Management method and system of injection molding system and storage medium

Abstract

The embodiment of the invention provides a management method and system of an injection molding system and a storage medium, and belongs to the technical field of injection molding machining. The injection molding system comprises a plurality of injection molds and a plurality of movably arranged injection devices, and each injection device is provided with a first sensing unit; at least two injection molds are distributed on one side of the moving path of each injection device at intervals, and the number of the injection devices is smaller than that of the injection molds; the method comprises the following steps: acquiring a sensing result of a first sensing unit in each injection device; when the distance between one target injection mold and the injection head of the injection device corresponding to the induction result is within a preset injection distance, mold use information of the target injection mold is updated; the target injection mold is one of a plurality of injection molds; and each injection mold is managed and controlled according to the use information of each mold. According to the embodiment of the invention, the accuracy of mold use condition statistics can be improved.

Description

Technical Field

[0001] The present application relates to the field of injection molding technology, and in particular to a management method, system and storage medium for an injection molding system. Background Art

[0002] In the field of injection molding processing technology, the injection molding system often needs to cooperate with the injection device (such as the injection head) and the injection mold to realize the production of injection molded products. At the same time, in order to improve the production efficiency of injection molded products while saving costs, multiple injection devices and injection molds are usually set, and the number of injection devices is less than the number of injection molds, so that after the injection is completed, when the injection mold is performing product molding operations and cleaning stages, the injection device can be moved to an idle injection mold for injection. In the prior art, in order to further facilitate the management of the injection molding system, the use of the injection mold is usually monitored to facilitate the maintenance of the injection mold, etc. However, since the number of injection devices is less than the number of injection molds, some injection molds are idle at the same time, and some injection molds are in the molding operation stage / cleaning stage, and the accuracy of automated statistics on the use of injection molding jigs is not high. Therefore, in the prior art, in order to ensure the accuracy of mold usage statistics, manual counting is often used. This method is inefficient and prone to statistical errors, which makes subsequent management based on the mold usage prone to errors. Therefore, how to improve the accuracy of mold usage statistics and thus improve the management convenience of the injection molding system is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a management method, system and storage medium for an injection molding system, which can improve the accuracy of mold usage statistics.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a management method for an injection molding system, wherein the injection molding system comprises a plurality of injection molds and a plurality of movably arranged injection devices, each of which is provided with a first sensing unit; at least two injection molds are spaced apart on one side of a moving path of each of the injection devices, and the number of the injection devices is less than the number of the injection molds; the management method comprises: Acquire the sensing result of the first sensing unit in each of the injection devices; When there is a target injection mold whose distance to the injection head of the injection device corresponding to the sensing result is within a preset injection distance, updating the mold use information of the target injection mold; the target injection mold is one of the multiple injection molds; Determine life prediction data corresponding to each of the injection molds according to the number of uses, the duration of use, and the interval of use indicated by the use information of each mold; Determine usage efficiency data corresponding to the injection device according to the usage times and usage durations indicated by the usage information of each mold; Generate a mold optimization strategy and an injection molding optimization strategy according to the life prediction data and the usage efficiency data; The injection mold is adjusted according to the mold optimization strategy, and the injection device is adjusted according to the injection optimization strategy.

[0005] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides an injection molding system, the injection molding system comprising: Multiple injection devices; A plurality of injection molds, each of which can be movably arranged on the same side of the plurality of injection devices, each of which is provided with a first sensing unit; and the number of the injection devices is less than the number of the injection molds; A management module, wherein the management module executes any method described in the first aspect.

[0006] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer-readable storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the management method of the injection molding system described in the first aspect above.

[0007] The management method, system and storage medium of the injection molding system proposed in the present application realize the mutual decoupling of the injection control of the injection device and the statistics of the mold usage information by judging the sensing result of the first sensing unit for each injection device respectively. Since the update time of the mold usage information is when the distance between the injection head of the injection device and the target injection mold is within the injection distance, the mold usage information can be strongly associated with the actual injection molding operation, thereby further ensuring that the mold usage information reflects the actual usage status of the target injection mold, so that the usage of each injection mold can be correctly counted. Therefore, compared with the related art, the embodiment of the present application can realize the automatic statistics of the injection mold and the statistical accuracy of the usage is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a partial structural schematic diagram of an embodiment of the injection molding system provided by the present application; Figure 2 is a cross-sectional schematic diagram of an injection device of an embodiment of the injection molding system provided by the present application (excluding the first sensing unit); Figure 3 It is a flow chart of an embodiment of the management method of the injection molding system provided by the present application; Figure 4aIt is a schematic diagram of the positions of a target injection device and a corresponding target injection mold before the raw material is injected in one embodiment of the management method of the injection molding system provided by the present application; Figure 4b It is a schematic diagram of the positions of a target injection device and a corresponding target injection mold when starting to inject raw materials in one embodiment of the management method of the injection molding system provided by the present application; Figure 5 Hardware block diagram corresponding to the management method of the injection molding system provided in this application.

