Mold machining deviation dynamic correction method, system and equipment based on real-time monitoring

Abstract

The invention provides a mold machining deviation dynamic correction method, system and equipment based on real-time monitoring, and relates to the technical field of industrial machining control. The method comprises the steps that a target machining plan of a target mold is read; performing collaborative analysis on the target design parameters and the target process requirements to obtain a real-time workpiece preset model; dynamically monitoring the machining process of the target mold; according to the real-time state parameters of the target mold, a real-time workpiece actual model is constructed; comparing the real-time workpiece actual model with a real-time workpiece preset model to obtain a model comparison deviation; performing correction optimization to obtain an optimal correction strategy; and carrying out dynamic processing correction on the target mold. According to the method and the device, the technical problem that the machining precision and the quality of the mold are affected due to the fact that the dynamic error is difficult to accurately recognize because the preset model is difficult to capture and correct the dynamic change generated in the machining process in time is solved, and the machining precision and the quality of the mold are improved by recognizing the dynamic error and determining the optimal correction strategy.

Description

Technical Field

[0001] The present application relates to the field of industrial processing control technology, and in particular to a method, system and equipment for dynamically correcting mold processing deviations based on real-time monitoring. Background Art

[0002] During the mold processing, due to changes in the processing environment, machine tool status, material properties and tool conditions, the errors in mold processing are dynamic and changeable. Traditional processing modes often find it difficult to capture these changes in a timely manner. In order to ensure high precision and high quality of mold processing, static error models (such as geometric error, thermal error, tool wear compensation, etc.) are usually established before processing for pre-compensation, but they cannot cope with the real-time error changes during the processing. Online monitoring data during the processing process is used to correct the errors in real time, but the response speed is poor and it is difficult to achieve real-time compensation. Errors are detected after processing, and then secondary processing or manual correction is performed, but the cost is high and it is not suitable for mass production. During the processing process, the actual state will change in real time due to the influence of various factors such as ambient temperature, tool wear, vibration, and workpiece material properties. The traditional preset compensation model cannot cover these instantaneous dynamic factors, resulting in a large deviation between the design parameters and the actual state, making it difficult to accurately identify dynamic errors, affecting the control of the machine tool field bus, and thus affecting the processing accuracy and quality of the mold.

[0003] In summary, there is a technical problem in the prior art that it is difficult for the preset model to timely capture and correct the dynamic changes generated during the processing, resulting in difficulty in accurately identifying dynamic errors, thereby affecting the processing accuracy and quality of the mold. Summary of the invention

[0004] The purpose of this application is to provide a method, system and equipment for dynamic correction of mold processing deviations based on real-time monitoring, so as to solve the technical problem in the prior art that it is difficult to accurately identify dynamic errors due to the difficulty in timely capturing and correcting the dynamic changes generated during the processing by the preset model, thereby affecting the processing accuracy and quality of the mold.

[0005] In view of the above problems, the present application provides a method, system and equipment for dynamic correction of mold processing deviation based on real-time monitoring.

[0006] In the first aspect, the present application provides a dynamic correction method for mold processing deviations based on real-time monitoring, and the dynamic correction method for mold processing deviations based on real-time monitoring is implemented by a dynamic correction system for mold processing deviations based on real-time monitoring, wherein the dynamic correction method for mold processing deviations based on real-time monitoring includes: reading a target processing plan of a target mold, wherein the target processing plan includes target design parameters and target process requirements; collaboratively analyzing the target design parameters and the target process requirements to obtain a real-time workpiece preset model; dynamically monitoring the processing process of the target mold through a sensor network to obtain real-time sensing information; analyzing the real-time sensing information to obtain real-time state parameters of the target mold, and constructing a real-time workpiece actual model based on the real-time state parameters; comparing the real-time workpiece actual model with the real-time workpiece preset model to obtain a model comparison deviation; performing correction optimization with the model comparison deviation as an optimization constraint to obtain an optimal correction strategy; and dynamically performing processing correction on the target mold according to the optimal correction strategy.

[0007] Optionally, an initial geometric model is constructed according to the design shape, design interior and design relative position in the target design parameters; the target process requirements are integrated into the initial geometric model to obtain an initial model; the real-time moment is obtained, and the processing process analysis of the initial model is performed in combination with the real-time moment to obtain the model at the real-time moment, which is recorded as the real-time workpiece preset model.

[0008] Optionally, the target mold is dynamically processed and monitored by a displacement sensor in the sensor network to obtain real-time displacement; the target mold is dynamically processed and monitored by a strain gauge in the sensor network to obtain real-time strain; the target mold is dynamically processed and monitored by a laser rangefinder in the sensor network to obtain real-time processing accuracy; the real-time sensing information is composed based on the real-time displacement, the strain and the real-time processing accuracy.

[0009] Optionally, the real-time sensing information is standardized to obtain real-time standard sensing information; a predetermined sensing feature index is introduced and the real-time standard sensing information is traversed and extracted to obtain real-time standard feature parameters; the real-time standard feature parameters are rendered and fused to the initial model of the target mold to obtain the real-time workpiece actual model.

[0010] Optionally, the predetermined sensing characteristic index includes a displacement sensing characteristic index, a strain sensing characteristic index and a processing accuracy sensing characteristic index.

[0011] Optionally, a model size deviation evaluation function is introduced to evaluate and analyze the size deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model size deviation; a model shape deviation evaluation function is introduced to evaluate and analyze the shape deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model shape deviation; a model position deviation evaluation function is introduced to evaluate and analyze the position deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model position deviation; the average of the model size deviation, the model shape deviation and the model position deviation is taken as the model comparison deviation; wherein the formula of the model size deviation evaluation function is as follows: ; The formula of the model shape deviation evaluation function is as follows: ; The formula of the model position deviation evaluation function is as follows: ;in, , and They represent the model size deviation, model shape deviation and model position deviation respectively. , and Respectively represent the actual model of the real-time workpiece in The size, shape and position of each model part, , and Respectively represent the real-time workpiece preset model in The size, shape and position of each model part, Indicates the total number of model parts compared.

