Injection molding machine motion cycle measurement method, device, electronic equipment and storage medium

Abstract

The present application discloses a method, device, electronic device, and storage medium for measuring the motion cycle of an injection molding machine, comprising the following steps: obtaining voltage data of the injection molding machine; the voltage data including template voltage data; flattening the template voltage data to obtain a template voltage data group; screening the template voltage data group to obtain a target array; segmenting the template voltage data according to the target dwell period of the target array to obtain a plurality of single-cycle template data to be integrated; integrating the single-cycle template data to be integrated with corresponding pressure data, injection motion data, and ejector motion data according to the position of each single-cycle template data to be integrated in a data table to obtain a plurality of target single-cycle data; flattening each target single-cycle data, and merging the target single-cycle data that meet the conditions to obtain motion characteristic data of each component in the injection molding machine. The method can monitor injection molding production and predict maintenance requirements of the production process, and can be widely used in the field of data processing technology.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, electronic device and storage medium for measuring the motion cycle of an injection molding machine. Background Art

[0002] With the growing demand for large-scale injection molded products in modern production, such as aerospace, automotive, defense, and medical fields, efficient, energy-saving, and intelligent large-scale injection molding machines and their manufacturing technologies are rapidly developing. Meanwhile, methods for measuring the motion cycle of injection molding machines exist in related technologies. However, these technologies present certain challenges when measuring the motion cycle of injection molding machines, such as unstable data, difficulty in accurately extracting key motion features, and insufficiently detailed data analysis. Consequently, these technologies are unable to effectively monitor production stability and predict maintenance needs.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The embodiments of the present application are intended to at least partially address one of the technical issues in the related art. To this end, the primary purpose of the embodiments of the present application is to provide a method, device, electronic device, and storage medium for measuring the motion cycle of an injection molding machine, which can monitor the stability of injection molding production, predict maintenance requirements for the production process, and improve the production efficiency and quality of the injection molding machine.

[0005] To achieve the above objectives, one aspect of an embodiment of the present application provides a method for measuring a motion cycle of an injection molding machine, the method comprising:

[0006] Obtain voltage data of the injection molding machine from a data table; wherein the voltage data is used to represent motion data of various components in the injection molding machine, and the voltage data at least includes template voltage data of a template;

[0007] performing a flattening process on the template voltage data of the template to obtain a plurality of template voltage data groups;

[0008] Filtering each of the template voltage data groups according to a preset filtering condition to obtain a plurality of target arrays including target dwell periods;

[0009] Performing period segmentation on the template voltage data according to the target dwell period corresponding to each target array to obtain a plurality of single-period template data to be integrated;

[0010] According to the position of each of the single-cycle template data to be integrated in the data table, the single-cycle template data to be integrated is integrated with the pressure data, injection motion data, and ejector motion data corresponding to the single-cycle template data to be integrated to obtain a plurality of target single-cycle data;

[0011] Each of the target single-cycle data is flattened, and the target single-cycle data that meets a preset merging condition is merged to obtain motion characteristic data of each component movement in the injection molding machine.

[0012] In some embodiments, the template voltage data of the template is flattened to obtain a plurality of template voltage data groups, including:

[0013] A flattening function is called to flatten the template voltage data of the template to obtain a plurality of template voltage data groups; wherein the flattening function is provided with a voltage value fluctuation range, and the voltage value fluctuation range is used to divide the template voltage data into a plurality of template voltage data groups.

[0014] In some embodiments, the filtering process of each of the template voltage data groups according to a preset filtering condition to obtain a plurality of target arrays including target dwell periods includes:

[0015] Calculating the number of data elements in each of the template voltage data groups, and setting a quantity filtering condition corresponding to each of the data elements according to the number of each of the data elements;

[0016] Each of the template voltage data groups is screened according to the quantity filtering condition to obtain a plurality of target arrays including target dwell periods.

[0017] In some embodiments, the target dwell period is the dwell period of the template after mold closing and the dwell period of the template after mold opening. The template voltage data is periodically segmented according to the target dwell periods corresponding to the target arrays to obtain a plurality of single-cycle template data to be integrated, including:

[0018] The template voltage data is periodically segmented according to the dwell time of the templates corresponding to each target array after mold closing and the dwell time of the templates after mold opening, to obtain a plurality of single-cycle template data to be integrated.

[0019] In some embodiments, the preset merging condition is a voltage difference and an interval distance between target single-cycle data. Flattening each target single-cycle data and merging the target single-cycle data that meet the preset merging condition to obtain motion characteristic data of each component in the injection molding machine includes:

[0020] Each target single-cycle data is flattened, and a merging function is called to merge the target single-cycle data that meet the preset voltage difference and interval distance between the target single-cycle data to obtain the motion characteristic data of each component in the injection molding machine.

[0021] In some embodiments, after flattening the target single-cycle data and merging the target single-cycle data that meet preset merging conditions to obtain motion characteristic data of each component in the injection molding machine, the method further includes:

[0022] fusing the target motion data of each component in each target single-cycle data to obtain curves corresponding to each target single-cycle data;

[0023] Calculating the time domain eigenvalues ​​and frequency domain eigenvalues ​​of the target motion data in each of the curves;

[0024] Performing correlation analysis on the target action data in each of the curves to obtain a correlation coefficient characteristic value corresponding to each of the curves;

[0025] performing dimensionality reduction processing on the time domain eigenvalues, the frequency domain eigenvalues, and the correlation coefficient eigenvalues, and performing cluster analysis on the time domain eigenvalues, the frequency domain eigenvalues, and the correlation coefficient eigenvalues ​​after the dimensionality reduction processing to obtain a plurality of clustering results;

[0026] Calculating the centroid of each clustering result to obtain the maximum centroid distance between each clustering result;

[0027] The target single-cycle data is monitored according to the maximum centroid distance.

[0028] In some embodiments, before calculating the time domain feature value and the frequency domain feature value of the target motion data in each of the curves, the method further includes:

[0029] Performing Fourier transform on the target motion data in each of the curves to obtain the frequency and amplitude corresponding to the target motion data in each of the curves.

