Plate thickness self-adaptive adjusting system based on sensor

Abstract

The invention discloses a sensor-based plate thickness adaptive adjustment system, which comprises a multi-source sensing fusion acquisition module, a dynamic parameter adjustment calculation module and a closed-loop feedback execution module. The multi-source sensing fusion acquisition module comprises a laser displacement sensor, a vibration sensor, a temperature sensor and a tool wear sensor which are respectively used for acquiring a plate thickness original signal, a processing environment vibration signal, a processing area environment temperature signal and a tool accumulated wear loss signal; the dynamic parameter adjustment calculation module receives the signal and calculates a machining parameter adjustment instruction; the closed-loop feedback execution module comprises a servo controller and an execution mechanism, and the execution mechanism adjusts the machining action under the control of the servo controller. According to the system, the problems of inaccurate thickness measurement and parameter adjustment lag caused by multi-factor interference in the prior art are solved, and the plate processing quality and consistency are improved.

Description

Technical Field

[0001] This invention relates to the field of sheet metal processing equipment technology, and more specifically to a sensor-based sheet metal thickness adaptive adjustment system. Background Technology

[0002] Traditional edge banding machines on the market rely heavily on manual operation or single sensor feedback for monitoring sheet thickness and adjusting processing parameters during sheet processing. Manual operation requires sampling sheet thickness measurements using tools such as calipers, followed by manual parameter input to adjust the processing system. This not only introduces human measurement errors but also fails to cover every sheet, resulting in poor consistency within the same batch. Even some machines using a single laser displacement sensor to monitor thickness do not consider the effects of vibration, temperature, and tool wear in the processing environment. Vibration in the processing environment can cause the sensor to shift relative to the sheet, leading to deviations in thickness measurements. Temperature changes in the processing area cause thermal expansion and contraction of the sheet and fluctuations in sensor accuracy, further exacerbating measurement errors. Furthermore, tool wear over long-term use alters the processing baseline, causing processing parameters to mismatch with actual requirements. These combined factors mean that in high-precision processing processes such as servo-tracked filleting, the tool trajectory cannot match the actual sheet thickness, often resulting in tool damage or substandard fillet quality. Existing solutions cannot correct these interfering factors in real time, leading to low processing efficiency and difficulty in improving product yield.

[0003] Based on the above problems, there is an urgent need for a technical solution that can comprehensively eliminate interference from multiple factors and achieve adaptive thickness monitoring and dynamic parameter adjustment. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a sensor-based adaptive plate thickness adjustment system, comprising a multi-source sensor fusion acquisition module, a dynamic parameter adjustment calculation module, and a closed-loop feedback execution module. The multi-source sensor fusion acquisition module is signal-connected to the dynamic parameter adjustment calculation module, and the dynamic parameter adjustment calculation module is signal-connected to the closed-loop feedback execution module. The multi-source sensor fusion acquisition module includes a laser displacement sensor, a vibration sensor, a temperature sensor, a humidity sensor, and a vision-inductive composite tool wear sensor. The laser displacement sensor is used to acquire the original plate thickness signal, the vibration sensor is used to acquire the vibration signal of the processing environment, the temperature sensor is used to acquire the ambient temperature signal of the processing area, the humidity sensor is used to acquire the ambient humidity signal of the processing area, and the vision-inductive composite tool wear sensor is used to accurately acquire the cumulative tool wear signal. The dynamic parameter adjustment calculation module receives the signal from the multi-source sensor fusion acquisition module and calculates the processing parameter adjustment command. The closed-loop feedback execution module includes a servo controller and an execution mechanism. The servo controller receives the processing parameter adjustment command, and the execution mechanism adjusts the processing action under the control of the servo controller.

[0005] Preferably, the multi-source sensor fusion acquisition module further includes a signal preprocessing unit, which is connected to the signals of the laser displacement sensor, vibration sensor, temperature sensor, humidity sensor and vision-inductive composite tool wear sensor, respectively, and is used to filter the acquired raw signals.

[0006] More preferably, the dynamic parameter adjustment calculation module includes a signal conversion unit, a data storage unit, and a calculation unit; the signal conversion unit is signal-connected to the multi-source sensor fusion acquisition module and is used to convert the acquired signal into a digital signal; the data storage unit stores the converted digital signal and preset processing standard parameters; the calculation unit is data-connected to the signal conversion unit and the data storage unit respectively and is used to call the data to calculate and obtain the processing parameter adjustment command.

[0007] More preferably, the actuator includes a tool drive assembly and a parameter adjustment assembly, the tool drive assembly and the parameter adjustment assembly being signal-connected to the servo controller; the tool drive assembly is used to adjust the tool movement trajectory, and the parameter adjustment assembly is used to adjust the machining pressure and speed parameters.

[0008] More preferably, the calculation unit calculates the true thickness of the board using a correction formula for the true thickness of the board. This correction formula is used to eliminate the interference of vibration, temperature, and humidity on the measurement results of the board thickness. Based on the difference between the true thickness of the board and the preset processing standard parameters, the calculation unit initially determines the direction of processing parameter adjustment.

