One-key self-adaptive energy-saving parameter optimization method and system for injection molding machine

Abstract

The invention relates to the field of energy-saving control of injection molding equipment, and particularly discloses a one-key self-adaptive energy-saving parameter optimization method and system for an injection molding machine. According to the method, after a user sets the minimum mold clamping force and related constraint conditions on a human-computer interface of an injection molding machine and triggers a one-key starting instruction, a system can collect operation data in the operation process in real time, and the operation data comprise the injection pressure, the pressure pad movement amount, the mold clamping force, the plasticizing rotating speed, the plasticizing time, the cooling time and the like; and an optimal injection pressure limit value, an optimal mold closing force and an optimal plasticizing rotating speed are automatically obtained by combining an optimal calculation method, and results are applied to a subsequent production cycle so as to realize efficient and energy-saving dynamic regulation and control. According to the method, dynamic optimization of the injection pressure, the mold clamping force and the plasticizing rotating speed is achieved through self-adaptive calculation and safety margin superposition, the product forming quality and the production stability are guaranteed, and energy consumption is remarkably reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of energy-saving control of injection molding equipment, and particularly relates to a one-key self-adaptive energy-saving parameter optimization method and system for an injection molding machine. BACKGROUND

[0002] Injection molding, as one of the core processes for producing plastic products, is widely used in many fields such as automobiles, home appliances, electronics, medical treatment, packaging, etc. With the continuous expansion of industrial scale, as one of the devices with the highest energy consumption in the production process, the energy efficiency of injection molding machines has attracted more and more attention.

[0003] In the running process of a traditional injection molding machine, the process parameters such as injection pressure, clamping force and plasticizing speed are usually set by the experience of an operator, and there is a lack of self-adaptive adjustment capability for different products, different molds and different production batches, so there are often situations of high energy consumption, low equipment utilization and parameter redundancy. For example, in order to avoid quality problems such as flash or weld marks, the operator often sets the clamping force and injection pressure to be too high, which ensures the product quality but brings a large waste of electric energy and hydraulic energy; and if the plasticizing speed is not set reasonably, the plasticizing time is likely to be too long or the material temperature is not uniform, which affects the molding efficiency and causes energy loss.

[0004] With the proposal of the concepts of intelligent manufacturing and green production, how to realize energy saving and consumption reduction by optimizing parameters under the premise of ensuring product quality and production stability has become a technical problem to be solved in the industry.

[0005] Although some researches in the prior art attempt to introduce running data for process monitoring, most of them still stay at the level of data collection and simple monitoring, and lack dynamic optimization calculation and full-automatic control of parameters. In addition, the existing energy-saving methods are mostly improvements of single links, such as reducing energy consumption by improving servo motor control or shortening the cycle by optimizing cooling time, and fail to form a systematic and integrated solution for overall process parameters. SUMMARY

[0006] One object of the present application is to provide a one-key self-adaptive energy-saving parameter optimization method and system for an injection molding machine, which can dynamically calculate and superimpose a safety margin by real-time collection of running data in combination with optimization mechanisms such as iterative step-down, self-learning cycle and relaxation factor, and then obtain optimal injection pressure, optimal clamping force and optimal plasticizing speed. Compared with the traditional methods relying on manual experience or single-point optimization, the present application realizes self-adaptive global optimization across multiple process links, effectively avoids energy waste caused by excessive conservative parameters, and ensures the stability of the process and the quality of the products.

[0007] To achieve the above objectives, the first aspect of this application provides a one-click adaptive energy-saving parameter optimization method for injection molding machines, comprising the following steps: S1: After the user sets the basic conditions through the human-machine interface, a one-click start command is triggered. The basic conditions include the minimum clamping force and constraint conditions. S2: Real-time acquisition of operating data during the injection molding machine's operation; S3: Based on the aforementioned operational data, the optimal parameters are obtained through iterative optimization calculations within the maximum self-learning cycle. These optimal parameters include the optimal injection pressure limit value P. optimal Optimal clamping force F optimal and optimal plasticizing speed N optimal ; S4: Apply the calculated optimal parameters to subsequent production cycles, and verify the effectiveness and stability of the optimal parameters through testing; S5: Output the parameter results before and after optimization.

[0008] Furthermore, in step S1 above, the constraints include the maximum number of self-learning cycles and the pressure pad movement threshold ΔS. threshold Preset step size F of clamping force step Relaxation factor α and safety margin.