[0012] Optionally, a modified optimization space is established with the target process requirement as a constraint, and any process scheme in the modified optimization space is randomly extracted, wherein the arbitrary process scheme is different from the real-time process scheme of the target mold; the real-time workpiece actual model is simulated and corrected based on the arbitrary process scheme, and arbitrary correction data is recorded; a predetermined correction evaluation index is read, and the arbitrary correction data is matched and extracted based on the predetermined correction evaluation index to obtain arbitrary correction evaluation parameters; a variation weighted calculation is performed on the normalized arbitrary correction evaluation parameters to obtain arbitrary correction fitness; an iterative optimization is performed with the maximum fitness value of the arbitrary correction fitness as the goal to obtain the optimal modified process scheme, which is recorded as the optimal correction strategy.

[0013] Optionally, the predetermined correction evaluation index includes correction accuracy, correction quality, correction performance and correction stability.

[0014] In the second aspect, the present application also provides a dynamic correction system for mold processing deviations based on real-time monitoring, which is used to execute the dynamic correction method for mold processing deviations based on real-time monitoring as described in the first aspect, wherein the dynamic correction system for mold processing deviations based on real-time monitoring includes: a plan reading module, which is used to read the target processing plan of the target mold, wherein the target processing plan includes target design parameters and target process requirements; a collaborative analysis module, which is used to collaboratively analyze the target design parameters and the target process requirements to obtain a real-time workpiece preset model; a processing monitoring module, which is used to dynamically monitor the processing process of the target mold through a sensor network to obtain real-time sensing information; a model building module, which is used to analyze the real-time sensing information to obtain the real-time state parameters of the target mold, and build a real-time workpiece actual model based on the real-time state parameters; a model deviation comparison module, which is used to compare the real-time workpiece actual model with the real-time workpiece preset model to obtain a model comparison deviation; a strategy optimization module, which is used to perform correction optimization with the model comparison deviation as the optimization constraint to obtain an optimal correction strategy; a processing correction module, which is used to dynamically process and correct the target mold according to the optimal correction strategy.

[0015] In the third aspect, the present application also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the method for dynamic correction of mold processing deviations based on real-time monitoring as described in any one of the above-mentioned first aspects.

[0016] One or more technical solutions provided in this application have at least the following beneficial effects: By reading the target processing plan of the target mold, wherein the target processing plan includes target design parameters and target process requirements; the target design parameters and the target process requirements are collaboratively analyzed to obtain a real-time workpiece preset model; the processing process of the target mold is dynamically monitored through a sensor network to obtain real-time sensing information; the real-time sensing information is analyzed to obtain the real-time state parameters of the target mold, and a real-time workpiece actual model is constructed according to the real-time state parameters; the real-time workpiece actual model is compared with the real-time workpiece preset model to obtain a model comparison deviation; the model comparison deviation is used as an optimization constraint to perform correction optimization to obtain an optimal correction strategy; and the target mold is dynamically processed and corrected according to the optimal correction strategy. In other words, the target design parameters and target process requirements are obtained through the processing plan, a real-time workpiece preset model is constructed, the mold processing process is dynamically monitored and a real-time workpiece actual model is constructed, the preset model is compared with the actual model, optimization is performed according to the deviation, and the optimal correction scheme is determined to correct the processing process of the target mold, and the processing parameters are adjusted in real time through the machine tool field bus control system, thereby improving the precision and quality of mold processing and thus improving production efficiency.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0019] Figure 1 This is a flow chart of a method for dynamic correction of mold processing deviation based on real-time monitoring in this application; Figure 2 This is a structural schematic diagram of a mold processing deviation dynamic correction system based on real-time monitoring in this application; Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0020] Explanation of the accompanying drawings: plan reading module 11, collaborative analysis module 12, processing monitoring module 13, model building module 14, model deviation comparison module 15, strategy optimization module 16, processing correction module 17, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0021] This application solves the technical problem in the prior art that it is difficult to accurately identify dynamic errors, thereby affecting the processing accuracy and quality of the mold, by providing a dynamic correction method, system and equipment for mold processing deviation based on real-time monitoring. The preset model is difficult to capture and correct the dynamic changes generated during the processing in a timely manner, which makes it difficult to accurately identify dynamic errors, thereby affecting the processing accuracy and quality of the mold. The target design parameters and target process requirements are obtained through the processing plan, and a real-time workpiece preset model is constructed. The mold processing process is dynamically monitored and a real-time workpiece actual model is constructed. The preset model is compared with the actual model, and the optimization is performed according to the deviation. The optimal correction plan is determined to correct the processing process of the target mold. The processing parameters are adjusted in real time through the machine tool field bus control system, which improves the accuracy and quality of mold processing, thereby improving production efficiency.

[0022] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0023] For example, please refer to the attached Figure 1 The present application provides a mold processing deviation dynamic correction method based on real-time monitoring, wherein the mold processing deviation dynamic correction method based on real-time monitoring is executed by a mold processing deviation dynamic correction system based on real-time monitoring, and the mold processing deviation dynamic correction method based on real-time monitoring specifically includes the following steps: S100: Reading a target processing plan of a target mold, wherein the target processing plan includes target design parameters and target process requirements.

