Automatic control method and system for injection molding equipment

Abstract

The invention discloses an automatic control method and system for injection molding equipment, and relates to the related technical field of production process control, and the method comprises the following steps: reading basic configuration information and work order injection molding tasks of the injection molding equipment; performing process control segmentation, and determining a task segmentation grid; traversing task segmentation grids, and carrying out grid independent control analysis and cooperative control adjustment; establishing communication connection between the programmable controller and the injection molding equipment, and setting a control program; a control response instruction is generated, equipment injection molding control is carried out, monitoring is synchronously carried out, and real-time control data is returned; and measuring control deviation data, performing deviation quality influence evaluation, and performing feedback operation regulation and control. The technical problems that control parameters cannot be intelligently and accurately determined through existing injection molding equipment control, consequently, injection molding task parameter control schemes are not matched, the positioning and calibration node quality problem is poor in accuracy and pertinence, and the injection molding quality cannot reach the expected effect are solved, and the technical effect of improving the injection molding quality is achieved.

Description

Technical Field

[0001] The present application relates to the field related to production process control technology, and specifically to an automated control method and system for injection molding equipment. Background Art

[0002] With the continuous development of science and technology and the increasing demand for industrialized production, the automated control of injection molding equipment has become an important trend in the manufacturing industry. By adding high-tech equipment such as sensors, actuators and PLC control systems to injection molding equipment, the intelligent and automated control of the equipment can be realized, and functions such as automatic metering, automatic feeding, automatic adjustment of process parameters, and automatic detection of product quality can be realized, thereby improving production efficiency and saving energy consumption. At the same time, digital management and remote monitoring of the production process can also be realized, and the transparency and traceability of the production process can be improved. However, traditional injection molding equipment requires manual operation, monitoring and adjustment, with low production efficiency and unstable injection molding production quality. It cannot realize intelligent control of the parameters of the entire injection molding process, and cannot monitor and locate each node in real time, and timely detect problems to ensure injection molding quality.

[0003] Therefore, in the current injection molding equipment control related technologies, there are technical problems such as the inability to intelligently and accurately determine control parameters, resulting in the incompatibility of injection molding task parameter control solutions, insufficient accuracy and specificity in positioning and calibrating quality issues at each node, and the injection molding quality failing to meet expected results. Summary of the invention

[0004] The present application provides an automated control method and system for injection molding equipment, and adopts technical means such as grid division, collaborative correlation analysis and calibration, programmable controller control, and deviation quality assessment to solve the technical problem that the existing injection molding equipment control cannot intelligently and accurately determine the control parameters, resulting in the incompatibility of the injection molding task parameter control scheme, the lack of accuracy and specificity in the quality problems of each node of the positioning and calibration, and the injection molding quality does not meet the expected effect. The application realizes the automated control of the injection molding task and the precise and targeted positioning and calibration, and achieves the technical effect of improving the injection molding quality.

[0005] The present application provides an automated control method for injection molding equipment, which includes reading basic configuration information and work order injection molding tasks of the injection molding equipment, wherein the basic configuration information includes equipment component dimensions and control system dimensions; performing process control segmentation on the work order injection molding task based on time and space dimensions to determine a task segmentation grid, wherein the time dimension segmentation is determined based on key control nodes, and the space dimension segmentation is determined based on equipment independent components; traversing the task segmentation grid, combining an adaptive operation and control model, performing grid independent control analysis and collaborative control adjustment, and determining an injection molding control scheme; establishing a communication connection between a programmable controller and the injection molding equipment, and setting a control program for the programmable controller based on the injection molding control scheme; generating a control response instruction to perform equipment injection molding control, and simultaneously performing real-time control monitoring and returning real-time control data; measuring control deviation data of the real-time control data, and performing a deviation quality impact assessment, and if the deviation impact coefficient meets the quality tolerance interval, performing feedback operation regulation of the injection molding equipment.

[0006] In a possible implementation, the equipment components include a mold, a clamping device, an injection device and a hydraulic stamping device; the control system includes a heating system, a cooling system, an electronic hydraulic system and a mechanical control system, and performs the following processing: based on the injection molding task of the work order, the parameter control features of the mapped equipment components and the control system are set and identified.

[0007] In a possible implementation, the work order injection molding task is segmented according to process control based on the time and space dimensions to determine the task segmentation grid, and the following processing is performed: the work order injection molding task is segmented and fitted according to the process level to determine multiple control stages; each control stage is traversed to identify stage-related components, and the components are independently segmented to determine the stage division information; the multiple control stages are fitted with the stage division information to determine the task segmentation grid; wherein, the process level segmentation and fitting method includes: segmenting the process level one based on the control mode to determine the first control division; segmenting the process level two based on the control risk point to determine the second control division; and cross-combining the first control division with the second control division to determine the multiple control stages.