[0030] To achieve the above-mentioned purpose, another aspect of the present application provides a device for measuring the motion cycle of an injection molding machine, the device comprising:

[0031] A data acquisition module is used to acquire voltage data of the injection molding machine in a data table; wherein the voltage data is used to represent motion data of each component in the injection molding machine, and the voltage data at least includes template voltage data of the template;

[0032] a data smoothing module, configured to perform a straightening process on the template voltage data of the template to obtain a plurality of template voltage data groups;

[0033] A data screening module, configured to screen each of the template voltage data groups according to a preset filtering condition to obtain a plurality of target arrays including target dwell periods;

[0034] a cycle segmentation module, configured to perform cycle segmentation on the template voltage data according to target dwell periods corresponding to the target arrays, to obtain a plurality of single-cycle template data to be integrated;

[0035] a cycle integration module, configured to integrate the single-cycle template data to be integrated with the pressure data, injection motion data, and ejector motion data corresponding to the single-cycle template data to be integrated according to the position of each single-cycle template data to be integrated in the data table, to obtain a plurality of target single-cycle data;

[0036] The motion feature acquisition module is used to perform flattening processing on each of the target single-cycle data and merge the target single-cycle data that meet the preset merging conditions to obtain the motion feature data of each component movement in the injection molding machine.

[0037] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method when executing the computer program.

[0038] To achieve the above objectives, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0039] The embodiments of the present application include at least the following beneficial effects: the present application provides a method, device, electronic device and storage medium for measuring the motion cycle of an injection molding machine, the scheme obtains voltage data of the injection molding machine from a data table; wherein the voltage data is used to characterize the motion data of each component in the injection molding machine, and the voltage data at least includes template voltage data of the template; the template voltage data of the template is flattened to obtain several template voltage data groups; each template voltage data group is screened according to preset filtering conditions to obtain several target arrays including target dwell periods; the template voltage data is periodically segmented according to the target dwell periods corresponding to each target array to obtain several single-cycle template data to be integrated; according to the position of each single-cycle template data to be integrated in the data table, the single-cycle template data to be integrated is integrated with the pressure data, injection motion data and ejector motion data corresponding to the single-cycle template data to be integrated to obtain several target single-cycle data; each target single-cycle data is flattened, and the target single-cycle data that meets the preset merging conditions are merged to obtain motion characteristic data of the motion of each component in the injection molding machine. In an embodiment of the present application, first, by flattening the voltage data of the template, a more accurate and stable data group can be obtained, and then the template voltage data group is screened according to preset filtering conditions to identify the data group containing the target dwell period, and the motion characteristics of the important period can be extracted and used for subsequent cycle segmentation. Then, the template voltage data is cycle-segmented according to the target dwell period to obtain a series of single-cycle template data to be integrated, which can carefully analyze the motion characteristics within each individual cycle, and then the single-cycle template data to be integrated is integrated with the pressure data, injection motion data and ejector motion data in the data table to obtain target single-cycle data containing comprehensive information, forming a data basis for a comprehensive analysis of the continuous working cycle of the injection molding machine, and finally, the target single-cycle data is flattened and the data that meets the preset conditions is merged to obtain refined motion feature data of the motion of each component of the injection molding machine. The refined motion feature data of the motion of each component of the injection molding machine obtained can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve the production efficiency and quality of the injection molding machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flowchart of the steps of the method for measuring the motion cycle of an injection molding machine provided in an embodiment of the present application;

[0041] Figure 2 This is a schematic diagram of the movement of components of an injection molding machine in multiple consecutive cycles provided by an embodiment of the present application;

[0042] Figure 3 This is a schematic diagram of component movement of a single cycle of an injection molding machine provided by an embodiment of the present application;

[0043] Figure 4 This is a schematic diagram of the cutting nodes of a single cycle of an injection molding machine provided in an embodiment of the present application;

[0044] Figure 5 is a schematic diagram of a fusion curve provided in an embodiment of the present application;

[0045] Figure 6 is a schematic diagram of the silhouette coefficient provided in an embodiment of the present application;

[0046] Figure 7 Schematic diagram of cluster analysis results provided in the embodiment of the present application;

[0047] Figure 8 1 is a schematic diagram of a periodic curve of abnormal off-peak value provided in an embodiment of the present application;

[0048] Figure 9 Schematic diagram of the structure of the injection molding machine motion cycle measurement device provided in an embodiment of the present application;

[0049] Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0051] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0052] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0054] With the growing demand for large-scale injection molded products in modern production, such as aerospace, automotive, defense, and medical fields, efficient, energy-saving, and intelligent large-scale injection molding machines and their manufacturing technologies are rapidly developing. Meanwhile, methods for measuring the motion cycle of injection molding machines exist in related technologies. However, these technologies present certain challenges when measuring the motion cycle of injection molding machines, such as unstable data, difficulty in accurately extracting key motion features, and insufficiently detailed data analysis. Consequently, these technologies are unable to effectively monitor production stability and predict maintenance needs.

[0055] In view of this, the present application provides a method, device, electronic device and storage medium for measuring the motion cycle of an injection molding machine. The solution obtains voltage data of the injection molding machine from a data table; wherein the voltage data is used to characterize the motion data of each component in the injection molding machine, and the voltage data at least includes template voltage data of the template; the template voltage data of the template is flattened to obtain several template voltage data groups; each template voltage data group is screened according to preset filtering conditions to obtain several target arrays including target dwell periods; the template voltage data is periodically segmented according to the target dwell periods corresponding to each target array to obtain several single-cycle template data to be integrated; according to the position of each single-cycle template data to be integrated in the data table, the single-cycle template data to be integrated is integrated with the pressure data, injection motion data and ejector motion data corresponding to the single-cycle template data to be integrated to obtain several target single-cycle data; each target single-cycle data is flattened, and the target single-cycle data that meets the preset merging conditions are merged to obtain motion characteristic data of the motion of each component in the injection molding machine. In an embodiment of the present application, first, by flattening the voltage data of the template, a more accurate and stable data group can be obtained, and then the template voltage data group is screened according to preset filtering conditions to identify the data group containing the target dwell period, and the motion characteristics of the important period can be extracted and used for subsequent cycle segmentation. Then, the template voltage data is cycle-segmented according to the target dwell period to obtain a series of single-cycle template data to be integrated, which can carefully analyze the motion characteristics within each individual cycle, and then the single-cycle template data to be integrated is integrated with the pressure data, injection motion data and ejector motion data in the data table to obtain target single-cycle data containing comprehensive information, forming a data basis for a comprehensive analysis of the continuous working cycle of the injection molding machine, and finally, the target single-cycle data is flattened and the data that meets the preset conditions is merged to obtain refined motion feature data of the motion of each component of the injection molding machine. The refined motion feature data of the motion of each component of the injection molding machine obtained can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve the production efficiency and quality of the injection molding machine.

[0056] The injection molding machine motion cycle measurement method provided by the embodiment of the present application relates to the field of data processing technology. The injection molding machine motion cycle measurement method provided by the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system consisting of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network, content distribution network) and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the injection molding machine motion cycle measurement method, etc., but is not limited to the above forms.