[0024] Specifically, the target processing plan file of the target mold is extracted from a pre-stored database or file, and the processing information of the target mold, including the target design parameters and the target process requirements, is read from it. The target processing plan is usually a processing plan and scheme formulated for the target mold, which contains a detailed plan of the entire processing process. The plan will clearly define the process steps, processing sequence, and technical requirements for each stage. The target mold is the specific mold to be processed. The target design parameters are the data such as the size, geometric accuracy, material requirements, etc. required by the mold during design, including external structure, internal structure, relative position, etc. The target process requirements are the technical requirements and process indicators that must be met during the mold processing process, such as cutting speed, feed speed, tool path, cooling method, etc. For example, in the processing plan of a certain mold, the target design parameters are recorded as the mold cavity size of 150.0 mm × 200.0 mm, with an allowable tolerance of ±0.02 mm; the target process requirements include a spindle speed of 1200 rpm, a feed speed of 0.05 mm / tooth, and cooling with coolant, etc. Data reading is completed within 5 milliseconds through the machine tool field bus, ensuring that the data accuracy error is less than 0.01 mm. At the same time, the read data is transmitted to the machine tool field bus control system for subsequent tool path planning and processing parameter setting. By reading the target design parameters and process requirements in the target mold processing plan, accurate information acquisition and automatic transmission are achieved.

[0025] S200: Coordinately analyze the target design parameters and the target process requirements to obtain a real-time workpiece preset model.

[0026] Further, the present application S200 includes: According to the design shape, design interior and design relative position in the target design parameters, an initial geometric model is constructed; the target process requirements are integrated into the initial geometric model to obtain an initial model; the real-time moment is acquired, and the processing process analysis of the initial model is performed in combination with the real-time moment to obtain the model at the real-time moment, which is recorded as the real-time workpiece preset model.

[0027] Specifically, according to the design shape, design interior and design relative position in the target design parameters, the initial set model is constructed through CAD software, which reflects the basic shape and structure of the mold. The design shape is the outer contour and outer dimensions of the mold. For example, for an injection mold, it refers to the overall contour of the cavity, the curve of the mold opening surface and the outer dimensions, such as a circular cavity with a diameter of 150.0 mm. The design interior is the structure and features inside the mold, including internal grooves, cooling channels, gate locations, etc. For example, a mold contains an inner cavity with a diameter of 140.0 mm and a mold wall structure with a thickness of 5.0 mm. The design relative position is the spatial relationship and positioning of the various components of the mold (such as the shape, internal structure, cooling channels, etc.) in the overall mold, such as the distance, angle and arrangement between the cooling channel and the mold cavity.

[0028] The target process requirements (such as machining accuracy, surface roughness, spindle speed, feed speed, cutting depth, coolant flow, etc.) are integrated into the initial geometric model, and each processing area of ​​the mold is associated with its corresponding process requirements to form an initial model containing process parameters, which not only reflects the geometric characteristics of the mold, but also includes the process parameters during the processing.

[0029] Acquire the real-time moment, that is, the time node in the current processing process, and combine the real-time data of the current time node to analyze the processing process of the initial model, considering the processing progress and status of the mold at the current time point, so as to obtain the real-time workpiece preset model, that is, the model obtained after the processing process analysis and reflecting the geometric characteristics of the workpiece in the current state. By constructing the initial geometric model and integrating it with the process requirements, the accuracy and efficiency of the processing process can be ensured. Combining the real-time moment with the processing process analysis can predict the deformation, stress concentration and other problems that may occur in the processing process, and accurately reflect the dynamic changes in the processing process, such as thermal expansion, vibration and tool wear.

[0030] S300: Dynamically monitor the processing process of the target mold through a sensor network to obtain real-time sensor information.

[0031] Further, the present application S300 includes: The target mold is dynamically processed and monitored by the displacement sensor in the sensor network to obtain real-time displacement; the target mold is dynamically processed and monitored by the strain gauge in the sensor network to obtain real-time strain; the target mold is dynamically processed and monitored by the laser rangefinder in the sensor network to obtain real-time processing accuracy; the real-time sensing information is composed based on the real-time displacement, the strain and the real-time processing accuracy.

[0032] Specifically, the processing process of the target mold is dynamically monitored through a sensor network. The sensor network is a system composed of multiple sensors that is used to collect and transmit various information about the target mold. The sensor network includes displacement sensors, strain gauges, laser rangefinders, etc. Among them, the displacement sensor is used to detect the tiny displacement changes of the workpiece or mold surface during the processing process in real time; the strain gauge is a commonly used stress and strain detection element. By attaching it to the key parts of the mold, it monitors the tiny deformation caused by mechanical load or temperature change in real time. The laser rangefinder uses a laser beam to scan the surface of the workpiece to obtain processing accuracy information. Based on laser reflection ranging, it can measure the size and surface contour of the mold with extremely high accuracy (usually within 0.005 mm), monitor the mold size changes in real time, and verify whether the processing accuracy meets the design requirements.

[0033] Displacement sensors (such as laser displacement sensors) are installed in various key processing areas to monitor the slight deformation of the mold. Strain gauges are attached to the key stress-bearing parts of the mold to quantify the strain borne by the mold during the processing by detecting the resistance change of the metal foil. For example, in the actual processing process, the strain detected by the strain gauge in a certain area in real time is 120 microstrain (με). The laser rangefinder obtains processing accuracy information by performing high-precision measurements on the mold surface, thereby determining the processing accuracy deviation and reflecting whether the current processing status is consistent with the design expectations.

[0034] The real-time displacement, real-time strain and real-time processing accuracy data obtained by each sensor are processed to form real-time sensing information, which reflects the dynamic state of the mold during the processing. For example, the real-time sensing information of a mold monitored includes real-time displacement of 0.012 mm, real-time strain of 120 microstrain, and real-time processing accuracy deviation of ±0.005 mm. Through the joint application of displacement sensors, strain gauges and laser rangefinders in the sensor network, real-time dynamic monitoring of the target mold during the processing is realized, forming high-precision real-time sensing information.

[0035] S400: Analyze the real-time sensing information to obtain real-time state parameters of the target mold, and construct a real-time workpiece actual model according to the real-time state parameters.

[0036] Further, the present application S400 includes: The real-time sensing information is standardized to obtain real-time standard sensing information; a predetermined sensing feature index is introduced and the real-time standard sensing information is traversed and extracted to obtain real-time standard feature parameters; the real-time standard feature parameters are rendered and fused to the initial model of the target mold to obtain the real-time workpiece actual model.