[0008] In a possible implementation, the adaptive operation and control model includes an independent analysis layer, a collaborative regulation layer and an integrated output layer. The adaptive operation and control model is combined to perform grid independent control analysis and collaborative control adjustment, and execute the following processing: based on the independent analysis layer, an independent control sub-scheme is determined, and the independent control sub-scheme corresponds one-to-one to the task segmentation grid; the task segmentation grid is subjected to a collaborative analysis of working conditions to determine a collaborative grid group; the collaborative grid group is traversed to perform frequency verification on the independent control sub-scheme to obtain a frequency verification result, and the frequency verification result indicates the sub-scheme fit; based on the frequency verification result, a collaborative control adjustment is performed.

[0009] In a possible implementation, collaborative control adjustment is performed based on the same-frequency verification result, and the following processing is also performed: identifying the same-frequency verification result, extracting the pre-adjustment sub-scheme whose sub-scheme fit does not meet the fit threshold, and identifying the fit deviation; determining the amplitude modulation constraint based on the fit deviation, and determining the control avoidance principle based on the importance of the control parameters; based on the amplitude modulation constraint and the control avoidance principle, combined with the collaborative control layer, the pre-adjustment sub-scheme is coordinated and optimized to determine the calibration sub-scheme.

[0010] In a possible implementation, the deviation quality impact assessment also performs the following processing: determining an assembly quality index value based on the work order injection molding task; determining a node quality index based on the control deviation data, mapping and allocating the assembly quality index value, and determining a node standard index value; evaluating the node quality index of the control deviation data to determine a node quality index value; calculating the difference between the node standard index value and the node quality index value to determine the deviation impact coefficient.

[0011] In a possible implementation, after the feedback operation control of the injection molding equipment is performed, the following processing is also performed: taking the feedback time of the real-time control data as the initial time, monitoring the feedback control data, and determining the calibration control trend; making an abnormal judgment on the calibration control trend, and generating a fault tracing instruction if there is an abnormal control trend; and performing equipment operation and maintenance management as the fault tracing instruction is received.

[0012] The present application also provides an automatic control system for injection molding equipment, comprising: An injection molding equipment information task reading module, which is used to read basic configuration information of the injection molding equipment and work order injection molding tasks, wherein the basic configuration information includes equipment component dimensions and control system dimensions; A task segmentation network determination module, the task segmentation network determination module is used to perform process control segmentation on the work order injection molding task based on the time and space dimensions, and determine the task segmentation grid, wherein the time dimension segmentation is determined based on the key control nodes, and the space dimension segmentation is determined based on the independent components of the equipment; An injection molding control scheme determination module, which is used to traverse the task segmentation grid, combine the adaptive operation and control model, perform grid independent control analysis and collaborative control adjustment, and determine the injection molding control scheme; A control program setting module, the control program setting module is used to establish a communication connection between the programmable controller and the injection molding equipment, and to set a control program for the programmable controller based on the injection molding control scheme; An equipment injection molding control module, which is used to perform equipment injection molding control based on generating control response instructions, and simultaneously perform real-time control monitoring and return real-time control data; The deviation quality impact assessment module is used to measure the control deviation data of the real-time control data and perform deviation quality impact assessment. If the deviation impact coefficient meets the quality tolerance interval, feedback operation control of the injection molding equipment is performed.

[0013] The invention proposes an automated control method and system for injection molding equipment, which reads the basic configuration information of the injection molding equipment and the injection molding tasks of the work order; performs process control segmentation and determines the task segmentation grid; traverses the task segmentation grid and performs grid independent control analysis and collaborative control adjustment; establishes a communication connection between the programmable controller and the injection molding equipment and sets the control program; generates control response instructions, performs equipment injection molding control, performs synchronous monitoring and sends back real-time control data; measures control deviation data, performs deviation quality impact assessment, and performs feedback operation regulation. This solves the technical problems existing in the control of existing injection molding equipment, such as the inability to intelligently and accurately determine control parameters, resulting in the incompatibility of the injection molding task parameter control scheme, the lack of accuracy and pertinence in positioning and calibrating the quality problems of each node, and the injection molding quality not meeting the expected effect. This realizes the automated control of injection molding tasks and precise and pertinent positioning and calibration, and achieves the technical effect of improving the injection molding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic flow chart of an automated control method for injection molding equipment provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart for performing process control segmentation in an automated control method for injection molding equipment provided in an embodiment of the present application; Figure 3 A schematic diagram of a process for evaluating the impact of deviation on quality in an automated control method for injection molding equipment provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an automated control system for an injection molding device provided in an embodiment of the present application.

