of the desiccant dryer, shorten the drainage period of the automatic drain valve at the bottom of the gas storage tank, shorten the action period of the solenoid valve at the bottom of the filter, and increase the drainage duration each time; when the ambient temperature is higher than the first ambient temperature threshold and the temperature of the air compressor motor is higher than the set value, the AI control device gives a high-temperature alarm and performs air compressor shutdown protection; When it is determined that the gas cleanliness is unqualified according to the monitoring data of the gas particle analyzer, control the air compressor to stop.

[0075] The control strategies and protection mechanisms set for factors such as environmental temperature, humidity, and gas cleanliness further improve the entire intelligent control system. While ensuring the stable operation of the air compressor and providing high-quality compressed air, it also ensures the safe and reliable operation of the equipment under various complex environmental conditions, improving the overall stability and reliability of the system.

[0076] As Figure 5 shown, the embodiment of the present invention also provides an intelligent control method for an air compressor based on the system described in the above embodiment, and the specific process is as follows: Step 1: The operator inputs processing parameters such as material properties, plate thickness, and processing speed on the interface of the AI control device. The AI control device establishes communication connections with the environmental monitoring module, status monitoring module, cleanliness detection module, etc., obtains external environmental parameters such as temperature, humidity, and atmospheric pressure, and real-time monitors the initial states of the air compressor and laser equipment to ensure that each device is in a normal standby state.

[0077] Step 2: The AI control device inputs the input processing parameters and the obtained environmental parameters into the AI algorithm processing module. The AI algorithm processing module is actually a model trained based on a large amount of historical data, which can analyze the correlation relationships between various parameters and predict the optimal processing air pressure and gas flow requirements of the laser equipment under different working conditions. Through model operations, the initial set values of the output pressure and flow rate of the air compressor suitable for the current cutting task are obtained.

[0078] It should be noted that the determination of the optimal cutting air pressure value is a comprehensive and delicate process, relying on the processing and analysis of multi-faceted data by the AI control device. It involves the comprehensive application of processing parameters, environmental parameters, process library data, and real-time feedback data. Through the calculation and analysis of the AI algorithm processing module, continuous optimization and adjustment are carried out to determine the accurate values under different working conditions.

[0079] The user inputs parameters such as the target processing material properties, cutting thickness, cutting speed, nozzle type, and focal position on the AI control device. At the same time, the device obtains environmental parameters such as environmental temperature, humidity, and pressure. The AI algorithm processing module will calculate based on these data using a pre-trained algorithm model. For example, for plates with high hardness and large thickness, a larger air pressure is required to ensure the cutting effect; when the cutting speed is fast, a higher air pressure is also required to meet the real-time cutting needs; different types of nozzles have different requirements for air pressure, and large-diameter nozzles may require a higher air pressure to ensure gas flow. And environmental parameters will affect air density and the working efficiency of the air compressor. The AI algorithm processing module comprehensively considers these factors and initially calculates the applicable air pressure range.

[0080] The system has accumulated a large amount of past processing data to form a process database. The best air pressure parameters under different combinations of processing parameters and environmental parameters are recorded in the process database. The AI control device will search for matching data in the process database according to the currently input parameters. If a completely matching record is found, the best air pressure parameter in this record will be directly called; if there is no completely matching one, the closest parameter will be selected as a reference through interpolation or approximation algorithms, and then combined with the calculation result of the algorithm processing module to adjust this reference parameter to obtain an air pressure value that better suits the current working conditions.

[0081] During the laser cutting process, the AI control device monitors the actual air pressure situation through the real-time air pressure data fed back inside the laser head. If there is a deviation between the actual air pressure and the calculated best air pressure, the AI control device will adjust the rotation speed of the air compressor in a timely manner according to the magnitude and direction of the deviation, and then adjust the output air pressure. For example, if slag hanging phenomenon appears on the cutting surface during the cutting process, it may mean that the air pressure is insufficient, and the AI control device will appropriately increase the air pressure. Moreover, after each cutting is completed, the cutting effect of this time will be evaluated according to indicators such as cutting quality and energy consumption, and the relevant data will be fed back to the process database to update and optimize the process database so that the best cutting air pressure can be calculated more accurately next time.

[0082] Step 3: The cleanliness detection module conducts real-time detection on the gas output by the air compressor to judge whether the gas cleanliness is qualified. If the gas cleanliness is unqualified, such as the detected dust particle size > 0.01μm or the oil content > 0.003ppm, the AI control device will immediately issue a shutdown alarm instruction to stop the operation of the air compressor and start the corresponding detection and treatment process to prevent the laser equipment from being polluted. Only when the gas cleanliness is qualified is the air compressor allowed to continue supplying gas to the laser equipment.

[0083] Step 4: During the laser cutting process, the status monitoring module monitors the gas pressure fluctuation situation in real time. When it is determined that the air pressure fluctuation during the cutting process is within the range of ±2%, it is considered that the air pressure is stable, and the air compressor maintains the current operating state; if the air pressure fluctuation exceeds this range, the AI control device automatically adjusts the rotation speed or other operating parameters of the air compressor according to the fluctuation situation to make the air pressure stable. For example, when the air pressure is low, the rotation speed of the air compressor is appropriately increased to increase the outlet air pressure; when the air pressure is high, the rotation speed is decreased.