[0009] Furthermore, the safety margin includes the injection pressure safety margin ΔP. safe Safety margin for clamping force ΔF safe Safety margin T for plasticizing speed safe .

[0010] Furthermore, the operational data includes injection pressure, pressure pad movement ΔS, and actual clamping force F. stable Plasticizing speed N curr Plasticization time T curr and cooldown time T cool .

[0011] Furthermore, the operational data includes injection pressure, pressure pad movement ΔS, and actual clamping force F. stable Plasticizing speed N curr All data are obtained from sensors built into the injection molding machine.

[0012] Furthermore, in step S3 above, the optimal parameter includes the optimal injection pressure limit value P. optimal and / or optimal clamping force F optimal and / or optimal plasticizing speed N optimal .

[0013] Furthermore, in step S3 above, the optimization calculation method includes an injection pressure optimization calculation method, / or a clamping force optimization calculation method, and / or a plasticizing speed optimization calculation method.

[0014] Furthermore, the specific operation method of the injection pressure optimization calculation method is as follows: Obtain the injection pressure at each stage within the current self-learning cycle from the runtime data, and thus obtain the maximum injection pressure P among all stages. max ; For the P max Superimposed injection pressure safety margin ΔP safe The optimal injection pressure limit value P was then calculated. optimal :P optimal =P max +ΔP safe .

[0015] Furthermore, the specific operation method of the optimization calculation method for the optimal clamping force is as follows: Obtain the pressure pad movement ΔS and actual clamping force F of the injection molding machine during the holding pressure stage in the current self-learning cycle from the operating data. actual ; The clamping force is gradually reduced using an iterative step-down method until it is reduced to the threshold value ΔS that triggers the pressure pad movement. threshold Or the minimum clamping force allowed by the equipment; Stop optimization and obtain the clamping force in the stage before triggering the preset constraint conditions; this is the optimal actual clamping force F. stable ; The optimal actual clamping force F stable Safety margin of clamping force ΔF safe The optimal clamping force F is thus obtained. optimal :F optimal = F stable +ΔF safe .

[0016] Furthermore, the specific process of the iterative step-down adjustment method is as follows: The obtained pad movement ΔS is compared with the preset pad movement stability threshold ΔS. threshold Comparison: If the pad movement ΔS > the pad movement threshold ΔS threshold If the adjustment fails, stop immediately to avoid burrs or product quality issues. If the pad movement ΔS ≤ pad movement threshold ΔS threshold The system will then use a preset step size F step The new clamping force F is obtained by gradually reducing the clamping force. new ; Subsequently, the new clamping force F was used. new Run the injection molding machine's mold closing process, and repeatedly iterate and adjust the step-down method based on the pressure pad movement ΔS obtained during the operation until the minimum mold closing force is met or the pressure pad displacement exceeds the threshold.

[0017] Furthermore, the new clamping force F new The specific calculation process is as follows: In the current actual clamping force F actual Based on this, according to the preset step size F of the clamping force step Gradually reduce the set value of the clamping force to obtain the new clamping force F. new :F new =F actual -F step .

[0018] Furthermore, the specific operation method of the optimization calculation method for the optimal plasticizing speed is as follows: Get cooldown time T cool Actual plasticizing time T curr Safety margin T for plasticizing speed safe This allows for the calculation of the maximum allowable time T that can be optimized. max .

[0019] Obtain plasticizing speed N curr With the corresponding plasticizing time T curr And according to the maximum allowed time T maxc Therefore, the optimal plasticizing speed N can be calculated. optimal .

[0020] Furthermore, the method for calculating the maximum allowable time is as follows: Based on the obtained cooling time T cool Safety margin T for plasticizing speed safe The maximum allowable time T is calculated. max :T max =T cool -T safe .

[0021] Furthermore, the optimal plasticizing speed N optimal The calculation process is as follows: Obtain the plasticizing speed N after the plasticizing time of the injection molding machine stabilizes from the operating data. curr With the corresponding plasticizing time T curr The constant k is calculated as: k = N curr ×T curr ; Determine the current plasticization time T curr Is it less than the maximum allowed time T? max : If so, calculate the new plasticizing speed; If not, it will automatically revert to the plasticizing speed value of the previous round and use it as the optimal plasticizing speed N. optimal .