[0016] Explanation of the reference numerals: injection molding equipment information task reading module 10 , task segmentation network determination module 20 , injection molding control scheme determination module 30 , control program setting module 40 , equipment injection molding control module 50 , deviation quality impact assessment module 60 . DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application embodiment provides an automatic control method for injection molding equipment, such as Figure 1 As shown, the method includes: Step S100, read the basic configuration information of the injection molding equipment and the work order injection molding task, wherein the basic configuration information includes the equipment component dimension and the control system dimension. Read the basic configuration information of the injection molding equipment and the work order injection molding task, wherein the basic configuration information refers to the basic components and configuration of the equipment, including the hardware composition and control system of the injection molding equipment, and the work order injection molding task refers to the specific injection molding operation task, which usually includes the product model, production quantity, production cycle, injection molding process parameters (such as injection pressure, injection molding speed, injection molding temperature, injection molding time) and other task information. In actual production, it is necessary to adjust the parameters and configuration of the injection molding equipment according to the requirements of the work order injection molding task to complete the injection molding of the product. Specifically, the equipment components include the injection molding machine body, injection system (injection device, injection cylinder, etc.), clamping system (clamping device, template, ejector), mold, hydraulic device, auxiliary device (feeder, air compressor, waste crusher), etc.; the control system is used to automatically monitor, adjust and control the production of the injection molding equipment, including heating system, cooling system, electronic hydraulic system and mechanical control system.

[0021] In a possible implementation, step S100 further includes step S110, based on the work order injection molding task, setting and identifying parameter control features of the mapped equipment components and the control system. According to the production requirements and process parameters in the work order injection molding task, the hardware components and control system of the injection molding equipment are identified with parameter control features, for example, heating, cooling system-mold-temperature uniformity. The equipment components include molds, locking devices, injection devices and hydraulic stamping devices; the control system includes a heating system, a cooling system, an electronic hydraulic system and a mechanical control system. Specifically, the mold is a tool used for molding injection molded products, including a mold core and a mold cavity; the locking device is used to fix the mold and keep it closed to prevent material overflow; the injection device is used to inject molten material into the mold cavity, including an injection cylinder, a nozzle and other components; the hydraulic stamping device is mainly used to provide the hydraulic power required in the injection molding process, including a hydraulic pump, a hydraulic cylinder, etc.; the control system is the control and regulation part of the injection molding equipment, which is a system used to control all aspects of the production process. The heating system is used to heat the mold and material; the cooling system is used to cool the molded injection molded products; the electronic hydraulic system is used to control the hydraulic action of the injection molding equipment to achieve precise adjustment of the injection action; the mechanical control system is the mechanical transmission system in the injection molding equipment, which realizes the mechanical action and position control in the injection molding process.

[0022] After reading the basic configuration information of the injection molding equipment and the work order injection molding task, execute step S200 to perform process control segmentation on the work order injection molding task based on the time and space dimensions to determine the task segmentation grid, wherein the time dimension segmentation is determined based on the key control nodes, and the space dimension segmentation is determined based on the independent components of the equipment. The process control segmentation of the injection molding task of the work order is carried out on the basis of the time and space dimensions, that is, the injection molding production task is further refined according to the characteristics and production requirements of the injection molding product. For example, different injection molding parameters, molds or auxiliary equipment may be required according to the different materials, structures or precision requirements of the product. The task segmentation network is determined, and the injection molding task is divided into multiple interrelated but relatively independent grid units to achieve refined management and control of the injection molding task. Specifically, each grid unit corresponds to a specific injection molding task and has clear time, space and process requirements, so as to understand the composition and distribution of the injection molding task more clearly. Among them, the time dimension segmentation is determined based on the key control nodes, and the space dimension segmentation is determined based on the independent components of the equipment. Specifically, the time dimension segmentation is to divide the injection molding task according to the production time. For example, according to the production plan, the entire injection molding task is divided into different time periods, each time period corresponds to a different production process, and the production progress can be better mastered through the segmentation of the time dimension; the space dimension refers to the segmentation of the injection molding task according to the spatial layout of the injection molding equipment or production line, and the injection molding task is divided into different areas, each area is responsible for a specific production task, and the segmentation of the space dimension helps to optimize the production process and improve the utilization rate of the injection molding equipment.

[0023] In one possible implementation, Figure 2As shown, step S200 further includes step S210, which performs process level segmentation and fitting on the work order injection molding task, and determines multiple control stages. The entire injection molding production process is divided into multiple stages according to different process levels and control requirements, and corresponding process control and adjustment are performed on each stage. Different process steps are gradually completed during the production process, and corresponding control and optimization are performed at each stage to ensure product quality and production efficiency. Specifically, according to the complexity and process requirements of the injection molding production process, the entire production process is divided into multiple process levels or stages. For example, the injection molding process is divided into a mold closing stage, a plastic injection stage, a pressure holding and curing stage, a cooling and temperature reduction stage, etc., and process fitting and optimization are performed on each control stage. For each process level, the corresponding control stage is determined, and corresponding process parameter control and adjustment are performed at each control stage. It also includes step S220, which traverses each control stage, identifies stage-related components, performs independent component segmentation, and determines stage division information. In the whole injection molding production process, each control stage is analyzed and identified in detail, the relevant equipment components involved in each stage are determined, and these components are independently segmented to determine the specific division information of each stage. Specifically, each control stage in the injection molding production process is traversed one by one, and the equipment components associated with each control stage are identified. For example, in the mold closing stage, components such as molds and clamping devices may be involved, and in the plastic injection stage, components such as injection devices and hydraulic systems may be involved. For each identified component, independent segmentation and differentiation are performed to clarify the function, role and specific task of the component in the injection molding process. Based on the result of independent segmentation of the components, the specific division information of each control stage is determined, including determining the list of components involved in each stage, the association relationship between components, the time range and operation requirements of each stage. It also includes step S230, fitting the multiple control stages and the stage division information to determine the task segmentation grid. The multiple control stages and their corresponding stage division information are integrated and matched to determine the segmentation grid of the entire injection molding task in time and space.

