A method and system for intelligent cutting control of PVC foam boards

Abstract

This invention belongs to the field of automation control technology, specifically relating to an intelligent cutting control method and system for PVC foam boards. The method includes the following steps: collecting and preprocessing cutting condition data, including the measured current of the spindle motor, the temperature inside the saw blade guard, and the current real-time feed speed; applying a moving average filter to the measured current to obtain a smoothed spindle current; and constructing a thermoviscous resistance index model, calculating the thermoviscous resistance index characterizing the degree of saw blade adhesion risk based on the smoothed spindle current, the rated operating voltage of the system spindle motor, the real-time feed speed, the effective cutting cross-sectional area, and the regional temperature. This invention not only eliminates severe saw blade sticking at its source, ensuring a smooth and flat cut surface, but also ensures the saw blade always operates within a suitable temperature range, significantly extending the tool's lifespan and improving the overall efficiency of the automated production line.

Description

Technical Field

[0001] This invention belongs to the field of automation control technology, specifically relating to an intelligent cutting control method and system for PVC foam boards. Background Technology

[0002] PVC (polyvinyl chloride) foam boards are widely used in building decoration, advertising production, and other fields due to their excellent properties such as light weight, sound insulation, and moisture resistance. However, the physical properties of PVC materials are greatly affected by temperature, making them typical heat-sensitive materials. In the board processing, the cutting process mainly relies on CNC panel saws or online fixed-length cutting machines. These devices typically operate with constant spindle speed and feed rate, or passively protect themselves by monitoring for current overload.

[0003] Existing cutting control methods have significant limitations when handling PVC materials. PVC has a low Vicat softening point, typically between 70℃ and 80℃. During high-speed continuous cutting, the instantaneous temperature at the saw blade tip can easily exceed this critical point, causing PVC powder to melt and adhere to the blade, resulting in tooth clogging. Once tooth clogging occurs, frictional resistance increases exponentially, leading to scorching, stringing, or wavy patterns on the cut surface, severely impacting product quality. Furthermore, existing overload protection often only triggers an alarm when the current spikes dramatically, by which time the saw blade is usually already severely stuck together or even damaged, causing irreversible losses.

[0004] Furthermore, different batches or densities of PVC foam boards exhibit varying sensitivities to cutting heat, making it impossible to balance processing efficiency and quality with fixed parameter control methods. Current control methods lack the ability to detect the material's thermal viscosity state early on, hindering effective intervention during the latent period of tool sticking, resulting in high scrap rates and rapid tool wear. Summary of the Invention

[0005] This invention provides an intelligent cutting control method and system for PVC foam boards to solve the technical problems of greasy cutting teeth, poor product quality, and rapid tool wear in the cutting of PVC foam boards in the prior art.

[0006] In a first aspect, the present invention provides an intelligent cutting control method for PVC foam boards, comprising the following steps:

[0007] The cutting condition data is collected and preprocessed. The cutting condition data includes the measured current of the spindle motor, the temperature inside the saw blade guard, and the current real-time feed speed. The measured current is filtered by moving average to obtain the smoothed spindle current.

[0008] A thermal viscous resistance index model was constructed, and the thermal viscous resistance index, which characterizes the degree of saw blade adhesion risk, was calculated based on the smoothed spindle current, the rated operating voltage of the system spindle motor, the real-time feed speed, the effective cutting cross-sectional area, and the regional temperature.

[0009] Based on the thermal viscous drag index, a nonlinear negative feedback control strategy is adopted to determine the target feed rate at the next moment. When the thermal viscous drag index exceeds the safe drag threshold, the speed decay ratio is calculated by the hyperbolic tangent function to reduce the target feed rate.

[0010] The target feed rate is converted into drive pulses and sent to the servo driver to perform variable speed control, and a shutdown protection is triggered when the speed drops to a preset lower limit and the thermal viscous resistance index continues to rise.

[0011] Furthermore, the formula for calculating the thermal viscous resistance index, which characterizes the degree of saw blade adhesion risk, is as follows:

[0012]

[0013] In the formula, for The thermal viscous drag index at any given time; for The smoothed spindle current at all times; This is the rated operating voltage of the system spindle motor; for Real-time feed rate at any given moment; For the effective cutting cross-sectional area; Let be the system stability constant; is the base of the natural logarithm; for The temperature of the region at any given time; This refers to the Vicat softening point temperature of PVC material. This is the thermosensitive weighting factor.

[0014] Furthermore, the effective cutting cross-sectional area Determined by the product of the plate thickness and the kerf width; system stability constant. This is used to prevent the denominator value from overflowing when the real-time feed rate approaches zero.