[0009] More preferably, the calculation unit obtains the machining parameter adjustment amount through a machining parameter adjustment amount calculation formula. The machining parameter adjustment amount calculation formula is used to quantify the initially determined machining parameter adjustment direction by combining the cumulative wear of the tool, the influence of temperature and humidity on the machining process, and obtain the specific machining parameter adjustment value.

[0010] More preferably, the calculation unit obtains the servo controller drive signal amplitude through a drive signal amplitude calculation formula. The drive signal amplitude calculation formula is used to determine the drive signal strength output by the servo controller based on the adjustment amount of machining parameters, vibration, temperature, humidity and tool wear, so as to ensure the precise action of the actuator.

[0011] More preferably, the signal preprocessing unit employs adaptive Kalman filtering and has a built-in filter parameter configuration subunit. The filter parameter configuration subunit dynamically adjusts the process noise covariance matrix and observation noise covariance matrix of the Kalman filter according to the changes in vibration signal, temperature signal, and humidity signal.

[0012] More preferably, the calculation unit adopts a hybrid algorithm combining PID control and fuzzy control to dynamically correct the deviation between the digital signal and the preset standard parameters, and the fuzzy control module is used to dynamically adjust the proportional coefficient and integral coefficient of the PID control.

[0013] More preferably, the closed-loop feedback execution module further includes a position detection component, which is signal-connected to the servo controller and is used to collect the actual position signals of the tool drive component and the parameter adjustment component and transmit them to the servo controller. The servo controller adjusts the control commands according to the difference between the actual position signal and the preset position signal.

[0014] The technical effects achieved by the above embodiments include:

[0015] The core inventive technology of this invention lies in setting up a multi-source sensor fusion acquisition module, which integrates a laser displacement sensor, a vibration sensor, a temperature sensor, and a tool wear sensor to simultaneously capture four key signals: thickness, vibration, temperature, and tool wear. This solves the problem of interference that cannot be eliminated by relying on a single signal in existing technologies. A dynamic parameter adjustment calculation module performs collaborative calculations on the multi-source signals to avoid parameter adjustment lag. Combined with a closed-loop feedback execution module, precise command execution is achieved. This solution specifically addresses the problems of inaccurate thickness measurement and processing parameter mismatch caused by multi-factor interference in the background technology, effectively avoiding tool damage to the sheet metal and substandard fillet quality, thus improving the processing quality and consistency of the sheet metal. Attached Figure Description

[0016] Figure 1This is a connection block diagram of the sensor-based adaptive thickness adjustment system for sheet metal in this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Traditional technical solutions have the following problems: existing edge banding machines rely on manual sampling to monitor the thickness of the board material, which has errors and cannot cover all boards; or they only use a single laser displacement sensor, which does not take into account the combined interference of vibration, temperature, humidity and tool wear, resulting in inaccurate thickness measurement, lagging adjustment of processing parameters, and quality problems that are prone to occur during servo tracking processing.

[0019] Based on this, this embodiment provides a sensor-based adaptive plate thickness adjustment system, including a multi-source sensor fusion acquisition module, a dynamic parameter adjustment calculation module, and a closed-loop feedback execution module. The multi-source sensor fusion acquisition module is signal-connected to the dynamic parameter adjustment calculation module, and the dynamic parameter adjustment calculation module is signal-connected to the closed-loop feedback execution module. The multi-source sensor fusion acquisition module includes a laser displacement sensor, a vibration sensor, a temperature sensor, a humidity sensor, and a vision-inductive composite tool wear sensor. The laser displacement sensor is used to acquire the original plate thickness signal, the vibration sensor is used to acquire the vibration signal of the processing environment, the temperature sensor is used to acquire the ambient temperature signal of the processing area, the humidity sensor is used to acquire the ambient humidity signal of the processing area, and the vision-inductive composite tool wear sensor is used to accurately acquire the cumulative tool wear signal. The dynamic parameter adjustment calculation module is used to receive the signal from the multi-source sensor fusion acquisition module and calculate the processing parameter adjustment command. The closed-loop feedback execution module includes a servo controller and an execution mechanism. The servo controller receives the processing parameter adjustment command, and the execution mechanism adjusts the processing action under the control of the servo controller.