[0024] In a possible implementation, the process level segmentation and fitting method of step S210 further includes step S211, performing process level segmentation based on the control mode to determine the first control division. The entire injection molding production process is divided into different levels according to different control modes, and the control division of the first level is determined, for example, the mold limit locking stage, the preheating treatment stage, the cooling treatment stage, the material injection molding stage, etc., and then the stage where control abnormalities and deviations are prone to occur is determined. It also includes step S212, performing process level segmentation based on the control risk point to determine the second control division. According to the risk points or key control nodes in the production process, the entire injection molding production process is further divided into different levels, and the control division of the second level is determined, wherein the control risk point may be a key operation node, a link that is susceptible to interference, or a node that is prone to quality problems in the injection molding production process. It also includes step S213, cross-combining the first control division and the second control division to determine the multiple control stages. The first control division and the second control division obtained by the process segmentation based on the control mode and the control risk point are cross-combined with each other to determine the multiple control stages in the entire injection molding production process. By cross-combining the first control division and the second control division, the entire injection molding production process is divided into multiple control stages with different control requirements and management priorities. Each control stage needs to implement corresponding process control and adjustment to ensure the stability of the injection molding production process and the controllability of product quality.

[0025] After determining the task segmentation grid, step S300 is executed to traverse the task segmentation grid, combine the adaptive operation and control model, perform grid independent control analysis and collaborative control adjustment, and determine the injection molding control solution. Each task segmentation grid in the injection molding production process is checked and analyzed one by one. At the same time, combined with the adaptive operation and control model, each grid is independently controlled and analyzed, and coordinated control adjustments are made to finally determine the control scheme suitable for the entire injection molding production process. Among them, the adaptive operation and control model is a model that can automatically adjust the control strategy according to real-time data and environmental changes. During the injection molding process, the adaptive operation and control model can collect the operation data of each grid unit in real time, such as temperature, injection pressure, injection speed, etc., and automatically adjust the control parameters according to these data. Specifically, the task segmentation grids divided in the injection molding production process are checked one by one, and each task segmentation grid is dynamically modeled and analyzed using the adaptive operation and control model. For each task segmentation grid, an independent control analysis is performed. On the basis of the independent control analysis of each grid, a coordinated control adjustment is performed to adjust the control parameters and strategies of each grid to achieve coordinated optimization of the overall production process. Finally, based on the results of the independent control analysis and coordinated control adjustment of the grid, a control scheme suitable for the entire injection molding production process is determined.

[0026] In a possible implementation, the adaptive operation and control model includes an independent analysis layer, a collaborative control layer and an integrated output layer. Step S300 combines the adaptive operation and control model to perform grid independent control analysis and collaborative control adjustment. It further includes step S310, based on the independent analysis layer, determining an independent control sub-scheme, and the independent control sub-scheme corresponds to the task segmentation grid one by one. According to the independent control analysis of each task segmentation grid, a corresponding control scheme is determined for each grid to ensure that each grid is controlled and adjusted in a targeted manner, wherein the independent analysis layer refers to a module in the adaptive operation and control model that performs independent control analysis and optimization adjustment on each task segmentation grid in the entire injection molding production process; the collaborative control layer is a module in the adaptive operation and control model that performs collaborative control optimization between each independent task; the integrated output layer refers to a module in the adaptive operation and control model that integrates and outputs information from the independent analysis layer and the collaborative control layer, and is used to generate control instructions to guide the operation and adjustment of the production process. It also includes step S320, performing collaborative analysis of the working conditions on the task segmentation grid to determine the collaborative grid group. Comprehensively analyze the task segmentation grids in the injection molding production process, identify the mutual influence and dependency between them, and then determine a group of collaborative grids to achieve collaborative optimization of the overall production process. It also includes step S330, traversing the collaborative grid group, performing frequency verification on the independent control sub-scheme, and obtaining the frequency verification result, which indicates the sub-scheme fit. The determined collaborative grid groups are checked and analyzed one by one to verify the adaptability and effectiveness of each independent control sub-scheme when working in collaboration. Specifically, the same-frequency verification is to verify whether the corresponding independent control sub-scheme can work in collaboration with other grids and maintain the same working frequency for each grid in the collaborative grid group; the sub-scheme fit is a fit index in the same-frequency verification result, which identifies the adaptability and effect of each independent control sub-scheme in collaborative work. A higher fit indicates that the sub-scheme can work well with other sub-schemes, and a lower fit may require further adjustment and optimization. For example, mold limiting and mold fixing can be matched, indicating that each targeted sub-scheme is highly adapted and meets the overall standard. Otherwise, coordination within appropriate limits is required. It also includes step S340, and collaborative control adjustment is performed based on the same-frequency verification result. The collaborative control scheme is adjusted and optimized according to the results of the same-frequency verification to ensure that each independent control sub-scheme can achieve the best effect and performance when working in collaboration.