[0057] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs (personal computers), minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0058] See also Figure 1 , Figure 1 This is an optional step flow chart of the method for measuring the motion cycle of an injection molding machine provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0059] Step S101, obtaining voltage data of an injection molding machine from a data table; wherein the voltage data is used to represent motion data of various components in the injection molding machine, and the voltage data at least includes template voltage data of a template;

[0060] The data table may be an Excel table for storing the voltage data of the injection molding machine recorded by the data recorder. It should be noted that, in addition to the data recorder, other instruments capable of collecting voltage signals from the injection molding machine are also feasible. It is understood that those skilled in the art can select the voltage data collection device based on actual conditions, and the present application does not impose any restrictions on this.

[0061] Injection molding machines exhibit distinct motion and dwell (or hold) characteristics across their components. For example, the movement of the mold plate can be roughly broken down into stages such as rapid mold closing, slow mold holding, mold locking, and mold opening. The injection process can be roughly broken down into stages such as injection, holding pressure, and melt. The ejector movement can be roughly broken down into stages such as ejector advance and ejector retraction. The mold locking process can be roughly broken down into stages such as clamping, holding pressure, and pressure relief. While the voltage recorded by a data recorder during the stop phase is generally linear, it can be affected by the accuracy of the recorder and equipment vibration, resulting in jitter in the recorded data. This requires filtering. It's understandable that voltage acquisition covers the entire injection molding process. While stop refers to a period of no motion, the corresponding voltage is still present during this period. While the accuracy of the recorder and vibration will cause some disturbance in the collected data, the disturbance is minimal, and simple filtering can be applied to address this jitter.

[0062] During the injection molding process, the movement of various components—such as the mold plate, injection screw, and ejector—as well as changes in system pressure, are reflected through changes in voltage signals. Voltage data is a digital representation of these dynamic parameters, and they have a one-to-one correspondence. For example, each change in the voltage signal can correspond to the opening and closing of the mold plate, the advancement or retraction of the screw, the rise or fall of the ejector, and an increase or decrease in system pressure.

[0063] The template voltage data is the voltage data corresponding to the template, and accordingly reflects the motion characteristic data of the template. In the embodiment of the present application, the main purpose is to implement period segmentation of the continuous multiple cycles of the template component movement to obtain multiple single cycles, and to process and analyze the multiple single cycles to obtain the fine motion characteristic data of each component. Figure 2 , Figure 2 Schematic diagram of the continuous multi-cycle component movement of the injection molding machine provided in the embodiment of the present application; Figure 2 As shown in the figure, the motion characteristic data of the components of the injection molding machine over multiple cycles are displayed using continuous voltage data. Figure 2 The horizontal axis represents time (in seconds), the vertical axis represents voltage signal (in volts), the red curve reflects the periodic motion of the movable plate, the blue curve reflects the periodic motion of the injection screw, and the green curve reflects the periodic motion of the ejector.

[0064] The voltage data at least includes the template voltage data of the template, and may also include the voltage data corresponding to the pressure changes of components such as the injection molding screw and the ejector pin, as well as the system.

[0065] In specific implementations, since the voltage signal corresponds to the movement stroke and pressure of the injection molding machine's template, screw, and ejector, injection molding production has a periodic characteristic. The continuous data can be periodically decomposed, and then the decomposed cycle can be carefully analyzed to obtain the refined motion characteristic data corresponding to each component in each cycle.

[0066] Step S102, performing a flattening process on the template voltage data of the template to obtain a plurality of template voltage data groups;

[0067] In some embodiments, step S102 may include: calling a flattening function to flatten the template voltage data of the template to obtain several template voltage data groups; wherein the flattening function is provided with a voltage value fluctuation range, and the voltage value fluctuation range is used to divide the template voltage data into several template voltage data groups.

[0068] The pseudo code of the flattening function is as follows:

[0069]

[0070]

[0071] The flattening function can segment data into different groups based on specific thresholds, and has the following characteristics: 1) Flexibility: The function can handle data segmentation based on different threshold conditions, making it suitable for a variety of complex data processing scenarios. 2) Dynamic threshold: The specific threshold is dynamically determined by combining the intermediate value parameter with the array, which increases the adaptability and accuracy when processing data. 3) Grouping logic: The decision of whether to create a new group is based on the difference between the data point and the current group average. This method can effectively segment data into meaningful groups and is particularly suitable for analyzing fluctuating data. 4) Wide application: This function can be applied to a variety of data analysis tasks and has good versatility.

[0072] Among them, flattening is a method of sequentially classifying data through the fluctuation range while using the intermediate value that can be adjusted as the processing node.

[0073] In the specific implementation, the flattening function is called, and the det_s parameter (greater than the intermediate voltage value) and det_x parameter (less than the intermediate voltage value) of the voltage value fluctuation range are set. The continuous dynamic model motion data recorded by the data recorder are flattened to obtain multiple template voltage data sets. In other words, each template voltage data set refers to a continuous multi-cycle data set divided by the voltage value fluctuation range set by the flattening function to obtain multiple small groups of unequal lengths. Then, the intermediate values ​​of each template voltage data set are filled to ensure that each template voltage data set has the same value, achieving the purpose of linearization. This is different from the linear fitting method used in related technologies.

[0074] It should be noted that the voltage value of the template after the mold is closed is more stable than that after the mold is opened. Therefore, it is necessary to set different voltage value fluctuation ranges by using the intermediate value as the limit.

[0075] In the embodiment of the present application, by flattening the voltage data of the template, a more accurate and stable data set can be obtained.

[0076] Step S103, filtering each of the template voltage data groups according to a preset filtering condition to obtain a plurality of target arrays including target dwell periods;

[0077] In some embodiments, step S103 may include: calculating the number of data elements in each template voltage data group, and setting a quantity filtering condition corresponding to each data element according to the number of each data element; screening each template voltage data group according to the quantity filtering condition to obtain several target arrays containing target dwell periods.

[0078] Among them, the preset filtering condition (quantity filtering condition) is the number of processed voltage values, which can be understood as the pause time. It should be noted that the filtering condition can be adjusted according to actual conditions. For example, if the pause time is long during the process production, a larger filtering condition value can be set. This embodiment of the application does not limit this.

[0079] For the target dwell period, it is the dwell period of the template after mold closing and the dwell period of the template after mold opening. It can be understood that the target array is the array of the dwell period of the template after mold closing and the dwell period of the template after mold opening.

