Networking injection molding machine servo drive control method

Abstract

The invention relates to the technical field of injection molding machine control, in particular to a networking injection molding machine servo drive control method. Firstly, a traditional continuous servo driving action is decomposed into a strong operation-weak voltage stabilization alternating unit sequence, and the instantaneous load peak value and the hydraulic pressure fluctuation are reduced. Secondly, product type characteristics are accurately matched based on a process database, action parameters are dynamically adjusted in combination with a multi-objective optimization algorithm, and the product quality consistency is remarkably improved. In addition, energy conservation and consumption reduction are achieved through a first-stage sleep mode and a second-stage sleep mode, a group control optimization mode is used for coordinating actions of multiple injection molding machines, load fluctuation of a workshop power grid is reduced, and the service life of equipment is prolonged. The system also supports real-time monitoring and networking management, constructs intelligent closed-loop control, enhances the production flexibility and the resource utilization rate, and provides an efficient, energy-saving and reliable solution for the injection molding industry.

Description

Technical Field

[0001] This invention relates to the field of injection molding machine control technology, and more specifically to a networked injection molding machine servo drive control method. Background Technology

[0002] In the injection molding field, the servo drive system, as the core control unit of the injection molding machine, directly impacts production efficiency, energy consumption, and equipment reliability. Traditional injection molding machine servo drive systems generally employ a continuous operation mode, where the servo motor operates continuously throughout the production cycle, and the hydraulic system outputs pressure according to a fixed sequence. This mode reveals significant shortcomings when dealing with high-load conditions: First, the problem of instantaneous torque overload is prominent. Taking the production of thin-walled parts as an example, the injection molding stage requires high-speed filling of molten plastic in a very short time. The instantaneous torque demand of the servo motor can reach 180%-220% of the rated torque, causing the drive current to surge to 1.5-2 times the rated value, frequently triggering overload protection shutdowns. According to industry statistics, the current overload failure rate of traditional systems during high-load stages reaches 35%-40%, and each overload shutdown requires an average of 5-10 minutes of restart and debugging, seriously affecting production continuity. Second, hydraulic shock leads to accelerated mechanical fatigue. In continuous drive mode, sudden pressure changes in the hydraulic system can generate impact loads with peak values ​​reaching 20%-30% of the system's rated pressure. Long-term operation leads to accelerated wear of key components such as the mold clamping mechanism and injection screw. Third, the power grid load fluctuates drastically when multiple machines are producing in tandem. When multiple injection molding machines in the workshop simultaneously enter the high-load stage, the peak instantaneous power demand of the power grid can reach 2.5-3 times the average load, easily causing power line overload tripping. In addition, standby energy consumption is significantly wasted. Current injection molding machine sleep modes only achieve a simple motor shutdown, while the hydraulic pump and heating coil continue to run. Actual measurement data shows that a single machine consumes 3-5 kWh of energy per hour in standby mode. If 20 machines in a workshop are idle for an average of 8 hours per day, the annual energy waste can reach 175,000-292,000 kWh. Traditional control methods lack intelligent sleep strategies based on load characteristics and cannot dynamically switch power states according to actual operating conditions, resulting in a widespread phenomenon of overpowered power supplies operating at insufficient capacity.

[0003] In existing technologies, although some studies have attempted to optimize servo drives through fuzzy control or frequency conversion regulation, none of them have solved the following core problems: 1) The continuous motion during high load phases has not been discretized and decomposed, making it impossible to fundamentally reduce the instantaneous torque peak; 2) There is a lack of a dynamic mapping mechanism between current threshold and hydraulic pressure, resulting in significant load regulation lag; 3) The reverse coupling model between motion parameters and the process system has not been constructed, leading to a decrease in product quality stability when parameters are adjusted; 4) Multi-machine group control only achieves process parameter synchronization and does not involve peak-shaving regulation of hydraulic impact energy spectrum, resulting in limited grid load optimization effect. Summary of the Invention

[0004] To address the challenges of overload, energy consumption, lifespan, and grid compatibility in traditional continuous drive systems, this invention proposes a networked servo drive control method for injection molding machines. Through discretized motion sequence design, dynamic load compensation, a multi-level sleep / wake-up mechanism, and group control optimization strategies, the method achieves efficient and stable operation of the injection molding machine servo system, meeting the urgent needs of the modern injection molding industry for efficient, energy-saving, and intelligent production.

[0005] The specific technical solution of the present invention is as follows: This invention provides a servo drive control method for a networked injection molding machine, comprising: When the injection molding machine starts, the production cycle is divided into multiple stages according to the product type characteristics in the preset process database. The high-load stage is selected. For the high-load stage, the traditional continuous servo drive action is decomposed into a discrete action sequence, namely a strong operation-weak voltage stabilization alternating unit sequence. Based on the load fluctuation characteristics of the discretized action sequence, a mapping relationship between servo motor current and hydraulic system pressure is established. The adjustment of the action unit parameters in the mapping relationship is back-coupled to the process control system. Based on the calculation of the injection molding machine physical model, the action parameters of each high load stage are readjusted and the action is executed. Power state switching is triggered based on the action execution result, including switching of load state and switching of sleep depth; When the network detects that multiple injection molding machines are producing homogeneous products, the group control optimization mode is activated based on the above control process. This causes the main frequency band of the hydraulic impact energy spectrum of the injection molding machines in the cluster to be staggered, ultimately reducing the load fluctuation of the workshop power grid and extending the fatigue life of the injection molding machine's mechanical structure.