[0027] In one possible implementation, step S340 further includes step S341, identifying the same-frequency verification result, extracting the pre-adjustment sub-scheme whose sub-scheme fit does not meet the fit threshold, and identifying the fit deviation. Based on the same-frequency verification result, the independent control sub-schemes whose fit does not meet the preset threshold are identified and regarded as candidate schemes for pre-adjustment. At the same time, these pre-adjustment sub-schemes are identified to indicate their deviation from the expected fit, that is, the degree of difference relative to the expected standard. It also includes step S342, determining the amplitude modulation constraint based on the fit deviation, and determining the control avoidance principle based on the importance of the control parameters. According to the size and influence of the fit deviation, the range limit of the control parameter adjustment is determined, that is, the amplitude modulation constraint, and the participating control parameters and their importance that should be given priority in the adjustment process are determined, that is, the control avoidance principle. Specifically, if the fit deviation is small, only a slight adjustment may be required, while if the fit deviation is large, a larger range of adjustments may be required. The amplitude modulation constraint can avoid production process fluctuations caused by excessive adjustment. The control avoidance principle indicates that in the adjustment process, those control parameters that have the greatest impact on the production process and are the most critical should be adjusted first to ensure the stability and optimization of the overall production process. It also includes step S343, based on the amplitude modulation constraint and the control avoidance principle, combined with the collaborative control layer, the pre-adjustment sub-scheme is coordinated and optimized to determine the calibration sub-scheme. On the basis of considering the amplitude modulation constraint and the control avoidance principle, the pre-adjustment sub-scheme is comprehensively optimized and adjusted using the collaborative control layer to determine the best calibration sub-scheme, thereby achieving the optimal control effect and performance of the overall production process.

[0028] After the injection molding control scheme is determined, step S400 is executed to establish a communication connection between the programmable controller and the injection molding equipment, and the control program of the programmable controller is set based on the injection molding control scheme. By establishing a communication connection, the programmable controller can exchange data and transmit instructions with the injection molding equipment. Based on the injection molding control scheme, the programmable controller is set accordingly to realize automatic control and regulation of the injection molding equipment. Specifically, it is ensured that the programmable controller and the injection molding equipment can be stably and reliably communicated. According to the determined injection molding control scheme, the programmable controller is set and programmed to realize automatic control and regulation of the injection molding equipment, including setting control logic, adjusting parameters, and writing program codes of the injection molding control scheme. By establishing a communication connection between the programmable controller and the injection molding equipment and setting the control program, automatic and precise control of the injection molding process can be realized, and production efficiency and product quality can be improved.

[0029] After the programmable controller is set up with the control program, step S500 is executed to generate a control response instruction, perform equipment injection molding control, and synchronously perform real-time control monitoring and return real-time control data. According to the control requirements and the set control strategy, the corresponding control instructions are generated from the injection molding control system to control the injection molding equipment, and the control process is monitored in real time and the monitored data is fed back to the control system. Specifically, according to the set injection molding control scheme and real-time production requirements, the corresponding control instructions are generated from the control system, such as adjusting parameters such as temperature, pressure, and speed. According to the generated control instructions, the injection molding equipment is subjected to corresponding control operations, and the working state and parameter settings of the equipment are adjusted. During the control process, real-time control monitoring is performed synchronously, and the working state and production parameters of the injection molding equipment are obtained through sensors or monitoring equipment, and the control effect is monitored in real time. The control data monitored in real time are fed back to the control system, including information such as equipment operating state, production parameters, and control effect, so as to adjust and optimize the control strategy in real time. By generating control response instructions, performing equipment injection molding control, and synchronously performing real-time control monitoring, and returning real-time control data, real-time monitoring and precise control of the injection molding production process can be achieved.

[0030] After the real-time control data is transmitted back, step S600 is executed to measure the control deviation data of the real-time control data, and to evaluate the impact of the deviation on the quality. If the deviation impact coefficient meets the quality tolerance interval, the feedback operation regulation of the injection molding equipment is performed. The control data monitored in real time is analyzed, the degree of deviation between the control parameters and the set values ​​is calculated, and the degree of impact of these deviations on the product quality is evaluated. If the deviation impact coefficient is within the quality tolerance interval, it means that the product quality is still within an acceptable range. At this time, the feedback operation regulation of the injection molding equipment is performed, that is, the equipment is adjusted to return it to the set value or the optimal state, so as to ensure the stability and consistency of the product quality. Among them, the deviation impact coefficient is an indicator of the impact of the degree of deviation of the control parameters on the quality of the injection molding product.