[0084] Step 5: If a batch cutting task is carried out, after a batch of cutting is completed, the AI control device will optimize and adjust the AI algorithm model according to the actual parameter data during this cutting process, such as the actual cutting speed, gas consumption situation, cutting quality feedback, etc. By continuously accumulating data and optimizing the model, the parameter setting during subsequent batch cutting can be made more accurate, improving the cutting efficiency and quality.

[0085] Step 6: The AI control device combines the operating data of the air compressor and laser equipment, such as operating time, gas flow, pressure fluctuation frequency, etc., and uses algorithms to predict the maintenance time of each consumable (such as filters, lubricating oil, etc.), and reminds the operator to replace consumables or maintain the equipment at the appropriate time to ensure the long-term stable operation of the equipment and reduce the equipment failure rate.

[0086] Step 7: When the laser cutting task is completed, the AI control device controls the air compressor to stop running, and organizes and stores the data during the cutting process to provide data support for subsequent data analysis and model optimization. At the same time, the system returns to the initial standby state and waits for the next cutting task instruction.

[0087] It should be noted that when the gas pressure fluctuation exceeds the threshold of ±2%, the air compressor will regulate the motor speed, air intake volume, and outlet valve opening through the AI control device, and will also link auxiliary equipment to optimize gas quality and pressure stability to ensure stable operation of laser cutting.

[0088] The AI control device responds quickly and adjusts the air compressor motor speed according to the pressure fluctuation and AI algorithm. If the pressure is too high, the motor speed is reduced. As the motor speed decreases, the compression frequency of the air compressor will decrease, and the gas output will decrease, thereby reducing the output pressure. On the contrary, if the pressure is too low, the motor speed will be increased, the compression frequency will be accelerated, the gas output will be increased, and the output pressure will be increased. For example, when the pressure is detected to be 3% higher than the set value, the AI control device will issue a command to let the inverter reduce the motor speed, reduce the gas compression per unit time, and gradually return the pressure to normal.

[0089] In addition to adjusting the motor speed, the output pressure can also be controlled by adjusting the intake volume. When the pressure is too high, reduce the opening of the intake valve to reduce the amount of air entering the air compressor. In this way, during the compression process, the amount of air involved in the compression is reduced, and the output gas pressure will also be reduced accordingly. When the pressure is too low, increase the opening of the intake valve to increase the intake volume and provide more air source to increase the output pressure.

[0090] Adjusting the outlet valve opening can also effectively control the output pressure. When the pressure exceeds the upper threshold, the outlet valve opening is appropriately increased to discharge the gas faster, reduce the pressure in the gas tank, and thus reduce the output pressure. When the pressure is lower than the lower threshold, the outlet valve opening is reduced to slow down the gas discharge speed, increase the pressure in the gas tank, and thus increase the output pressure.

[0091] The air compressor will also work with related auxiliary equipment to stabilize the pressure. For example, when the pressure fluctuates greatly, the working intensity of the air dryer and the cold dryer will be increased to ensure the dryness of the gas and reduce the unstable pressure factors caused by gas humidity problems. If the pressure fluctuation is caused by the blockage of the gas line, the system will automatically check and clean the gas line to restore the smooth flow of air and ensure stable pressure.

[0092] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent control system for an air compressor used in laser cutting, characterized in that, It includes an AI control device, a cleanliness detection module, an environmental monitoring module, and a status monitoring module that are respectively connected to the AI control device; The cleanliness detection module is used to detect the cleanliness of the air in real time and feedback the detection data to the AI control device; The environmental monitoring module is used to monitor the values of environmental parameters and feedback the monitoring data to the AI control device; The environmental parameters include environmental temperature, humidity, and atmospheric pressure; The status monitoring module is used to monitor the operating status of the air compressor in real time, including motor temperature, output air pressure, and flow rate; and feedback the monitoring data to the AI control device; The AI control device obtains the values of the laser cutting processing parameters input by the user, combines the data feedback from the cleanliness monitoring device, the environmental monitoring module, and the status monitoring module, obtains the optimal operating speed and optimal cutting air pressure of the air compressor, and controls the operating status of the air compressor through a frequency converter according to the optimal operating speed and optimal cutting air pressure, and adjusts the output air pressure of the air compressor through an air pressure regulating valve.

2. The intelligent control system of the air compressor for laser cutting according to claim 1, wherein The cleanliness detection module uses a gas particle analyzer; The gas particle analyzer is used to detect the number and particle size distribution of suspended particles in the air in real time, and feedback the detection data to the AI control device through a gas particle analyzer communication line; The environmental monitoring module includes a temperature sensor, a humidity sensor, and a barometric pressure detector, which are respectively used to monitor environmental temperature, humidity, and atmospheric pressure, and transmit the monitoring data to the AI control device; The status monitoring module includes a motor temperature sensor, an outlet air pressure sensor installed on the air outlet pipeline of the air compressor, and an outlet air flow sensor, which are respectively used to monitor the temperature of the motor, the outlet air pressure, and the outlet air flow in real time, and send the monitoring data to the AI control device.