[0080] In a specific implementation, after flattening the template voltage data to obtain multiple template voltage data groups, the number of data elements (measured voltage data) in each template voltage data group is calculated, and a filtering condition for the number of data elements in the template voltage data group is set. The data in the template voltage data group is then filtered to an array greater than the filtered number. The resulting filtered array corresponds to the dwell period of the movable mold after mold closing and the dwell period after mold opening. That is, by filtering the number of elements in the template voltage data group, a series of arrays consisting solely of the dwell period of the movable mold after mold closing and the dwell period after mold opening can be obtained. It should be noted that each single cycle segmented from multiple consecutive cycles contains only one mold opening completion process and one mold closing completion process. Cycle segmentation can be achieved by combining arrays.

[0081] The filtered arrays need to be processed to ensure that the first array is the mold closing dwell array and the last array is the mold opening dwell array. The main purpose is to ensure positioning and avoid two situations.

[0082] In an embodiment of the present application, the template voltage data group is screened according to a preset filtering condition to identify the data group containing the target dwell period, so that the motion features of the important period can be extracted and used for subsequent period segmentation.

[0083] Step S104, periodically dividing the template voltage data according to the target dwell periods corresponding to the target arrays to obtain a plurality of single-cycle template data to be integrated;

[0084] In some embodiments, step S104 may include: periodically dividing the template voltage data according to the dwell period of the templates corresponding to each target array after mold closing and the dwell period of the templates after mold opening, to obtain a plurality of single-cycle template data to be integrated.

[0085] The template voltage data may be understood as continuous multiple cycles (data) of the template, or may be understood as continuous voltage data of the template.

[0086] For the single-cycle template data to be integrated, it is the single-cycle data obtained by period-segmenting the template voltage data of multiple consecutive cycles. It can also be referred to as a single cycle. The single-cycle template data can be used for subsequent data integration to obtain complete single-cycle data. Figure 3 , Figure 3 This is a schematic diagram of component movement of a single cycle of an injection molding machine provided by an embodiment of the present application; Figure 3 The horizontal axis represents time (in seconds), the vertical axis represents voltage signal (in volts), the red curve reflects the periodic motion of the movable platen, the blue curve reflects the periodic motion of the injection screw, the yellow dotted curve reflects the periodic motion of the ejector, and the purple curve reflects the system pressure.

[0087] See also Figure 4 , Figure 4 Schematic diagram of the cutting nodes of the injection molding machine for continuous multi-cycles provided in the embodiment of the present application; Figure 3 same, Figure 4 The horizontal axis represents time (in seconds), the vertical axis represents voltage signal (in volts), the red curve reflects the periodic motion of the movable plate, the blue curve reflects the periodic motion of the injection screw, the yellow dotted curve reflects the periodic motion of the ejector, and the purple curve reflects the system pressure. Figure 4 As shown in the figure, different parameters can be obtained by combining various cutting nodes, and the speed, acceleration and other process parameters of different areas can also be obtained by constraining the position of the cutting nodes. For example, if the template is cut, it can be divided into mold closing and mold opening. First, determine the time points of mold closing and mold opening, and then calculate the speed, acceleration and other parameters of this interval. For example, Figure 4 [3.56, 6.04] represent the time and voltage at which high-pressure clamping is completed, respectively. The voltage value can be proportionally calculated to determine the pressure of the high-pressure clamping. When high-pressure clamping is completed, the pressure value at that moment is the maximum value in the cycle. The node is confirmed by searching for the maximum voltage value collected by the pressure sensor during the cycle. [2.41, 0.85] represent the time and voltage value at which the movable platen is closed (the voltage at that moment is relative to the voltage at zero). The time node 2.41 is determined by horizontally segmenting the movable platen curve, specifically the time at which the segment's starting point is determined. The remaining time nodes are confirmed using similar methods as described above.

[0088] In the specific implementation, it is necessary to process the filtered array to ensure that the first array is the mold closing dwell array and the last array is the mold opening dwell array. Then the processed array will be tested. If the values ​​of two adjacent arrays are close, the program will be terminated and a prompt will be given to adjust the det_s parameter and the det_x parameter (if the voltage value fluctuation range is set too small, the fluctuation range will be very narrow, so that the data belonging to the same segment will be divided into several groups, thereby disrupting the sorting of the mold closing dwell-mold opening dwell grouping). If there is no prompt to adjust the det_s parameter and the det_x parameter, the array will be divided into a single cycle according to the mold opening dwell end point. Among them, the mold opening dwell end point is the end point of the mold opening dwell of a single cycle, and it can also be the starting point of the next cycle. Therefore, as long as the mold opening dwell points are correctly sorted, the cycle can be divided.

[0089] In the embodiment of the present application, the template voltage data is periodically segmented according to the target dwell period to obtain a series of single-cycle template data to be integrated, which enables detailed analysis of the motion characteristics within each individual cycle.

[0090] Step S105, integrating the single-cycle template data to be integrated with the pressure data, injection motion data, and ejector motion data corresponding to the single-cycle template data to be integrated according to the position of each single-cycle template data to be integrated in the data table, to obtain a plurality of target single-cycle data;

[0091] The target single-cycle data is a complete single cycle, including data information of each component of the injection molding machine.

[0092] In the specific implementation, the positions of each single cycle divided by the moving plate movement in the data table are compared, and other data such as pressure data, injection movement data, and ejector movement data are re-integrated into a cycle according to the corresponding positions, and the integrated individual cycles are recorded and stored.

[0093] In an embodiment of the present application, by integrating the single-cycle template data to be integrated with the pressure data, injection motion data, and ejector motion data in the data table, target single-cycle data containing comprehensive information is obtained, forming a data basis for a comprehensive analysis of the continuous working cycle of the injection molding machine.

[0094] Step S106 , performing a flattening process on each of the target single-cycle data, and merging the target single-cycle data that meet a preset merging condition, to obtain motion characteristic data of each component movement in the injection molding machine.

[0095] The preset merging conditions are the voltage difference and spacing between the target single-cycle data. For example, the merging array determines whether to merge based on the voltage difference between the two arrays and the spacing between the two arrays. The values ​​in the preset merging conditions can be adjusted based on actual conditions and are not limited in this embodiment of the present application.

[0096] In some embodiments, step S106 may include: flattening each target single-cycle data, and calling a merging function to merge the target single-cycle data that meets the preset voltage difference and interval distance between the target single-cycle data to obtain the motion characteristic data of each component movement in the injection molding machine.

[0097] Among them, the pseudo code of the adjacent group merging function is as follows:

[0098]

[0099] In the specific implementation, each integrated individual cycle is processed in sequence, and the flattening function is called on the motion data of each component. In order to more accurately obtain the specific motion characteristics of each component, the det_s parameter and det_x parameter settings are reduced. At the same time, the merging function of adjacent data is called to merge the arrays. At the same time, restrictions are set on the flattened and merged arrays. This can obtain the fine motion characteristics of each component's motion, and the parameters of each component's motion are cyclically saved and plotted. Cycle saving means saving the measured values ​​of each single cycle, such as cycle time, mold opening and closing time, and maximum injection speed, for reference in subsequent data analysis or maintenance needs.