[0031] In one possible implementation, Figure 3As shown, the deviation quality impact assessment described in step S600 further includes step S610, determining the assembly quality index value based on the work order injection molding task. According to the specific injection molding work order task requirements and product specifications, the quality of the final product produced by injection molding is evaluated to obtain a comprehensive quality index value, namely the assembly quality index value, which reflects the comprehensive quality performance of the products produced by the entire work order injection molding task, wherein the assembly quality index value can be obtained by comprehensive calculation of multiple quality indicators, such as product dimensional accuracy, surface finish, material composition qualified rate, etc. It also includes step S620, determining the node quality index based on the control deviation data, mapping and apportioning the assembly quality index value, and determining the node standard index value. According to the deviation data and impact assessment results in the control process, the overall product quality index is decomposed into quality indicators of each node or link, so as to more finely monitor and adjust the quality problems in the production process, and the overall quality index is apportioned to the current control node to determine the node index quality that the current node needs to meet when the assembly standard is reached, and to verify. Specifically, the node quality index refers to the key node or link in the production process, and the corresponding quality index is set, such as each process on the production line, the production batch of each injection mold, etc. The assembly quality index mapping and apportionment refers to mapping and apportioning according to the contribution of each node in the entire production process according to the overall product quality index value, so as to determine the standard quality index value that each node should reach, and according to the result obtained by the apportionment, the standard quality index value that each node should reach is determined, that is, the node standard index value. It also includes step S630, evaluating the node quality index of the control deviation data and determining the node quality index value. It also includes step S640, performing difference calculation between the node standard index value and the node quality index value to determine the deviation influence coefficient. Calculate the difference between the node quality index value and the node standard index value to determine the coefficient of the degree of influence of the difference on product quality. By calculating the deviation influence coefficient, the influence degree of each node quality deviation from the standard value can be quantified, and quality control and injection molding production parameter adjustment can be accurately carried out to improve product quality and production efficiency.

[0032] In a possible implementation, after step S600 performs feedback operation control of the injection molding equipment, it further includes step S650, taking the return time of the real-time control data as the initial time, monitoring the feedback control data, and determining the calibration control trend. In the real-time control process, the control data monitored in real time is compared with the set value, and adjusted according to the feedback data to determine the calibration control trend. Specifically, after the calibration control is executed, its dynamic control change trend deviates too much from the expected control trend, that is, there is a deviation in the first control and there is still a deviation after the second calibration, indicating that there may still be a fault. It also includes step S660, making an abnormal judgment on the calibration control trend, and generating a fault tracing instruction if there is an abnormal control trend. The calibrated control trend is monitored and analyzed to detect whether there is an abnormal situation or a deviation from the expected trend. If an abnormality is found, the system will generate a fault tracing instruction to determine and trace the cause of the abnormality, quickly locate and solve the abnormal situation, and ensure the stability of the production process and the consistency of product quality. It also includes step S670, with the reception of the fault tracing instruction, equipment operation and maintenance management is performed. After receiving the fault tracing instruction, take corresponding measures to operate and maintain the equipment to solve possible problems of the equipment and ensure the normal operation and production efficiency of the equipment.

[0033] In the above, refer to Figure 1 The present invention describes in detail an automatic control method of an injection molding device according to an embodiment of the present invention. Figure 4 An automatic control system for injection molding equipment according to an embodiment of the present invention is described.

[0034] An automated control system for injection molding equipment according to an embodiment of the present invention is used to solve the technical problem that the existing injection molding equipment control cannot intelligently and accurately determine the control parameters, resulting in the incompatibility of the injection molding task parameter control scheme, the lack of accuracy and pertinence in the positioning and calibration of the quality problems of each node, and the injection molding quality does not meet the expected effect. It realizes the automated control of the injection molding task, the precise and pertinent positioning and calibration, and achieves the technical effect of improving the injection molding quality. An automated control system for injection molding equipment includes: an injection molding equipment information task reading module 10, a task segmentation network determination module 20, an injection molding control scheme determination module 30, a control program setting module 40, an equipment injection molding control module 50, and a deviation quality impact assessment module 60.

[0035] An injection molding equipment information task reading module 10, the injection molding equipment information task reading module 10 is used to read basic configuration information of the injection molding equipment and work order injection molding tasks, the basic configuration information includes equipment component dimensions and control system dimensions; A task segmentation network determination module 20, the task segmentation network determination module 20 is used to perform process control segmentation on the work order injection molding task based on the time and space dimensions, and determine the task segmentation grid, wherein the time dimension segmentation is determined based on the key control nodes, and the space dimension segmentation is determined based on the independent components of the equipment; An injection molding control scheme determination module 30, which is used to traverse the task segmentation grid, combine the adaptive operation and control model, perform grid independent control analysis and collaborative control adjustment, and determine the injection molding control scheme; A control program setting module 40, the control program setting module 40 is used to establish a communication connection between the programmable controller and the injection molding equipment, and to set a control program for the programmable controller based on the injection molding control scheme; The equipment injection molding control module 50 is used to perform equipment injection molding control based on the generated control response instruction, and simultaneously perform real-time control monitoring and return real-time control data; The deviation quality impact assessment module 60 is used to measure the control deviation data of the real-time control data and perform deviation quality impact assessment. If the deviation impact coefficient meets the quality tolerance interval, feedback operation control of the injection molding equipment is performed.

