Non-linear correction method and system for pressure sensor of injection molding machine and injection molding machine

Abstract

The invention discloses a nonlinear correction method and system for a pressure sensor of an injection molding machine and the injection molding machine. The nonlinear correction method comprises the following steps: firstly, according to at least four different calibration pressures applied to a pressure sensor, recording calibration voltages correspondingly output by the pressure sensor, and taking the calibration pressures and the calibration voltages as calibration data; secondly, according to the calibration data and a preset algorithm, constructing and fitting to obtain a fitting model capable of reflecting the relationship between the calibration pressure and the calibration voltage; then acquiring an actual voltage value output by the pressure sensor, substituting the actual voltage value into the fitting model, and calculating to obtain a preliminary corrected pressure value; and finally, acquiring the real-time temperature near the pressure sensor, introducing a temperature compensation item according to the real-time temperature, and further correcting the preliminarily corrected pressure value to obtain a final corrected pressure value. According to the invention, the nonlinear error of the pressure sensor in a full-scale range can be obviously reduced, and the absolute precision of pressure measurement is improved.

Description

Technical Field

[0001] This invention belongs to the field of injection molding machine pressure detection technology, and specifically relates to a nonlinear correction method, system and injection molding machine for injection molding machine pressure sensors. Background Technology

[0002] In the field of plastic molding and processing, high-speed electric injection molding machines have become core equipment for the production of precision injection molded products due to their advantages such as fast response speed, high control precision, and low energy consumption. Among them, the ring pressure sensor, as a key detection element, is widely deployed at the nozzle or front end of the mold cavity of the injection molding machine to monitor the pressure parameters of the molten plastic in real time.

[0003] The quality of this pressure detection signal plays a decisive role in the core control aspects of the injection molding process. On the one hand, it directly determines the accuracy of the switch from speed control to pressure control (i.e., V / P switching) during the injection stage. The accuracy of the V / P switching point is a prerequisite for ensuring the stability of the melt filling state. On the other hand, this signal is also the core feedback basis for the closed-loop control of pressure during the holding pressure stage, directly affecting the stability of the holding pressure. The accuracy of V / P switching and the stability of holding pressure control together constitute key factors affecting the weight repeatability and dimensional accuracy of injection molded products, and are crucial to the yield rate and production consistency of precision injection molded products.

[0004] Although ring pressure sensors play an irreplaceable role in high-speed electric injection molding machines, existing technologies still have many prominent shortcomings, making it difficult to meet the stringent requirements of high-speed precision injection molding: Currently available ring pressure sensors typically have a nominal linearity ranging from ±0.6% to ±2% FS (full scale). Taking a sensor with a full scale of 362 kN as an example, a linearity error of only ±1% FS can result in a pressure feedback deviation of up to ±3.6 kN across the entire scale. In high-speed (e.g., 2 kHz) pressure closed-loop control systems, this nonlinear error can cause hysteresis or distortion in the pressure feedback signal, leading to a shift in the injection V / P switching point, severe pressure oscillations during the holding pressure stage, and ultimately significant fluctuations in the weight of the injection-molded product, resulting in a substantial decrease in the stability of the production process.

[0005] Furthermore, the injection molding industry commonly employs single-point or two-point linear calibration methods for annular pressure sensors, meaning that only the zero point and full-scale point of the sensor are calibrated. This simplified calibration method can only fit the linear characteristic curve under ideal conditions and cannot effectively fit and accurately correct the actual nonlinear characteristics of the sensor across the entire measurement range. This results in a significant gap between the actual measurement accuracy and the theoretical accuracy of the sensor, making it difficult to meet the high-precision pressure measurement requirements of high-speed precision injection molding and severely restricting the improvement of precision and technological breakthroughs in injection molding processes. Summary of the Invention

[0006] To address the aforementioned problems, this invention discloses a nonlinear correction method, system, and injection molding machine for an injection molding machine pressure sensor, in order to overcome or at least partially solve the aforementioned problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: This invention discloses a nonlinear correction method for a pressure sensor in an injection molding machine, the nonlinear correction method comprising: Based on at least four different calibration pressures applied to the pressure sensor, the corresponding calibration voltage output by the pressure sensor is recorded, and the calibration pressure and the calibration voltage are used as calibration data; Based on the calibration data and the preset algorithm, a fitting model that reflects the relationship between the calibration pressure and the calibration voltage is constructed and fitted. The actual voltage value output by the pressure sensor is obtained, and the actual voltage value is substituted into the fitting model to calculate the preliminary corrected pressure value; The real-time temperature near the pressure sensor is obtained, and a temperature compensation term is introduced based on the real-time temperature to further correct the preliminary corrected pressure value, thereby obtaining the final corrected pressure value.