[0006] As a further option of the present invention, dividing the production cycle into multiple stages based on the product type characteristics in the preset process database includes: Match product types from the process database. Product types include thin-walled parts, thick-walled parts, and composite structural parts. Based on the matching product type, the wall thickness distribution, the number of ribs, and the ratio of runner length are extracted as key geometric parameters. Combined with the material viscosity and temperature change patterns or multi-source sensor data fusion, the production cycle is dynamically divided into several stages, including melting and plasticizing, injection molding, and pressure holding and cooling.

[0007] As a further option of the present invention, the high-load screening stage includes: Real-time monitoring of servo motor current and hydraulic system pressure; When the servo motor current continuously exceeds the preset ratio of the rated current and the hydraulic system pressure fluctuation exceeds the stable threshold, the current stage is determined to be a high load stage.

[0008] As a further option of the present invention, establishing the mapping relationship between the servo motor current and the hydraulic system pressure includes: A piecewise linear model is used to describe the mapping relationship between servo motor current and hydraulic system pressure; The mapping relationship is configured such that when the servo motor current exceeds the dynamic current threshold, the voltage stabilization time in the next action unit is automatically extended and the operation time is shortened accordingly.

[0009] As a further option of the present invention, the dynamic current threshold is calculated based on the rated current of the servo motor, a preset correction coefficient, and a safety margin, and the calculation formula is as follows: ;in, For current threshold, Rated current, For correction factor, For safety margin.

[0010] As a further option of the present invention, the calculation based on the physical model of the injection molding machine, and the readjustment of the action parameters for each high-load stage, includes: Extract dynamic adjustment parameters, including the percentage increase in voltage stabilization time and the percentage decrease in operating time, where the percentage increase in voltage stabilization time is calculated using a formula: ;in, To extend the voltage stabilization time by a certain percentage, This is a dynamic scaling factor. For real-time current, For current threshold, The duration of the current exceeding the limit. The time decay coefficient, It is the hyperbolic tangent function; The pressure stabilization time adjustment and operation time adjustment extracted from the mapping relationship are input into the injection molding machine physical model; the injection molding machine physical model includes a hydraulic system model, a melt flow model, and a thermodynamic model; A multi-objective optimization function was constructed with the objectives of consistent injection molded part quality, single-cycle energy consumption, and equipment vibration intensity as the goals, and an optimization algorithm was used to iteratively optimize the motion parameters.

[0011] As a further option of the present invention, the readjustment of the operating parameters for each high-load stage also includes: During the injection molding stage, the injection speed and melt temperature are adjusted synchronously according to the pressure stabilization time adjustment. During the pressure holding and cooling stage, the pressure holding time and clamping force are adjusted according to pressure fluctuations.

[0012] As a further option of the present invention, the power state switching triggered based on the action execution result includes: Real-time statistics on the proportion of the voltage stabilization phase during high load periods; When the proportion of the pressure stabilization stage continuously exceeds the preset ratio, it is determined that the machine has entered a low load condition and the injection molding machine is controlled to switch to the first-level sleep mode. The first-level sleep mode includes shutting down the hydraulic pump and reducing the power of the heating coil.

[0013] As a further option of the present invention, if the duration of the first-level sleep mode exceeds a preset standby threshold, the injection molding machine is controlled to enter the second-level deep sleep mode. Upon waking from deep sleep mode, reverse action sequence compensation is performed, including step-by-step loading of hydraulic system pressure and preheating of the heating coil.

[0014] As a further option of the present invention, the network detection of multiple injection molding machines in the production state of isomorphic products includes: Extract and compare the product type characteristics and process parameters currently produced by multiple injection molding machines; When the similarity of process parameters reaches the preset matching threshold, it is determined to be a production state of isomorphic products; In the group control optimization mode, the start time of the action sequence of each injection molding machine in the cluster is adjusted in a centralized manner to achieve a staggered distribution of the main frequency band of the hydraulic impact energy spectrum.

[0015] The beneficial effects of the technical solutions provided in this application include at least the following: By discretizing continuous servo drive actions into alternating strong operation and weak pressure stabilization unit sequences, instantaneous load peaks and hydraulic system pressure fluctuations are effectively reduced, equipment vibration intensity is decreased, and the stability and reliability of injection molding machine operation are significantly improved.

[0016] By employing discrete action sequences and dynamic sleep modes to optimize energy consumption, single-cycle energy consumption is reduced by approximately 10.6% during high-load phases. Furthermore, by using a group control optimization mode to achieve staggered distribution of hydraulic impact energy spectrum, the fluctuation of workshop power grid load is further reduced, resulting in significant energy-saving effects.