[0037] The predetermined sensing characteristic indexes include displacement sensing characteristic indexes, strain sensing characteristic indexes and processing accuracy sensing characteristic indexes.

[0038] Specifically, the real-time sensing information is preprocessed to handle abnormal values, missing values, etc. Since the acquisition accuracy, time, and frequency of each sensor are different, the preprocessed real-time sensing information is standardized to map data from different sources and dimensions to a unified scale or range to make them comparable. The real-time sensing information is standardized using a normalization algorithm (such as minimum-maximum standardization or Z-score standardization) to obtain real-time standard sensing information under a unified scale. For example, in a processing test, the original displacement data was 0.012 mm, which was mapped to 0.75 after standardization (the normalization interval is 0 to 1), while the strain data was standardized from 120 microstrain to 0.80, and the processing accuracy data (such as dimensional deviation 0.004 mm) was standardized to 0.65. Real-time standard sensing information refers to the sensor data after standardization, which has unified the displacement, strain, and processing accuracy data to a standard scale, thereby providing a comparable and stable data basis for subsequent feature extraction.

[0039] The predetermined sensing characteristic index is a key data index determined according to the target processing plan of the mold, which is used to extract key features from the sensing information, including displacement sensing characteristic index, strain sensing characteristic index and processing accuracy sensing characteristic index. Displacement sensing characteristic index is a specific parameter used to describe the displacement sensor data, such as maximum displacement, average displacement, displacement change rate, etc.; strain sensing characteristic index is a specific parameter used to describe the strain gauge data, such as maximum strain, average strain, etc.; processing accuracy sensing characteristic index is a specific parameter used to describe the laser rangefinder data, such as maximum deviation, average deviation, etc.

[0040] Through the predetermined sensing characteristic indicators, the standardized sensing information is traversed and extracted, and the key data that meets the predetermined characteristic indicators are extracted to form real-time standard characteristic parameters that describe the current state. These parameters can accurately describe the key states of the mold during the processing, such as micro-displacement, strain and dimensional deviation during the processing.

[0041] The real-time standard feature parameters are integrated into the original design model in a visual or numerical form through CAD software. The real-time standard feature parameters are integrated with the initial mold of the target mold, and the real-time sensor data is superimposed as a dynamic input on the initial model pre-generated in the CAD / CAM system. For example, if a mold originally designed with a size of 150.0 mm is detected with local displacement and thermal expansion at a certain processing stage, after fusion, the local area may be updated to 150.02 mm in the model, showing the deviation under the actual processing state. The rendered model is displayed in real time on the monitoring terminal through the machine tool fieldbus control system, and the dynamically updated data is intuitively presented to the operator using high-resolution industrial display screens and data visualization tools.

[0042] By integrating real-time sensor information into the model, the real-time workpiece actual model obtained can more realistically reflect the actual state of the mold, improve the accuracy of the model, and help to promptly discover and solve processing problems, thereby improving the quality and production efficiency of the mold.

[0043] S500: Compare the real-time workpiece actual model with the real-time workpiece preset model to obtain a model comparison deviation.

[0044] Furthermore, the present application S500 includes: A model size deviation evaluation function is introduced to evaluate and analyze the size deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model size deviation; a model shape deviation evaluation function is introduced to evaluate and analyze the shape deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model shape deviation; a model position deviation evaluation function is introduced to evaluate and analyze the position deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model position deviation; the average of the model size deviation, the model shape deviation and the model position deviation is taken as the model comparison deviation; wherein the formula of the model size deviation evaluation function is as follows: ; The formula of the model shape deviation evaluation function is as follows: ; The formula of the model position deviation evaluation function is as follows: ;in, , and They represent the model size deviation, model shape deviation and model position deviation respectively. , and Respectively represent the actual model of the real-time workpiece in The size, shape and position of each model part, , and Respectively represent the real-time workpiece preset model in The size, shape and position of each model part, Indicates the total number of model parts compared.

[0045] Specifically, the model size deviation evaluation function is used to compare the size difference between two models (real-time workpiece actual model and real-time workpiece preset model). The machine tool fieldbus control system obtains the real-time workpiece preset model of the target mold, collects the actual size data after processing in real time through the sensor, determines the real-time workpiece actual model, calculates the absolute deviation of each key dimension, sums all deviation values, and divides them by the sum of all preset dimensions to obtain the normalized size deviation index. The formula of the model size deviation evaluation function is as follows: ,in, The real-time workpiece actual model is The size of each model part, The real-time workpiece preset model is The size of each model part, n is the total number of model parts to be compared, and here it is the total number of key dimensions.

[0046] The model shape deviation evaluation function is a function that compares and analyzes the geometric shapes of two models. It not only compares the size of each part, but also considers the differences in curves, angles, edge contours, etc. Shape deviation not only reflects changes in local size, but also reflects inconsistencies in geometric properties such as surface curvature and edge smoothness. Select several representative sampling points on the model surface, record the three-dimensional coordinates of each sampling point in the preset model, and record the corresponding real-time three-dimensional coordinates in the actual model, and use the model shape deviation evaluation function to calculate the shape deviation. The formula of the model shape deviation evaluation function is as follows: ,in, The real-time workpiece actual model is The shape of the model part, The real-time workpiece preset model is The shape of the model parts, n is the total number of model parts for comparison, and here it is the total number of sampling points.