[0008] Furthermore, the at least four different calibration pressures uniformly cover the full range of the pressure sensor.

[0009] Further, the execution of constructing and fitting a fitting model that reflects the relationship between the calibration pressure and the calibration voltage based on the calibration data and the preset algorithm includes: Constructing a polynomial function: F = a0 + a1·V + a2·V 2 + ... + a m ·V m Where F is the pressure applied to the pressure sensor; V is the voltage output by the pressure sensor; m is the polynomial order; a0 to a m The coefficients of the polynomial; The calibration data is input into the polynomial function, and the coefficients in the polynomial function are determined based on the least squares method to obtain the fitting model.

[0010] Furthermore, the step of introducing a temperature compensation term based on the real-time temperature to further correct the initial corrected pressure value and obtain the final corrected pressure value includes: Construct the temperature compensation function: F corrT = F corr+ k·(T - T0) Among them, F corrT The final corrected pressure value; k is the temperature compensation coefficient; F corr This is for initial pressure value correction; T is the real-time temperature near the pressure sensor; T0 is the reference temperature; The initial corrected pressure value and the real-time temperature are input into the temperature compensation function to obtain the final corrected pressure value.

[0011] Furthermore, the nonlinear correction method further includes: During the no-load phase of the injection molding machine, the coefficients a0 in the fitted model are updated when zero pressure is applied to the pressure sensor.

[0012] Furthermore, the nonlinear correction method further includes: The injection screw of the injection machine is controlled to advance, and a stable known pressure is established in the closed barrel. Then, the corresponding voltage value output by the pressure sensor is recorded to form a calibration data point. The calibration data point is added to the calibration data as new calibration data. Based on the new calibration data, recalculate and update the coefficients a0 to a in the fitted model. m .

[0013] Furthermore, the nonlinear correction method further includes: The temperature compensation coefficient k is updated periodically.

[0014] Another aspect of the present invention discloses a nonlinear correction system for an injection molding machine pressure sensor. The nonlinear correction system is applied to the aforementioned nonlinear correction method for an injection molding machine pressure sensor. The nonlinear correction system includes: The data acquisition unit is used to record the calibration voltage output by the pressure sensor based on at least four different calibration pressures applied to the pressure sensor, and to use the calibration pressure and the calibration voltage as calibration data; The training data construction unit is used to construct and fit a fitting model that reflects the relationship between the calibration pressure and the calibration voltage based on the calibration data and the preset algorithm. The first correction unit is used to obtain the actual voltage value output by the pressure sensor, and input the actual voltage value into the fitting model to calculate the preliminary corrected pressure value; The second correction unit is used to acquire the real-time temperature near the pressure sensor, introduce a temperature compensation term based on the real-time temperature, and further correct the preliminary corrected pressure value to obtain the final corrected pressure value.

[0015] Furthermore, the nonlinear correction system also includes: The learning unit controls the injection screw of the injection molding machine to advance, establishing a stable, known pressure within the closed barrel. It then records the corresponding voltage value output by the pressure sensor, forming calibration data points. These calibration data points are added to the existing calibration data as new calibration data. Based on this new calibration data, the coefficients a0 to a1 in the fitted model are recalculated and updated. m The temperature compensation coefficient k is updated periodically.

[0016] In another aspect, the present invention discloses an injection molding machine, the injection molding machine comprising: Processor; and Memory for storing the executable instructions of the processor; The processor executes the executable instructions to enable the injection molding machine to implement the nonlinear correction method for the injection molding machine pressure sensor described above.