[0020] In this technical solution, the multi-source sensor fusion acquisition module is the core innovation. The laser displacement sensor is a triangular reflector type, installed 100mm above the edge banding machine processing station, facing the upper surface of the board, to collect the original signal of the board thickness in real time. The vibration sensor is a piezoelectric type, fixed on the processing frame near the tool, to collect the voltage signal generated by the frame vibration and convert it into a vibration velocity signal. The temperature sensor is a platinum resistance type, embedded in the worktable of the processing area, to collect the ambient temperature around the board. The humidity sensor is a capacitive humidity sensor, installed in the processing area near the board, no more than 50mm away from the temperature sensor, to collect the relative humidity of the processing area, with a measurement range of 0-100%RH and an accuracy of ±2%RH. The vision-inductive composite tool wear sensor integrates a high-definition industrial camera and an inductive detection unit, installed on the side of the tool spindle. The industrial camera captures the tool edge image at macro distance, and analyzes the edge wear morphology through image grayscale processing and edge extraction algorithms. The inductive detection unit obtains wear data by detecting the change in inductance between the tool edge and the sensor. The two data are fused to obtain the accurate cumulative tool wear. Each sensor is connected to the dynamic parameter adjustment and calculation module via shielded cables to avoid signal transmission interference. The dynamic parameter adjustment and calculation module uses an industrial-grade control board, integrating signal processing and computation functions. After receiving signals from each sensor, it calculates machining parameter adjustment commands using a built-in algorithm and then sends them to the servo controller of the closed-loop feedback execution module. The servo controller is a Siemens series, which outputs drive signals to the actuator according to the commands. The tool drive component of the actuator uses a servo motor to adjust the Z-axis height and X-axis feed trajectory of the tool. The parameter adjustment component uses a pneumatic regulating valve and a stepper motor to adjust the machining pressure and feed speed, respectively.

[0021] This solution uses multi-source sensors to collaboratively collect data, comprehensively capturing key factors affecting processing quality. The dynamic parameter adjustment calculation module achieves the fusion calculation of multiple signals, and the closed-loop feedback execution module ensures accurate execution of instructions. This solves the processing quality problems caused by multiple factors in existing technologies and enables adaptive adjustment of sheet thickness.

[0022] Traditional technical solutions have the following technical problems: the raw signals collected by multi-source sensors contain environmental noise, such as voltage noise caused by power grid fluctuations and vibration noise generated by mechanical friction. Directly using the raw signals will lead to subsequent calculation errors and affect the accuracy of processing parameter adjustment.

[0023] Based on this, the multi-source sensor fusion acquisition module also includes a signal preprocessing unit, which is connected to the signals of the laser displacement sensor, vibration sensor, temperature sensor, humidity sensor and vision-inductive composite tool wear sensor, respectively, and is used to filter the acquired raw signals.

[0024] The signal preprocessing unit employs an FPGA-based hardware filtering circuit, integrating multiple signal conditioning channels, each corresponding to a sensor signal. The analog signal output from the laser displacement sensor is first amplified by an operational amplifier, with the amplification factor set according to the sensor output signal amplitude to ensure the signal is within the optimal range for ADC acquisition. The signals output from the vibration and temperature sensors are respectively filtered by low-pass and high-pass filters to remove high-frequency noise and low-frequency drift. The analog signal output from the humidity sensor is filtered by a band-pass filter to eliminate noise caused by environmental electromagnetic interference. The digital signal output from the vision-inductive composite tool wear sensor is passed through an opto-isolator to eliminate electromagnetic interference. The core filtering algorithm employs adaptive Kalman filtering. The signal preprocessing unit incorporates a filter parameter configuration subunit, which receives vibration velocity signals from the vibration sensor, temperature signals from the temperature sensor, and humidity signals from the humidity sensor in real time. When the vibration velocity increases, the process noise covariance matrix value of the Kalman filter is dynamically increased to improve the filter's adaptability to dynamic interference. When the temperature change exceeds 5°C, the observation noise covariance matrix is ​​adjusted to compensate for the temperature's impact on sensor accuracy. When the humidity change exceeds 10%RH, the observation noise covariance matrix is ​​further optimized to correct signal drift caused by humidity.

[0025] This solution effectively eliminates noise interference in the original signal through hardware conditioning and adaptive filtering in the signal preprocessing unit, providing a precise input signal for the subsequent dynamic parameter adjustment calculation module and improving the accuracy of processing parameter adjustment.

[0026] Traditional technical solutions have the following technical problems: the signals collected by multi-source sensors are of different types, some are analog signals and some are digital signals, and the amount of signal data is large. If the data is directly processed, it will result in low computational efficiency and it will be impossible to store historical data for subsequent analysis and parameter optimization.

[0027] Based on this, the dynamic parameter adjustment calculation module includes a signal conversion unit, a data storage unit, and a calculation unit; the signal conversion unit is connected to the multi-source sensor fusion acquisition module and is used to convert the acquired signal into a digital signal; the data storage unit stores the converted digital signal and preset processing standard parameters; the calculation unit is connected to the signal conversion unit and the data storage unit respectively and is used to call the data to calculate and obtain the processing parameter adjustment command.

[0028] The signal conversion unit uses a 16-bit ADC chip, ADS1256, with eight analog input channels. It can simultaneously acquire analog signals from laser displacement sensors, vibration sensors, temperature sensors, and humidity sensors, with a sampling frequency set to 1kHz, converting the analog signals into digital signals. The digital signal output from the vision-inductive composite tool wear sensor is directly read via the SPI interface. The data storage unit uses an EEPROM chip, AT24C256, and an SD card. The EEPROM stores preset machining standard parameters, such as standard thicknesses, machining pressure ranges, and feed rate ranges for different sheet materials. The SD card stores the converted digital signals, with a storage period set to 1 minute, retaining historical data from the last 72 hours for subsequent troubleshooting and parameter optimization. The computing unit uses an STM32H743 microprocessor with a 480MHz clock speed, possessing powerful computing capabilities. It connects to the signal conversion unit and data storage unit via an internal bus, reading the converted digital signals in real time, calling the preset standard parameters in the data storage unit, calculating machining parameter adjustment commands through built-in algorithms, and then sending them to the servo controller via an RS485 interface.