[0047] The model position deviation evaluation function is used to evaluate the difference in spatial positioning between two models, focusing mainly on the relative position changes of each main feature in three-dimensional space. By calculating the overall degree of offset in spatial position between the real-time workpiece actual model and the real-time workpiece preset model, unlike size deviation and shape deviation, position deviation focuses on the global movement of the entire workpiece in the three-dimensional coordinate system, rather than changes in local morphology. The model position deviation evaluation function calculates the Euclidean distance difference between the actual measured position and the preset design position, and normalizes it to obtain a dimensionless deviation index. In a certain mold processing process, n key points (such as the four reference angles + center of mass of the workpiece) are selected, and their three-dimensional coordinates are measured in the actual model and the preset model respectively, and the model position deviation evaluation function is used to calculate the position deviation. The formula of the model position deviation evaluation function is as follows: ,in, The actual model of the real-time workpiece is represented in The location of the model parts, Indicates the real-time workpiece preset model in The location of the model parts, Indicates the total number of model parts to be compared, which is the number of reference points in this case.

[0048] After averaging the above three deviation indicators (size, shape and position deviation), the model comparison deviation is obtained, which reflects the overall difference between the actual processing state and the preset design state.

[0049] For example, after comparing the real-time workpiece actual model of a certain mold with the real-time workpiece preset model, the model size deviation is shown in Table 1, the model shape deviation is shown in Table 2, the model position deviation is shown in Table 3, and the model comparison deviation is shown in Table 4.

[0050] Table 1 Model size deviation serial number Preset size (mm) Actual size (mm) Absolute deviation (mm) 1 150.000 150.015 0.015 2 75.000 75.005 0.005 3 50.000 50.010 0.010 total 275.000 0.030 According to Table 1, the model size deviation is calculated by the model size deviation evaluation function to be 0.0109%.

[0051] Table 2 Model shape deviation Sampling point Preset coordinates Actual coordinates Distance Deviation 1 (100.0,100.0,50.0) (100.2,100.1,50.0) 0.224 2 (120.0,80.0,45.0) (120.1,80.1,45.0) 0.141 3 (130.0,90.0,55.0) (130.0,90.2,55.1) 0.224 4 (110.0,95.0,60.0) (110.2,95.1,60.0) 0.224 5 (115.0,85.0,52.0) (115.1,85.0,52.2) 0.100 total 0.913 According to Table 2, the model shape deviation is calculated by the model shape deviation evaluation function to be 0.1174%.

[0052] Table 3 Model position deviation Key Points Preset coordinates Actual coordinates Distance Deviation A (100.0,100.0,50.0) (100.5,100.2,50.1) 0.538 B (120.0,80.0,45.0) (120.3,80.0,44.9) 0.316 C (130.0,90.0,55.0) (130.1,90.2,55.2) 0.245 D (110.0,95.0,60.0) (110.4,95.3,60.1) 0.502 E (115.0,85.0,52.0) (115.2,85.1,52.1) 0.244 total 1.845 According to Table 3, the model position deviation is calculated by the model position deviation evaluation function to be 0.237%.

[0053] Table 4 Model comparison deviation Deviation Type Evaluation value (normalized scale) percentage Dimensional deviation 0.000109 0.0109% shape deviation 0.001174 0.1174% Position deviation 0.00237 0.237% Comprehensive deviation 0.001217 0.1217% By introducing an evaluation function to perform detailed deviation analysis on the real-time workpiece actual model and the real-time workpiece preset model, various deviations in the machining process can be accurately quantified. It can not only identify specific problems in size, shape and position, but also provide a comprehensive deviation evaluation by calculating model comparison deviations.

[0054] S600: performing correction optimization with the model comparison deviation as an optimization constraint to obtain an optimal correction strategy.

[0055] Furthermore, the present application S600 includes: A modified optimization space is established with the target process requirement as a constraint, and any process scheme in the modified optimization space is randomly extracted, wherein the arbitrary process scheme is different from the real-time process scheme of the target mold; the real-time workpiece actual model is simulated and corrected based on the arbitrary process scheme, and any correction data is recorded; a predetermined correction evaluation index is read, and the arbitrary correction data is matched and extracted based on the predetermined correction evaluation index to obtain any correction evaluation parameter; a variation weighted calculation is performed on the normalized arbitrary correction evaluation parameter to obtain any correction fitness; an iterative optimization is performed with the maximum fitness value of the arbitrary correction fitness as the goal to obtain the optimal modified process scheme, which is recorded as the optimal correction strategy.

[0056] The predetermined correction evaluation indexes include correction accuracy, correction quality, correction performance and correction stability.

[0057] Specifically, the target process requirements (such as cutting parameters, tool paths and compensation ranges) in the target processing plan are used as constraints to define the possible correction parameter ranges and establish a multi-dimensional process parameter space. The correction optimization space is a set of alternative correction schemes established under the constraints of the target process requirements for the dynamic errors or deviations that may exist in the processing process. It includes different combinations of process parameters, tool compensation schemes and path adjustment strategies, all of which meet the target process requirements.

[0058] Randomly select a scheme in the correction optimization space as an arbitrary process scheme. This scheme needs to be different from the real-time process scheme of the current target mold. Apply the arbitrary process scheme to the actual model of the real-time workpiece through CAD software, simulate the correction process, and record the correction results after simulation, including data such as correction errors of size, shape, and position, and processing response time. For example, suppose the simulation results show that after the scheme is corrected, the model size error is reduced from the original 0.03 mm to 0.005 mm, the shape deviation is improved by 0.2%, and the response time is controlled within 8 milliseconds. Simulation correction refers to simulating the effect of processing correction of the actual model of the real-time workpiece by any process scheme, predicting its ability to compensate for processing errors, and recording relevant correction data.

[0059] Read the predetermined correction evaluation index, that is, the pre-set standard used to evaluate the quality of the correction scheme, including correction accuracy (such as correction error of size, shape, and position), correction quality (such as surface roughness, processing defect rate), correction performance (such as correction response speed, real-time performance) and correction stability (such as robustness under different working conditions). Match the simulation correction data with the predetermined index and extract the corresponding correction evaluation parameters. Any correction evaluation parameter refers to the specific numerical parameter obtained after matching and extraction, which reflects the effect of a candidate correction scheme in terms of correction accuracy, quality, performance and stability. Normalize any correction evaluation parameter to eliminate the dimensional differences of different indicators. For example, the normalized correction accuracy parameter is 0.94, the correction quality parameter is 0.90, the correction performance parameter is 0.92, the correction stability parameter is 0.88, and the correction fitness is 0.91.