[0009] Reference numerals: Injection device 100, first sensing unit 110, tube body 120, screw 130, hopper 140, driving member 150, heater 160, injection port 170, Injection mold 200, second sensing unit 210, Management module 300, Linear drive track 400. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0011] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0013] In the field of injection molding technology, the injection molding system often needs to cooperate with the injection device (such as the injection head) and the injection mold to realize the production of injection molded products. At the same time, in order to improve the production efficiency of injection molded products and save costs, multiple injection devices and injection molds are usually set, and the number of injection devices is less than the number of injection molds, so that after the injection is completed, when the injection mold performs product molding operation and cleaning stage, the injection device can be moved to the idle injection mold for injection. In the prior art, in order to further facilitate the management of the injection molding system, the use of the injection mold is usually monitored to facilitate the maintenance of the injection mold, etc. For example, it can be estimated based on the number of times the injection mold cover is opened and closed. However, because the injection mold may be opened and closed due to non-production reasons such as maintenance and inspection, the recording method based on the number of times the cover is opened and closed cannot accurately reflect the actual use of the injection mold. Since the number of injection devices is less than the number of injection molds, at the same time, some injection molds are in an idle state, and some injection molds are in the molding operation stage / cleaning stage. The existing statistics based on the injection device will also cause usage statistics errors for the injection molds that have passed. Therefore, in the prior art, in order to ensure the accuracy of mold usage statistics, manual counting is mainly used. This method is inefficient and prone to statistical errors, which makes subsequent management based on the mold usage prone to errors. Therefore, how to improve the accuracy of mold usage statistics and thus improve the management convenience of the injection molding system is a technical problem that needs to be solved urgently. Based on this, the embodiments of the present application provide a management method, system, and storage medium for an injection molding system, which can improve the accuracy of mold usage statistics.

[0014] Understandably, referring to Figure 1 As shown, according to an embodiment of the present application, an injection molding system is provided, and the injection molding system includes: A plurality of injection molds 200; A plurality of injection devices 100, each of which can be movably arranged on the same side of the plurality of injection molds 200, each of which is provided with a first sensing unit 110; and the number of the injection devices 100 is less than the number of the injection molds 200; Management module 300, the management module 300 performs the following steps: Obtaining the sensing result of the first sensing unit 110 in each injection device 100; When there is a target injection mold and the distance between the injection head of the injection device 100 corresponding to the sensing result is within the preset injection distance, the mold use information of the target injection mold is updated; the target injection mold is one of the multiple injection molds 200; Each injection mold 200 is controlled and managed according to the usage information of each mold.

[0015] It is understandable that the management module 300 can also be used to control the injection device 100 to move to an idle injection mold 200 to perform a raw material injection operation. Since the first sensing unit 110 is set in a one-to-one correspondence with the injection device 100, the update of the mold usage information can be independent of the raw material injection operation of the injection device 100, and the mold usage information can be counted without changing the existing control logic, thereby simplifying the control logic of the management module 300 and improving the convenience of deployment and commissioning of the injection molding system. Exemplarily, if the first sensing unit 110 is set as a distance sensor or other sensor that can identify the distance, it is configured for periodic real-time detection. If the first sensing unit 110 is set as a reader / writer, it can be configured to be activated within the injection distance.

[0016] The embodiment of the present application does not limit the number of the injection mold 200 and the injection device 100, and those skilled in the art can selectively set them based on cost and production efficiency.

[0017] The embodiment of the present application does not limit the arrangement path of the injection mold 200, and those skilled in the art can selectively set it according to actual needs. For example, the arrangement path of the injection mold 200 is set to a straight line or a curve or other shapes.

[0018] The embodiment of the present application does not limit the type of the injection mold 200, nor does it limit the type of product molded by the injection mold 200. Those skilled in the art can selectively set it according to actual needs.

[0019] The injection distance represents the maximum distance of the injection head of the injection device 100 when injecting an injection mold 200. Those skilled in the art can selectively set it according to the deployment position of the injection device 100 and the injection mold 200, and the embodiment of the present application does not limit this.

[0020] The embodiment of the present application does not limit the options of the first sensing unit 110 , and those skilled in the art may select a suitable first sensing unit 110 according to the environment required for the type of product to be injection molded by the injection mold 200 .

[0021] The update of the mold usage information is based on the mold parameters of the target injection mold. The embodiment of the present application does not limit how to determine the mold parameters of the target injection mold. For example, it can be determined based on the position of the injection device when the injection distance is met. For example, a second sensing unit 210 can be configured on the injection mold 200 and determined by the parameters sent by the second sensing unit 210, and so on.