[0017] By optimizing motion parameters and smoothing load transitions, the weight deviation of injection molded parts is improved by approximately 25%, while melt flowability compensation technology ensures the molding quality of the products, thereby significantly improving the consistency of product quality.

[0018] Based on the process database, the system accurately matches the characteristics of product types and dynamically adjusts the action parameters using a multi-objective optimization algorithm. This supports intelligent closed-loop control and group control optimization modes, greatly improving the flexibility and intelligence of production.

[0019] By reducing equipment vibration intensity, decreasing hydraulic shock energy, and controlling servo motor temperature rise, mechanical fatigue and high-temperature aging problems are effectively alleviated, significantly extending the service life of the injection molding machine's mechanical structure and key components.

[0020] This system enables multiple injection molding machines to work together in a network. By monitoring load fluctuations in real time and dynamically adjusting motion parameters, it builds an intelligent management system, optimizes overall production scheduling in the workshop, and improves resource utilization efficiency and management level.

[0021] In summary, the networked injection molding machine servo drive control method of this application, by introducing discretized motion sequences, dynamic mapping relationships, parameter optimization algorithms, and group control optimization modes, achieves efficient, energy-saving, intelligent, and reliable operation of the injection molding machine servo drive control. It not only significantly improves equipment performance and product quality but also reduces energy consumption and maintenance costs, providing a highly competitive technical solution for the injection molding industry. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall process of a networked injection molding machine servo drive control method; Figure 2 A detailed flowchart of steps S100 for a networked injection molding machine servo drive control method; Figure 3 A detailed flowchart of steps S200 for a networked injection molding machine servo drive control method; Figure 4 A detailed flowchart of steps S300 for a networked injection molding machine servo drive control method; Figure 5 A detailed flowchart of the steps in a networked injection molding machine servo drive control method S400; Figure 6 This is a detailed flowchart of the steps in a networked injection molding machine servo drive control method S500. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] In the injection molding field, traditional servo drive systems typically operate in continuous mode. During high-load phases, they are prone to instantaneous torque overload, leading to excessively high current peaks, significant hydraulic system shocks, and consequently, problems such as motor overheating and accelerated mechanical fatigue. Simultaneously, existing control methods lack optimization mechanisms for grid load fluctuations in multi-machine collaborative production, resulting in instantaneous load spikes in the workshop power grid and significant energy waste during equipment standby. For solutions to these problems, please refer to [link to relevant documentation]. Figure 1 This illustrates a networked injection molding machine servo drive control method according to an embodiment of the present invention, the method comprising: S100: When the injection molding machine starts, the production cycle is divided into multiple stages according to the product type characteristics in the preset process database. The high load stage is selected. For the high load stage, the traditional continuous servo drive action is decomposed into a discrete action sequence, namely a strong operation-weak voltage stabilization alternating unit sequence.

[0025] S200: Based on the load fluctuation characteristics of the discretized action sequence, a mapping relationship between servo motor current and hydraulic system pressure is established.

[0026] S300: The adjustment of the motion unit parameters in the mapping relationship is back-coupled to the process control system. Based on the calculation of the injection molding machine physical model, the motion parameters of each high load stage are readjusted and the actions are executed.

[0027] S400: Triggers power state switching based on action execution results, including load state switching and sleep depth switching.

[0028] S500: When the network detects that multiple injection molding machines are in the production state of homogeneous products, based on the control process of S100-S400, the group control optimization mode is activated to make the main frequency band of the hydraulic impact energy spectrum of the injection molding machines in the cluster staggered, ultimately reducing the load fluctuation of the workshop power grid and extending the fatigue life of the mechanical structure of the injection molding machines.

[0029] The S100-S500 provides a networked injection molding machine servo drive computer control method that solves the overload defects of traditional continuous drive by discretizing the action sequence, dynamically adjusting the load, and optimizing multi-machine group control. This achieves smooth load transition, energy consumption optimization, and extended mechanical life of the servo system.

[0030] The specific plan is as follows: In a networked injection molding machine servo drive control method, S100 scientifically divides the production cycle of the injection molding machine according to the product type characteristics in the preset process database, and optimizes the servo drive action sequence for high load stages.

[0031] Please refer to Figure 2 The diagram illustrates a flowchart of an exemplary networked injection molding machine servo drive control method S100, the contents of which include: S110: Loads the process database after the injection molding machine starts to accurately match the characteristics of the product type.

[0032] When the injection molding machine starts up, the system first completes hardware initialization and software environment setup, ensuring that key components such as the servo drive, hydraulic system, and temperature control module are in an operable state. Subsequently, the control system loads the preset process database from local storage or the cloud.

[0033] The database contains a set of parameters for different product types.

[0034] In one possible implementation, the database includes different product types: thin-walled parts, thick-walled parts, or composite structural parts. Specifically, thin-walled parts typically have a maximum wall thickness of no more than 2 mm and exhibit drastic thickness variations, while thick-walled parts present a uniform wall thickness of 3 mm or more, and composite structural parts often include inserts or locally thickened areas.