[0100] Because a smaller fluctuation range is used within a single cycle, a line segment that originally belonged to one line segment may be processed into two. If the difference between the two segments is small, they should be merged. If the segment is larger than two, it indicates that the fluctuation range is set incorrectly, and the det_s and det_x parameters need to be reset. Merging arrays is determined by the voltage values ​​of the two arrays and the distance between them.

[0101] In an embodiment of the present application, the target single-cycle data is flattened and the data that meets the preset conditions is merged, so that the data can be segmented within a single cycle. Through the combination of various segmentation points, refined motion characteristic data of the movement of each component of the injection molding machine can be obtained, such as mold closing time, injection holding time, etc. The obtained refined motion characteristic data of the movement of each component of the injection molding machine can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve the production efficiency and quality of the injection molding machine.

[0102] It should be noted that the first straightening process is applied within the entire cycle (continuous multiple cycles) to achieve segmentation within a single cycle. However, the straightening process here is performed within a single cycle to achieve segmentation of the motion patterns of each component within the single cycle.

[0103] In other embodiments, after step S106, the following may also be included: fusing the target action data of each component in each target single-cycle data separately to obtain each curve corresponding to each target single-cycle data; calculating the time domain eigenvalues ​​and frequency domain eigenvalues ​​of the target action data in each curve; performing correlation analysis on the target action data in each curve to obtain the correlation coefficient eigenvalues ​​corresponding to each curve; performing dimensionality reduction processing on the time domain eigenvalues, frequency domain eigenvalues ​​and correlation coefficient eigenvalues, and performing cluster analysis on the time domain eigenvalues, frequency domain eigenvalues ​​and correlation coefficient eigenvalues ​​after dimensionality reduction processing to obtain several clustering results; performing centroid calculation on each clustering result to obtain the maximum centroid distance between each clustering result; and monitoring the target single-cycle data based on the maximum centroid distance.

[0104] See also Figure 5 , Figure 5 is a schematic diagram of a fusion curve provided in an embodiment of the present application; Figure 5 The horizontal axis represents time (in seconds) and the vertical axis represents voltage signal (in volts).

[0105] The target action data refers to the effective or significant actions of the template, ejector, injection molding, system pressure, etc. within the cycle.

[0106] The calculation method of the correlation coefficient eigenvalue can be as follows:

[0107]

[0108]

[0109] Optionally, before calculating the time domain eigenvalues ​​and frequency domain eigenvalues ​​of the target motion data in each curve, the method may further include: performing Fourier transform on the target motion data in each curve to obtain the frequency and amplitude corresponding to the target motion data in each curve.

[0110] See also Figure 6 , Figure 6 is a schematic diagram of the silhouette coefficient provided in an embodiment of the present application; Figure 6 The horizontal axis represents the profile value, and the vertical axis represents the number of groups (clusters). Figure 7 , Figure 7 Schematic diagram of cluster analysis results provided in the embodiment of the present application; Figure 7 As shown, the points with serial numbers 1, 2, 5, 6 and 7 are a group, the points with serial numbers 13, 17 and 18 are a group, the points with serial numbers 10, 11, 14, 15, 16, 19 and 20 are a group, and the points with serial numbers 3, 4, 8, 9 and 12 are abnormal outliers ( Figure 7 At the blue dot in the middle), for example, after comparing the measured parameters, a fault occurred during the 8th cycle of production, so it appears as an abnormal point. Figure 7 The abnormal cycle point with sequence number 8 is far away from other cycle points, while other abnormal cycle points (such as the abnormal cycle points with sequence numbers 3, 4, 9 and 12) are closer to other cycle points (except the abnormal cycle point with sequence number 8). This phenomenon indicates that the abnormal cycle point has a certain difference from the cycle in the group and has not yet reached a fault similar to the 8th cycle. Figure 8 , Figure 8 Schematic diagram of the periodic curve of abnormal off-peak value provided by the embodiment of the present application, such as Figure 8 As shown in the figure, Figure 7 The periodic curve corresponding to the abnormal value of the blue point in the middle, Figure 8The horizontal axis represents time (in seconds), the vertical axis represents voltage signal (in volts), the red curve reflects the periodic motion of the movable platen, the blue curve reflects the periodic motion of the injection (screw), the yellow dotted curve reflects the periodic motion of the ejector, and the purple curve reflects the system pressure.

[0111] The calculation time for calculating the maximum distance between the centroids of the classifications and analyzing the stable trend of injection molding can be divided into: 1) cumulative analysis, that is, the data of each calculation is continuously accumulated; 2) fixed-length calculation, each calculation of the same length of data. It is understood that the time for calculating the maximum distance between the centroids of the classifications can be adjusted according to actual conditions, and this embodiment of the application does not impose any restrictions on this.

[0112] In the specific implementation, the effective actions (significant actions) of the template, ejector, injection molding, and system pressure within the cycle are fused into a curve in chronological order by means of data fusion. The frequency and amplitude of each single cycle can be obtained by Fourier transforming the fused curve. At the same time, the eigenvalue statistics of each single cycle in the time domain and frequency domain can be performed. In addition, the fused curves of different cycles are subjected to correlation analysis, and the correlation coefficients of the curve with other curves are accumulated and averaged as a correlation coefficient eigenvalue of the single cycle currently being calculated. Then, the PCA (Principal Component Analysis) method is used to reduce the dimensionality of the multidimensional features (time domain eigenvalues, frequency domain eigenvalues, and correlation coefficient eigenvalues), and the features with a cumulative contribution rate of 95% are included. Then, the k-means (K-Means C l useri g a g r ithm, K-means clustering algorithm) performs cluster analysis on the multidimensional features after dimensionality reduction processing, uses the silhouette coefficient as the judgment condition for the number of categories, can perform multiple k-means clustering analysis operations, selects the value with the largest number of categories as the number of categories, and extracts the outliers from the matrix. Finally, the centroids of different categories can be obtained through cluster analysis, and the distance between the centroids is calculated using the first three dimensions of the centroid to obtain the maximum centroid distance between classification categories. By continuously detecting the maximum centroid distance, the stability and trend observation between continuous long periods and segmented long periods can be achieved.