[0015] Furthermore, the formula for determining the target feed rate at the next moment using a nonlinear negative feedback control strategy is as follows:

[0016]

[0017] In the formula, The target feed rate for the next control cycle; This is the maximum feed rate allowed by the process. The braking depth coefficient is used to set the minimum speed limit of the system under harsh operating conditions. It is the hyperbolic tangent function; This is a function to find the maximum value. for The thermal viscous drag index at any given time; This is the safety resistance threshold.

[0018] Furthermore, the data collected on the cutting process includes:

[0019] The effective current value of the spindle motor is obtained by a Hall current sensor installed at the input of the cutting motor driver;

[0020] The temperature of the area is obtained by an infrared temperature probe installed inside the saw blade guard.

[0021] The real-time feed speed is obtained by reading the encoder feedback of the servo drive or the pulse transmission frequency of the PLC.

[0022] Furthermore, the aiming point of the infrared temperature probe is set to the back face of the saw blade or the dust flow area where chips are discharged, in order to obtain the temperature of the area close to the actual temperature of the cutting point.

[0023] Furthermore, the safe resistance threshold is obtained by recording the average value of the thermal viscous resistance index during stable cutting when the equipment is unloaded or cutting a standard template in a cold state, and multiplying the average value by a preset safety factor as the safe resistance threshold.

[0024] Furthermore, the specific logic of the shutdown protection is as follows: determine whether the current real-time feed rate has been reduced to the minimum limit related to the maximum feed rate allowed by the process and the braking depth coefficient. If it has been reduced to the minimum limit and the detected thermal viscous resistance index continues to rise within a preset time period, it is determined to be abnormal and shutdown is executed.

[0025] Furthermore, the process of performing moving average filtering on the measured current includes: acquiring the original measured current values ​​at multiple consecutive sampling times, calculating the arithmetic mean of the multiple original measured current values, and using the arithmetic mean as the smoothed main shaft current at the current time to filter out high-frequency noise interference.

[0026] Secondly, the present invention provides an intelligent cutting control system for PVC foam boards, including a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent cutting control method for PVC foam boards is realized.

[0027] The beneficial effects are as follows: This invention, through physical modeling, deeply integrates current, speed, and temperature data to creatively construct a resistance evaluation model that includes a temperature penalty term, making it highly sensitive to the thermal adhesion phenomenon unique to PVC materials. During the latency period of tooth clogging, the system can proactively reduce the feed speed through a nonlinear negative feedback algorithm, cutting off the positive feedback loop of heat accumulation. This proactive control strategy, which "prevents disease," not only eliminates severe tooth clogging at its source, ensuring a smooth and flat cut surface, but also ensures that the saw blade always operates within a suitable temperature range, significantly extending the tool's lifespan and improving the overall efficiency of the automated production line. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention.

[0029] Figure 2 This is a schematic diagram illustrating the speed adaptive control logic and effect based on the resistance model of the present invention.

[0030] Figure 3 This is a schematic diagram comparing the control effects of existing technologies with those of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] An embodiment of the intelligent cutting control method for PVC foam boards provided by this invention:

[0033] like Figure 1 As shown, the intelligent cutting control method for PVC foam boards includes the following steps:

[0034] S1: Collect cutting condition data and perform preprocessing.

[0035] This step is mainly responsible for obtaining the key physical quantities during the cutting process from the hardware device.

[0036] Firstly, in terms of hardware deployment, a Hall current sensor is installed at the input of the cutting motor driver to acquire the effective current value of the spindle motor in real time. The sensor outputs a 0-5V analog signal, which is converted into a digital measured current by an AD conversion module. Meanwhile, an infrared temperature sensor is installed inside the saw blade guard. The sensor's aiming point is set to the area of ​​the saw blade's back face or the dust flow area where chips are discharged, as this area has the temperature closest to the actual cutting point temperature. The infrared temperature sensor outputs the real-time temperature of this area. The response time is set to less than 100ms. Furthermore, the current real-time feed speed is obtained by reading the encoder feedback from the servo drive or the pulse transmission frequency from the PLC. .

[0037] Secondly, the raw measured current collected A moving average filter is applied to remove high-frequency noise caused by power grid fluctuations, resulting in a smoothed spindle current. .

[0038] For example, if the system sets the filter window length to 5, and the current values ​​collected for 5 consecutive times are [10.2A, 10.5A, 9.8A, 10.4A, 10.1A], then the smoothed spindle current... This process ensures that the subsequent formula calculations use a stable effective load current.

[0039] By collecting and filtering the dataset in real time, a more accurate dataset with noise interference removed can be obtained, laying the foundation for accurate calculation of the subsequent model.

[0040] S2, construct the thermoviscous resistance index model.

[0041] This step involves building a physical model to assess whether the saw blade is in a dangerous state of impending adhesion.