[0022] For example, refer to Figure 1As shown, it is assumed that n injection molds 200 are provided and k injection devices 100 are provided, where n is 2k. In the initial state, the n injection molds 200 are all in an idle state, and the k injection devices 100 are moved to the first k consecutively distributed injection molds 200 for injection molding operation. At this time, Figure 1 As shown, when it is detected that the distance between the injection device 1~injection device k and the corresponding injection mold 200 is within the injection distance, that is, the injection device 1~injection device k is performing the injection material operation on the corresponding injection mold 1~injection mold k, it is determined that there is a target injection mold for each of the injection devices 1~injection device k, and the corresponding mold usage information is updated, wherein the injection mold 1~injection mold k is respectively used as the target injection mold for the injection device 1~injection device k. After the injection of the injection mold 1~injection mold k is completed, the injection device 1~injection device k is moved to the injection mold k+1~injection mold 2k to perform the injection material operation. In some embodiments, due to the difference in the cleaning time after the injection mold 200 completes an injection material operation, the movement timing of the multiple injection devices 100 is triggered at different times. For example, if the cleaning time of injection mold 2k is long after injection molds k+1~2k complete the previous injection, the injection device k that starts the injection into injection mold 2k will lag behind other injection devices 100. At this time, since the update of the mold usage information is only strongly correlated with the sensing result of the corresponding injection device 100, the accuracy of the statistical convenience can be improved.

[0023] The management module 300 can be wirelessly connected to each first sensing unit 110, the injection device 100, and each second sensing unit 210. The management module 300 includes a device with a logic operation function such as a PLC or a microcontroller unit. In some embodiments, the management module 300 also includes a display screen to display the production status of the injection molded product. The present application embodiment does not limit the composition and other functions of the management module 300, and those skilled in the art can selectively set it according to actual conditions.

[0024] Understandably, referring to Figure 1 and Figure 2 As shown, each injection mold 200 is provided with a second sensing unit 210; the first sensing unit 110 is arranged at the injection port 170 of the corresponding injection device 100, and the distance between the first sensing unit 110 and the second sensing unit 210 at the corresponding injection position is less than or equal to the injection distance.

[0025] The first sensing unit 110 and the second sensing unit 210 are respectively arranged in the injection mold 200 and the injection device 100, so that when the distance between the second sensing unit 210 and the second sensing unit 210 is less than or equal to the injection distance, the first sensing unit 110 can directly obtain the mold parameters on the injection mold 200 ready to receive the injection material through the second sensing unit 210, thereby realizing the monitoring of a more complex production process with the control logic of a simpler software program, reducing the complexity of the deployment and debugging of the injection molding system.

[0026] For example, Figure 2 As shown, the injection device 100 includes a tube body 120, a screw 130, a hopper 140 and a driving member 150. The feed port of the hopper 140 is embedded in the tube body 120. One end of the screw 130 is connected to the driving end of the driving member 150. The other end of the screw 130 is inserted into the tube body 120. A heater 160 is provided on the outer wall of the tube body 120. The heater 160 is used to heat the raw material in the tube body 120. The driving member 150 is used to drive the screw 130 to rotate, so that the heated raw material in the tube body 120 is squeezed and the raw material is injected into the corresponding injection mold 200 from the injection port 170 away from the driving member 150. The driving member 150 can be set as a device with a driving function such as a motor.

[0027] Understandably, referring to Figure 1 As shown, the injection system also includes a linear transmission track 400, each injection device 100 can be movably set on the linear transmission track 400, each injection mold 200 is distributed at intervals along one side of the linear transmission track 400, and each injection mold 200 is located on the same straight line.

[0028] By distributing the injection devices 100 along a straight line, the moving efficiency of the injection device 100 can be improved, thereby improving the efficiency of the injection molding process.

[0029] Reference Figure 3 As shown, according to an embodiment of the present application, a management method of an injection molding system is provided, the injection molding system includes a plurality of injection molds 200 and a plurality of movably arranged injection devices 100, each injection device 100 is provided with a first sensing unit 110; at least two injection molds 200 are spaced apart on one side of the moving path of each injection device 100, and the number of injection devices 100 is less than the number of injection molds 200; the management method includes: Step S100, obtaining the sensing result of the first sensing unit 110 in each injection device 100; Step S200: when there is a target injection mold and the distance between the injection head of the injection device 100 corresponding to the sensing result is within the preset injection distance, the mold use information of the target injection mold is updated; the target injection mold is one of the multiple injection molds 200; Step S300, determining life prediction data corresponding to each injection mold according to the number of uses, use duration and use interval indicated by the use information of each mold; Specifically, a life prediction model based on machine learning or statistical analysis is used, combined with a large amount of historical injection mold usage data and actual life conditions for training and optimization. For example, a multivariate linear regression model can be used to model the number of times the mold is used, the length of time it is used, and the interval between uses as independent variables, and the actual service life of the mold (in units of injection times or time) as the dependent variable. At the same time, considering the impact of different mold models, materials, processing technology and other factors on life, the corresponding characteristic variables are introduced for model training to improve the accuracy of the prediction.