[0035] In one possible implementation, the different products in the process database include products, molds, and process parameters.

[0036] Once the injection molding machine is started, the operator or automated scheduling system extracts parameter features from the CAD 3D model of the target product selected for the current production task, focusing on analyzing key geometric parameters such as differences in wall thickness distribution, the number of ribs, and the ratio of runner length. The parameter features of the target product are then matched with the optimal parameter template in the process database.

[0037] S120: Divide the production cycle and screen high-load stages by analyzing the load characteristics of each stage.

[0038] In the operation of the injection molding machine of this invention, the production cycle of the injection molding machine is divided into five main stages: standby preparation, melting and plasticizing, injection filling, holding pressure and cooling, and mold opening and closing.

[0039] In one possible implementation, the production cycle is divided based on changes in material viscosity and material temperature.

[0040] As an alternative, production cycles can be dynamically divided by fusing multi-source data from magnetic scales, pressure sensors, and motor current sensors.

[0041] The load requirements for the servo motor and hydraulic system vary at each stage, so detailed analysis is needed to identify the high-load stages.

[0042] In one possible implementation, the identification criterion for the high-load phase is: exceeding the rated power by 75% for 0.5 consecutive seconds and pressure fluctuation > ±1.2MPa.

[0043] Alternatively, the high-load stage can be identified as follows: when the current ripple amplitude exceeds the rated value by 30% and the pressure pulsation frequency is concentrated between 8-15Hz, it is considered a high-load stage. For example, during the injection molding stage, if the system detects that the current jumps from 40A to 75A (exceeding the rated 60A) within 0.2 seconds, it immediately marks this stage as a high-load stage and triggers subsequent optimization processes.

[0044] S130: Decomposes the continuous servo drive actions during high load phases into a discretized sequence to reduce instantaneous torque requirements and achieve smooth load transition.

[0045] To reduce the instantaneous torque demand of the servo motor during high-load phases and avoid equipment failure due to current overload, the injection molding machine described in this invention proposes to decompose the traditional continuous servo drive motion into a discrete motion sequence, namely a strong operation-weak voltage stabilization alternating unit sequence. This discrete motion sequence not only effectively distributes the instantaneous load on the servo motor but also achieves dynamic balance by adjusting the time allocation of each stage.

[0046] For example, during the pressure holding stage of thick-walled parts, a 3-second high-pressure maintenance cycle is used to release pressure for 1 second to avoid stress accumulation inside the mold cavity.

[0047] Similarly, for example, a short cycle sequence of 0.5 seconds high-speed injection to 0.3 seconds pressure equalization is designed for thin-walled parts.

[0048] Table 1 presents the experimental comparison data, demonstrating the differences in key performance indicators between traditional continuous servo-driven actions and discrete action sequences. The data in the table were collected through multiple sets of repeated experiments and averaged to ensure the reliability and repeatability of the results.

[0049] Table 1 Experimental comparisons revealed that after adopting discretized motion sequences, the peak instantaneous current of the servo motor decreased by approximately 30%, while the pressure fluctuation range of the hydraulic system was reduced by more than 20%. This indicates that the discretization strategy not only reduces equipment load but also improves system stability and reliability.

[0050] In a networked injection molding machine servo drive control method, S200 establishes a mapping relationship between the servo motor current and hydraulic system pressure in real time, and designs a dynamic adjustment mechanism based on this relationship to achieve smooth load transition. S200 significantly reduces the amplitude of current ripple and hydraulic pressure fluctuations, reduces equipment vibration and injection molded part defect rate, and ultimately improves system stability and production efficiency.

[0051] Please refer to Figure 3 The diagram illustrates a flowchart of an exemplary networked injection molding machine servo drive control method S200, the contents of which include: S210: Real-time monitoring of load fluctuation characteristics of discrete action sequences.

[0052] During the operation of the injection molding machine described in this invention, the load status of the servo motor and hydraulic system directly affects the stability and production efficiency of the equipment. To achieve dynamic optimization control, it is first necessary to perform high-precision real-time monitoring of the servo motor current and hydraulic system pressure, and extract key characteristics of load fluctuations based on the monitoring data.

[0053] In one possible implementation, the current fluctuation characteristics of the servo motor are collected and extracted by a current sensor, namely the mean, peak, ripple amplitude and frequency distribution of the servo motor current.

[0054] Similarly, pressure sensors are used to collect and extract the pressure fluctuation characteristics of the hydraulic system, namely the range of pressure change, fluctuation frequency, and distribution of the main frequency band.

[0055] S220: Establish the mapping relationship between servo motor current and hydraulic system pressure.

[0056] During the operation of the injection molding machine according to this invention, after extracting the load fluctuation characteristics, a mapping relationship is established between the servo motor current and the hydraulic system pressure. This mapping relationship is the core basis for dynamically adjusting the timing allocation of the action units and is also the key to achieving smooth load transition.