[0029] This solution achieves unified conversion of multiple signal types through a signal conversion unit, classifies and stores data through a data storage unit, and performs efficient calculations through a computing unit. The three work together to ensure the stable operation of the dynamic parameter adjustment calculation module and provide data and computational support for the adjustment of processing parameters.

[0030] Traditional technical solutions have the following technical problems: the actuators of existing edge banding machines mostly use a single motor control, which cannot adjust the tool movement trajectory and processing parameters separately. This leads to mutual interference between tool trajectory adjustment and pressure and speed parameter adjustment, making it difficult to meet the requirements of high-precision processing. For example, when servo tracking rounded corners, tool trajectory deviation and excessive pressure are likely to occur simultaneously, exacerbating processing defects.

[0031] Based on this, the actuator includes a tool drive assembly and a parameter adjustment assembly, which are respectively connected to the servo controller via signals; the tool drive assembly is used to adjust the tool movement trajectory, and the parameter adjustment assembly is used to adjust the machining pressure and speed parameters.

[0032] The tool drive assembly includes a Z-axis servo motor and an X-axis servo motor. The Z-axis servo motor is connected to the tool mount via a ball screw, controlling the vertical movement of the tool and adjusting the vertical distance between the tool and the sheet metal, i.e., the machining reference in the thickness direction. The X-axis servo motor is connected to the tool mount via a synchronous belt, controlling the forward and backward movement of the tool and adjusting the tool's feed trajectory to adapt to the processing of sheets of different lengths. Each servo motor is equipped with a 17-bit absolute encoder, providing real-time feedback on motor position to ensure trajectory adjustment accuracy. The parameter adjustment assembly includes a pneumatic regulating valve and a stepper motor. The pneumatic regulating valve is connected to the pressure roller via an air pipe, adjusting the pressure of the pressure roller on the sheet metal based on the current signal output from the servo controller. The pressure adjustment range covers the commonly used 0.2-0.8MPa for edge banding machines. The stepper motor is connected to the feed roller via a reduction gear, adjusting the speed of the feed roller based on the pulse signal output from the servo controller, thereby changing the feed speed of the sheet metal. The speed adjustment range is 1-5m / min. The tool drive assembly and the parameter adjustment assembly independently receive commands from the servo controller and complete their respective adjustment actions to avoid mutual interference.

[0033] This solution divides the actuator into a tool drive component and a parameter adjustment component, enabling independent adjustment of the tool path and machining parameters, thereby improving the control accuracy of the actuator and meeting the requirements of high-precision machining.

[0034] Traditional technical solutions have the following technical problems: Existing technologies directly use the original thickness value collected by the laser displacement sensor as the processing benchmark, without considering sensor offset caused by vibration, thermal expansion and contraction of the board caused by temperature, and moisture absorption and expansion of the board caused by humidity. The thickness measurement value deviates significantly from the actual value. Adjusting the processing parameters based on this will lead to a mismatch between the parameters and the actual requirements.

[0035] Based on this, the calculation unit calculates the true thickness of the board using the corrective formula for the true thickness of the board. The corrective formula for the true thickness of the board is used to eliminate the interference of vibration, temperature and humidity on the measurement results of the board thickness. Based on the difference between the true thickness of the board and the preset processing standard parameters, the calculation unit initially determines the direction of the processing parameter adjustment.

[0036] The formula for correcting the true value of the plate thickness is as follows: The definitions and dimensions of each parameter are as follows: This represents the actual thickness of the sheet metal, measured in mm. The original value of the plate thickness collected by the laser displacement sensor is in mm. The vibration influence coefficient, with dimensions in mm·s / ℃, is determined experimentally under common edge banding machine processing conditions. The value range is 0.005-0.01 mm·s / ℃. The greater the vibration velocity, the more significant the impact on thickness measurement. and The product term (the vibration velocity value of the processing environment collected by the vibration sensor, in mm / s) is used to correct the measurement deviation caused by vibration; The temperature decay coefficient, with dimensions 1 / ℃, ranges from 0.02 to 0.051 / ℃. As temperature increases, the effect of thermal expansion and contraction of the sheet metal on the measurement exhibits an exponential decay trend. Therefore, by... ( Adjust the ambient temperature value of the processing area (in °C) collected by the temperature sensor; This is the temperature quadratic effect coefficient, with dimensions in mm / ℃², and a value range of 0.0001-0.0003 mm / ℃². It is used to correct for nonlinear effects when the temperature is too high or too low. Because the effect of temperature on the thickness of the sheet exhibits a quadratic variation under extreme temperatures, it is added... item; The second-order influence coefficient of humidity, with dimensions in mm / %RH², and a value range of 0.00005-0.0001 mm / %RH², is used to correct the nonlinear effect of humidity on board expansion. The humidity value of the processing area is collected by a humidity sensor, and the dimension is %RH.