[0017] The advantages and beneficial effects of this invention are: In the nonlinear correction method of this invention, the pressure sensor measurement is first preliminarily corrected using a fitting model obtained from calibration data, which can significantly reduce the nonlinear error of the pressure sensor across its entire range. Then, by introducing a temperature compensation term, the interference of ambient temperature changes on the pressure sensor measurement accuracy can be eliminated, solving the measurement deviation problem caused by temperature drift, thereby improving the absolute accuracy of pressure measurement. Furthermore, the nonlinear correction method of this invention is a purely software-based compensation solution, eliminating the need for additional high-cost hardware calibration modules and significantly reducing the implementation cost of correction. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a diagram illustrating the implementation steps of a nonlinear correction method for an injection molding machine pressure sensor in one embodiment of the present invention. Figure 2 A comparison graph of the polynomial fitting curve and the ideal linear relationship curve; Figure 3 This is a comparison chart of the errors before and after correction; Figure 4 This is a schematic diagram of the injection molding machine in another embodiment of the present invention. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or sets.

[0021] To keep the drawings concise, only the parts relevant to the invention are shown schematically in each figure, and they do not represent the actual structure of the product. Furthermore, for ease of understanding, in some figures, components with the same structure or function are shown only schematically, or only one is labeled. In this document, "a" not only means "only one," but can also mean "more than one."

[0022] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0024] One embodiment of the present invention provides a nonlinear correction method for an injection molding machine pressure sensor, aiming to solve the problems of inherent nonlinear error in existing injection molding machine ring pressure sensors and the limitations of traditional calibration methods, and to achieve high-precision pressure measurement across the entire range. Figure 1 As shown, the nonlinear correction method includes: Step 01: Based on at least four different calibration pressures applied to the pressure sensor, record the corresponding calibration voltage output by the pressure sensor, and use the calibration pressure and calibration voltage as calibration data.

[0025] Understandably, this step can be performed before the pressure sensor leaves the factory, i.e., by setting up a dedicated calibration test bench to calibrate the pressure sensor. Specifically, n (n is a natural number not less than 4) different calibration pressures {F1, F2, ..., F} are applied to the pressure sensor. n Furthermore, n different calibration pressures evenly cover the entire range (operating range) of the pressure sensor to ensure the representativeness of the calibration data; for example, in the range of 0 ~ 362 kN, five calibration pressure points are selected: 0 N, 90.5 kN, 181 kN, 271.5 kN and 362 kN.

[0026] After each calibration pressure stabilizes, record the corresponding calibration voltage V output by the pressure sensor. n Calibration voltage V n Covering the key areas of the entire calibration range (low-pressure, medium-pressure, and high-pressure sections), each pair of calibration pressure and calibration voltage values ​​is used as calibration data to form complete calibration data { (F1, V1), (F2, V2), ..., (F n V n Compared to traditional single-point or two-point calibration, multiple sets of calibration data can fully present the nonlinear characteristics of the pressure sensor across its entire range.

[0027] Step 02: Based on the calibration data and the preset algorithm, construct and fit a fitting model that can reflect the relationship between the calibration pressure and the calibration voltage.

[0028] Understandably, to accurately characterize the nonlinear relationship between the calibration pressure and calibration voltage of the injection molding machine pressure sensor, this embodiment employs the least squares method to fit the collected calibration data into an m-order polynomial function with voltage V as the independent variable and pressure F as the dependent variable; specifically: First, construct the polynomial function: F = a0 + a1·V + a2·V 2 + ... + a m ·V m Where F is the pressure applied to the pressure sensor; V is the voltage output by the pressure sensor; m is the polynomial order; a0 to a m are the coefficients of the polynomial.

[0029] The calibration data is input into a polynomial function, and an objective function S, representing the sum of squared errors of all calibration data fitting errors, is constructed based on the least squares method. The coefficients a0, a1, ..., a... in the polynomial function are determined when the objective function S reaches its minimum value. m Substituting this coefficient into the polynomial function yields the fitted model.

[0030] Compared to traditional single-point / two-point linear calibration, m-order polynomial fitting can accurately fit the nonlinear output curve of the sensor across its entire range, significantly reducing nonlinear errors. By adjusting the polynomial order m, a balance can be achieved between computational complexity and fitting accuracy. 2nd or 3rd order polynomials can meet the pressure measurement accuracy requirements of high-speed precision injection molding and reduce the complexity of calculations. The least squares method achieves fitting by minimizing the sum of squared errors, which can effectively suppress random interference in the calibration process and ensure the stability and reliability of the fitting model.

[0031] Step 03: Obtain the actual voltage value output by the pressure sensor, and input the actual voltage value into the fitting model to calculate the preliminary corrected pressure value.