[0022] Furthermore, the calculation process for the new plasticizing speed is as follows: Based on the optimal allowable time T max and plasticizing time T curr Calculate the optimizable time T opt :T opt =T max -T curr ; Target plasticization time T is calculated based on relaxation factor α. target :T target =T curr +α×T opt ; According to the obtained T target Calculate the new plasticizing speed N next :N next =k / T target ; Subsequently, at the new plasticizing speed N next Run the injection molding machine during the plasticizing process, and repeat the optimized calculation method of plasticizing speed based on the actual plasticizing time obtained from the operation, until the actual plasticizing time approaches or reaches the maximum allowable time T. max .

[0023] This application also provides a one-click adaptive energy-saving parameter optimization system for injection molding machines, which applies the above-mentioned one-click adaptive energy-saving parameter optimization method, including: User settings module, which is used to set basic conditions; A real-time sensor is used to collect the operating data of the injection molding machine and transmit the operating data to the data acquisition module; A data acquisition module, which is used to acquire operational data transmitted by real-time sensors in real time; An optimization calculation module optimizes parameters based on the running data collected by the data acquisition module and the basic conditions set by the user setting module. The testing and verification module is used to verify the optimization parameters obtained by the optimization calculation module in a real production cycle, monitor product quality and cycle time, and collect energy consumption data as feedback.

[0024] The embodiments of this application have the following technical effects: (1) This application aims to improve the adaptability and long-term economic benefits of the entire production system on the basis of achieving energy saving and automation. Its optimization process is not a one-time event, but a closed-loop system of continuous iteration and self-learning.

[0025] (2) In the optimization of clamping force, this application adopts an iterative step-down method to gradually approach the minimum clamping force boundary allowed by the equipment, and uses the pressure pad movement threshold for real-time monitoring and termination protection. This method accurately finds the best balance between energy consumption and quality while ensuring that the mold is not stretched open, and directly reduces the reactive power loss of the hydraulic system.

[0026] (3) In the optimization of plasticizing speed, this application innovatively uses cooling time as the constraint benchmark and gradually adjusts plasticizing time by introducing relaxation factor α, so that it safely approaches the maximum allowable time. Thus, without extending the total cycle time, the speed of plasticizing motor is reduced, achieving the energy-saving goal of this process and reducing mechanical wear. Attached Figure Description

[0027] The accompanying drawings, as part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application, but do not constitute an undue limitation of this application. Obviously, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of a one-click adaptive energy-saving parameter optimization method for injection molding machines according to this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0029] In the description of this application, it should be noted that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0030] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0031] Those skilled in the art should understand that the embodiments described below are merely a part of the embodiments of this application, and not all of the embodiments of this application. These partial embodiments are intended to explain the technical principles of this application and are not intended to limit the scope of protection of this application. Based on the embodiments provided in this application, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of this application.

[0032] The following reference Figure 1 This application provides a detailed description of a one-click adaptive energy-saving parameter optimization method for injection molding machines.

[0033] like Figure 1 As shown, in some embodiments of this application, a one-click adaptive energy-saving parameter optimization method for injection molding machines is provided, including the following steps: S1: After the user sets the basic conditions through the human-machine interface, a one-click start command is triggered. The basic conditions include the minimum clamping force and constraint conditions. S2: Real-time acquisition of operating data during the injection molding machine's operation; S3: Based on the operational data, loop within the maximum self-learning cycle until the optimal parameters are obtained or the maximum number of self-learning cycles is reached; the optimal parameters include the optimal injection pressure limit value P. optimal Optimal clamping force F optimal and optimal plasticizing speed N optimal ; S4: Apply the calculated optimal parameters to subsequent production cycles, and verify the effectiveness and stability of the optimal parameters through testing; S5: Output the parameter results before and after optimization.

[0034] In the current technological context, injection molding machines, widely used in plastic product processing, typically rely on operators to set injection pressure, clamping force, and plasticizing speed based on experience or historical process parameters. However, since the optimal process parameters are not fixed for different molds, materials, and operating conditions, traditional methods relying on manual experience can easily lead to problems such as excessive energy consumption, accelerated equipment wear, and even unstable product quality. For example, setting the clamping force too high not only wastes energy but may also cause excessive damage to the mold; an unreasonable injection pressure setting directly affects the molding quality and defect rate of the product; and if the plasticizing speed cannot be dynamically adjusted according to cooling time and plasticizing conditions, a contradiction between energy consumption and production cycle time can easily arise. These problems are prevalent in existing energy-saving control technologies for injection molding machines, and due to the lack of real-time data-driven optimization mechanisms, traditional solutions often lag behind in adapting to complex changes in operating conditions, resulting in limited energy-saving effects.

