A method, system and storage medium for quickly formulating energy consumption limits for injection molding

Abstract

The present invention discloses a method, system, and storage medium for quickly establishing energy consumption limits for injection molding. The method includes: determining a working energy consumption model and all working energy consumption parameters; performing a sensitivity analysis on the working energy consumption parameters to determine process parameters that have a greater impact on normal working energy consumption; establishing a probability statistical distribution model, generating random vector samples that obey the probability statistical distribution model through the Monte Carlo method, and obtaining a number of working energy consumption sample values; drawing a control chart based on the working energy consumption sample values, and determining the normal working energy consumption limit for a single processing cycle through the control chart. This application takes into account the characteristics of random fluctuations in energy consumption during the manufacturing process, and combines SPC technology, an injection molding energy consumption model, a sensitivity function, and a Monte Carlo method to achieve the establishment of energy consumption limits. The method provided by this application has greater objectivity, improves the applicability of energy consumption limits, and can therefore more efficiently, scientifically, and rationally establish energy consumption limits quickly.

Description

Technical Field

[0001] The present invention relates to the field of injection molding control technology, and in particular to a method, system and storage medium for quickly formulating energy consumption limits for injection molding. Background Art

[0002] Injection molding refers to the process of producing semi-finished parts of a certain shape from molten raw materials through operations such as pressurization, injection, cooling, and separation. It is the main processing method for the production of plastic products, and has a high installed power density and degree of automation. The injection molding process of plastic parts mainly includes seven steps: mold closing, plasticization, filling, pressure holding, cooling, mold opening, and demolding. Each step in the entire injection molding cycle requires a certain amount of energy consumption. The problem of high energy consumption and low energy efficiency in the entire process is very significant. There are multiple energy conversions and a high proportion of energy consumption in non-molding actions, which leads to a large amount of energy waste. Therefore, the establishment of energy consumption limits for injection molding processing is an important measure to strengthen the practice of energy consumption management, monitoring, and carbon reduction in the production process of injection molding products.

[0003] However, the entire injection molding machine system has numerous energy consumption sources, complex energy consumption patterns, and difficult to obtain energy consumption. Most existing energy consumption limit formulation methods start with the inherent specific energy of the processing process, and then determine it by setting a corresponding scaling factor based on the service life of the processing equipment and the company's processing needs. This method is highly subjective, and the applicability of the energy consumption limit obtained by this method is limited. At the same time, the random fluctuations in energy consumption during the production process are not taken into account in the process of formulating energy consumption limits, which leads to low accuracy of the energy consumption limit obtained, and it is unable to truly achieve the effect of controlling the energy consumption of the injection molding process. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system and storage medium for quickly formulating energy consumption limits for injection molding, so as to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0005] The solution of the present invention to solve the technical problem is as follows: First, the present application provides a method for quickly formulating energy consumption limits for injection molding, comprising the following steps:

[0006] According to the injection molding process, calculate the total working energy consumption of a single processing cycle, determine the working energy consumption model and all working energy consumption parameters;

[0007] Performing sensitivity analysis on all the working energy consumption parameters in turn to determine the process parameters that have the greatest impact on normal working energy consumption among all the working energy consumption parameters;

[0008] Establishing a probability statistical distribution model based on the process parameters, generating random vector samples that obey the probability statistical distribution model through the Monte Carlo method, and substituting the random vector samples into the working energy consumption model to obtain working energy consumption sample values ​​of N single processing cycles;

[0009] A control chart is drawn according to the working energy consumption sample values ​​of the N single processing cycles, and the normal working energy consumption limit of the single processing cycle is determined through the control chart.

[0010] As a further improvement of the above technical solution, the total working energy consumption of a single processing cycle is calculated according to the injection molding process, and the working energy consumption model and all working energy consumption parameters are determined, including:

[0011] The injection molding process is defined as consisting of seven sub-processes: mold closing, plasticizing, filling, pressure holding, cooling, mold opening, and ejector advance and retreat;

[0012] Calculate the energy consumption E1 of the mold closing and opening process, E2 of the ejector advance and retreat process, E3 of the plasticizing process, E4 of the injection process, E5 of the pressure holding process, and E6 of the cooling process in a single processing cycle.

[0013] The total working energy consumption E in a single injection cycle is calculated by energy consumption E1, E2, ..., E6. s , through the total working energy consumption E s To express the objective function of the working energy consumption model of a single injection cycle;

[0014] Among them, the total working energy consumption E s Satisfies the following formula:

[0015] E s=E1+E2+E3+E4+E5+E6;

[0016] All working energy consumption parameters are determined according to the working energy consumption model.