[0030] For each injection mold, its current number of uses, usage duration, and usage interval data are substituted into the trained life prediction model to calculate the corresponding life prediction value. The life prediction value can be presented in intuitive forms such as the remaining number of injections and the remaining available time, which are not limited here. For example, the model predicts that a mold has 5,000 injection opportunities left under the current usage state, or can still work normally for 100 hours, providing data basis for the mold maintenance and replacement plan. At the same time, the reliability of the life prediction results is evaluated by comparing with historical data, model error analysis, and other means, and abnormal predictions are discovered and adjusted in a timely manner.

[0031] Step S400, determining usage efficiency data corresponding to the injection devices respectively according to the usage times and usage durations indicated by the usage information of each mold; Specifically, the efficiency of the injection device is measured from two aspects: one is the number of injections per unit time, which reflects its production speed; the other is the time consumed for each injection (including injection, pressure holding, cooling and other stages), which reflects its energy utilization and production cycle efficiency. The efficiency data is obtained by statistically analyzing the number of times and duration of use in the mold usage information. For example, within a production cycle (such as 8 hours), an injection device completes 1,000 injections, and the total usage time is 6 hours. The number of injections per unit time is 1,000 / 6≈166.7 times / hour, and the average time consumed for each injection is 6*3600 / 1000=21.6 seconds.

[0032] As the injection molding production continues, new mold usage information is obtained in real time, and the usage efficiency data of the injection device 100 is dynamically updated. By drawing efficiency curves (such as time-injection number curves, time-single injection molding time curves), the performance change trend of the injection device 100 is analyzed. For example, if it is found that the number of injections per unit time of a certain injection device 100 is gradually decreasing, or the single injection molding time is gradually increasing, it indicates that the equipment has problems such as wear, potential failures, or unreasonable process parameters, which need to be checked and optimized in a timely manner.

[0033] Step S500, generating a mold optimization strategy and an injection molding optimization strategy according to the life prediction data and the usage efficiency data; Specifically, according to the life prediction data, for injection molds that are near the end of their service life, arrange maintenance, overhaul, reduce the number of times used, or replace them in advance. For example, if the remaining injection molding times of a mold's life prediction are less than 1,000 times, and the current production task is tight, consider quickly replacing the mold in the near future production gap to avoid the risk of mold damage and production suspension during the production process; for molds that still have a certain remaining life but have a long usage interval, reasonably arrange maintenance cycles, perform routine maintenance work such as cleaning, lubrication, and parts inspection to extend the mold's service life. Or in the case of difficult mold replacement, by reducing the use of short-lived molds, that is, giving priority to long-lived molds, so that the end of the life of multiple molds is similar, it is convenient for unified replacement and maintenance.

[0034] Injection molds can be allocated rationally based on the efficiency data of injection devices. For example, injection devices with high production efficiency can be allocated first to injection molds with complex processes and high dimensional accuracy requirements, so as to give full play to the advantages of the equipment and improve product quality. For injection devices with low use efficiency, they can be matched with relatively simple molds that do not require high production efficiency. At the same time, the reasons for their low efficiency can be analyzed, such as whether there are mechanical failures, hydraulic system problems, etc., and targeted repairs or upgrades can be carried out.

[0035] Mould optimization strategies include: reasonably arranging the order of mould use, giving priority to moulds with longer remaining life; regularly maintaining and servicing moulds to extend their service life; replacing severely worn moulds in a timely manner according to production needs and mould status, etc.

[0036] Injection molding optimization strategies include: adjusting the process parameters of the injection device (such as injection pressure, injection speed, holding time, etc.) to improve production efficiency and product quality; regularly inspecting and maintaining the injection device to ensure its normal operation; optimizing the movement path of the injection device according to the distribution and use of the mold, reducing movement time and energy consumption, etc.

[0037] Step S600: adjusting the injection mold according to the mold optimization strategy, and adjusting the injection device according to the injection optimization strategy.

[0038] Specifically, according to the mold optimization strategy, the injection mold is adjusted accordingly. For example, if the strategy recommends maintenance of a mold, then professionals need to be arranged to clean, lubricate, inspect and repair the mold; if it is recommended to replace the mold, a new mold needs to be purchased in time and installed and debugged.

[0039] According to the injection molding optimization strategy, the injection device is adjusted. For example, if the process parameters need to be adjusted, the injection pressure, speed and other parameters need to be accurately set through the control system; if the injection device is found to have a fault, it needs to be repaired or replaced in time.