[0113] In an embodiment of the present application, in the cyclic movement of the injection molding machine, the waiting time after the template movement is completed by clamping and the waiting time after the mold opening are completed are relatively long. By setting the filtering conditions, it is possible to obtain data arrays of two stops of the moving template movement within a single cycle, thereby achieving the purpose of segmenting multi-cycle data, and realizing the measurement of parameters such as the single-cycle movement time, mold opening time, mold closing time, ejector running time, ejector stroke, mold opening stroke, mold opening distance repetition accuracy, maximum injection speed, and maximum speed of mold opening and closing movement. The various parameters obtained can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve production efficiency and quality. In addition, by extracting the features of time domain eigenvalues, frequency domain eigenvalues ​​and single-cycle correlation coefficient eigenvalues, the PCA method is used to reduce the dimensionality of the time domain eigenvalues, frequency domain eigenvalues ​​and single-cycle correlation coefficient eigenvalues ​​(the dimension is determined by the cumulative contribution rate), and the outliers are extracted. Then, the k-means method is used to perform cluster analysis on the multidimensional eigenvalues. By obtaining the maximum average silhouette coefficient, the number of classification categories is determined to calculate the maximum distance between the centroids of the categories, thereby realizing the observation of continuous long-cycle stability and segmented long-cycle stability and trends, providing an efficient solution for the monitoring and optimization of injection molding production of injection molding machines.

[0114] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0115] The steps of the method for measuring the motion cycle of an injection molding machine provided in the embodiment of the present application are as follows:

[0116] Step 1: Re-read the continuous voltage data recorded by the data logger in a data table (such as Excel) at regular intervals.

[0117] Step 2: Call the flattening function, set the det_s parameter (greater than the middle voltage value) and the det_x parameter (less than the middle voltage value) of the voltage value fluctuation range, flatten the continuous data of the dynamic mold motion recorded by the data recorder, and obtain multiple data groups. Then calculate the number of data elements (measured voltage data) in each data group, and set the filtering conditions for the number of data elements in the data group, and then filter the data in the data group into an array greater than the filter number. The array obtained after filtering is the array corresponding to the residence period of the dynamic mold after the mold is closed and the residence period after the mold is opened.

[0118] That is, each data group refers to a plurality of groups of unequal lengths obtained by dividing the continuous multi-cycle data through the voltage value fluctuation range set by the flattening function, and then filling the intermediate values ​​of each data group so that each data group has the same value to achieve the purpose of linearization, which is different from the linear fitting method in the related technology; then, by filtering the number of elements in the data group, a series of arrays consisting of the dwell period of the movable mold after the mold is closed and the dwell period after the mold is opened can be obtained. It should be noted that each single cycle divided out of the continuous multi-cycles has only one mold opening completion process and one mold closing completion process. By combining the arrays, cycle cutting can be achieved.

[0119] Step 3: Process the filtered arrays to ensure that the first array is the mold closing dwell array and the last array is the mold opening dwell array. The main purpose is to ensure positioning and avoid the two situations.

[0120] Step 4: Check the array processed in step 3. If the values ​​of two adjacent arrays are close, the program will be terminated and a prompt will be given to adjust the det_s parameter and the det_x parameter (if the voltage value fluctuation range is set too small, the fluctuation range will be very narrow, so that the data belonging to the same segment will be divided into several groups, thereby disrupting the sorting of the mold closing dwell and mold opening dwell groups). If there is no prompt to adjust the det_s parameter and the det_x parameter, the array will be divided into single cycles according to the mold opening dwell end point. Among them, the mold opening dwell end point is the end point of the mold opening dwell of a single cycle, and it can also be the starting point of the next cycle. Therefore, as long as the mold opening dwell point is correctly sorted, the cycle can be divided.

[0121] Step 5: Compare the positions of the single cycles divided by the moving plate motion in the data table, and re-integrate other data such as pressure data, injection motion data, and ejector motion data into one cycle according to the corresponding positions, and record and store the integrated individual cycles.

[0122] Step 6: Process each integrated individual cycle in sequence, call the straightening function for the motion data of each component, and in order to obtain the specific motion characteristics of each component more accurately, reduce the settings of the det_s parameter and the det_x parameter, and call the merge function of adjacent data to merge the arrays at the same time. At the same time, set restrictions on the straightened and merged arrays, so that the fine motion characteristics of each component can be obtained, and the parameters of the motion of each component can be saved in a loop and plotted.

[0123] Because a smaller fluctuation range is used within a single cycle, a line segment that originally belonged to one line segment may be processed into two. If the difference between the two segments is small, they should be merged. If the segment is larger than two, it indicates that the fluctuation range is set incorrectly, and the det_s and det_x parameters need to be reset. Merging arrays is determined by the voltage values ​​of the two arrays and the distance between them.

[0124] It should be noted that the adjustment of the det_s and det_x parameters in step 2 is to segment continuous multi-cycle data. In order to ensure the reliability of the segmentation, a larger fluctuation range should be used. The same method is also required when the single-cycle data is processed independently in step 6. When it is necessary to obtain more accurate specific motion characteristics of each component, a smaller voltage fluctuation value can be set for calculation.

[0125] Step 7: By means of data fusion, the effective actions (significant actions) of the template, ejector, injection molding, and system pressure within the cycle are fused into a curve in chronological order.

[0126] Step 8: By performing a Fourier transform on the fused curve, the frequency and amplitude of each single cycle can be obtained. Eigenvalue statistics can also be performed on each single cycle in the time and frequency domains. Furthermore, correlation analysis is performed on the fused curves of different cycles. The correlation coefficients of the curve and other curves are accumulated and averaged to form the correlation coefficient eigenvalue of the current single cycle being calculated.

[0127] Step nine: Use the PCA (Principal Component Analysis) method to reduce the dimensionality of the multidimensional features (time domain eigenvalues, frequency domain eigenvalues, and correlation coefficient eigenvalues), include the features with a cumulative contribution rate of 95%, and then use k-means to perform cluster analysis on the multidimensional features after dimensionality reduction. Use the silhouette coefficient as the judgment condition for the number of categories. Multiple k-means cluster analysis operations can be performed, and the value with the largest number of categories is selected as the number of categories, and outliers are extracted from the matrix.

[0128] Step 10: Through cluster analysis, we can obtain the centroids of different categories. The distance between the centroids is calculated using the first three dimensions of the centroids to obtain the maximum centroid distance between classification categories. By continuously detecting the maximum centroid distance, we can observe the stability and trend between continuous long periods and segmented long periods.

[0129] Step 11: Merge the above calculated data into an Excel spreadsheet and save it in the specified folder. At the same time, save the drawing in the specified folder to prepare data for subsequent continuous development.