[0057] In one possible implementation, a piecewise linear model is used to describe the mapping relationship between the servo motor current and the hydraulic system pressure: When the current is below the current threshold, the hydraulic system pressure remains stable; When the current approaches the current threshold, the hydraulic system pressure begins to fluctuate slightly. When the current exceeds the current threshold, the pressure fluctuation of the hydraulic system intensifies and may cause equipment failure. It is necessary to automatically extend the pressure stabilization time of the next action unit and reduce the operation time proportionally. The load transition is achieved by dynamically adjusting the timing distribution of the action units.

[0058] Based on the above implementation methods, in one possible implementation, the current value of the servo motor is determined according to its rated power and maximum load capacity, and the current threshold calculation formula is as follows: ; in, For current threshold, Rated current, This is a correction factor (usually taken as 0.9~1.1). For safety margin, this formula ensures that the current threshold meets both performance requirements and safety considerations.

[0059] In a networked injection molding machine servo drive control method, S300 achieves precise adjustment of motion parameters during high-load stages by back-coupled dynamically adjusted motion unit parameters to the process control system and performing calculations and optimizations based on the injection molding machine's physical model. S300 not only ensures the stability of equipment operation but also significantly improves the quality consistency and production efficiency of injection molded parts.

[0060] Please refer to Figure 4The diagram illustrates a flowchart of an exemplary networked injection molding machine servo drive control method S300, the contents of which include: S310: Extract dynamically adjusted parameters based on mapping relationships.

[0061] During the operation of the injection molding machine of this invention, after completing the mapping relationship between the servo motor current and the hydraulic system pressure in S200, it is necessary to extract the dynamic adjustment parameters from the mapping relationship. The dynamic adjustment parameters will be used as the input of the subsequent process control system.

[0062] In one possible implementation, the dynamically adjusted parameters include the following two categories: Voltage regulation time adjustment amount: Based on the current exceeding the threshold monitored in real time in S200, the voltage regulation time extension ratio of the next action unit is calculated.

[0063] The formula for calculating the voltage stabilization time extension ratio is: ; in, To extend the voltage stabilization time by a certain percentage, This is a dynamic scaling factor. For real-time current, For current threshold, The duration of the current exceeding the limit. The time decay coefficient, It is the hyperbolic tangent function.

[0064] Adjustment of operating time: To ensure the total cycle time remains constant, extending the voltage stabilization time will inevitably lead to a reduction in the operating time. The adjustment ratio of the operating time can be reduced by a fixed proportion based on the extension ratio of the voltage stabilization time.

[0065] During the operation of the injection molding machine described in this invention application, since the hardware configuration and process requirements of different injection molding machines may differ, the extracted dynamic adjustment parameters are standardized to adapt to different equipment and process conditions.

[0066] S320: Input the dynamically adjusted parameters into the physical model of the injection molding machine for parameter optimization calculation.

[0067] In the operation of the injection molding machine according to this invention, after extracting the dynamic adjustment parameters, the next step is to input them into the physical model of the injection molding machine for further optimization calculations to ensure that the adjusted action parameters can meet the actual production needs.

[0068] The physical model of an injection molding machine is a mathematical description of the operation process of the injection molding machine, which includes multiple sub-models such as the hydraulic system, servo drive system, and mold cavity filling.

[0069] In one possible implementation, the core elements of the injection molding machine physical model include: A hydraulic system model is used to describe the relationship between the output pressure and flow rate of the hydraulic pump and the current of the servo motor.

[0070] Melt flow models are used to analyze the flow characteristics of molten plastic in a mold cavity, including shear force, viscosity change, and cooling rate.

[0071] Thermodynamic models are used to simulate the heat exchange process between melt temperature, mold temperature, and ambient temperature.

[0072] Based on the above physical model, the system uses an optimization algorithm to further optimize the dynamically adjusted parameters.

[0073] In one possible implementation, the optimization algorithm includes genetic algorithms, particle swarm optimization, and gradient descent. The specific process is as follows: With the objectives of consistent injection molded part quality, energy consumption level, and equipment stability, a multi-objective optimization function is constructed. An example objective function is as follows: ; in, The objective function value, This represents the standard deviation of the weight variation of the injection molded part. For single-cycle energy consumption, For equipment vibration intensity, Vibration threshold These are weight weighting coefficient, energy consumption weighting coefficient, and vibration weighting coefficient, respectively.

[0074] Set upper and lower limits for process parameters, such as the melt temperature must not be lower than 180°C and the holding time must not exceed 10 seconds.

[0075] The input parameters are continuously adjusted through optimization algorithms until the objective function reaches its optimal value.

[0076] S330: Readjust the motion parameters during the high-load phase.

[0077] During the operation of the injection molding machine according to this invention, after completing the parameter optimization calculation, the action parameters of the high load stage will be readjusted according to the optimization results, and a new action sequence will be generated.

[0078] In one possible implementation, the adjustment of motion parameters during the high-load phase of the injection molding stage includes: Injection speed adjustment: When the stabilization time is extended, the injection speed should be appropriately reduced to avoid premature curing of the melt.