[0042] Specifically, the formula for calculating the thermal viscous resistance index, which characterizes the risk of saw blade adhesion, is as follows:

[0043]

[0044] in, for The thermal viscous resistance index at any given time; the larger the value, the higher the risk of tooth clogging. for The smoothed spindle current at all times; The rated operating voltage of the system spindle motor is read from the equipment nameplate; for Real-time feed rate at any given moment; For the effective cutting cross-sectional area; This is the system stability constant, which can be 1.0. Its function is to prevent... When the value approaches zero, the denominator tends to be zero, causing numerical overflow. for The temperature of the region at any given time; This is the Vicat softening point temperature of PVC material, usually taken as 75℃; is the base of the natural logarithm, approximately 2.718; This is the thermo-sensitive weighting factor, for example, 1.5.

[0045] Among them, the effective cutting cross-sectional area Directly from the thickness of the sheet material With kerf width The product is obtained.

[0046] The calculation example is as follows:

[0047] Assuming the current cutting is of thickness PVC board, kerf width ,but ;

[0048] Assume system parameters: , ℃, , ;

[0049] At some point The collected data is: smoothed spindle current. Real-time feed rate Regional temperature ℃.

[0050] First, the first part of the calculation formula, the numerator: ; Denominator: The first half of the value .

[0051] Secondly, the latter part of the calculation formula, the temperature ratio: Logarithmic internal values: Logarithmic calculation: Exponential weighting: .

[0052] Finally, calculate the final index: .

[0053] As can be seen, without the temperature factor, the value might only be around 5700, but with the temperature factor introduced, the value is amplified to over 8400, which keenly points out the risk of adhesion under the current high temperature.

[0054] By constructing this index model, the complex cutting thermodynamic state can be transformed into a single numerical index, accurately assessing the risk of tooth clogging under the current working conditions and compensating for the lag of single current detection.

[0055] S3 determines the target feed rate based on the thermoviscous resistance index.

[0056] The controller is based on the calculated The target feed rate at the next moment is calculated using a nonlinear negative feedback algorithm. .

[0057] Specifically, the formula for determining the target feed rate at the next moment using a nonlinear negative feedback control strategy is as follows:

[0058]

[0059] In the formula, The target feed rate for the next control cycle; The maximum feed rate allowed by the process, for example ; This is the braking depth coefficient, for example, 0.6. This coefficient determines the minimum speed limit of the system under the worst operating conditions. It is the hyperbolic tangent function; Used to filter out negative values; This is the safety resistance threshold.

[0060] Assuming the safety resistance threshold is determined by passing an unloaded test and multiplying it by a preset safety factor (e.g., 1.2). .

[0061] Continuing from the example in step S2, the calculated result is... .

[0062] The first step is to calculate the out-of-limit deviation: Relative deviation ratio: .

[0063] The second step is to calculate the hyperbolic tangent adjustment: ,because The function has the property that its value smoothly approaches 1 as the input increases.

[0064] The third step is to calculate the speed attenuation ratio: Attenuation amount = This means a reduction in speed of approximately 22.8%; retention rate = .

[0065] Step 4: Calculate the target velocity: .

[0066] As can be seen, because the resistance index exceeded the safety threshold, the system automatically and smoothly reduced the speed from the maximum value of 0.15m / s to 0.116m / s. The speed reduction will directly reduce frictional heat generation, causing the temperature of the subsequent area to drop, thereby inhibiting the further deterioration of the tooth paste.

[0067] By using the nonlinear adjustment of the hyperbolic tangent function, smooth deceleration is achieved when risks first appear, and strong braking is achieved when risks are high, effectively balancing processing efficiency and equipment safety.

[0068] S4 performs speed control and shutdown protection.

[0069] This step is responsible for implementing the calculated instructions into physical actions.

[0070] The PLC will calculate The signal is converted into a corresponding pulse frequency and sent to the servo driver for speed adjustment. Simultaneously, the system performs a fuse protection check: the system continuously monitors whether the current speed has dropped to the minimum limit. .

[0071] For example, the minimum value under the above parameters is If the system detects that the speed has been maintained at around 0.06 m / s, but the detected speed is... If the blade continues to rise within a preset time period (e.g., 5 seconds), it is determined that the saw blade is completely unusable or the cooling system has failed. In this case, the system will immediately stop feeding and sound an alarm, prompting the operator to replace the saw blade.

[0072] like Figure 2 As shown, the speed adaptive control logic and effect based on the drag model are illustrated. to Within a certain timeframe, the thermal viscous drag index is below the safety threshold, and the feed rate command curve shows that the feed rate remains within a certain range. The maximum value. Around the second mark, the thermoviscous drag index begins to climb and exceeds the threshold. At this point, the system algorithm intervenes, and the feed rate command curve rapidly and smoothly declines. As the speed decreases, the upward trend of the thermoviscous drag index is curbed and begins to fall back, eventually stabilizing near the threshold.