[0036] The specific configuration of the injection molding equipment information task reading module 10 will be described in detail below. As described above, the basic configuration information of the injection molding equipment and the work order injection molding task are read, and the basic configuration information includes the equipment component dimension and the control system dimension. The injection molding equipment information task reading module 10 may further include: mold, clamping device, injection device and hydraulic stamping device; the control system includes a heating system, a cooling system, an electronic hydraulic system and a mechanical control system; based on the work order injection molding task, the mapped equipment components and the control system are marked with parameter control features.

[0037] The specific configuration of the task segmentation network determination module 20 will be described in detail below. As described above, the process control segmentation of the work order injection molding task is performed based on the time and space dimensions to determine the task segmentation grid. The task segmentation network determination module 20 further includes: performing process level segmentation and fitting on the work order injection molding task to determine multiple control stages; traversing each control stage, identifying stage-related components, performing independent component segmentation, and determining stage division information; fitting the multiple control stages and the stage division information to determine the task segmentation grid; wherein, the process level segmentation and fitting method includes: performing process level one segmentation based on the control mode to determine the first control division; performing process level two segmentation based on the control risk point to determine the second control division; cross-combining the first control division with the second control division to determine the multiple control stages.

[0038] The specific configuration of the injection molding control scheme determination module 30 will be described in detail below. As described above, the task segmentation grid is traversed, and the grid independent control analysis and collaborative control adjustment are performed in combination with the adaptive operation and control model to determine the injection molding control scheme. The injection molding control scheme determination module 30 further includes: an independent analysis layer, a collaborative regulation layer and an integrated output layer. The method also includes: based on the independent analysis layer, determining an independent control sub-scheme, and the independent control sub-scheme corresponds to the task segmentation grid one by one; performing a collaborative analysis of the working conditions on the task segmentation grid to determine a collaborative grid group; traversing the collaborative grid group, performing a same-frequency verification on the independent control sub-scheme, and obtaining a same-frequency verification result, and the same-frequency verification result indicates the sub-scheme fit; based on the same-frequency verification result, performing a collaborative control adjustment.

[0039] The specific configuration of the injection molding control scheme determination module 30 will be described in detail below. As described above, based on the same-frequency verification result, the collaborative control adjustment is performed, and the injection molding control scheme determination module 30 further includes: identifying the same-frequency verification result, extracting the pre-adjusted sub-scheme whose sub-scheme fit does not meet the fit threshold, and marking the fit deviation; determining the amplitude modulation constraint based on the fit deviation, and determining the control avoidance principle based on the importance of the parameter control; based on the amplitude modulation constraint and the control avoidance principle, combined with the collaborative control layer, the pre-adjusted sub-scheme is coordinated and optimized to determine the calibration sub-scheme.

[0040] The specific configuration of the deviation quality impact assessment module 60 will be described in detail below. As described above, the deviation quality impact assessment module 60 further includes: determining the assembly quality index value based on the work order injection molding task; determining the node quality index based on the control deviation data, mapping and allocating the assembly quality index value, and determining the node standard index value; evaluating the node quality index of the control deviation data to determine the node quality index value; performing difference calculation between the node standard index value and the node quality index value to determine the deviation impact coefficient.

[0041] The specific configuration of the deviation quality impact assessment module 60 will be described in detail below. As described above, after the feedback operation control of the injection molding equipment is performed, the deviation quality impact assessment module 60 further includes: taking the return time of the real-time control data as the initial time, monitoring the feedback control data, and determining the calibration control trend; making an abnormal judgment on the calibration control trend, and generating a fault tracing instruction if there is an abnormal control trend; and performing equipment operation and maintenance management as the fault tracing instruction is received.

[0042] An automatic control system for injection molding equipment provided by an embodiment of the present invention can execute an automatic control method for injection molding equipment provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0043] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0044] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. An automatic control method for injection molding equipment, characterized in that: The method comprises: Read the basic configuration information of the injection molding equipment and the injection molding task of the work order, wherein the basic configuration information includes the equipment component dimension and the control system dimension; Performing process control segmentation on the injection molding task of the work order based on the time and space dimensions to determine the task segmentation grid, wherein the time dimension segmentation is determined based on the key control nodes, and the space dimension segmentation is determined based on the independent components of the equipment; Traversing the task segmentation grid, combining the adaptive operation and control model, performing grid independent control analysis and collaborative control adjustment, and determining the injection molding control scheme; Establishing a communication connection between a programmable controller and the injection molding equipment, and setting a control program for the programmable controller based on the injection molding control scheme; Generate control response instructions, perform equipment injection control, and simultaneously perform real-time control monitoring and send back real-time control data; The control deviation data of the real-time control data is measured, and the deviation quality impact assessment is performed. If the deviation impact coefficient satisfies the quality tolerance interval, feedback operation regulation of the injection molding equipment is performed.