[0040] By judging the sensing result of the first sensing unit 110 for each injection device 100, the injection control of the injection device 100 and the statistics of the mold usage information are decoupled from each other, and since the update timing of the mold usage information is when the distance between the injection head of the injection device 100 and the target injection mold is within the injection distance, the mold usage information can be strongly associated with the actual injection operation, so as to further ensure that the mold usage information reflects the actual usage status of the target injection mold, so that the usage of each injection mold 200 can be correctly counted. Therefore, the embodiment of the present application can realize the automatic statistics of the injection mold 200 and the statistical accuracy of the usage is higher.

[0041] Each injection mold 200 is provided with an injection position, so as to ensure that the injection device 100 can be aligned with the injection port 170 on the injection mold 200. The injection device 100 and the injection mold 200 can inject raw materials one by one. The injection distance characterization can identify the maximum distance at which the injection device 100 starts the injection process for the corresponding injection mold 200 at the injection position. For example, when the injection distance is set to 90 and the injection device 100 does not inject raw materials, the longest distance between the injection device 100 and each injection mold 200 is 400 and the shortest distance is less than 80. For example, refer to Figure 4a As shown in FIG. 1 , when the distance between the injection device 100 and the corresponding injection mold 200 at the injection position is 400, it means that the injection device 100 has not started the raw material injection. Figure 4b As shown, when the distance between the injection device 100 and the injection mold 200 corresponding to the injection position is 80, it means that the injection device 100 is in a raw material injection ready state and is about to start raw material injection. At this time, the update of the mold usage information of the injection mold 200 can be triggered.

[0042] In some optional embodiments, the mold optimization strategy and the injection optimization strategy are generated according to the life prediction data and the usage efficiency data, including: generating a life list for each of the injection molds according to the life prediction data, the life list indicating the remaining life of each injection mold, and sorting the molds in descending order according to the remaining life; when each of the injection molds needs to be injected, selecting the idle injection molds in turn according to the life list for injection molding; confirming the adjustment information of each of the injection devices according to the usage efficiency data and the preset efficiency; and adjusting the injection pressure and injection time of each of the injection devices according to the adjustment information.

[0043] Specifically, the life prediction data is obtained based on the previously acquired usage information of each injection mold (number of uses, usage duration, and usage interval) through a specific life prediction method (such as empirical formulas, machine learning models, etc.). These data will be collected in the system's database, and each injection mold will correspond to a unique identifier and the corresponding remaining life value.

[0044] The system generates a life list based on these life prediction data. In this list, each row represents an injection mold, including the mold identifier and the corresponding remaining life. Then, the system sorts the list in order of remaining life from large to small. For example, efficient sorting algorithms such as quick sort and merge sort are used to complete this operation. Through sorting, molds with longer remaining life are used first, avoiding frequent mold replacement in a short period of time, making the end time of mold life of the production line similar, facilitating unified replacement and maintenance, thereby improving the continuity and stability of production.

[0045] When the system receives an instruction to perform injection molding on each injection mold, it will first confirm the current injection molding demand, including the number of products to be produced, specifications and other information. The system will search for injection molds that are in an idle state in the life list one by one. The idle state can be determined by the mold's usage status identification (such as whether injection molding is in progress, whether maintenance is in progress, etc.). Once an idle mold is found, the mold will be selected for injection molding. Giving priority to idle molds with long remaining life helps to make full use of the mold's service life and reduce the cost of mold replacement. For example, if there are multiple idle molds to choose from, the system will give priority to the mold with the longest remaining life for injection molding.

[0046] The usage efficiency data is mainly calculated based on the number of times and duration of use indicated by the usage information of each mold, such as the number of injections per unit time, the average time consumed for each injection, etc. The preset efficiency is a standard value pre-set based on production requirements, equipment performance and other factors. The system will compare and analyze the actual usage efficiency data of each injection device with the preset efficiency.

[0047] If the actual efficiency of an injection device is lower than the preset efficiency, the system will further analyze the reasons, such as insufficient injection pressure, too long injection time, etc. Based on these analysis results, the system will determine the corresponding adjustment information. The adjustment information may include the injection pressure value and injection time value that need to be adjusted. For example, if the injection speed of the injection device is found to be slow, the injection pressure can be increased or the injection time can be shortened to improve efficiency; if the product quality is unstable, the injection speed can be appropriately reduced and the injection time can be increased to ensure product quality.

[0048] The system will convert the determined adjustment information into specific adjustment instructions and send them to the corresponding injection device. These instructions will accurately indicate the specific values ​​of the injection pressure and injection time that the injection device needs to adjust.