[0032] Understandably, during the actual operation of the injection molding machine, the actual voltage value output by the pressure sensor is collected in real time. This actual voltage value is then substituted into the fitting model obtained in step 02. Through the inverse operation of the fitting model or direct mapping calculation, a preliminary corrected pressure value is obtained.

[0033] This step can eliminate the measurement deviation caused by the inherent nonlinear characteristics of the pressure sensor. Compared with the traditional calibration method, the error of the initial pressure value correction can be controlled within ±0.3% FS, laying the foundation for subsequent high-precision control.

[0034] Step 04: Obtain the real-time temperature near the pressure sensor, introduce a temperature compensation term based on the real-time temperature, further correct the initial corrected pressure value, and obtain the final corrected pressure value.

[0035] Understandably, a high-precision temperature sensor (such as a thermistor or thermocouple) is deployed near the pressure sensor to collect real-time temperature data of the pressure sensor's operating environment, denoted as the real-time temperature T. Based on the pre-calibrated sensor temperature drift characteristics, a temperature compensation function is first constructed: F corrT = F corr + k·(T - T0) Among them, F corrT The final corrected pressure value; k is the temperature compensation coefficient; F corr The pressure value is initially corrected; T is the real-time temperature near the pressure sensor; T0 is the reference temperature, specifically 25℃.

[0036] The initial corrected pressure value and real-time temperature are then input into the temperature compensation function to obtain the final corrected pressure value. Temperature compensation eliminates the influence of ambient temperature changes on sensor measurement accuracy, ensuring the stability and accuracy of pressure measurement within the complex operating temperature range of the injection molding machine.

[0037] It should be noted that the temperature compensation coefficient k is obtained as follows: First, install the pressure sensor and temperature sensor in a temperature-controlled chamber, and apply a constant and precise known pressure F to the pressure sensor. ref For example, using standard weights or a calibrated standard pressure generator.

[0038] Secondly, the temperature chamber is slowly heated from a low temperature (e.g., 10°C) to a high temperature (e.g., 60°C), covering the entire temperature range in which the pressure sensor operates. Simultaneously, the actual temperature T and the pressure reading F output by the pressure sensor after polynomial compensation are recorded. corr (T); where the pressure Fref is kept constant throughout the temperature change process.

[0039] Then, calculate the error at each temperature point T: Error(T) = F corr (T) F ref Plot a graph with temperature T on the x-axis and error on the y-axis; the data points usually show an approximately linear distribution.

[0040] Finally, a linear regression (least squares fitting) was performed on these data points to obtain a straight line Error = k·(T) T0), and then the temperature compensation coefficient k is calculated.

[0041] In summary, the nonlinear correction method for the injection molding machine pressure sensor in this embodiment, through the core algorithm of "multi-point calibration + polynomial fitting + temperature compensation," effectively overcomes the shortcomings of existing technologies, such as large sensor nonlinearity errors, measurement deviations caused by temperature drift, and poor adaptability, and has the following significant beneficial effects: I. Significantly improves the absolute accuracy of pressure measurement and eliminates nonlinear errors across the entire measurement range.

[0042] During the calibration phase, the output voltages at at least four known pressure points are collected, and the pressure sensor output signal is corrected in real time during operation based on the fitted model. Compared with traditional single-point or two-point linear calibration methods (see Table 1), this invention can significantly reduce the inherent nonlinear error of the pressure sensor across the entire measurement range, greatly compressing the sensor measurement error from the traditional ±0.6% ~ ±2% FS range. This effectively improves the absolute accuracy of pressure measurement, laying a high-precision data foundation for the precise control of V / P switching and the stable adjustment of holding pressure in injection molding machines. Consequently, it can significantly improve the weight repeatability and dimensional accuracy of injection molded products, and increase the yield of precision injection molded products.

[0043] Table 1

[0044] Second, it meets the requirements of high-speed closed-loop control and achieves real-time signal compensation without delay.

[0045] This invention employs a lightweight software algorithm architecture to perform compensation calculations, making it adaptable to high-speed closed-loop pressure control scenarios with a 2 kHz bandwidth. Its compensation calculation delay is significantly lower than the injection molding machine's pressure control cycle, thus avoiding interference with the real-time performance of high-speed closed-loop control. While ensuring high-precision compensation, it also guarantees the timeliness and effectiveness of pressure feedback signals during high-speed injection molding, avoiding problems such as pressure control lag and holding pressure oscillation caused by compensation algorithm delays, thereby ensuring the stability of high-speed precision injection molding production.