[0037] Formula derivation logic: First, vibration will cause the laser displacement sensor to shift relative to the plate, and the amount of shift is proportional to the vibration velocity. Therefore, we first introduce... First, the vibration deviation is corrected; second, the effect of temperature on vibration deviation has a decay characteristic. When the temperature rises, the change in material stiffness slows down the vibration transmission. Therefore, an exponential function is used to correct the vibration deviation. The vibration deviation term is attenuated and adjusted. Finally, temperature directly causes the thermal expansion and contraction of the board material, and its effect is quadratic nonlinear at extreme temperatures; therefore, an adjustment is introduced. The nonlinear effect of temperature is corrected, and humidity causes the board to absorb moisture and expand, with the expansion amount approximately proportional to the square of the humidity. This nonlinear characteristic is particularly pronounced in high humidity environments. Therefore, a correction is introduced... The nonlinear effect of humidity was corrected to obtain the true value of the plate thickness. The computing unit will With the standard plate thickness value preset in the data storage unit In comparison, if Therefore, it is preliminarily determined that the machining parameters need to be adjusted in the direction of reducing the tool feed depth and lowering the machining pressure; if Therefore, it is preliminarily determined that the machining parameters need to be adjusted in the direction of increasing the tool feed depth and increasing the machining pressure.

[0038] This scheme eliminates the interference of vibration, temperature and humidity on thickness measurement by using a formula to correct the true value of the plate thickness, thereby obtaining an accurate true value of the plate thickness. This provides a reliable basis for adjusting processing parameters and avoids parameter mismatch caused by measurement deviations.

[0039] Traditional technical solutions have the following technical problems: Existing technologies only make preliminary adjustments to processing parameters based on thickness deviations, without considering changes in processing references caused by tool wear, or the effects of temperature and humidity on the processing process. The parameter adjustment amounts lack quantitative basis, resulting in the adjusted parameters still failing to meet processing requirements. For example, even if the thickness deviation is corrected after tool wear, burrs may still appear on the edges of the processed sheet.

[0040] Based on this, the calculation unit obtains the machining parameter adjustment amount through the machining parameter adjustment amount calculation formula. The machining parameter adjustment amount calculation formula is used to quantify the initially determined machining parameter adjustment direction by combining the cumulative wear of the tool, temperature and humidity on the machining process, and obtain the specific machining parameter adjustment value.

[0041] The formula for calculating the adjustment amount of processing parameters is: The definitions and dimensions of each parameter are as follows: The dimensions of the parameters are different depending on the type of parameter. For example, the dimension of the pressure adjustment is MPa, and the dimension of the speed adjustment is m / min. This is a dimensionless parameter adjustment coefficient used during pressure regulation. The value is 0.1-0.3 MPa / mm, during speed adjustment. The value is 0.5-1.5m / (min·mm), and it is determined according to the material of the board. The larger value is used for rigid boards and the smaller value is used for soft boards. This represents the actual thickness of the sheet metal, measured in mm. The preset standard plate thickness value, in mm; The thickness deviation is the basis for parameter adjustment; The tool wear influence coefficient is dimensionless and ranges from 0.02 to 0.05. The greater the tool wear, the more significant the impact on machining parameters. The cumulative wear of the tool, measured in mm, is collected by a vision-inductive composite tool wear sensor. This item is used to correct the impact of tool wear on machining parameters; the greater the wear, the larger the adjustment needs to be. The coefficient represents the cubic influence of temperature, with dimensions 1 / ℃³, and a value ranging from 0.00001 to 0.000031 / ℃³. The effect of temperature on the processing exhibits a cubic nonlinear variation over long processing times; therefore, it is determined through... Adjust the items accordingly. The ambient temperature of the processing area is expressed in °C. The humidity effect coefficient is dimensionless and ranges from 0.001 to 0.003. The higher the humidity, the more obvious the change in the plasticity of the sheet and the more significant the demand on processing parameters. The value represents the ambient humidity of the processing area, expressed in %RH.

[0042] Formula derivation logic: First, take thickness deviation as an example. Based on, multiplied by the parameter adjustment coefficient This yields an initial adjustment value that does not consider the effects of tool wear, temperature, and humidity. Secondly, tool wear can dull the tool edge, requiring increased machining pressure or reduced feed rate. Therefore, [the adjustment is made by...]. The initial adjustment amount is amplified and corrected; finally, long-term temperature changes affect the plasticity of the sheet metal and the cutting performance of the cutting tool, and the influence law is approximately a cubic function, therefore, through... The term undergoes non-linear correction. Simultaneously, humidity alters the moisture content of the board, affecting its hardness and plasticity. Therefore, [the following is a more detailed explanation / analysis]. By correcting the humidity level and adjusting the processing parameters, a precise adjustment amount for the processing parameters can be obtained. .

[0043] This solution quantifies the adjustment amount of machining parameters by calculating the adjustment amount. It also takes into account the effects of tool wear, temperature and humidity to ensure that the adjustment amount matches the actual machining requirements and improves the accuracy of machining parameter adjustment.