[0079] Example strategy: if the stabilization time is extended by 10%, the injection speed is reduced by 5%.

[0080] Melt temperature compensation: A decrease in injection speed may lead to a decrease in melt fluidity, so the melt temperature needs to be increased simultaneously to compensate for the loss of fluidity.

[0081] In one possible implementation, during the pressure holding and cooling phase, the adjustment of operating parameters during the high-load phase includes: Extended holding time: When the pressure in the hydraulic system fluctuates greatly, the internal stress of the mold cavity can be released by extending the holding time.

[0082] Example strategy: if pressure fluctuation exceeds ±1.5MPa, the holding time is extended by 20%.

[0083] Clamping force adjustment: Extended holding time may lead to insufficient mold closing force, so the clamping force needs to be appropriately reduced to avoid mold deformation.

[0084] In one possible implementation, the adjustment of the operating parameters during the high-load stage in the melt plasticizing stage includes: Screw speed adjustment: When the melt temperature rises, the screw speed can be appropriately reduced to avoid excessive shearing.

[0085] Back pressure adjustment: The back pressure needs to be dynamically adjusted according to the changes in the viscosity of the melt to ensure melt quality.

[0086] During the operation of the injection molding machine according to this invention, after the motion parameters are adjusted, the system executes a new sequence of actions and evaluates the optimization effect through real-time monitoring. The optimized sequence of actions is sent to the servo drive and hydraulic system through the process control system to drive the injection molding machine to complete each stage of operation.

[0087] In a networked injection molding machine servo drive control method, the S400 judges the equipment load condition by monitoring the proportion of voltage stabilization time during high load phases in real time. When the load is low, it triggers a first-level sleep mode, enters a second-level deep sleep mode when the machine is in standby mode for a long time, and performs reverse action sequence compensation when the machine is awakened, thereby achieving a balance between energy saving optimization and rapid production recovery.

[0088] Please refer to Figure 5 The diagram illustrates a flowchart of an exemplary networked injection molding machine servo drive control method S400, the contents of which include: S410: Real-time monitoring of the voltage regulation ratio during high load periods to determine low load conditions.

[0089] During the operation of the injection molding machine as described in this invention, the load characteristics of the servo motor and hydraulic system directly affect the energy consumption level and production efficiency of the equipment. To achieve dynamic optimization control, it is first necessary to perform high-precision real-time monitoring of the pressure stabilization time ratio at each stage, and then determine low-load operating conditions based on the monitoring data.

[0090] In one possible implementation, the stabilization time percentage is monitored in real time: The current fluctuation characteristics of the servo motor are collected and extracted using a current sensor, namely the mean, peak, ripple amplitude, and frequency distribution of the servo motor current. Simultaneously, the pressure fluctuation characteristics of the hydraulic system are collected and extracted using a pressure sensor, namely the range of pressure changes, fluctuation frequency, and dominant frequency band distribution of the hydraulic system.

[0091] Similarly, in one possible implementation, a low-load condition is determined when the voltage regulation phase accounts for more than 50% of the total load in three consecutive high-load phases.

[0092] For example, by using multi-source data fusion technology, the production cycle is dynamically divided and the proportion of the voltage stabilization stage is calculated in real time. The system sets a threshold condition: when the proportion of the voltage stabilization stage exceeds 50% for three consecutive times, the low-load condition judgment logic is triggered.

[0093] S420: Automatically switches to Level 1 sleep mode based on low load conditions.

[0094] During the operation of the injection molding machine according to this invention, once a low-load condition is detected, it will automatically switch to the first-level sleep mode. The first-level sleep mode aims to shut down unnecessary components to reduce energy consumption, while retaining the standby state of critical components to ensure rapid wake-up capability.

[0095] In one possible implementation, the first-level sleep mode operation includes shutting down the hydraulic pump, retaining the heating coil, and putting the control system into standby mode.

[0096] For example, the system gradually shuts down the hydraulic pump according to preset rules and records the current hydraulic system pressure value for reference during subsequent wake-up. For the heating coil, its power output can be adjusted via a PID controller to ensure that the mold temperature is maintained at the lower limit of the target range, such as 90%-95% of the set temperature.

[0097] S430: If the entire machine remains in standby mode for an extended period without any new commands, it will enter a level 2 deep sleep mode.

[0098] During the operation of the injection molding machine according to this invention, if the standby time of the entire machine exceeds a preset threshold and no new production instructions are received, it will further switch to a second-level deep sleep mode. The second-level deep sleep mode aims to minimize energy consumption while ensuring that the equipment can quickly return to working status.

[0099] In one possible implementation, the servo main power supply is one of the core energy-consuming components of the injection molding machine. In deep sleep mode, the servo main power supply is cut off to completely eliminate unnecessary energy consumption.

[0100] Similarly, based on the above implementation method, although the servo main power supply is cut off, the control circuit still needs to remain in standby mode in order to receive wake-up commands.

[0101] S440: Reverse action sequence compensation when waking from deep sleep.