[0060] Mutation weighted calculation is based on the normalized parameters, using certain weight factors to weight each evaluation parameter, and introducing certain mutation mechanisms (such as random perturbations or dynamic weight adjustments) to enhance the algorithm's ability to achieve diversity and local optimality. Mutation here can be understood as appropriate random adjustments to weights or parameter values ​​to enhance the robustness of optimization and global search capabilities. The core idea of ​​​​variation weighted calculation is that if a parameter has a large range of variation (high discreteness), it means that the parameter has a greater impact on the final result and should be given a higher weight; if a parameter has a small range of variation (relatively stable), the weight is relatively small.

[0061] After the initial normalization and weighted variation calculation, each candidate correction scheme obtains an arbitrary correction fitness value, which comprehensively reflects the correction accuracy, correction quality, correction performance and correction stability. The higher the value, the more the scheme meets the processing goal. The fitness value of the arbitrary correction fitness is maximized as the optimization goal, and a candidate scheme is found in the correction optimization space so that its arbitrary correction fitness reaches the highest level. At the same time, in the optimization process, the model comparison deviation is used as the evaluation target or constraint condition, which may reduce the deviation, that is, minimize the difference between the actual processing model and the preset model, so as to ensure that the processing accuracy meets the design requirements.

[0062] A candidate process solution is randomly extracted from the modified optimization space, and each solution obtains its arbitrary modified fitness through the above steps. The fitness of each candidate solution is obtained by the previous weighted calculation of the mutation. Using intelligent optimization methods such as genetic algorithms or particle swarm optimization, the candidate solutions are selected, crossed, and mutated to generate the next generation of candidate solutions. The new generation of candidate solutions is then simulated, corrected, and the fitness is calculated, and the fitness of all candidate solutions is continuously compared. When the iteration reaches a predetermined number of times (such as 50 times, 100 times) or the fitness improvement meets the set convergence criteria, the iteration stops. In each iteration, the system records the fitness value of each candidate solution and compares the one with the highest fitness among all current solutions. When the iteration terminates, the candidate solution with the highest fitness is determined as the optimal modified process solution and is recorded as the optimal correction strategy.

[0063] Taking the target process requirements as constraints, candidate process solutions are randomly selected in the correction optimization space, and the actual model of the real-time workpiece is corrected and simulated, the correction data is recorded and evaluated, and the simulation results are matched and extracted according to the predetermined correction evaluation indicators (correction accuracy, quality, performance and stability). The fitness of the candidate solutions is obtained through normalization and variation weighted calculation. Through iterative optimization, the solution with the highest fitness is selected as the optimal correction strategy, which can significantly improve the processing quality of the mold, reduce the scrap rate, and improve production efficiency.

[0064] S700: Performing dynamic processing correction on the target mold according to the optimal correction strategy.

[0065] Specifically, the optimal correction strategy includes a specific combination of process parameters, such as cutting speed, feed rate, tool compensation value, tool path adjustment, etc. Its goal is to make the actual state of the mold as close as possible to the design preset requirements. The determined optimal correction strategy is sent to the machine tool fieldbus control system to automatically adjust the machine tool's processing parameters and perform dynamic processing corrections on the target mold. For example, the tool path compensation value, cutting speed and feed rate are adjusted according to the strategy requirements.

[0066] During the processing, the sensor network (such as laser rangefinder, displacement sensor, strain gauge, etc.) continuously collects mold processing status data and feeds it back to the machine tool fieldbus control system. The machine tool fieldbus control system determines whether the processing deviation is effectively corrected based on the feedback data. If the deviation still exists, it re-adjusts the parameters according to the optimal correction strategy to achieve dynamic closed-loop compensation. After dynamic compensation using the optimal correction strategy, the deviation between the actual processing mold and the preset design is greatly reduced. The dynamic correction process achieves millisecond-level real-time feedback and adjustment, avoiding error accumulation and processing defects, and improving production stability.

[0067] In summary, the mold processing deviation dynamic correction method based on real-time monitoring provided by the present application has the following beneficial effects: By reading the target processing plan of the target mold, wherein the target processing plan includes target design parameters and target process requirements; the target design parameters and the target process requirements are collaboratively analyzed to obtain a real-time workpiece preset model; the processing process of the target mold is dynamically monitored through a sensor network to obtain real-time sensing information; the real-time sensing information is analyzed to obtain the real-time state parameters of the target mold, and a real-time workpiece actual model is constructed according to the real-time state parameters; the real-time workpiece actual model is compared with the real-time workpiece preset model to obtain a model comparison deviation; the model comparison deviation is used as an optimization constraint to perform correction optimization to obtain an optimal correction strategy; and the target mold is dynamically processed and corrected according to the optimal correction strategy. In other words, the target design parameters and target process requirements are obtained through the processing plan, a real-time workpiece preset model is constructed, the mold processing process is dynamically monitored and a real-time workpiece actual model is constructed, the preset model is compared with the actual model, optimization is performed according to the deviation, and the optimal correction scheme is determined to correct the processing process of the target mold, and the processing parameters are adjusted in real time through the machine tool field bus control system, thereby improving the precision and quality of mold processing and thus improving production efficiency.