[0035] In summary, this application proposes a one-click adaptive energy-saving parameter optimization method and system for injection molding machines. Addressing the shortcomings of existing injection molding machines in energy-saving control, process parameter setting, and dynamic adaptability, this application provides a complete optimization mechanism and execution process. This application not only solves the drawbacks of traditional technologies, such as reliance on experience-based parameter setting, lack of dynamic adjustment capabilities, and inaccurate energy consumption control, but also achieves dynamic optimization and energy-saving control of key process parameters such as injection pressure, clamping force, and plasticizing speed while ensuring product quality and equipment stability.

[0036] Specifically, the operation process of step S1 is as follows: (1) Users can activate the "adaptive energy-saving optimization" function of the injection molding machine through the human-machine interface.

[0037] (2) Set the minimum clamping force and constraints through the human-machine interface. The constraints include the maximum number of self-learning cycles and the pressure pad movement threshold ΔS. threshold Preset step size F of clamping force step Relaxation factor α and safety margin. The safety margin includes the injection pressure safety margin ΔP. safe Safety margin for clamping force ΔF safe Safety margin T for plasticizing speed safe The constraints are custom-defined by the user based on the product to be manufactured.

[0038] The constraints not only ensure the stability and safety of the energy-saving parameter optimization process for injection molding machines, but also significantly promote the effectiveness and feasibility of the entire optimization method. First, the maximum number of self-learning cycles sets a reasonable upper limit for the system's learning and optimization cycles, avoiding ineffective loops and over-computation during excessively long optimization periods. This constraint not only limits the system's computational resource consumption but also ensures that the system can complete adaptive learning and provide optimization results within a reasonable timeframe, thereby maximizing production efficiency. The pressure pad movement threshold is another key constraint, used to control the stability of the clamping force optimization process. When the pressure pad movement exceeds the preset threshold, the optimization immediately terminates, preventing uneven mold stress due to insufficient clamping force, which could lead to quality problems such as flash. This constraint effectively guarantees product quality, ensuring energy saving while avoiding the risk of production quality issues caused by insufficient clamping force. The preset clamping force step size and relaxation factor are important means of finely controlling the adjustment mechanism during parameter optimization. The preset clamping force step size is used to gradually adjust the clamping force, ensuring that each adjustment meets actual production needs and avoiding instability in the production process due to excessive or rapid adjustments. The relaxation factor plays a role in optimizing the plasticizing speed. It determines the flexibility of plasticizing time adjustment during the optimization process, making the optimization process smoother and more gradual. This helps the optimized speed to accurately adapt to changes in cooling time. These constraints work together to ensure that the system maintains precise control over the production process during parameter optimization, preventing over-optimization from leading to excessively long or short production cycles.

[0039] Safety margins are crucial for ensuring that optimization results can cope with different production environments and uncertainties. Safety margins provide an additional guarantee value for each optimized parameter, ensuring that even slight errors during optimization maintain production safety and stability. For example, the safety margin for injection pressure ensures that even slight pressure fluctuations in actual operation maintain product quality and the normal operation of the injection molding machine. In the plasticizing speed optimization process, constraints also play a balancing role in the coupling relationship between cooling time and plasticizing time. The maximum allowable time is calculated using cooling time and the safety margin for plasticizing speed, ensuring that the entire injection molding cycle operates within the optimal range. When the actual plasticizing time approaches the maximum allowable time, the system automatically adjusts the speed to ensure it does not exceed the cooling time, thus avoiding energy waste due to excessive speed. At this point, the use of safety margins and relaxation factors in plasticizing speed optimization ensures that the optimization process more closely matches the needs of actual production.

[0040] Specifically, the operation process of step S2 is as follows: (1) The system collects the following data during the production process through built-in sensors: Injection pressure: Record the maximum injection pressure at each injection stage.

[0041] Pressure pad movement ΔS: Monitors the movement of the pressure pad during the pressure holding phase.

[0042] Actual clamping force F stable Record the actual clamping force used during the pressure holding stage.

[0043] Plasticizing speed N curr Record the actual plasticizing speed and time.

[0044] Plasticization time T curr and cooldown time T cool : Calculate the actual plasticizing time and cooling time.