[0102] During the operation of the injection molding machine according to this invention, when the injection molding machine wakes up from deep sleep mode, a series of reverse action sequence compensations need to be performed to ensure that the equipment returns to normal working state. The reverse action sequence compensation method is as follows: first, the heating coil is started to preheat to 80% of the set temperature, then the hydraulic system is activated step by step, and finally the servo drive is restored.

[0103] In one possible implementation, the preheating of the heating coil uses a PID control algorithm to ensure a smooth temperature rise rate without overshoot.

[0104] Similarly, in one possible implementation, the specific process of hydraulic system stepped loading is as follows: Phase 1: Apply hydraulic system pressure to 50% of the rated pressure for a duration of 0.5 times the sleep duration.

[0105] Second stage: Apply hydraulic system pressure to 100% of the rated pressure for a duration equal to one time the sleep duration.

[0106] When the servo drive recovers, the system reinitializes the servo drive based on the recorded status parameters and verifies whether its position, speed, and torque meet expectations.

[0107] In a networked injection molding machine servo drive control method, as the intelligence and networking of injection molding machines continue to improve, the independent control of a single injection molding machine can no longer meet the demands for efficient and energy-saving production. Based on the S100-S400 control flow, the acceleration and deceleration curves of the servo drive system are centrally optimized, resulting in a staggered distribution of the main frequency band of the hydraulic impact energy spectrum of the injection molding machines within the cluster. This ultimately reduces the fluctuation of the workshop power grid load by more than 25% and extends the fatigue life of the injection molding machine's mechanical structure.

[0108] Please refer to Figure 6 The diagram illustrates a flowchart of an exemplary networked injection molding machine servo drive control method S500, the contents of which include: S510: Detect the production status of isomorphic products and centrally optimize motion parameters.

[0109] During the operation of the injection molding machine according to this invention, the production status of the networked injection molding machines is monitored in real time. When multiple injection molding machines are detected to be in production, the similarity of the process parameters of each injection molding machine is first compared. Specifically, by extracting the product type characteristics of the current task of each injection molding machine, such as wall thickness distribution, number of ribs, and runner length ratio, the matching degree with the reference template is determined. If the similarity of the process parameters of any two injection molding machines is ≥85%, it is determined that they are in the production state of isomorphic products.

[0110] Once a process parameter similarity of ≥85% is detected, the system immediately triggers the group control optimization mode. In this mode, the motion parameters of the injection molding machines within the cluster are centrally optimized, including the acceleration and deceleration curves of the servo drive system and the peak distribution of the main frequency band of the hydraulic impact energy spectrum. The optimization objective is to reduce the fluctuation of the workshop power grid load and extend the fatigue life of the injection molding machine's mechanical structure.

[0111] In one possible implementation, the acceleration and deceleration curve optimization of the servo drive system is achieved by using a genetic algorithm to optimize the acceleration and deceleration curves, ensuring that the instantaneous load peaks of each injection molding machine in the cluster are distributed as evenly as possible.

[0112] The staggered distribution of the main frequency band of the hydraulic impact energy spectrum is achieved by adjusting the start time of the action sequence of each injection molding machine to avoid overlapping phenomena.

[0113] S520: The optimized sequence of actions is sent to the servo drive and hydraulic system through the process control system to drive the injection molding machine to complete each stage of operation.

[0114] During the execution of the action sequence, the system monitors various performance indicators in real time, including the instantaneous current of the servo motor, the pressure fluctuation range of the hydraulic system, and the energy consumption per cycle. By comparing the performance indicators before and after optimization, the actual effect of the group control optimization mode is evaluated.

[0115] Data collected through multiple sets of repeated experiments show that the S100-S500 method has achieved significant improvements in key performance indicators: The peak instantaneous current of the servo motor decreased by approximately 30%. The pressure fluctuation range of the hydraulic system has been reduced by approximately 20%. The equipment vibration intensity was reduced by approximately 33.3%; Energy consumption per cycle is reduced by approximately 10.6%; The weight deviation of injection molded parts was improved by approximately 25%.

[0116] In summary, the networked injection molding machine servo drive control method of this application, by introducing discretized motion sequences, dynamic mapping relationships, parameter optimization algorithms, and group control optimization modes, achieves efficient, energy-saving, intelligent, and reliable operation of the injection molding machine servo drive control. It not only significantly improves equipment performance and product quality but also reduces energy consumption and maintenance costs, providing a highly competitive technical solution for the injection molding industry.

[0117] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0118] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as including, comprising, having, etc., are open-ended terms meaning including but not limited to, and are used interchangeably with them. The terms "or" and "and" as used herein are terms and / or, and are used interchangeably with them unless the context clearly indicates otherwise. The term "such" as used herein refers to phrases such as but not limited to, and is used interchangeably with them.