[0046] Third, the system integrates multiple compensation mechanisms to ensure long-term stability and reliability.

[0047] This invention integrates a temperature drift compensation function. By collecting the real-time temperature near the pressure sensor and introducing a temperature compensation term, it can eliminate the interference of ambient temperature changes on the sensor's measurement accuracy and solve the measurement deviation problem caused by temperature drift.

[0048] Fourth, it is highly versatile and inexpensive, and can be adapted to various sensor specifications.

[0049] This invention is a purely software-based compensation solution, eliminating the need for additional high-cost hardware calibration modules and significantly reducing the implementation cost of calibration. Furthermore, its core polynomial fitting algorithm and compensation logic can flexibly adapt to different ranges and models of ring pressure sensors. High-precision calibration can be achieved simply by completing corresponding multi-point calibration and polynomial parameter updates for different sensor specifications. It possesses strong versatility and scalability, and can be widely applied to the upgrading and new construction projects of pressure measurement systems in various injection molding machines, providing the industry with a low-cost and easily implemented sensor accuracy upgrade solution.

[0050] In this embodiment, the nonlinear correction method further includes: During the no-load phase of the injection molding machine, the coefficients a0 in the fitted model are updated when zero pressure is applied to the pressure sensor.

[0051] Understandably, even the best pressure sensors will exhibit slow changes in their output characteristics over time (i.e., "time drift"). Minor creep in the mechanical structure and aging of electronic components can cause the initially calibrated fitting model to gradually become distorted. By automatically performing a zero-pressure calibration during the no-load phase (e.g., after daily power-on warm-up), the coefficients a0 of the fitting model are automatically updated, thereby eliminating "zero drift" and ensuring that the pressure reading reference remains accurate.

[0052] In other embodiments, the nonlinear correction method further includes: The injection screw of the injection molding machine is controlled to advance, establishing a stable, known pressure within the closed barrel. The corresponding voltage value output by the pressure sensor is then recorded, forming calibration data points. These calibration data points are added to the overall calibration data as new calibration data. Based on this new calibration data, the coefficients a0 to a1 in the fitted model are recalculated and updated. m .

[0053] Understandably, if only a one-time calibration at the factory can be relied upon, the control accuracy will irreversibly and continuously decline as the equipment ages, leading to fluctuations in product quality. Traditional high-precision systems require periodic on-site calibration by professional technicians using expensive external equipment, or the removal of sensors for factory calibration, resulting in long downtime and high maintenance costs. In this embodiment, during injection molding machine maintenance, when the mold is changed or the equipment is serviced, the operator can be guided through a simplified on-site calibration process (e.g., applying a known, stable system pressure), simplifying the complex calibration process into a "one-click" operation. The calibration data points are added to the new calibration data, and based on this new calibration data, combined with the least squares method, the coefficients a0 to a1 in the fitted model are recalculated and updated. m This allows for fine-tuning of the coefficients of the fitting model to better fit the current actual working environment, compensate for pressure sensor aging and long-term drift, maintain repeatability accuracy ≤0.1% FS, thereby combating performance and accuracy degradation and ensuring the quality consistency of every product produced.

[0054] The nonlinear correction method in this embodiment has a long-term aging self-learning function, which can adaptively learn and compensate for the aging characteristics of the pressure sensor during long-term use. This effectively counteracts the accuracy decay caused by the aging of the pressure sensor, greatly improves the long-term stability and operational reliability of the pressure measurement system, increases the injection molding pressure control accuracy by about 30%, reduces product weight fluctuation by more than 50%, extends the effective service life of the pressure sensor, and reduces equipment maintenance and replacement costs.

[0055] Furthermore, nonlinear correction methods also include: The temperature compensation coefficient k should be updated periodically.

[0056] Understandably, the temperature compensation coefficient k can be updated automatically on a regular basis, such as every 1000 hours of operation or after a certain batch of products (configurable). Alternatively, it can be triggered by the operator with a single button press during mold changes or equipment maintenance. The system can continue to operate normally during this update process without requiring machine shutdown or external calibration equipment. Specifically, during the operation of the injection molding machine, the output voltage value of the pressure sensor is first recorded under known system pressure, and the current ambient temperature T of the pressure sensor is recorded simultaneously. i Each point forms a data point (T). i Vi ); where V i This represents the output voltage of the pressure sensor at the corresponding temperature. Then, using multiple sets of collected (T...) i V i The data is used to fit a new temperature compensation coefficient k of the current pressure sensor using the least squares method, and the temperature compensation function k in the temperature compensation function is updated.