[0044] Traditional technical solutions have the following technical problems: In the existing technology, the amplitude of the drive signal output by the servo controller is fixed, and the influence of vibration, temperature, humidity and tool wear on the response characteristics of the actuator is not considered. When the vibration is large, the actuator may overshoot; when the temperature is too high, the humidity is too high or the tool wear is severe, the response speed of the actuator slows down, resulting in a decrease in motion accuracy and inability to accurately execute the machining parameter adjustment commands.

[0045] Based on this, the calculation unit obtains the amplitude of the servo controller's drive signal through a drive signal amplitude calculation formula. The drive signal amplitude calculation formula is used to determine the strength of the drive signal output by the servo controller based on the adjustment amount of machining parameters, vibration, temperature, humidity and tool wear, so as to ensure the precise action of the actuator.

[0046] The formula for calculating the amplitude of the driving signal is: The definitions and dimensions of each parameter are as follows: The amplitude of the servo controller drive signal is expressed in V. The reference drive signal amplitude is in V, and its value is 5-10V, which is set according to the rated voltage of the actuator. The dimensions of the adjustment amount for processing parameters are determined according to the parameter type; The vibration influence coefficient, with dimensions in mm·s / ℃, is used in the correction formula for the true thickness of the plate. Consistent; The vibration velocity value in the processing environment is expressed in mm / s. This item is used to correct the impact of vibration on the drive signal. The greater the vibration speed, the smaller the value of this item, which reduces the amplitude of the drive signal and avoids overshoot of the actuator. This is the temperature secondary influence coefficient, with dimensions in mm / ℃², and is used in the correction formula for the true value of the plate thickness. Consistent; The ambient temperature of the processing area is expressed in °C. The tool wear influence coefficient is dimensionless and is used in the calculation formula for machining parameter adjustment. Consistent; This represents the cumulative wear of the cutting tool, measured in mm. The humidity influence coefficient is used in the calculation formula for processing parameter adjustment. Consistent; The humidity value of the processing area is expressed in %RH. This item is used to correct the effects of temperature, tool wear, and humidity on the drive signal. When the temperature is too high, the wear is severe, or the humidity is too high, the value of this item increases, thereby increasing the amplitude of the drive signal and compensating for the decrease in the response speed of the actuator.

[0047] Formula derivation logic: First, the amplitude of the reference drive signal is used. Multiply by the processing parameter adjustment amount First, a basic drive signal amplitude matching the adjustment amount is obtained; second, vibration increases the dynamic interference of the actuator, so the drive signal amplitude needs to be reduced to avoid overshoot, hence the introduction of... The attenuation of the vibration is adjusted; the higher the vibration speed, the more significant the attenuation. Finally, excessively high temperatures increase the resistance of the actuator's motor windings, tool wear increases cutting resistance, and excessive humidity alters the friction between the sheet metal and the tool. All three factors reduce the actuator's response speed, necessitating an increase in the drive signal amplitude to compensate. Therefore, the following adjustment is introduced: The amplitude of the drive signal is amplified and adjusted; the higher the temperature, the more severe the wear, and the greater the humidity, the more obvious the amplification, ultimately obtaining a drive signal amplitude adapted to the current operating conditions. .

[0048] This solution dynamically adjusts the amplitude of the servo controller's drive signal by calculating the drive signal amplitude, adapting to different vibration, temperature, humidity, and tool wear conditions, ensuring precise actuator movements, and improving the execution accuracy of machining parameter adjustments.

[0049] Traditional technical solutions have the following technical problems: Existing signal filtering uses Kalman filtering with fixed parameters. When the vibration, temperature and humidity of the processing environment change, the fixed filtering parameters cannot adapt to the new noise characteristics, resulting in a decrease in filtering effect. The filtered signal still contains a lot of noise, which affects the accuracy of subsequent calculations.

[0050] Based on this, the signal preprocessing unit adopts adaptive Kalman filtering and has a built-in filter parameter configuration subunit. The filter parameter configuration subunit dynamically adjusts the process noise covariance matrix and observation noise covariance matrix of the Kalman filter according to the changes in vibration signal, temperature signal and humidity signal.

[0051] In the FPGA chip of the signal preprocessing unit, the core parameter of the Kalman filter algorithm is the process noise covariance matrix. and observation noise covariance matrix The filter parameter configuration subunit receives the vibration velocity signal from the vibration sensor in real time. Temperature signal from temperature sensor Humidity signal from humidity sensor ,Establish and and , , The association model. When When the speed increases by 1 mm / s, Increasing the diagonal element values ​​of the matrix by 5% increases the uncertainty of the system model due to increased vibration, necessitating an increase in the process noise covariance; when When the temperature rises by 5°C, Increasing the diagonal element values ​​of the matrix by 8% increases the uncertainty of sensor measurements due to increased temperature, necessitating an increase in the observation noise covariance; when When the RH increases by 10%, The diagonal elements of the matrix are increased by an additional 3% because increased humidity leads to increased uncertainty in sensor measurements and the condition of the substrate, necessitating a further increase in the observation noise covariance. For example, when , , hour, Matrix set as , Matrix set as ;when , , hour, Matrix adjustment to , Matrix adjustment to .