[0049] After receiving the adjustment instruction, the injection device will adjust the injection pressure and injection time according to the instruction requirements. During the adjustment process, the system will monitor the operating status and actual parameters of the injection device in real time to ensure that the adjusted parameters meet the requirements of the adjustment instruction. At the same time, the injection device will feed back the adjusted operating data to the system, and the system will further evaluate the adjustment effect based on the feedback data. If necessary, it will make secondary adjustments to achieve the preset efficiency standards.

[0050] In some optional embodiments, the life prediction data corresponding to each of the injection molds is determined according to the number of uses, usage duration and usage interval indicated by the mold usage information, including: inputting the number of uses, usage duration and usage interval indicated by the mold usage information into a preset detection model; generating first life data according to the number of uses through a first intermediate layer of the preset detection model; generating second life data after correcting the first life data according to the usage duration through a second intermediate layer of the preset detection model; and generating the life prediction data after correcting the second life data according to the usage interval through a third intermediate layer of the preset detection model.

[0051] Specifically, the preset detection model is a trained machine learning or deep learning model, which is used to predict the life of the injection mold based on the number of times the injection mold is used, the length of time it is used, and the interval between uses. This model can be a neural network model (such as a multi-layer perceptron MLP), a decision tree model, a support vector machine model, etc. Here, the neural network model is used as an example for explanation. The model contains multiple intermediate layers, each of which has a specific function, which is used to gradually process the input data and generate the final life prediction data.

[0052] Input the mold usage information into the preset detection model: The mold usage information needs to be sorted and preprocessed. The number of uses, usage duration, and usage interval are actual data collected from the injection molding system, and there are different units and ranges. In order for the model to better process these data, normalization processing is required to map these data to a specific interval, such as [0,1]. After preprocessing, the number of uses, usage duration, and usage interval are used as input vectors and input into the input layer of the preset detection model. The number of neurons in the input layer is the same as the dimension of the input data, that is, 3 neurons, which receive the normalized values ​​of the number of uses, usage duration, and usage interval respectively.

[0053] The first intermediate layer is the first hidden layer of the preset detection model. Its main function is to extract and transform the number of uses to generate the first life data. This layer contains multiple neurons, each of which performs a weighted summation of the input number of uses and performs a nonlinear transformation through an activation function.

[0054] The second intermediate layer receives the first life data and the normalized value of the usage time as input. Its function is to consider the impact of the usage time on the mold life, correct the first life data, and thus generate the second life data. This layer also contains multiple neurons for further feature extraction and transformation of the input data.

[0055] The third intermediate layer receives the second life data and the normalized value of the use interval as input. Its purpose is to consider the impact of the use interval on the mold life, correct the second life data, and finally generate life prediction data. This layer also contains multiple neurons for the final feature extraction and transformation of the input data.

[0056] By presetting the detection model, the impact of the number of uses, usage duration and usage interval on the mold life is considered, and finally the life prediction data corresponding to each injection mold is generated. These life prediction data can provide corresponding data for decisions such as mold maintenance and replacement.

[0057] The embodiment of the present application does not limit the type of the first sensing unit 110, and it can be set as a distance sensor, a reader / writer, etc. For example, if the first sensing unit 110 is a distance sensor, the distance between the injection device 100 and the injection mold 200 can be periodically checked. When the distance is less than the preset injection distance, it means that the injection device 100 is in the process of injecting raw materials, and the mold usage information needs to be updated. If the first sensing unit 110 is a reader / writer, since the reader / writer has a reading and writing distance range, after the reading and writing distance range of the reader / writer is selected to be within the injection distance, when the reader / writer can read the information of the injection mold 200, it means that the injection device 100 is in the process of injecting raw materials; at this time, the mold usage information of the corresponding injection mold 200 can be updated.

[0058] It is understandable that each injection mold 200 is provided with a second sensing unit 210; the sensing distance of the first sensing unit 110 to each second sensing unit 210 is less than or equal to the injection distance, and when there is a target injection mold and the distance between the injection head of the injection device 100 corresponding to the sensing result is within the preset injection distance, the mold use information of the target injection mold is updated, including: Determine whether the sensing result carries mold parameters; wherein the mold parameters are mold parameters sent by the second sensing unit 210 in the target injection mold to the first sensing unit 110; When there are mold parameters, it is determined that there is a target injection mold and the distance between the injection head of the injection device 100 corresponding to the sensing result is within the injection distance; The mold usage information of the target injection mold is updated according to the mold parameters.

[0059] The mold parameters sent by each second sensing unit 210 are the recorded mold parameters of the mold itself. In some embodiments, the mold parameters may only include the mold index, and accordingly, some mold parameters of each injection mold 200, such as model, maintenance record, etc., will be pre-configured in the database. In other embodiments, the mold parameters may also include information such as the model of the mold. The embodiments of the present application can selectively set the mold parameters according to actual conditions.