3. The intelligent control system of an air compressor for laser cutting according to claim 2, characterized in that, The system further includes a cooling device, a refrigerant dryer, and a desiccant dryer connected to the AI control device; The AI control device is configured as: During the operation of the air compressor, when the environmental temperature is higher than the set first environmental temperature threshold, control the cooling device to start; When the environmental humidity is higher than the set humidity threshold, control to lower the dew point of the refrigerant dryer and shorten the A-B tower exchange time, the gas storage tank drainage period, and the filter drainage period of the desiccant dryer; When the environmental temperature is higher than the first environmental temperature threshold and the temperature of the air compressor motor is higher than the set value, trigger a high-temperature alarm and control the air compressor to stop.

4. The intelligent control system of the air compressor for laser cutting according to claim 3, characterized in that, The AI control device is further configured as: When it is determined that the gas cleanliness detected by the gas particle analyzer is unqualified, control the air compressor to stop and issue an alarm signal.

5. The intelligent control system for an air compressor used in laser cutting according to claim 4, wherein, The AI control device is further configured as: When the air compressor is started for the first time, receive the temperature, humidity, and atmospheric pressure data collected by the environmental monitoring module, calculate the maximum speed of the air compressor according to the received data, and control the air compressor to operate at the maximum speed to quickly load the pressure of the gas storage tank to the rated pressure.

6. The intelligent control system of an air compressor for laser cutting according to claim 5, characterized in that, The AI control device includes a control panel, an AI algorithm processing module, and a PLC control module; The control panel is connected to the AI algorithm processing module, the AI algorithm processing module is connected to the PLC control module, and the PLC control module is connected to the air compressor through a frequency converter; The user inputs the values of the laser cutting processing parameters to the AI algorithm processing module through the control panel; The AI algorithm processing module is configured as: Obtain the values of the target processing material properties, cutting thickness, cutting speed, nozzle type, and focus position parameters input through the control panel, and combine the data fed back by the cleanliness monitoring device, the environment monitoring module, and the status monitoring module to obtain the optimal operating speed and the optimal cutting air pressure of the air compressor; The PLC control module is configured to: According to the optimal operating speed and the optimal cutting air pressure, control the operation of the air compressor through the frequency converter, and adjust the output air pressure through the air pressure regulating valve.

7. The intelligent control system of an air compressor for laser cutting according to claim 6, wherein, The AI algorithm processing module is configured to obtain the optimal cutting air pressure: Match in the process database according to the values of the processing parameters input by the user and the values of the monitored environmental parameters; If there is a historical record in the process database that exactly matches the current processing parameters and environmental parameters, directly call the optimal air pressure parameters in this record; If there is no exact match record, select the closest reference air pressure from the process database through interpolation or approximation algorithm, and dynamically adjust it in combination with the values of the processing parameters and environmental parameters to generate the optimal cutting air pressure.

8. The intelligent control system of the air compressor for laser cutting according to claim 7, wherein The PLC control module is configured to: During the cutting process, continuously receive the monitoring data of the outlet air pressure sensor; If there is a deviation between the actual air pressure and the optimal cutting air pressure, dynamically adjust the air compressor speed or the opening of the air pressure regulating valve to maintain constant pressure air supply.

9. An intelligent control method for an air compressor used in laser cutting, characterized in that, Include: Collect the environmental temperature, humidity, and atmospheric pressure in real time through the environment monitoring module, obtain the motor temperature, outlet pressure, and flow rate of the air compressor through the status monitoring module, and detect the cleanliness data of the compressed air in real time through the cleanliness detection module; Receive the values of the processing parameters input by the user, and the processing parameters include material properties, cutting thickness, cutting speed, nozzle type, and focus position; Calculate the optimal operating speed and the optimal cutting air pressure according to the values of the processing parameters, the monitoring data of the environment monitoring module, and the detection data of the cleanliness detection module; Adjust the air compressor speed through the frequency converter, and control the output air pressure through the air pressure regulating valve to make the actual air pressure dynamically match the optimal cutting air pressure.

10. The intelligent control method of the air compressor for laser cutting according to claim 9, wherein, The steps of calculating the optimal operating speed and the optimal cutting air pressure according to the values of the processing parameters, the monitoring data of the environment monitoring module, and the detection data of the cleanliness detection module include: Match in the process database according to the values of the processing parameters input by the user and the values of the monitored environmental parameters; If there is a historical record in the process database that exactly matches the current processing parameters and environmental parameters, directly call the optimal air pressure parameters in this record; If there is no exact match record, select the closest reference air pressure from the process database through interpolation or approximation algorithm, and dynamically adjust it in combination with the processing parameters and the values of environmental parameters to generate the optimal cutting air pressure.

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