[0052] The filter parameter configuration subunit updates every 100ms. and The adjusted matrix parameters are written into the register of the Kalman filter algorithm in real time to ensure that the Kalman filter can adapt to changes in environmental noise characteristics in real time.

[0053] This scheme improves the adaptability of Kalman filtering by dynamically adjusting the filtering parameters, ensuring the accuracy of the filtered signal and providing reliable data input for subsequent calculations.

[0054] Traditional technical solutions have the following technical problems: The existing computing unit uses a single PID control algorithm with fixed PID parameters. When the processing conditions change, such as an increase in the thickness deviation of the sheet material or a sudden change in temperature, the fixed-parameter PID algorithm cannot respond quickly to the deviation changes, resulting in overshoot or lag in parameter adjustment, which affects the accuracy of processing parameter adjustment.

[0055] Based on this, the calculation unit adopts a hybrid algorithm combining PID control and fuzzy control to dynamically correct the deviation between the digital signal and the preset standard parameters. The fuzzy control module is used to dynamically adjust the proportional coefficient and integral coefficient of the PID control.

[0056] The core of the hybrid algorithm is the dynamic adjustment of PID parameters by the fuzzy control module, with the thickness deviation as the input to the fuzzy control module. (Unit: mm) and deviation change rate (Dimensions are mm / s), the output is the proportional coefficient adjustment of the PID controller. and integral coefficient adjustment amount .

[0057] The fuzzy control module will Divided into 5 fuzzy subsets: negative large (NB), negative small (NS), zero (ZO), positive small (PS), and positive large (PB), corresponding to The range is [-0.5, -0.3), [-0.3, -0.1), [-0.1, 0.1), [0.1, 0.3), [0.3, 0.5]; Divided into 5 fuzzy subsets: negative large (NB), negative small (NS), zero (ZO), positive small (PS), and positive large (PB), corresponding to The range is [-0.2, -0.1), [-0.1, -0.05), [-0.05, 0.05), [0.05, 0.1), [0.1, 0.2]. A fuzzy rule table is established based on engineering experience, for example, when... For PB, When it is PB, For PB, To increase the proportional and integral coefficients, the response speed is accelerated; when For NB, When it is NB, For NB, NS, which means reducing the proportional and integral coefficients to avoid overshoot.

[0058] Real-time calculation by computing unit and The input is processed by the fuzzy control module, and through fuzzy inference and defuzzification, the result is obtained. and Then, compared with the PID's reference proportional coefficient and benchmark integral coefficients By superimposing the parameters, the real-time PID parameters are obtained. , The PID control module calculates the thickness deviation based on real-time PID parameters and outputs a correction amount for adjusting the processing parameters. This correction amount is then added to the original processing parameter adjustment amount to obtain the final processing parameter adjustment command. This scheme combines PID and fuzzy control to achieve dynamic adjustment of PID parameters, improving the response speed of the computing unit to changes in operating conditions, avoiding parameter adjustment overshoot or lag, and improving the accuracy of processing parameter adjustment.

[0059] Traditional technical solutions have the following technical problems: Existing closed-loop feedback execution modules only drive the actuator according to the processing parameters and do not monitor the actual position of the actuator in real time. They cannot know whether the actuator is accurately executing the instructions. If the actuator has a mechanical failure that causes positional deviation, it will lead to a mismatch between the processing parameters and the actual action, which will aggravate processing defects and make it impossible to detect the fault in time.

[0060] Based on this, the closed-loop feedback execution module also includes a position detection component, which is signal-connected to the servo controller and is used to collect the actual position signals of the tool drive component and the parameter adjustment component and transmit them to the servo controller. The servo controller adjusts the control commands according to the difference between the actual position signal and the preset position signal.

[0061] The position detection component includes a linear encoder and a rotary encoder. The linear encoder, with a resolution of 0.1 μm, is mounted on the Z-axis and X-axis guide rails of the tool drive component, and acquires the actual position signal of the tool mount in real time, transmitting it to the servo controller. The rotary encoder is mounted on the pneumatic regulating valve core and the stepper motor shaft of the parameter adjustment component. The rotary encoder of the pneumatic regulating valve has a resolution of 1000 lines / revolution, acquiring the valve core rotation angle signal and converting it into a position signal corresponding to the actual pressure. The rotary encoder of the stepper motor has a resolution of 2000 lines / revolution, acquiring the motor rotation angle signal and converting it into a position signal corresponding to the actual feed speed. The position detection component is connected to the servo controller via an RS485 interface, transmitting the actual position signal every 5 ms. The servo controller has an internal position comparison module that compares the received actual position signal with the preset position signal and calculates the position deviation. If the absolute value of the deviation is greater than 0.01mm (tool drive component), 0.05MPa (parameter adjustment component), or 0.01m / min (parameter adjustment component), the servo controller adjusts the output drive signal, increasing or decreasing the drive signal amplitude until the deviation between the actual position signal and the preset position signal is less than the threshold.