[0045] The optimization method provided in this application collects operational data during the production process using sensors built into the injection molding machine itself, eliminating the need for additional sensors. Through the collaborative work of these built-in sensors, the system can collect data from each operational stage of the injection molding machine in real time, ensuring that optimization calculations are based on accurate real-time data, thereby improving the energy efficiency and stability of the entire injection molding process. The collected operational data includes injection pressure, pressure pad movement ΔS, and actual clamping force F. stable Plasticizing speed N curr Plasticization time T curr and cooldown time T cool Among them, the pressure pad movement ΔS and the actual clamping force F stable Plasticizing speed N curr These three operational data points are acquired directly from sensors, while the plasticizing time T... curr and cooldown time T cool This is calculated by the injection molding machine's software.

[0046] Specifically, the injection pressure is obtained from the injection pressure sensor on the injection molding machine itself. This sensor detects the maximum injection pressure at each injection stage and captures pressure changes in real time during the injection process, especially during the filling and holding stages. This data is crucial for optimizing and obtaining the optimal injection pressure, helping the system calculate the optimal injection pressure limit for each stage, thereby optimizing energy consumption and production efficiency.

[0047] Specifically, the actual clamping force F stable The clamping pressure is obtained from the clamping pressure sensor on the injection molding machine itself. This sensor measures the actual clamping force applied during the clamping process. This data helps determine the state of mold clamping and is used for iterative calculations in the clamping force optimization submodule. By providing real-time feedback from the clamping pressure sensor, the system can gradually reduce the clamping force based on the relationship between the pressure pad movement and the clamping force until the optimal clamping force is reached.

[0048] Specifically, the pressure pad movement ΔS is obtained by the pressure pad displacement sensor of the injection molding machine itself. This sensor is the core feedback signal in the clamping force optimization process, monitoring the movement of the pressure pad in real time and determining whether it exceeds the preset stability threshold. If the pressure pad movement exceeds this threshold, the optimization process will terminate to prevent quality problems such as flash.

[0049] Specifically, plasticizing speed N curr The data is obtained from the plasticizing speed sensor on the injection molding machine itself. This sensor monitors the screw speed of the injection molding machine. This data is crucial for optimizing the plasticizing speed. By modeling the coupling relationship between plasticizing time and cooling time, the system can adjust the speed based on real-time data, optimize energy consumption, and avoid over-plasticizing.

[0050] Specifically, step S3 includes S3-1, an injection pressure optimization calculation method, and / or S3-2, a clamping force optimization calculation method, and / or a plasticizing speed optimization calculation method. In the one-click adaptive energy-saving parameter optimization method for injection molding machines provided in this application, at least one of the three optimization processes—injection pressure optimization, clamping force optimization, and plasticizing speed optimization—can be implemented simultaneously, or any one or any combination of two can be selected for optimization. By implementing these three modules simultaneously, the system can comprehensively consider the influence of multiple factors, thereby achieving more refined and comprehensive optimization; while implementing only one module allows the system to perform targeted optimization for specific needs, which is flexible and efficient. The advantage of implementing these three modules simultaneously is that it allows for comprehensive optimization of the injection molding process from a global perspective. The optimization of each module will affect each other; for example, the optimization of injection pressure may have a certain impact on the adjustment of clamping force, and the optimization of clamping force may also require adjusting the plasticizing speed to maintain the stability of the production cycle. By running these three modules simultaneously, the system can minimize energy waste, ensure optimal parameters at each stage, and ultimately improve overall production efficiency and energy-saving effects. Furthermore, simultaneous optimization allows for real-time parameter adjustments, preventing poor overall performance due to improper adjustment of a single parameter and ensuring the stability and efficiency of the injection molding process.

[0051] However, optimizing any one or any combination of any two modules can also bring significant benefits. Under specific production requirements, optimizing a single module can quickly improve the efficiency of a specific process. For example, excessive pressure during injection may lead to energy waste, while optimizing the injection pressure alone can solve this problem without considering other factors. Similarly, in the process of mold clamping force control, optimizing the clamping force alone can simplify adjustments, reduce system complexity, and improve operational convenience.

[0052] Furthermore, in step S3, the optimization calculation method provided in this application will loop within the maximum self-learning cycle until the optimized parameter is the optimal parameter or the number of loops reaches the maximum number of self-learning cycles, at which point the optimization will terminate. If the optimal parameter has not been optimized by the time the maximum number of self-learning cycles is reached, the optimization process will fail and stop.