2. The automatic control method of injection molding equipment according to claim 1, characterized in that: The equipment components include molds, clamping devices, injection devices and hydraulic stamping devices; the control system includes heating system, cooling system, electronic hydraulic system and mechanical control system; Based on the injection molding task of the work order, parameter control features of the mapped equipment components and the control system are set and identified.

3. The automatic control method of injection molding equipment according to claim 1, characterized in that: The injection molding task of the work order is segmented by process control based on the time and space dimensions to determine the task segmentation grid, and the method further includes: Performing process level segmentation and fitting on the injection molding task of the work order, and determining multiple control stages; Traverse each control stage, identify the relevant components of the stage, perform independent component segmentation, and determine the stage division information; Fitting the multiple control stages and the stage division information to determine the task division grid; Among them, the process level segmentation and fitting methods include: Based on the control mode, the process is divided into one layer and the first control division is determined; Perform a second-level process segmentation based on the control risk points and determine the second control division; The first control division and the second control division are cross-combined to determine the multiple control stages.

4. The automatic control method for injection molding equipment according to claim 1, characterized in that: The adaptive operation and control model includes an independent analysis layer, a collaborative control layer and an integrated output layer. The adaptive operation and control model is combined to perform grid independent control analysis and collaborative control adjustment. The method also includes: Based on the independent analysis layer, determining an independent control sub-scheme, wherein the independent control sub-scheme corresponds one-to-one to the task segmentation grid; Performing collaborative analysis on the task segmentation grids to determine a collaborative grid group; Traversing the collaborative grid group, performing same-frequency verification on the independent control sub-scheme, and obtaining a same-frequency verification result, wherein the same-frequency verification result indicates the sub-scheme compatibility; Based on the same-frequency verification result, collaborative control adjustment is performed.

5. The automatic control method of injection molding equipment according to claim 4, characterized in that: Based on the same-frequency verification result, collaborative control adjustment is performed, and the method further includes: Identify the same-frequency verification result, extract the pre-adjusted sub-scheme whose sub-scheme fit does not meet the fit threshold, and mark the fit deviation; Determine the amplitude modulation constraint based on the fit degree deviation, and determine the control avoidance principle based on the importance of the parameter control; Based on the amplitude modulation constraint and the control avoidance principle, in combination with the collaborative control layer, the pre-regulation sub-scheme is coordinated and optimized to determine the calibration sub-scheme.

6. The automatic control method for injection molding equipment according to claim 1, characterized in that: The method further comprises: Determine an assembly quality index value based on the injection molding task of the work order; Determine a node quality index based on the control deviation data, map and apportion the assembly quality index value, and determine a node standard index value; Evaluate the node quality index of the control deviation data to determine the node quality index value; The difference between the node standard index value and the node quality index value is calculated to determine the deviation influence coefficient.

7. The automatic control method for injection molding equipment according to claim 1, characterized in that: After performing feedback operation control of the injection molding equipment, the method further includes: Taking the time of returning the real-time control data as the initial time, monitoring the feedback control data, and determining the calibration control trend; Performing abnormality determination on the calibration control trend, and generating a fault tracing instruction if an abnormal control trend exists; With the receipt of the fault tracing instruction, equipment operation and maintenance management is performed.

8. An automatic control system for injection molding equipment, characterized in that: The system is used to implement an automatic control method for injection molding equipment according to any one of claims 1 to 7, and the system comprises: An injection molding equipment information task reading module, which is used to read basic configuration information of the injection molding equipment and work order injection molding tasks, wherein the basic configuration information includes equipment component dimensions and control system dimensions; A task segmentation network determination module, the task segmentation network determination module is used to perform process control segmentation on the work order injection molding task based on the time and space dimensions, and determine the task segmentation grid, wherein the time dimension segmentation is determined based on the key control nodes, and the space dimension segmentation is determined based on the independent components of the equipment; An injection molding control scheme determination module, which is used to traverse the task segmentation grid, combine the adaptive operation and control model, perform grid independent control analysis and collaborative control adjustment, and determine the injection molding control scheme; A control program setting module, the control program setting module is used to establish a communication connection between the programmable controller and the injection molding equipment, and to set a control program for the programmable controller based on the injection molding control scheme; An equipment injection molding control module, which is used to perform equipment injection molding control based on generating control response instructions, and simultaneously perform real-time control monitoring and return real-time control data; The deviation quality impact assessment module is used to measure the control deviation data of the real-time control data and perform deviation quality impact assessment. If the deviation impact coefficient meets the quality tolerance interval, feedback operation control of the injection molding equipment is performed.

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