[0060] The sensing distance of the first sensing unit 110 to each second sensing unit 210 is less than or equal to the injection distance, which means that the first sensing unit 110 can normally receive the mold parameters transmitted by the second sensing unit 210 only within the injection distance range.

[0061] It is understandable that the first sensing unit 110 is configured as a reader / writer; and the second sensing unit 210 is configured as an RFID tag.

[0062] By configuring the first sensing unit 110 as a reader / writer and the second sensing unit 210 as an RFID tag, the operation logic can be further simplified while reducing the production cost.

[0063] Exemplarily, taking the activation of the RFID tag within the injection distance as an example, the injection distance can be set to the distance between the first sensing unit 110 and the second sensing unit 210 when the injection device 100 is inserted into the corresponding target injection mold for raw material injection, or the distance between the first sensing unit 110 and the second sensing unit 210 when the injection device 100 is close to the target injection mold, or the distance between the first sensing unit 110 and the second sensing unit 210 when the injection device 100 is in contact with the target injection mold. At this time, within the injection distance, the reader is automatically activated, and the reader recognizes the information of the RFID tag on the corresponding target injection mold, and records the current time as the time point for the target injection mold to be used. When the reader and the RFID tag do not meet the injection distance, the reader will not sense, that is, when there is actually no injection, it will not sense any information of the injection mold 200 and update the mold usage information.

[0064] It is understandable that the mold usage information of the target injection mold is updated, including: Analyze the index information of the target injection mold from the sensing results; Obtain the timestamp corresponding to the sensing result and determine the historical usage count of the target injection mold based on the index information; Update the historical usage count; A usage record of the target injection mold is generated according to the updated historical usage count and timestamp.

[0065] By associating the injection timestamp with the number of injection molds 200, more detailed management of the production process can be achieved, thereby further improving the convenience of analyzing the efficiency of the production process.

[0066] It is understandable that each injection mold 200 is controlled and processed according to the usage information of each mold, including: According to the usage information of each mold, life prediction data corresponding to each injection mold 200 is determined respectively; According to the usage information of each mold, respectively determine the usage efficiency data corresponding to the injection device 100; Generate mold optimization strategy and injection molding optimization strategy based on life prediction data and usage efficiency data.

[0067] In some embodiments, the life prediction data can be determined by the number of uses recorded in the mold usage information. In other embodiments, the usage interval and usage duration of each injection mold 200 can be counted through the timestamp recorded in the mold usage information. Therefore, the remaining number of uses, usage duration and other information of the corresponding injection mold 200 can be determined through one or more parameters among the number of uses, usage interval or usage duration to obtain the life prediction data.

[0068] The usage efficiency data can be used to obtain a single usage duration according to the working duration of the injection device 100 and the number of times each injection mold 200 is used, so that the change in usage efficiency within a historical period can be determined based on the single usage duration.

[0069] In some embodiments, the information of the injection device 100 is associated with the mold usage information, so that the efficiency of each injection device 100 can be further effectively tracked.

[0070] The mold optimization strategy is used to optimize the injection mold 200, such as the mold life, the number of times the mold is used, etc., so that the cost and production efficiency can be taken into account.

[0071] The injection molding optimization strategy is an optimization of the injection molding process, including optimization of the injection device 100 and the control timing of the injection device 100. The embodiment of the present application does not limit how to generate the mold optimization strategy and the injection molding optimization strategy, and those skilled in the art can selectively set them according to actual conditions, such as machine learning or deep learning, etc.

[0072] It is understandable that each injection mold 200 is located on the same straight line; the first sensing unit 110 is determined in the following manner: Obtaining the distance range between each injection device 100 and the adjacent injection mold 200 when the injection device 100 is located at the injection position; Obtaining an operating temperature range of the injection device 100; The first sensing unit 110 is determined according to the operating temperature range and the distance range.

[0073] The accuracy of the first sensing unit 110 is related to the actual use environment. Therefore, by selecting a suitable first sensing unit 110 in combination with the environment, the accuracy of the mold use information statistics can be further ensured.

[0074] It is understandable that if Figure 5 As shown, an embodiment of the present application further provides an electronic device, the electronic device comprising: The processor 501 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 502 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 502 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 502, and the processor 501 calls and executes the management method of the injection molding system of the embodiment of this application; Input / output interface 503, used to implement information input and output; Communication interface 504, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 505 that transmits information between various components of the device (e.g., the processor 501, the memory 502, the input / output interface 503, and the communication interface 504); The processor 501 , the memory 502 , the input / output interface 503 and the communication interface 504 are connected to each other in communication within the device via the bus 505 .

[0075] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the management method of the injection molding system is implemented.

[0076] 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 disposed 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.