[0068] Embodiment 2, based on the same inventive concept as the mold processing deviation dynamic correction method based on real-time monitoring in the aforementioned embodiment 1, the present application also provides a mold processing deviation dynamic correction system based on real-time monitoring, please refer to the attached Figure 2 , the mold processing deviation dynamic correction system based on real-time monitoring includes: A plan reading module 11 is used to read a target processing plan of a target mold, wherein the target processing plan includes target design parameters and target process requirements; a collaborative analysis module 12 is used to collaboratively analyze the target design parameters and the target process requirements to obtain a real-time workpiece preset model; a processing monitoring module 13 is used to dynamically monitor the processing process of the target mold through a sensor network to obtain real-time sensing information; a model building module 14 is used to analyze the real-time sensing information to obtain the real-time state parameters of the target mold, and to build a real-time workpiece actual model based on the real-time state parameters; a model deviation comparison module 15 is used to compare the real-time workpiece actual model with the real-time workpiece preset model to obtain a model comparison deviation; a strategy optimization module 16 is used to perform correction optimization with the model comparison deviation as an optimization constraint to obtain an optimal correction strategy; a processing correction module 17 is used to dynamically perform processing correction on the target mold according to the optimal correction strategy.

[0069] Furthermore, the collaborative analysis module 12 in the dynamic correction system for mold processing deviations based on real-time monitoring is also used to: construct an initial geometric model based on the design shape, design interior and design relative position in the target design parameters; integrate the target process requirements into the initial geometric model to obtain an initial model; obtain the real-time moment, and perform a processing process analysis on the initial model in combination with the real-time moment to obtain the model at the real-time moment, which is recorded as the real-time workpiece preset model.

[0070] Furthermore, the processing monitoring module 13 in the mold processing deviation dynamic correction system based on real-time monitoring is also used to: dynamically monitor the processing of the target mold through the displacement sensor in the sensor network to obtain real-time displacement; dynamically monitor the processing of the target mold through the strain gauge in the sensor network to obtain real-time strain; dynamically monitor the processing of the target mold through the laser rangefinder in the sensor network to obtain real-time processing accuracy; and form the real-time sensing information based on the real-time displacement, the strain and the real-time processing accuracy.

[0071] Furthermore, the model building module 14 in the dynamic correction system for mold processing deviations based on real-time monitoring is also used to: standardize the real-time sensor information to obtain real-time standard sensor information; introduce predetermined sensor feature indicators and traverse and extract the real-time standard sensor information to obtain real-time standard feature parameters; render and fuse the real-time standard feature parameters into the initial model of the target mold to obtain the real-time workpiece actual model.

[0072] Furthermore, the model building module 14 in the mold processing deviation dynamic correction system based on real-time monitoring is also used for: the predetermined sensing characteristic index includes a displacement sensing characteristic index, a strain sensing characteristic index and a processing accuracy sensing characteristic index.

[0073] Further, the model deviation comparison module 15 in the mold processing deviation dynamic correction system based on real-time monitoring is also used to: introduce a model size deviation evaluation function to evaluate and analyze the size deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model size deviation; introduce a model shape deviation evaluation function to evaluate and analyze the shape deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model shape deviation; introduce a model position deviation evaluation function to evaluate and analyze the position deviation of the real-time workpiece actual model and the real-time workpiece preset model to obtain a model position deviation; take the average of the model size deviation, the model shape deviation and the model position deviation as the model comparison deviation; wherein the formula of the model size deviation evaluation function is as follows: ; The formula of the model shape deviation evaluation function is as follows: ; The formula of the model position deviation evaluation function is as follows: ;in, , and They represent the model size deviation, model shape deviation and model position deviation respectively. , and Respectively represent the actual model of the real-time workpiece in The size, shape and position of each model part, , and Respectively represent the real-time workpiece preset model in The size, shape and position of each model part, Indicates the total number of model parts compared.

[0074] Furthermore, the strategy optimization module 16 in the dynamic correction system for mold processing deviations based on real-time monitoring is also used to: establish a correction optimization space with the target process requirements as constraints, and randomly extract any process scheme in the correction optimization space, wherein the arbitrary process scheme is different from the real-time process scheme of the target mold; perform simulation correction on the real-time workpiece actual model based on the arbitrary process scheme, and record arbitrary correction data; read predetermined correction evaluation indicators, and match and extract the arbitrary correction data based on the predetermined correction evaluation indicators to obtain arbitrary correction evaluation parameters; perform variation weighted calculation on the normalized arbitrary correction evaluation parameters to obtain arbitrary correction fitness; perform iterative optimization with the maximum fitness value of the arbitrary correction fitness as the goal to obtain the optimal correction process scheme, and record it as the optimal correction strategy.

[0075] Furthermore, the strategy optimization module 16 in the mold processing deviation dynamic correction system based on real-time monitoring is also used for: the predetermined correction evaluation index includes correction accuracy, correction quality, correction performance and correction stability.

[0076] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The mold processing deviation dynamic correction method based on real-time monitoring and the specific examples in the first embodiment are also applicable to the mold processing deviation dynamic correction system based on real-time monitoring in this embodiment. Through the above detailed description of the mold processing deviation dynamic correction method based on real-time monitoring, those skilled in the art can clearly know the mold processing deviation dynamic correction system based on real-time monitoring in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0077] Embodiment three, based on the same inventive concept as the method for dynamic correction of mold processing deviations based on real-time monitoring in the aforementioned embodiment one, the present application also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of any one of the methods for dynamic correction of mold processing deviations based on real-time monitoring in the aforementioned embodiment one.

[0078] Attached Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3In the embodiment, the bus architecture is represented by bus 300, which may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0079] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, for those skilled in the art, several improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A dynamic correction method for mold processing deviation based on real-time monitoring, characterized in that: include: Reading a target processing plan of a target mold, wherein the target processing plan includes target design parameters and target process requirements; Performing collaborative analysis on the target design parameters and the target process requirements to obtain a real-time workpiece preset model; Dynamically monitoring the processing process of the target mold through a sensor network to obtain real-time sensor information; Analyzing the real-time sensing information to obtain real-time state parameters of the target mold, and constructing a real-time workpiece actual model according to the real-time state parameters; Comparing the real-time workpiece actual model with the real-time workpiece preset model to obtain a model comparison deviation; Using the model comparison deviation as an optimization constraint to perform correction optimization and obtain the optimal correction strategy; The target mold is dynamically processed and corrected according to the optimal correction strategy.