[0017] As a further improvement to the above technical solution, the energy consumption E1 of the mold closing and opening process, the energy consumption E2 of the ejector advance and retreat process, the energy consumption E3 of the plasticizing process, the energy consumption E4 of the injection molding process, the energy consumption E5 of the pressure holding process, and the energy consumption E6 of the cooling process in a single processing cycle are calculated separately, including:

[0018] The energy consumption E1 of the mold closing and mold opening process in a single processing cycle is determined by the pressure information of the hydraulic cylinder of the injection molding machine during the mold closing and mold opening process; E1 satisfies: E1 = ∫P1A1dS;

[0019] Among them, P1 represents the liquid pressure in the hydraulic cylinder during mold closing and mold opening; A1 represents the difference between the piston area and the piston rod area of ​​the hydraulic cylinder during mold closing and mold opening; S represents the stroke of the piston rod;

[0020] The energy consumption E2 of the demoulding process in a single processing cycle is determined by the pressure information of the hydraulic cylinder during the demoulding process; E2 satisfies: E2 = ∫P2A2dS;

[0021] Among them, P2 represents the liquid pressure in the hydraulic cylinder during the demoulding process; A2 represents the difference between the piston area and the piston rod area of ​​the hydraulic cylinder during the demoulding process; S represents the stroke of the piston rod;

[0022] The energy consumption E3 of the plasticizing process in a single processing cycle is determined by the information of the processed material during the plasticizing process; E3 satisfies: E3=cmΔT+M T ωt3+P3A1s;

[0023] Where c represents the specific heat capacity of the processed material, m represents the mass of the processed material, ΔT represents the temperature difference between the temperature during plasticization and the reference temperature, and M T represents the screw driving torque, ω represents the screw angular velocity, t3 represents the time required for the plasticizing process, P3 represents the back pressure during the plasticizing process, and s represents the screw retreat distance after plasticizing;

[0024] The energy consumption E4 of the injection molding process in a single processing cycle is determined by the filling pressure information and the processing melt information during the injection molding process; E4 satisfies: E4 = ∫P4Q4dt1;

[0025] Among them, P4 represents the filling pressure of the injection molding machine during the injection molding process, Q4 represents the volume flow rate of the melt during the injection molding process, and t1 represents the time required for the injection molding process;

[0026] The energy consumption E5 of the holding process in a single processing cycle is determined by the pressure information and the processing melt information during the holding process; E5 satisfies: E5 = ∫P5Q5dt2;

[0027] Among them, P5 represents the holding pressure during the holding process, Q5 represents the volume flow rate of the melt during the holding process, and t2 represents the time required for the holding process;

[0028] The energy consumption E6 of the cooling process in a single processing cycle is determined by the output work of the water pump of the injection molding machine during the cooling process; E6 satisfies: E6=P6Q6t4;

[0029] Among them, P6 represents the output pressure of the water pump during the cooling process, Q6 represents the output flow of the water pump during the cooling process, and t4 represents the time required for the cooling process.

[0030] As a further improvement of the above technical solution, the determining of all working energy consumption parameters according to the working energy consumption model includes:

[0031] According to the objective function E of the working energy consumption model s , determine the working energy consumption parameters x1, x2, ..., x 22 Among them, E s Expressed as: E s =E1+E2+E3+E4+E5+E6=∫P1A1dS+∫P2A2dS+cmΔT+M T ωt3+P3A1s+∫P4Q4dt1+∫P5Q5dt2+P6Q6t4;

[0032] Among them, x1=P1, x2=A1, x3=S, x4=P2, x5=A2, x6=c, x7=m, x8=ΔT, x9=M T , x 10 =ω,x 11 =t3,x 12 =P3,x 13 =s,x 14 =P4,x 15 =Q4,x 16 =t1,x 17 =P5,x 18 =Q5,x 19 =t2,x 20 =P6,x 21 =Q6,x 22 =t4.

[0033] As a further improvement to the above technical solution, the sensitivity analysis is performed on all the working energy consumption parameters in sequence to determine the process parameters that have a greater impact on the normal working energy consumption among all the working energy consumption parameters, including:

[0034] According to the preset sensitivity function, calculate the working energy consumption parameter x in turn i Sensitive function value, i = 1, 2, 3, ..., 22;

[0035] Compare the sensitive function values ​​of all working energy consumption parameters and reorder all working energy consumption parameters into x according to the sensitive function values j ′,j=1,2,3,...,22;

[0036] Summarize the working energy consumption parameters x1′, x2′, …, x1′ whose sensitive function value is greater than the sensitive threshold m ′, m<22, x1′, x2′, …, x m ′, m<22 is the process parameter that has a greater impact on the energy consumption of normal operation.