[0073] Figure 3 The invention demonstrates a comparison with existing technologies. Existing technologies exhibit an exponential increase in resistance over time, exceeding the material softening threshold and leading to severe tooth clogging. In contrast, the invention's resistance index, through adaptive control, successfully maintains the cutting state within a safe range.

[0074] Through strict execution logic and circuit breaker protection mechanisms, the effective implementation of control strategies is ensured, and a final safety line is provided for the equipment, preventing serious production accidents from occurring.

[0075] An embodiment of the intelligent cutting control system for PVC foam boards provided by this invention:

[0076] The intelligent cutting control system for PVC foam boards includes a processor and a memory. The memory stores computer program instructions, which are executed by the processor to implement the aforementioned intelligent cutting control method for PVC foam boards.

[0077] The intelligent cutting control system for PVC foam boards also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0078] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0079] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent cutting control of PVC foam boards, characterized in that, Includes the following steps: The cutting condition data is collected and preprocessed. The cutting condition data includes the measured current of the spindle motor, the temperature inside the saw blade guard, and the current real-time feed speed. The measured current is filtered by moving average to obtain the smoothed spindle current. A thermal viscous resistance index model is constructed. Based on the smoothed spindle current, the rated operating voltage of the system spindle motor, the real-time feed rate, the effective cutting cross-sectional area, and the regional temperature, the thermal viscous resistance index, which characterizes the degree of saw blade adhesion risk, is calculated, including: , for Thermoviscous drag index at time t, for The smoothed spindle current at all times This refers to the rated operating voltage of the system's spindle motor. for Real-time feed rate at all times To achieve an effective cutting cross-sectional area, Let be the system stability constant. is the base of the natural logarithm. for The temperature of the region at that moment, This refers to the Vicat softening point temperature of PVC material. Thermosensitive weighting factor; effective cutting cross-sectional area Determined by the product of the plate thickness and the kerf width; system stability constant. Used to prevent the denominator value from overflowing when the real-time feed rate approaches zero; Based on the thermoviscous drag index, a nonlinear negative feedback control strategy is used to determine the target feed rate at the next moment, including: , The target feed rate for the next control cycle. This is the maximum feed rate allowed by the process. The braking depth coefficient is used to set the minimum speed limit of the system under harsh operating conditions. It is the hyperbolic tangent function. To find the maximum value function, The safety resistance threshold; When the thermal viscous drag index exceeds the safe drag threshold, the velocity decay ratio is calculated using the hyperbolic tangent function to reduce the target feed rate. The target feed rate is converted into drive pulses and sent to the servo driver to perform variable speed control, and a shutdown protection is triggered when the speed drops to a preset lower limit and the thermal viscous resistance index continues to rise.

2. The intelligent cutting control method for PVC foam boards according to claim 1, characterized in that, The data collected for the cutting process includes: The effective current value of the spindle motor is obtained by a Hall current sensor installed at the input of the cutting motor driver; The temperature of the area is obtained by an infrared temperature probe installed inside the saw blade guard. The real-time feed speed is obtained by reading the encoder feedback of the servo drive or the pulse transmission frequency of the PLC.

3. The intelligent cutting control method for PVC foam boards according to claim 2, characterized in that, The aiming point of the infrared temperature probe is set to the back face of the saw blade or the dust flow area where chips are discharged, in order to obtain the temperature of the area close to the actual temperature of the cutting point.

4. The intelligent cutting control method for PVC foam boards according to claim 1, characterized in that, The safe resistance threshold is obtained by recording the average value of the thermal viscous resistance index during stable cutting when the equipment is unloaded or cutting a standard template in a cold state, and multiplying the average value by a preset safety factor as the safe resistance threshold.

5. The intelligent cutting control method for PVC foam boards according to claim 1, characterized in that, The specific logic of the shutdown protection is as follows: determine whether the current real-time feed rate has been reduced to the minimum limit related to the maximum feed rate allowed by the process and the braking depth coefficient. If it has been reduced to the minimum limit and the detected thermal viscous resistance index continues to rise within a preset time period, it is judged as abnormal and shutdown is executed.

6. The intelligent cutting control method for PVC foam boards according to claim 1, characterized in that, The process of applying a moving average filter to the measured current includes: acquiring the original measured current values ​​at multiple consecutive sampling times, calculating the arithmetic mean of the multiple original measured current values, and using the arithmetic mean as the smoothed main shaft current at the current time to filter out high-frequency noise interference.

7. A smart cutting control system for PVC foam boards, characterized in that, The device includes a memory and a processor. The memory stores computer program instructions, which, when executed by the processor, implement the intelligent cutting control method for PVC foam boards according to any one of claims 1-6.

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