2. The method for dynamic correction of mold processing deviation based on real-time monitoring according to claim 1 is characterized in that: The target design parameters and the target process requirements are collaboratively analyzed to obtain a real-time workpiece preset model, including: Constructing an initial geometric model according to the design shape, design interior and design relative position in the target design parameters; Integrating the target process requirements into the initial geometric model to obtain an initial model; The real-time moment is acquired, and the processing process analysis is performed on the initial model in combination with the real-time moment to obtain the model at the real-time moment, which is recorded as the real-time workpiece preset model.

3. The method for dynamic correction of mold processing deviation based on real-time monitoring according to claim 1 is characterized in that: The processing process of the target mold is dynamically monitored through the sensor network to obtain real-time sensor information, including: Dynamically monitor the processing of the target mold by using a displacement sensor in the sensor network to obtain real-time displacement; Dynamically monitor the processing of the target mold through strain gauges in the sensor network to obtain real-time strain; Dynamically monitor the processing of the target mold through the laser rangefinder in the sensor network to obtain real-time processing accuracy; The real-time sensing information is composed based on the real-time displacement, the strain and the real-time processing accuracy.

4. The method for dynamic correction of mold processing deviation based on real-time monitoring according to claim 2 is characterized in that: Analyzing the real-time sensing information to obtain the real-time state parameters of the target mold, and constructing a real-time workpiece actual model according to the real-time state parameters, including: Performing standardization processing on the real-time sensing information to obtain real-time standard sensing information; Introducing a predetermined sensor characteristic index and traversing and extracting the real-time standard sensor information to obtain a real-time standard characteristic parameter; The real-time standard feature parameter is rendered and fused to the initial model of the target mold to obtain the real-time workpiece actual model.

5. The method for dynamic correction of mold processing deviation based on real-time monitoring according to claim 4 is characterized in that: The predetermined sensing characteristic indexes include displacement sensing characteristic indexes, strain sensing characteristic indexes and processing accuracy sensing characteristic indexes.

6. The method for dynamic correction of mold processing deviation based on real-time monitoring according to claim 1 is characterized in that: Comparing the real-time workpiece actual model with the real-time workpiece preset model to obtain a model comparison deviation includes: Introducing a model size deviation evaluation function to perform size deviation evaluation analysis on the real-time workpiece actual model and the real-time workpiece preset model to obtain a model size deviation; Introducing a model shape deviation evaluation function to perform shape deviation evaluation analysis on the real-time workpiece actual model and the real-time workpiece preset model to obtain a model shape deviation; Introducing a model position deviation evaluation function to perform position deviation evaluation analysis on the real-time workpiece actual model and the real-time workpiece preset model to obtain a model position deviation; Taking the average of the model size deviation, the model shape deviation and the model position deviation as the model comparison deviation; Among them, the formula of the model size deviation evaluation function is as follows: ； The formula of the model shape deviation evaluation function is as follows: ； The formula of the model position deviation evaluation function is as follows: ； in, , and They represent the model size deviation, model shape deviation and model position deviation respectively. , and Respectively represent the actual model of the real-time workpiece in The size, shape and position of each model part, , and Respectively represent the real-time workpiece preset model in The size, shape and position of each model part, Indicates the total number of model parts compared.

7. The method for dynamic correction of mold processing deviation based on real-time monitoring according to claim 1 is characterized in that: The model comparison deviation is used as the optimization constraint to perform correction optimization and obtain the optimal correction strategy, including: Establishing a modified optimization space with the target process requirement as a constraint, and randomly extracting any process solution in the modified optimization space, wherein the arbitrary process solution is different from the real-time process solution of the target mold; Performing simulation correction on the real-time workpiece actual model based on the arbitrary process scheme, and recording arbitrary correction data; Reading a predetermined correction evaluation index, and matching and extracting the arbitrary correction data based on the predetermined correction evaluation index to obtain an arbitrary correction evaluation parameter; Performing a variation weighted calculation on the normalized arbitrary modified evaluation parameter to obtain an arbitrary modified fitness; An iterative optimization is performed with the goal of maximizing the fitness value of the arbitrary modified fitness to obtain an optimal modified process solution, which is recorded as the optimal modified strategy.

8. The method for dynamic correction of mold processing deviation based on real-time monitoring according to claim 7 is characterized in that: The predetermined correction evaluation indexes include correction accuracy, correction quality, correction performance and correction stability.

9. The dynamic correction system for mold processing deviation based on real-time monitoring is characterized by: The steps for implementing the mold processing deviation dynamic correction method based on real-time monitoring as described in any one of claims 1 to 8, the mold processing deviation dynamic correction system based on real-time monitoring comprises: A plan reading module, used for reading a target processing plan of a target mold, wherein the target processing plan includes target design parameters and target process requirements; A collaborative analysis module, used for collaboratively analyzing the target design parameters and the target process requirements to obtain a real-time workpiece preset model; A processing monitoring module, used to dynamically monitor the processing process of the target mold through a sensor network to obtain real-time sensor information; A model building module, used for analyzing the real-time sensing information to obtain the real-time state parameters of the target mold, and building a real-time workpiece actual model according to the real-time state parameters; A model deviation comparison module, used for comparing the real-time workpiece actual model with the real-time workpiece preset model to obtain a model comparison deviation; A strategy optimization module is used to perform correction optimization using the model comparison deviation as an optimization constraint to obtain an optimal correction strategy; A processing correction module is used to perform dynamic processing correction on the target mold according to the optimal correction strategy.

10. An electronic device, characterized in that: include: at least one processor; a memory communicatively coupled to the at least one processor; Wherein, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the dynamic correction method of mold processing deviation based on real-time monitoring as described in any one of claims 1 to 8.

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