[0037] As a further improvement of the above technical solution, the working energy consumption parameter x is calculated in sequence according to the preset sensitive function. i Sensitive function values ​​include:

[0038] Determine all working energy consumption parameters x i The value range of i is 1, 2, 3, ..., 22;

[0039] When calculating the i-th working energy consumption parameter x i When the sensitive function value of the working energy consumption parameter x i Within the corresponding value range, a value is randomly selected as the working energy consumption parameter x i The new value of Other working energy consumption parameters take the baseline value; among them, Not equal to x i The baseline value;

[0040] Calculate the value of the objective function of the working energy consumption model in the current single processing cycle, and record the current objective function value as the current function value E si ;

[0041] According to E si and Calculate the working energy consumption parameter x i Sensitive function value S k (x i ):

[0042] ΔE si =E s ′-E si ,

[0043] Where: E s ′ represents the value of the objective function of the working energy consumption model in a single processing cycle when all working energy consumption parameters take the baseline value; x′ i Represents the working energy consumption parameter x i The baseline value of S k is a set of dimensionless non-negative real numbers; S k The larger the value, the higher the E si x i The more sensitive, that is, x i To E si The greater the impact;

[0044] After calculating the i-th working energy consumption parameter x i When the sensitive function value of is obtained, let i=i+1 and repeat the above steps until the calculation of the sensitive function values ​​of all working energy consumption parameters is completed.

[0045] As a further improvement to the above technical solution, the method of establishing a probability statistical distribution model based on the process parameters, generating random vector samples that obey the probability statistical distribution model through the Monte Carlo method, and substituting the random vector samples into the working energy consumption model to obtain working energy consumption sample values ​​of N single processing cycles includes:

[0046] Establish m probability statistical distribution models P1, P2, P3, ..., P m as a distribution function of said process parameter;

[0047] Determine the sampling plan and combine the Monte Carlo method to generate the probability statistical distribution model P1, P2, P3, ..., P m A random vector sample x i (j) ; Wherein, i represents the number of the random vector sample, i = 1, 2, 3, ..., N, j represents the number of the process parameter, j = 1, 2, 3, ..., m;

[0048] The sampling scheme is to perform n1 random samplings to form n1 sampling subgroups; in a single random sampling process, n2 random vector samples corresponding to each process parameter are generated through m probability statistical distribution models; wherein N = n1×n2;

[0049] The random vector sample x i (j) Substitute the energy consumption values ​​into the working energy consumption model to obtain N working energy consumption sample values ​​of a single processing cycle.

[0050] As a further improvement to the above technical solution, the step of drawing a control chart based on the working energy consumption sample values ​​of the N single processing cycles and determining the normal working energy consumption limit of a single processing cycle through the control chart includes:

[0051] Calculating energy consumption sampling data through the working energy consumption sample values ​​of the N single processing cycles;

[0052] The energy consumption sampling data includes N working energy consumption sample values ​​a k , working energy consumption sample value a k Average value and standard deviation s i ,i=1,2,3,...,N,k=1,2,3,...,N;

[0053] According to the energy consumption sample data, draw Control chart, the Control charts include s charts and Graph, calculate s graph and control limits of the graph, and determining the normal operating energy consumption limit of a single processing cycle based on the control limits;

[0054] Among them, the The control chart satisfies the following formula:

[0055] Among them, CL s is the center line of the s graph, UCL s and LCL s are the upper and lower control limits of the s chart, for The center line of the figure, and They are Upper and lower control limits of the chart; is the average value of the standard deviation of the n1 group samples, is the average of the sample means of group n1, and A3, B4 and B3 are constants.

[0056] In a second aspect, the present application provides a system for quickly formulating energy consumption limits for injection molding, comprising:

[0057] A model building unit is used to calculate the total working energy consumption of a single processing cycle according to the injection molding process, and to determine the working energy consumption model and all working energy consumption parameters;

[0058] A sensitivity analysis unit is used to perform sensitivity analysis on all the working energy consumption parameters in sequence to determine the process parameters that have a greater impact on normal working energy consumption among all the working energy consumption parameters;

[0059] a sample generating unit, configured to establish a probability statistical distribution model based on the process parameters, and generate random vector samples that obey the probability statistical distribution model by using a Monte Carlo method;

[0060] A first calculation unit is used to substitute the random vector sample into the working energy consumption model to obtain working energy consumption sample values ​​of N single processing cycles;

[0061] A drawing unit, configured to draw a control chart based on the working energy consumption sample values ​​of the N single processing cycles;

[0062] The second calculation unit is used to determine the normal working energy consumption limit of a single processing cycle through a control chart.