[0062] For example, if the preset Z-axis position of the tool drive assembly is 10mm, and the actual detected position is 10.02mm, the deviation is 0.02mm, which is greater than the threshold of 0.01mm. The servo controller then reduces the amplitude of the Z-axis servo motor's drive signal, controlling the motor to rotate in the opposite direction and adjusting the tool mount downwards by 0.02mm until the actual position is 10mm. This solution monitors the actuator position in real time through the position detection assembly, and the servo controller adjusts the control commands based on the position deviation, forming a secondary closed-loop control. This ensures precise actuator movement, timely detection and correction of position deviations, and avoids machining defects.

[0063] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A sensor-based adaptive thickness adjustment system for sheet metal, characterized in that, The system includes a multi-source sensor fusion acquisition module, a dynamic parameter adjustment and calculation module, and a closed-loop feedback execution module. The multi-source sensor fusion acquisition module is signal-connected to the dynamic parameter adjustment and calculation module, and the dynamic parameter adjustment and calculation module is signal-connected to the closed-loop feedback execution module. The multi-source sensor fusion acquisition module includes a laser displacement sensor, a vibration sensor, a temperature sensor, a humidity sensor, and a vision-inductive composite tool wear sensor. The laser displacement sensor is used to acquire the original signal of the plate thickness, the vibration sensor is used to acquire the vibration signal of the processing environment, the temperature sensor is used to acquire the ambient temperature signal of the processing area, the humidity sensor is used to acquire the ambient humidity signal of the processing area, and the vision-inductive composite tool wear sensor is used to accurately acquire the cumulative tool wear signal. The dynamic parameter adjustment and calculation module receives the signal from the multi-source sensor fusion acquisition module and calculates the processing parameter adjustment command. The closed-loop feedback execution module includes a servo controller and an execution mechanism. The servo controller receives the processing parameter adjustment command, and the execution mechanism adjusts the processing action under the control of the servo controller.

2. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 1, characterized in that, The multi-source sensor fusion acquisition module also includes a signal preprocessing unit, which is connected to the signals of the laser displacement sensor, vibration sensor, temperature sensor, humidity sensor and vision-inductive composite tool wear sensor, respectively, and is used to filter the various raw signals acquired.

3. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 1, characterized in that, The dynamic parameter adjustment calculation module includes a signal conversion unit, a data storage unit, and a calculation unit. The signal conversion unit is connected to the multi-source sensor fusion acquisition module and is used to convert the acquired signal into a digital signal. The data storage unit stores the converted digital signal and preset processing standard parameters. The calculation unit is connected to the signal conversion unit and the data storage unit respectively and is used to call the data to calculate and obtain the processing parameter adjustment command.

4. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 1, characterized in that, The actuator includes a tool drive assembly and a parameter adjustment assembly, which are respectively connected to the servo controller via signals. The tool drive assembly is used to adjust the tool movement trajectory, and the parameter adjustment assembly is used to adjust the machining pressure and speed parameters.

5. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 3, characterized in that, The calculation unit calculates the true thickness of the board using a correction formula for the true thickness of the board. This correction formula is used to eliminate the interference of vibration, temperature, and humidity on the measurement results of the board thickness. Based on the difference between the true thickness of the board and the preset processing standard parameters, the calculation unit initially determines the direction of processing parameter adjustment.

6. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 5, characterized in that, The calculation unit obtains the machining parameter adjustment amount through the machining parameter adjustment amount calculation formula. The machining parameter adjustment amount calculation formula is used to quantify the initially determined machining parameter adjustment direction by combining the cumulative wear of the tool, temperature and humidity on the machining process, and obtain the specific machining parameter adjustment value.

7. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 6, characterized in that, The calculation unit obtains the amplitude of the servo controller's drive signal through a drive signal amplitude calculation formula. The drive signal amplitude calculation formula is used to determine the strength of the drive signal output by the servo controller based on the adjustment amount of machining parameters, vibration, temperature, humidity and tool wear, so as to ensure the precise action of the actuator.

8. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 2, characterized in that, The signal preprocessing unit employs adaptive Kalman filtering and has a built-in filter parameter configuration subunit. The filter parameter configuration subunit dynamically adjusts the process noise covariance matrix and observation noise covariance matrix of the Kalman filter according to the changes in vibration signal, temperature signal, and humidity signal.

9. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 3, characterized in that, The calculation unit uses a hybrid algorithm combining PID control and fuzzy control to dynamically correct the deviation between the digital signal and the preset standard parameters. The fuzzy control module is used to dynamically adjust the proportional coefficient and integral coefficient of the PID control.

10. The sensor-based adaptive thickness adjustment system for sheet metal according to claim 4, characterized in that, The closed-loop feedback execution module also includes a position detection component, which is signal-connected to the servo controller and is used to collect the actual position signals of the tool drive component and the parameter adjustment component and transmit them to the servo controller. The servo controller adjusts the control commands according to the difference between the actual position signal and the preset position signal.

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