[0057] In one specific embodiment, the nonlinear correction method for the pressure sensor is as follows: Equipment and Sensor Configuration: Injection Molding Machine Type: Electric injection molding machine, closed-loop pressure control bandwidth of 2 kHz. Pressure Sensor: Ring pressure sensor, full scale of 362 kN, output voltage of 0 ~ 10 V, nominal linearity of ±1% FS. Controller: PLC (Programmable Logic Controller) / MCU (Microcontroller Unit) with built-in pressure acquisition module, 12-bit A / D conversion.

[0058] ① Multi-point calibration: First, install the pressure sensor inside the injection cylinder of the injection molding machine and apply six known force points to the pressure sensor: 0 kN, 60 kN, 120 kN, 180 kN, 240 kN, and 362 kN. Then, record the corresponding output voltages of the pressure sensor (considering ±1% FS nonlinearity): 0.05 V, 1.63 V, 3.28 V, 4.98 V, 6.63 V, and 10.00 V. Finally, establish calibration data. {(0,0.05), (60,1.63), (120,3.28), (180,4.98), (240,6.63), (362,10.00)}.

[0059] ② Polynomial fitting Perform quadratic polynomial fitting (least squares method) on the calibration data: F= 1.5 + 36.2 V 0.48·V 2 (kN) Error between fitting results and calibration points: maximum ±1kN (≈0.28%FS) Depend on Figure 2 It can be seen that the polynomial fitting curves of the pressure sensor input pressure and output voltage obtained by fitting the model are close to the ideal linear relationship curve and are more in line with the actual situation.

[0060] ③Preliminary revisions The controller reads the output voltage V of the pressure sensor in real time. real It then calls the fitting function to calculate the initial corrected pressure value: F corr = 1.5 + 36.2 V real 0.48·V real 2 ④ Temperature compensation Based on calculations, the temperature compensation coefficient k is -0.00051 kN / ℃, and the measured current temperature is 60℃. Substituting the initial corrected pressure value into the temperature compensation function, the final corrected pressure value is obtained: F corrT = F corr + (-0.00051 kN / ℃) · (60℃ - 25℃) Depend on Figure 3 As can be seen, before compensation, the error range was relatively large, ranging from ±1% of the full scale (FS) of the pressure sensor; after compensation, the error range was significantly reduced, decreasing to ±0.28% of the full scale of the pressure sensor. After calibrating the pressure sensor using the above nonlinear correction method, the measurement accuracy of the system was greatly improved, and the error was reduced by approximately 70%.

[0061] Another embodiment of the present invention provides a nonlinear correction system for an injection molding machine pressure sensor. The nonlinear correction system is applied to the nonlinear correction method for the injection molding machine pressure sensor described in the above embodiment. The nonlinear correction system includes: The data acquisition unit is used to record the corresponding calibration voltage output by the pressure sensor based on at least four different calibration pressures applied to the pressure sensor, and to use the calibration pressure and calibration voltage as calibration data.

[0062] The training data construction unit is used to construct and fit a fitting model that reflects the relationship between calibration pressure and calibration voltage based on calibration data and a preset algorithm.

[0063] The first correction unit is used to obtain the actual voltage value output by the pressure sensor, and input the actual voltage value into the fitting model to calculate the preliminary corrected pressure value.

[0064] The second correction unit is used to acquire the real-time temperature near the pressure sensor, introduce a temperature compensation term based on the real-time temperature, and further correct the initial corrected pressure value to obtain the final corrected pressure value.

[0065] This nonlinear correction system can significantly reduce the nonlinear error of the pressure sensor across the entire measurement range and eliminate the interference of ambient temperature changes on the measurement accuracy of the pressure sensor, thus solving the measurement deviation problem caused by temperature drift.

[0066] In some embodiments, the at least four different calibration pressures uniformly cover the full range of the pressure sensor.