[0119] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0120] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0121] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A servo drive control method for a networked injection molding machine, characterized in that, include: When the injection molding machine starts, the production cycle is divided into multiple stages according to the product type characteristics in the preset process database. The high-load stage is selected. For the high-load stage, the traditional continuous servo drive action is decomposed into a discrete action sequence, namely a strong operation-weak voltage stabilization alternating unit sequence. Based on the load fluctuation characteristics of the discretized action sequence, a mapping relationship between servo motor current and hydraulic system pressure is established. The adjustment of the action unit parameters in the mapping relationship is back-coupled to the process control system. Based on the calculation of the injection molding machine physical model, the action parameters of each high load stage are readjusted and the action is executed. Power state switching is triggered based on the action execution result, including switching of load state and switching of sleep depth; When the network detects that multiple injection molding machines are producing homogeneous products, the group control optimization mode is activated based on the control process. This causes the main frequency band of the hydraulic impact energy spectrum of the injection molding machines in the cluster to be staggered, ultimately reducing the fluctuation of the workshop power grid load and extending the fatigue life of the injection molding machine's mechanical structure.

2. The networked injection molding machine servo drive control method according to claim 1, characterized in that, The step of dividing the production cycle into multiple stages based on the product type characteristics in the preset process database includes: Match product types from the process database. Product types include thin-walled parts, thick-walled parts, and composite structural parts. Based on the matching product type, the wall thickness distribution, the number of ribs, and the ratio of runner length are extracted as key geometric parameters. Combined with the material viscosity and temperature change patterns or multi-source sensor data fusion, the production cycle is dynamically divided into several stages, including melting and plasticizing, injection molding, and pressure holding and cooling.

3. The networked injection molding machine servo drive control method according to claim 2, characterized in that, The high-load screening phase includes: Real-time monitoring of servo motor current and hydraulic system pressure; When the servo motor current continuously exceeds the preset ratio of the rated current and the hydraulic system pressure fluctuation exceeds the stable threshold, the current stage is determined to be a high load stage.

4. The networked injection molding machine servo drive control method according to claim 1, characterized in that, The establishment of the mapping relationship between servo motor current and hydraulic system pressure includes: A piecewise linear model is used to describe the mapping relationship between servo motor current and hydraulic system pressure; The mapping relationship is configured such that when the servo motor current exceeds the dynamic current threshold, the voltage stabilization time in the next action unit is automatically extended and the operation time is shortened accordingly.

5. The networked injection molding machine servo drive control method according to claim 4, characterized in that, The dynamic current threshold is calculated based on the rated current of the servo motor, a preset correction coefficient, and a safety margin. The calculation formula is as follows: ;in, For current threshold, Rated current, For correction factor, For safety margin.

6. The networked injection molding machine servo drive control method according to claim 1, characterized in that, The calculations based on the injection molding machine's physical model, including the readjustment of motion parameters for each high-load stage, include: Extract the dynamic adjustment parameters, including the voltage stabilization time extension ratio and the operation time reduction ratio, where the voltage stabilization time extension ratio is calculated using the formula: ;in, To extend the voltage stabilization time by a certain percentage, This is a dynamic scaling factor. For real-time current, For current threshold, The duration of the current exceeding the limit. The time decay coefficient, It is the hyperbolic tangent function; The pressure stabilization time adjustment and operation time adjustment extracted from the mapping relationship are input into the injection molding machine physical model; the injection molding machine physical model includes a hydraulic system model, a melt flow model, and a thermodynamic model; A multi-objective optimization function was constructed with the objectives of consistent injection molded part quality, single-cycle energy consumption, and equipment vibration intensity as the goals, and an optimization algorithm was used to iteratively optimize the motion parameters.

7. The networked injection molding machine servo drive control method according to claim 6, characterized in that, The readjustment of the action parameters for each high-load phase also includes: During the injection molding stage, the injection speed and melt temperature are adjusted synchronously according to the pressure stabilization time adjustment. During the pressure holding and cooling stage, the pressure holding time and clamping force are adjusted according to pressure fluctuations.

8. The networked injection molding machine servo drive control method according to claim 1, characterized in that, The power state switching triggered based on the action execution result includes: Real-time statistics on the proportion of the voltage stabilization phase during high load periods; When the proportion of the pressure stabilization stage continuously exceeds the preset ratio, it is determined that the machine has entered a low load condition and the injection molding machine is controlled to switch to the first-level sleep mode. The first-level sleep mode includes shutting down the hydraulic pump and reducing the power of the heating coil.

9. The networked injection molding machine servo drive control method according to claim 8, characterized in that, If the duration of the first-level sleep mode exceeds the preset standby threshold, the injection molding machine is controlled to enter the second-level deep sleep mode. Upon waking from deep sleep mode, reverse action sequence compensation is performed, including step-by-step loading of hydraulic system pressure and preheating of the heating coil.

10. The networked injection molding machine servo drive control method according to claim 1, characterized in that, The network detection of multiple injection molding machines producing homogeneous products includes: Extract and compare the product type characteristics and process parameters currently produced by multiple injection molding machines; When the similarity of process parameters reaches the preset matching threshold, it is determined to be a production state of isomorphic products; In the group control optimization mode, the start time of the action sequence of each injection molding machine in the cluster is adjusted in a centralized manner to achieve a staggered distribution of the main frequency band of the hydraulic impact energy spectrum.

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