[0053] Specifically, the operation method of the injection pressure optimization calculation method in step S3-1 above is as follows: (1) Obtain the injection pressure of each stage in the current self-learning cycle from the running data, and thus obtain the maximum injection pressure P among all stage injection pressures. max Pressure data for each injection stage is monitored in real time using an injection pressure sensor. Particularly during the filling and holding phases, the maximum injection pressure value P across all stages is recorded and calculated. max .

[0054] (2) is the P max Superimposed safety margin ΔP safe The optimal injection pressure limit value P was then calculated. optimal : P optimal =P max +ΔP safe .

[0055] At this point, the optimal injection pressure P is obtained. optimal During each production cycle, the injection molding machine will operate according to the obtained P. optimal The adjusted and optimized injection pressure will be fed back to the injection molding machine system and tested and verified for at least two production cycles to ensure its stability and energy-saving effect.

[0056] Specifically, the operation method for optimizing the clamping force in step S3-1 above is as follows: (1) Obtain the pressure pad movement ΔS and actual clamping force F of the injection molding machine during the holding pressure stage in the current self-learning cycle from the running data. actual The clamping force sensor monitors the force during the clamping process in real time, and combines this with the pressure pad displacement data collected by the pressure pad displacement sensor. The pressure pad displacement reflects the force on the mold during the clamping process; excessive displacement may lead to mold instability.

[0057] (2) The clamping force is gradually reduced using an iterative step-down method until the clamping force is reduced to the threshold value ΔS that triggers the movement of the pressure pad. threshold Or the minimum clamping force allowed by the equipment.

[0058] (3) Stop optimization and obtain the clamping force of the previous stage before triggering the preset constraint conditions, which is the optimal actual clamping force F. stable .

[0059] (4) is the optimal actual clamping force F. stable Safety margin of clamping force ΔF safe The optimal clamping force F is thus obtained. optimal :F optimal = F stable +ΔF safe .

[0060] Specifically, in the above-mentioned optimization calculation method for optimal clamping force, the iterative step-down adjustment method is as follows: (1) Compare the obtained pad movement amount ΔS with the preset pad movement amount stability threshold ΔS threshold Comparison: If the pad movement ΔS > the pad movement threshold ΔS threshold If the adjustment fails, stop immediately to avoid burrs or product quality issues.

[0061] If the pad movement ΔS ≤ pad movement threshold ΔS threshold The system will then use a preset step size (F) step Gradually reduce the clamping force. At the current actual clamping force F... actual Based on this, according to the preset step size F of the clamping force step Gradually reduce the set value of the clamping force to obtain the new clamping force F. new :F new =F actual -F step .

[0062] (2) Then, a new clamping force F is applied. new Run the injection molding machine's mold closing process, and repeatedly iterate and adjust the step-down method based on the pressure pad movement ΔS obtained during the operation until the minimum mold closing force is met or the pressure pad displacement exceeds the threshold.

[0063] Specifically, the operation method for optimizing the plasticizing speed in step S3-1 above is as follows: (1) Obtain the cooldown time T cool Actual plasticizing time T curr Safety margin T for plasticizing speed safe This allows for the calculation of the maximum allowable time T that can be optimized. max .

[0064] (2) Obtain the plasticizing speed N curr With the corresponding plasticizing time T curr And according to the maximum allowed time T maxc Therefore, the optimal plasticizing speed N can be calculated. optimal .

[0065] Specifically, the maximum allowed time is determined by the cooldown time T.cool Safety margin T for plasticizing speed safe The calculation yielded the maximum permissible time T. max The calculation formula is T max =T cool -T safe .

[0066] Specifically, the optimal plasticizing speed N optimal The calculation process is as follows: (1) Obtain the plasticizing speed N after the plasticizing time of the injection molding machine stabilizes from the operating data. curr With the corresponding plasticizing time T curr The constant k is calculated as: k = N curr ×T curr .

[0067] (2) Determine the current plasticization time T curr Is it less than the maximum allowed time T? max : If so, calculate the new plasticizing speed; If not, it will automatically revert to the plasticizing speed value of the previous round and use it as the optimal plasticizing speed N. optimal .

[0068] Specifically, the calculation process for the new plasticizing speed is as follows: (1) Based on the optimal allowable time T max and plasticizing time T curr Calculate the optimizable time T opt :T opt =T max -T curr .

[0069] (2) Calculate the target plasticization time T based on the relaxation factor α. target :T target =T curr +α×T opt .