[0067] In some embodiments, the execution of constructing and fitting a fitting model that reflects the relationship between the calibration pressure and the calibration voltage based on the calibration data and a preset algorithm includes: Constructing a polynomial function: F = a0 + a1·V + a2·V 2 + ... + a m ·V m Where F is the pressure applied to the pressure sensor; V is the voltage output by the pressure sensor; m is the polynomial order; a0 to a m are the coefficients of the polynomial.

[0068] The calibration data is input into the polynomial function, and the coefficients in the polynomial function are determined based on the least squares method to obtain the fitting model.

[0069] In some embodiments, the step of introducing a temperature compensation term based on the real-time temperature to further correct the initial corrected pressure value and obtain the final corrected pressure value includes: Construct the temperature compensation function: F corrT = F corr + k·(T - T0) Among them, F corrT The final corrected pressure value; k is the temperature compensation coefficient; F corr The pressure value is initially corrected; T is the real-time temperature near the pressure sensor; T0 is the reference temperature.

[0070] The initial corrected pressure value and the real-time temperature are input into the temperature compensation function to obtain the final corrected pressure value.

[0071] In some embodiments, the nonlinear correction system further includes: The learning unit controls the injection screw of the injection molding machine to advance, establishing a stable, known pressure within the closed barrel. It then records the corresponding voltage values ​​output by the pressure sensor, forming calibration data points. These calibration data points are added to the overall calibration data as new calibration data. Based on this new calibration data, the coefficients a0 to a1 in the fitted model are recalculated and updated. m The temperature compensation coefficient k should be updated periodically.

[0072] It is understood that the nonlinear correction system for the injection molding machine pressure sensor described above can realize all the steps of the nonlinear correction method for the injection molding machine pressure sensor provided in the foregoing embodiments. The relevant explanations regarding the nonlinear correction method for the injection molding machine pressure sensor are applicable to the nonlinear correction system for the injection molding machine pressure sensor, and will not be repeated here.

[0073] In another embodiment of the present invention, an injection molding machine is provided, such as... Figure 4 As shown, the injection molding machine includes: One or more processors (or processing units); may also include one or more memories coupled to the processor for storing executable instructions of the processor, and may also include a communication module coupled to the processor. The processor executes the executable instructions to cause the injection molding machine to implement a nonlinear correction method for an injection molding machine pressure sensor as described in the foregoing embodiments.

[0074] A communication module can be used to communicate with other devices or apparatuses, such as sending or receiving data and / or signals. A communication module may have at least one communication module for communication. A communication module may include any interface necessary for communicating with other devices. Exemplarily, a communication module may be a transceiver, circuit, bus, module, or other type of communication module.

[0075] The processor may include, but is not limited to, one or more of the following: a general-purpose computer, a special-purpose computer, a microcontroller, a digital signal processor (DSP), or a controller-based multi-core controller architecture. The device may have multiple processors, such as application-specific integrated circuit (ASIC) chips, which are time-dependent on a clock synchronized with the main processor.

[0076] The memory may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, at least one of the following: read-only memory (ROM), electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD), or other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, at least one of the following: random access memory (RAM), or other volatile memories that do not persist during the duration of a power outage.

[0077] A computer program consists of computer-executable instructions that are executed by an associated processor. Programs can be stored in ROM. A processor can perform any appropriate action and processing by loading the program into RAM.

[0078] Possible implementations of this application can be achieved through a program, enabling the communication device to execute any of the processes discussed in the foregoing embodiments. Possible implementations of this application can also be achieved through hardware or a combination of software and hardware.

[0079] In some implementations, the program may be tangibly contained in a computer-readable storage medium, which may include in a device (such as in memory) or other storage device accessible by the device. The program may be loaded from the computer-readable storage medium into RAM for execution. The computer-readable storage medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc.

[0080] The above description is merely a specific embodiment of the present invention. Under the teachings of the present invention, those skilled in the art can make other improvements or modifications based on the above embodiments. Those skilled in the art should understand that the above specific description is only to better explain the purpose of the present invention, and the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A nonlinear correction method for a pressure sensor in an injection molding machine, characterized in that, The nonlinear correction method includes: Based on at least four different calibration pressures applied to the pressure sensor, the corresponding calibration voltage output by the pressure sensor is recorded, and the calibration pressure and the calibration voltage are used as calibration data; Based on the calibration data and the preset algorithm, a fitting model that can reflect the relationship between the calibration pressure and the calibration voltage is constructed and fitted. The actual voltage value output by the pressure sensor is obtained, and the actual voltage value is substituted into the fitting model to calculate the preliminary corrected pressure value; The real-time temperature near the pressure sensor is obtained, and a temperature compensation term is introduced based on the real-time temperature to further correct the initial corrected pressure value, thereby obtaining the final corrected pressure value.