[0130] As can be seen from the above, the injection molding machine motion cycle measurement method of the embodiment of the present application is to decompose the continuously collected voltage data into multiple independent single cycles, and in the independent cycles, the motion characteristics of the template, ejector, and injection part of the injection molding machine are decomposed according to the change pattern of the voltage, and the motion combination of each component in the cycle is measured. By flattening the jumping data and combining and merging adjacent grouped data according to thresholds and distances, the accuracy and continuity of the data flattening can be flexibly achieved, and various basic parameters such as the template, injection, ejector movement and clamping pressure can be accurately measured. The various basic parameters obtained can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve production efficiency and quality.

[0131] In addition, the fusion of the voltage values ​​of the template, injection molding, and ejector movements and pressure within the cycle can better realize the feature extraction in the time domain and frequency domain. Through dimensionality reduction and unsupervised classification processing of multi-dimensional feature values, outliers can be found and the maximum centroid distance between classification categories can be obtained. Through uninterrupted detection, the observation of stability and trends during continuous long cycles and segmented long cycles can be achieved.

[0132] It should be pointed out that this embodiment only provides a brief schematic illustration of the general process of the injection molding machine motion cycle measurement method. The detailed description of each step can refer to the relevant content in the aforementioned embodiment and will not be repeated here. It can be understood that the present invention is not limited to this.

[0133] The embodiment of the present application obtains voltage data of an injection molding machine from a data table; wherein the voltage data is used to characterize motion data of each component in the injection molding machine, and the voltage data at least includes template voltage data of a template; the template voltage data of the template is flattened to obtain a plurality of template voltage data groups; each template voltage data group is screened according to a preset filtering condition to obtain a plurality of target arrays including a target dwell period; the template voltage data is periodically segmented according to the target dwell period corresponding to each target array to obtain a plurality of single-cycle template data to be integrated; the single-cycle template data to be integrated is integrated with the pressure data, injection motion data, and ejector motion data corresponding to the single-cycle template data to be integrated according to the position of each single-cycle template data to be integrated in the data table to obtain a plurality of target single-cycle data; each target single-cycle data is flattened, and the target single-cycle data that meets the preset merging condition is merged to obtain motion characteristic data of the motion of each component in the injection molding machine. In an embodiment of the present application, first, by flattening the voltage data of the template, a more accurate and stable data group can be obtained, and then the template voltage data group is screened according to preset filtering conditions to identify the data group containing the target dwell period, and the motion characteristics of the important period can be extracted and used for subsequent cycle segmentation. Then, the template voltage data is cycle-segmented according to the target dwell period to obtain a series of single-cycle template data to be integrated, which can carefully analyze the motion characteristics within each individual cycle, and then the single-cycle template data to be integrated is integrated with the pressure data, injection motion data and ejector motion data in the data table to obtain target single-cycle data containing comprehensive information, forming a data basis for a comprehensive analysis of the continuous working cycle of the injection molding machine, and finally, the target single-cycle data is flattened and the data that meets the preset conditions is merged to obtain refined motion feature data of the motion of each component of the injection molding machine. The refined motion feature data of the motion of each component of the injection molding machine obtained can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve the production efficiency and quality of the injection molding machine.

[0134] That is, the embodiments of the present application can accurately decompose the recorded continuous data into multiple independent single cycles. In the independent cycles, the motion of the template, ejector, and injection part of the injection molding machine is decomposed by the change pattern of voltage, and the motion combination of each component in the cycle is analyzed to obtain basic measurement values ​​such as cycle time, mold opening and closing speed (including maximum speed), maximum injection speed, ejector operation time, ejector stroke, mold opening stroke, mold opening repeatability, etc. The refined motion characteristic data of the various components of the injection molding machine obtained can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve the production efficiency and quality of the injection molding machine.

[0135] In addition, the fusion of the voltage value of the template, injection molding, and ejector action and pressure within the cycle can better realize the feature extraction in the time domain and frequency domain. Through the dimensionality reduction and unsupervised classification processing of the multi-dimensional eigenvalues, the changes in the category centroid position and the maximum centroid position can be continuously updated, and the outliers can be expressed. It can realize the continuous measurement of stability and trend during continuous long cycles and segmented long cycles, and the measurement of intra-category variance and change trend.

[0136] See also Figure 9 The present application also provides an injection molding machine motion cycle measurement device 900, which can implement the above-mentioned injection molding machine motion cycle measurement method. The device 900 includes:

[0137] The data acquisition module 901 is used to acquire voltage data of the injection molding machine from the data table; wherein the voltage data is used to represent the motion data of each component in the injection molding machine, and the voltage data at least includes the template voltage data of the template;

[0138] A data smoothing module 902 is used to perform a flattening process on the template voltage data of the template to obtain a plurality of template voltage data groups;

[0139] The data screening module 903 is used to screen each of the template voltage data groups according to a preset filtering condition to obtain a plurality of target arrays including target dwell periods;

[0140] A cycle segmentation module 904 is configured to cycle segment the template voltage data according to target dwell periods corresponding to the target arrays to obtain a plurality of single-cycle template data to be integrated;

[0141] The cycle integration module 905 is used to integrate the single-cycle template data to be integrated with the pressure data, injection motion data and ejector motion data corresponding to the single-cycle template data to be integrated according to the position of each single-cycle template data to be integrated in the data table to obtain a plurality of target single-cycle data;

[0142] The motion feature acquisition module 906 is used to perform a flattening process on each of the target single-cycle data and merge the target single-cycle data that meet a preset merging condition to obtain motion feature data of each component movement in the injection molding machine.

[0143] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0144] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method for measuring the motion cycle of an injection molding machine. The electronic device can be any smart terminal, such as a tablet computer or an in-vehicle computer.

[0145] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0146] See also Figure 10 , Figure 10 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0147] The processor 1001 may be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0148] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the injection molding machine motion cycle measurement method of the embodiment of this application;

[0149] Input / output interface 1003, used to implement information input and output;

[0150] Communication interface 1004, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.);

[0151] Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 );

[0152] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0153] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned injection molding machine motion cycle measurement method is implemented.