[0070] (3) Based on the obtained T target Calculate the new plasticizing speed N next :N next =k / T target .

[0071] (4) Then, at the new plasticizing speed N next Run the injection molding machine during the plasticizing process, and repeat the optimized calculation method of plasticizing speed based on the actual plasticizing time obtained from the operation, until the actual plasticizing time approaches or reaches the maximum allowable time T. max .

[0072] Specifically, in step S4 above, the effectiveness and stability of the obtained optimal parameters are verified online. Step S4 collects real-time data on energy consumption, product quality, and cycle time of the injection molding machine after applying the optimal parameters in a real production cycle, and compares this data with the data before optimization. If energy consumption is significantly reduced, and product quality and cycle time remain good, the optimized parameters are proven to be effective. If energy consumption optimization is not significant or product quality issues arise, further parameter optimization adjustments can be made.

[0073] Specifically, in step S5, after completing the verification step in step S4, the injection molding machine system will output the optimization results on the human-machine interface. These results display the injection pressure, clamping force, and plasticizing speed before and after optimization, as well as the corresponding energy consumption, production cycle, product quality, and energy-saving effects. This comparative data helps users intuitively see the optimization effect, confirm whether the energy-saving and production efficiency goals have been achieved, and then choose whether to apply the new parameters.

[0074] This application also provides a one-click adaptive energy-saving parameter optimization system for injection molding machines, which applies the above-mentioned one-click adaptive energy-saving parameter optimization method, including: User settings module, which is used to set basic conditions; A real-time sensor is used to collect the operating data of the injection molding machine and transmit the operating data to the data acquisition module; A data acquisition module, which is used to acquire operational data transmitted by real-time sensors in real time; An optimization calculation module optimizes parameters based on the running data collected by the data acquisition module and the basic conditions set by the user setting module. The testing and verification module is used to verify the optimization parameters obtained by the optimization calculation module in a real production cycle, monitor product quality and cycle time, and collect energy consumption data as feedback.

[0075] The above description is merely a preferred embodiment of this application and is not intended to limit this application in any way. Although this application has disclosed the preferred embodiment as above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-mentioned technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. The implementation schemes in the above embodiments can be further combined or replaced. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of this application.

Claims

1. A one-button adaptive energy-saving parameter optimization method for injection molding machines, characterized in that, Includes the following steps: S1: After the user sets the basic conditions through the human-machine interface, a one-click start command is triggered. The basic conditions include the minimum clamping force and constraint conditions. S2: Real-time acquisition of operating data during the injection molding machine's operation; S3: Based on the running data, loop within the maximum self-learning cycle until the optimal parameters are obtained or the maximum number of self-learning cycles is reached; S4: Apply the calculated optimal parameters to subsequent production cycles, and verify the effectiveness and stability of the optimal parameters through testing; S5: Output the parameter results before and after optimization.

2. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 1, characterized in that, In step S1 above, the constraints include the maximum number of self-learning cycles and the pressure pad movement threshold ΔS. threshold Preset step size F of clamping force step Relaxation factor α and safety margin.

3. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 2, characterized in that, The safety margin includes the injection pressure safety margin ΔP. safe Safety margin for clamping force ΔF safe Safety margin T for plasticizing speed safe .

4. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 1, characterized in that, In step S2 above, the operating data includes injection pressure, pressure pad movement ΔS, and actual clamping force F. stable Plasticizing speed N curr Plasticization time T curr and cooldown time T cool .

5. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 4, characterized in that, The operational data includes injection pressure, pressure pad movement ΔS, and actual clamping force F. stable Plasticizing speed N curr All data are obtained from sensors built into the injection molding machine.

6. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 4, characterized in that, In step S3 above, the optimal parameter includes the optimal injection pressure limit value P. optimal and / or optimal clamping force F optimal and / or optimal plasticizing speed N optimal .

7. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 4, characterized in that, In step S3 above, the optimization calculation method includes an injection pressure optimization calculation method, / or a clamping force optimization calculation method, and / or a plasticizing speed optimization calculation method.

8. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 7, characterized in that, The specific operation method of the injection pressure optimization calculation method is as follows: Obtain the injection pressure at each stage within the current self-learning cycle from the runtime data, and then obtain the maximum injection pressure P among all stages. max ; For the P max Superimposed injection pressure safety margin ΔP safe The optimal injection pressure limit value P was then calculated. optimal :P optimal =P max +ΔP safe .

9. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 7, characterized in that, The specific operation method of the optimization calculation method for the optimal clamping force is as follows: Obtain the pressure pad movement ΔS and actual clamping force F of the injection molding machine during the holding pressure stage in the current self-learning cycle from the operating data. actual ; The clamping force is gradually reduced using an iterative step-down method until it is reduced to the threshold value ΔS that triggers the pressure pad movement. threshold Or the minimum clamping force allowed by the equipment; Stop optimization and obtain the clamping force in the stage before triggering the preset constraint conditions; this is the optimal actual clamping force F. stable ; The optimal actual clamping force F stable Safety margin of clamping force ΔF safe The optimal clamping force F is thus obtained. optimal :F optimal = F stable +ΔF safe .

10. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 9, characterized in that, The specific process of the iterative step-down adjustment method is as follows: The obtained pad movement ΔS is compared with the preset pad movement stability threshold ΔS. threshold Comparison: If the pad movement ΔS > the pad movement threshold ΔS threshold If the adjustment fails, stop immediately to avoid burrs or product quality issues. If the pad movement ΔS ≤ pad movement threshold ΔS threshold The system will then use a preset step size F step The new clamping force F is obtained by gradually reducing the clamping force. new ; Subsequently, the new clamping force F was used. new Run the injection molding machine's mold closing process, and repeatedly iterate and adjust the step-down method based on the pressure pad movement ΔS obtained during the operation until the minimum mold closing force is met or the pressure pad displacement exceeds the threshold.

11. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 10, characterized in that, The new clamping force F new The specific calculation process is as follows: In the current actual clamping force F actual Based on this, according to the preset step size F of the clamping force step Gradually reduce the set value of the clamping force to obtain the new clamping force F. new :F new =F actual -F step .

12. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 7, characterized in that, The specific operation method of the optimization calculation method for the optimal plasticizing speed is as follows: Get cooldown time T cool Actual plasticizing time T curr Safety margin T for plasticizing speed safe This allows for the calculation of the maximum allowable time T that can be optimized. max . Obtain plasticizing speed N curr With the corresponding plasticizing time T curr And according to the maximum allowed time T max Therefore, the optimal plasticizing speed N is calculated. optimal .

13. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 12, characterized in that, The method for calculating the maximum allowable time is as follows: Based on the obtained cooling time T cool Safety margin T for plasticizing speed safe The maximum allowable time T is calculated. max :T max =T cool -T safe .

14. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 12, characterized in that, The optimal plasticizing speed N optimal The calculation process is as follows: Obtain the plasticizing speed N after the plasticizing time of the injection molding machine stabilizes from the operating data. curr With the corresponding plasticizing time T curr The constant k is calculated as: k = N curr ×T curr ; Determine the current plasticization time T curr Is it less than the maximum allowed time T? max : If so, calculate the new plasticizing speed; If not, it will automatically revert to the plasticizing speed value of the previous round and use it as the optimal plasticizing speed N. optimal .

15. The one-button adaptive energy-saving parameter optimization method for injection molding machines according to claim 14, characterized in that, The calculation process for the new plasticizing speed is as follows: Based on the optimal allowable time T max and plasticizing time T curr Calculate the optimizable time T opt :T opt =T max -T curr ; Target plasticization time T is calculated based on relaxation factor α. target :T target =T curr +α×T opt ; According to the obtained T target Calculate the new plasticizing speed N next :N next =k / T target ; Subsequently, at the new plasticizing speed N next Run the injection molding machine during the plasticizing process, and repeat the optimized calculation method of plasticizing speed based on the actual plasticizing time obtained from the operation, until the actual plasticizing time approaches or reaches the maximum allowable time T. max .

16. A one-button adaptive energy-saving parameter optimization system for injection molding machines, characterized in that, The one-click adaptive energy-saving parameter optimization method according to any one of claims 1-15 includes: User settings module, which is used to set basic conditions; A real-time sensor is used to collect the operating data of the injection molding machine and transmit the operating data to the data acquisition module; A data acquisition module, which is used to acquire operational data transmitted by real-time sensors in real time; An optimization calculation module optimizes parameters based on the running data collected by the data acquisition module and the basic conditions set by the user setting module. The testing and verification module is used to verify the optimization parameters obtained by the optimization calculation module in a real production cycle, monitor product quality and cycle time, and collect energy consumption data as feedback.

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