2. The nonlinear correction method for the injection molding machine pressure sensor according to claim 1, characterized in that, The execution of constructing and fitting a fitting model that reflects the relationship between the calibration pressure and the calibration voltage based on the calibration data and a preset algorithm includes: Constructing a polynomial function: F = a0 + a1·V + a2·V 2 + ... + a m ·V m Where F is the pressure applied to the pressure sensor; V is the voltage output by the pressure sensor; m is the polynomial order; a0 to a m The coefficients of the polynomial; The calibration data is input into the polynomial function, and the coefficients in the polynomial function are determined based on the least squares method to obtain the fitting model.

3. The nonlinear correction method for the injection molding machine pressure sensor according to claim 1, characterized in that, The step of introducing a temperature compensation term based on the real-time temperature to further correct the initial corrected pressure value and obtain the final corrected pressure value includes: Construct the temperature compensation function: F corrT = F corr + k·(T - T0) Among them, F corrT The final corrected pressure value; k is the temperature compensation coefficient; F corr This is for initial pressure value correction; T is the real-time temperature near the pressure sensor; T0 is the reference temperature; The initial corrected pressure value and the real-time temperature are input into the temperature compensation function to obtain the final corrected pressure value.

4. The nonlinear correction method for the injection molding machine pressure sensor according to claim 1, characterized in that, The nonlinear correction method further includes: During the no-load phase of the injection molding machine, the coefficients a0 in the fitted model are updated when zero pressure is applied to the pressure sensor.

5. The nonlinear correction method for the injection molding machine pressure sensor according to claim 1, characterized in that, The nonlinear correction method further includes: The injection screw of the injection machine is controlled to advance, and a stable known pressure is established in the closed barrel. Then, the corresponding voltage value output by the pressure sensor is recorded to form a calibration data point. The calibration data point is added to the calibration data as new calibration data. Based on the new calibration data, recalculate and update the coefficients a0 to a in the fitted model. m .

6. The nonlinear correction method for the injection molding machine pressure sensor according to claim 1, characterized in that, The nonlinear correction method further includes: The temperature compensation coefficient k is updated periodically.

7. The nonlinear correction method for the injection molding machine pressure sensor according to claim 1, characterized in that, The at least four different calibration pressures uniformly cover the full range of the pressure sensor.

8. A nonlinear correction system for an injection molding machine pressure sensor, characterized in that, The nonlinear correction system is applied to the nonlinear correction method for the injection molding machine pressure sensor according to any one of claims 1 to 7, and the nonlinear correction system comprises: The data acquisition unit is used to record the calibration voltage output by the pressure sensor based on at least four different calibration pressures applied to the pressure sensor, and to use the calibration pressure and the calibration voltage as calibration data; The training data construction unit is used to construct and fit a fitting model that reflects the relationship between the calibration pressure and the calibration voltage based on the calibration data and the preset algorithm. The first correction unit is used to obtain the actual voltage value output by the pressure sensor, and input the actual voltage value into the fitting model to calculate the preliminary corrected pressure value; The second correction unit is used to acquire the real-time temperature near the pressure sensor, introduce a temperature compensation term based on the real-time temperature, and further correct the preliminary corrected pressure value to obtain the final corrected pressure value.

9. The nonlinear correction system for the injection molding machine pressure sensor according to claim 8, characterized in that, The nonlinear correction system also includes: The learning unit controls the injection screw of the injection molding machine to advance, establishing a stable, known pressure within the closed barrel. It then records the corresponding voltage value output by the pressure sensor, forming calibration data points. These calibration data points are added to the existing calibration data as new calibration data. Based on this new calibration data, the coefficients a0 to a1 in the fitted model are recalculated and updated. m The temperature compensation coefficient k is updated periodically.

10. An injection molding machine, characterized in that, The injection molding machine includes: Processor; and Memory for storing the executable instructions of the processor; The processor executes the executable instructions to enable the injection molding machine to implement the nonlinear correction method for the injection molding machine pressure sensor as described in any one of claims 1 to 7.

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