[0154] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0155] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0156] The embodiments of the present application provide a method for measuring the motion cycle of an injection molding machine, an apparatus for measuring the motion cycle of an injection molding machine, an electronic device, and a storage medium. The method obtains voltage data of the injection molding machine from a data table, wherein the voltage data is used to characterize the motion data of each component in the injection molding machine, and the voltage data at least includes template voltage data of a template; the template voltage data of the template is flattened to obtain a plurality of template voltage data groups; each template voltage data group is screened according to preset filtering conditions to obtain a plurality of target arrays including target dwell periods; the template voltage data is periodically segmented according to the target dwell periods corresponding to each target array to obtain a plurality of single-cycle template data to be integrated; the single-cycle template data to be integrated is integrated with the pressure data, injection motion data, and ejector motion data corresponding to the single-cycle template data to be integrated according to the position of each single-cycle template data to be integrated in the data table to obtain a plurality of target single-cycle data; each target single-cycle data is flattened, and the target single-cycle data that meets the preset merging conditions are merged to obtain motion characteristic data of the motion of each component in the injection molding machine. In an embodiment of the present application, first, by flattening the voltage data of the template, a more accurate and stable data group can be obtained, and then the template voltage data group is screened according to preset filtering conditions to identify the data group containing the target dwell period, and the motion characteristics of the important period can be extracted and used for subsequent cycle segmentation. Then, the template voltage data is cycle-segmented according to the target dwell period to obtain a series of single-cycle template data to be integrated, which can carefully analyze the motion characteristics within each individual cycle, and then the single-cycle template data to be integrated is integrated with the pressure data, injection motion data and ejector motion data in the data table to obtain target single-cycle data containing comprehensive information, forming a data basis for a comprehensive analysis of the continuous working cycle of the injection molding machine, and finally, the target single-cycle data is flattened and the data that meets the preset conditions is merged to obtain refined motion feature data of the motion of each component of the injection molding machine. The refined motion feature data of the motion of each component of the injection molding machine obtained can be used to monitor the stability of injection molding production, predict the maintenance requirements of the production process, and improve the production efficiency and quality of the injection molding machine.

[0157] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0158] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0159] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0160] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0161] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0162] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0164] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0166] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0167] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for measuring the motion cycle of an injection molding machine, characterized in that: The method comprises: Obtain voltage data of the injection molding machine from a data table; wherein the voltage data is used to represent motion data of various components in the injection molding machine, and the voltage data at least includes template voltage data of a template; performing a flattening process on the template voltage data of the template to obtain a plurality of template voltage data groups; Filtering each of the template voltage data groups according to a preset filtering condition to obtain a plurality of target arrays including target dwell periods; Performing period segmentation on the template voltage data according to the target dwell period corresponding to each target array to obtain a plurality of single-period template data to be integrated; According to the position of each of the single-cycle template data to be integrated in the data table, the single-cycle template data to be integrated is integrated with the pressure data, injection motion data, and ejector motion data corresponding to the single-cycle template data to be integrated to obtain a plurality of target single-cycle data; Each of the target single-cycle data is flattened, and the target single-cycle data that meets a preset merging condition is merged to obtain motion characteristic data of each component movement in the injection molding machine.

2. The method according to claim 1, characterized in that The template voltage data of the template is flattened to obtain a plurality of template voltage data groups, including: A flattening function is called to flatten the template voltage data of the template to obtain a plurality of template voltage data groups; wherein the flattening function is provided with a voltage value fluctuation range, and the voltage value fluctuation range is used to divide the template voltage data into a plurality of template voltage data groups.

3. The method according to claim 1, characterized in that The filtering process is performed on each of the template voltage data groups according to the preset filtering conditions to obtain a plurality of target arrays including target dwell periods, including: Calculating the number of data elements in each of the template voltage data groups, and setting a quantity filtering condition corresponding to each of the data elements according to the number of each of the data elements; Each of the template voltage data groups is screened according to the quantity filtering condition to obtain a plurality of target arrays including target dwell periods.

4. The method according to claim 1, wherein The target dwell period is the dwell period of the template after mold closing and the dwell period of the template after mold opening. The template voltage data is periodically segmented according to the target dwell period corresponding to each target array to obtain a plurality of single-cycle template data to be integrated, including: The template voltage data is periodically segmented according to the dwell time of the templates corresponding to each target array after mold closing and the dwell time of the templates after mold opening, to obtain a plurality of single-cycle template data to be integrated.

5. The method according to claim 1, characterized in that The preset merging conditions are the voltage difference and the interval distance between the target single-cycle data. The target single-cycle data are flattened and the target single-cycle data that meet the preset merging conditions are merged to obtain the motion characteristic data of each component in the injection molding machine, including: Each target single-cycle data is flattened, and a merging function is called to merge the target single-cycle data that meet the preset voltage difference and interval distance between the target single-cycle data to obtain the motion characteristic data of each component in the injection molding machine.

6. The method according to claim 1, wherein After flattening the target single-cycle data and merging the target single-cycle data that meet preset merging conditions to obtain motion characteristic data of the motion of each component in the injection molding machine, the method further includes: fusing the target motion data of each component in each target single-cycle data to obtain curves corresponding to each target single-cycle data; Calculating the time domain eigenvalues ​​and frequency domain eigenvalues ​​of the target motion data in each of the curves; Performing correlation analysis on the target action data in each of the curves to obtain a correlation coefficient characteristic value corresponding to each of the curves; performing dimensionality reduction processing on the time domain eigenvalues, the frequency domain eigenvalues, and the correlation coefficient eigenvalues, and performing cluster analysis on the time domain eigenvalues, the frequency domain eigenvalues, and the correlation coefficient eigenvalues ​​after the dimensionality reduction processing to obtain a plurality of clustering results; Calculating the centroid of each clustering result to obtain the maximum centroid distance between each clustering result; The target single-cycle data is monitored according to the maximum centroid distance.

7. The method according to claim 6, characterized in that Before calculating the time domain eigenvalues ​​and frequency domain eigenvalues ​​of the target motion data in each of the curves, the method further includes: Performing Fourier transform on the target motion data in each of the curves to obtain the frequency and amplitude corresponding to the target motion data in each of the curves.

8. A device for measuring the motion cycle of an injection molding machine, characterized in that: The device comprises: A data acquisition module is used to acquire voltage data of the injection molding machine in a data table; wherein the voltage data is used to represent motion data of each component in the injection molding machine, and the voltage data at least includes template voltage data of the template; A data smoothing module is used to perform a straightening process on the template voltage data of the template to obtain a plurality of template voltage data groups; A data screening module, configured to screen each of the template voltage data groups according to a preset filtering condition to obtain a plurality of target arrays including target dwell periods; a cycle segmentation module, configured to perform cycle segmentation on the template voltage data according to target dwell periods corresponding to the target arrays, to obtain a plurality of single-cycle template data to be integrated; a cycle integration module, configured to integrate the single-cycle template data to be integrated with the pressure data, injection motion data, and ejector motion data corresponding to the single-cycle template data to be integrated according to the position of each single-cycle template data to be integrated in the data table, to obtain a plurality of target single-cycle data; The motion feature acquisition module is used to perform flattening processing on each of the target single-cycle data and merge the target single-cycle data that meet the preset merging conditions to obtain the motion feature data of each component movement in the injection molding machine.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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