[0077] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present 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 the present application are also applicable to similar technical problems.

[0078] Those skilled in the art will appreciate 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.

[0079] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0081] 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 sequence. 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 of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising 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.

[0082] It should be understood that in the present 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 objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single 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.

[0083] In the several embodiments provided in the present 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 only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, 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.

[0084] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] 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, 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 to enable 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 (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0087] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A management method for an injection molding system, characterized in that: The injection molding system comprises a plurality of injection molds and a plurality of movably arranged injection devices, each of which is provided with a first sensing unit; at least two injection molds are spaced apart on one side of a moving path of each injection device, and the number of the injection devices is less than the number of the injection molds; the management method comprises: Acquire the sensing result of the first sensing unit in each of the injection devices; When there is a target injection mold whose distance to the injection head of the injection device corresponding to the sensing result is within a preset injection distance, updating the mold use information of the target injection mold; the target injection mold is one of the multiple injection molds; Determine life prediction data corresponding to each of the injection molds according to the number of uses, the duration of use, and the interval of use indicated by the use information of each mold; Determine usage efficiency data corresponding to the injection device according to the usage times and usage durations indicated by the usage information of each mold; Generate a mold optimization strategy and an injection molding optimization strategy according to the life prediction data and the usage efficiency data; The injection mold is adjusted according to the mold optimization strategy, and the injection device is adjusted according to the injection optimization strategy.

2. The management method of the injection molding system according to claim 1, characterized in that: Each of the injection molds is provided with a second sensing unit; the sensing distance of the first sensing unit to each of the second sensing units is less than or equal to the injection distance, and when there is a target injection mold and the distance between the injection head of the injection device corresponding to the sensing result is within the preset injection distance, the mold use information of the target injection mold is updated, including: Determine whether the sensing result carries mold parameters; wherein the mold parameters are mold parameters sent by the second sensing unit in the target injection mold to the first sensing unit; When the mold parameters exist, it is determined that a distance between a target injection mold and an injection head of the injection device corresponding to the sensing result is within the injection distance; The mold usage information of the target injection mold is updated according to the mold parameters.

3. The management method of the injection molding system according to claim 2, characterized in that: The first sensing unit is configured as a reader / writer; the second sensing unit is configured as an RFID tag.

4. The management method of the injection molding system according to claim 1, characterized in that: The updating of the mold usage information of the target injection mold includes: Parsing the index information of the target injection mold from the sensing result; Obtaining a timestamp corresponding to the sensing result and determining a historical usage count of the target injection mold according to the index information; Updating the historical usage count; A usage record of the target injection mold is generated according to the updated historical usage count and the timestamp.

5. The management method of the injection molding system according to claim 1, characterized in that: Generating a mold optimization strategy and an injection molding optimization strategy according to the life prediction data and the usage efficiency data includes: Generate a life list of each of the injection molds according to the life prediction data, wherein the life list indicates the remaining life of each of the injection molds and sorts the molds in descending order according to the remaining life; When each of the injection molds needs to be injected, the idle injection molds are selected in sequence according to the life list for injection molding; confirming adjustment information of each of the injection devices according to the usage efficiency data and the preset efficiency; The injection pressure and injection time of each of the injection devices are adjusted according to the adjustment information.

6. The management method of the injection molding system according to claim 1, characterized in that: The method of determining life prediction data corresponding to each of the injection molds according to the number of uses, the duration of use, and the interval of use indicated by the use information of each mold comprises: Inputting the usage times, usage duration and usage interval indicated by the mold usage information into a preset detection model; Generate first life data according to the number of times of use through the first intermediate layer of the preset detection model; Generate second life data by correcting the first life data according to the usage time through the second intermediate layer of the preset detection model; The life prediction data is generated by correcting the second life data according to the usage interval through the third intermediate layer of the preset detection model.

7. The management method of the injection molding system according to claim 1, characterized in that: All the injection molds are located on the same straight line; the first sensing unit is determined in the following manner: Obtaining a distance range between each injection device and an adjacent injection mold when the injection device is located at a preset injection position; Obtaining an operating temperature range of the injection device; The first sensing unit is determined according to the operating temperature range and the distance range.

8. An injection molding system, characterized in that: The injection molding system comprises: Multiple injection devices; A plurality of injection molds, each of which can be movably arranged on the same side of the plurality of injection devices, each of which is provided with a first sensing unit; and the number of the injection devices is less than the number of the injection molds; A management module, wherein the management module executes the method according to claim 1.

9. The injection molding system according to claim 8, characterized in that: The injection molding system also includes a linear transmission track, each of the injection devices can be movably arranged on the linear transmission track, each of the injection molds is distributed at intervals along one side of the linear transmission track, and each of the injection molds is located on the same straight line.

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

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