[0063] In a third aspect, the present application further provides a storage medium storing processor-executable instructions, which, when executed by the processor, are used to execute the method for quickly formulating injection molding energy consumption limits.

[0064] The beneficial effects of the present invention are: providing a method, system and storage medium for quickly formulating energy consumption limits for injection molding, the method comprising: determining a working energy consumption model and all working energy consumption parameters; performing sensitivity analysis on all working energy consumption parameters in turn to determine the process parameters that have the greatest impact on normal working energy consumption among all working energy consumption parameters; establishing a probability statistical distribution model based on the process parameters, generating random vector samples that obey the probability statistical distribution model through the Monte Carlo method, and then obtaining working energy consumption sample values ​​for N single processing cycles; drawing a control chart based on the working energy consumption sample values, and determining the normal working energy consumption limit of a single processing cycle through the control chart, thereby achieving energy consumption control during the injection molding process. The present application utilizes the concept of control limits in SPC technology, combines the injection molding energy consumption model, sensitivity function and Monte Carlo method to achieve energy consumption limit formulation. The present application has stronger objectivity, and the energy consumption limit formulation process takes into account the characteristics of random fluctuations in energy consumption during the manufacturing process, thereby improving the applicability of the energy consumption limit, and thus being able to quickly formulate energy consumption limits more efficiently, scientifically and rationally. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of a method for quickly formulating energy consumption limits for injection molding provided in an embodiment of the present application;

[0066] Figure 2 A workflow diagram for performing sensitivity analysis on all working energy consumption parameters in sequence and determining process parameters provided in an embodiment of the present application;

[0067] Figure 3 A flow chart of a method for generating random vector samples provided in an embodiment of the present application;

[0068] Figure 4 A workflow diagram for establishing normal operating energy consumption limits is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0070] The present application is further described below in conjunction with the accompanying drawings and specific embodiments. The described embodiments should not be considered as limiting the present application. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0071] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0072] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0073] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0074] (1) Statistical Process Control (SPC) refers to the use of statistical methods to monitor, analyze, and control a process during a specific process change. Specifically, it is a quality management technology that uses applied statistical techniques to evaluate and monitor each stage of the process, establish and maintain the process at an acceptable and stable level, and thus ensure that products and services meet the specified requirements. It is part of process control and has two main aspects: one is to use control charts to analyze the stability of the process and provide early warnings for abnormal factors in the process; the other is to calculate the process capability index to analyze the degree to which the stable process capability meets the technical requirements and evaluate the process quality.

[0075] (2) Monte Carlo method is a numerical calculation method based on probability and statistics theory. Its core idea is to use random numbers or more commonly pseudo-random numbers to solve some complex computational problems. Monte Carlo method is mainly divided into three algorithms: rejection sampling, importance sampling and MCMC (Markov Chain Monte Carlo method).

[0076] Injection molding refers to the process of producing semi-finished products of a certain shape by pressurizing, injecting, cooling, and releasing molten raw materials. It is the main processing method for plastic product production and has a high installed power density and a high degree of automation. Generally, the injection molding cycle consists of mold closing time, filling time, holding time, cooling time, and demolding time. The injection molding process of plastic parts mainly includes the following seven steps:

[0077] The first step is mold closing.

[0078] The second step is plasticization. Plasticization refers to the whole process of heating the plastic in the barrel to reach a fluid state and good plasticity.

[0079] The third step is filling. Filling is the first step in the entire injection molding cycle, starting from the moment the mold closes and injection begins, until the mold cavity is approximately 95% filled. In theory, the shorter the filling time, the higher the molding efficiency. However, in actual production, molding time or injection speed is subject to many constraints.

[0080] The fourth step is holding pressure. The purpose of the holding stage is to continuously apply pressure to compact the melt and increase the plastic's density to compensate for shrinkage. During this process, the material density continues to increase, and the plastic part gradually takes shape. The holding stage continues until the gate solidifies and seals, at which point the mold cavity pressure reaches its maximum value.

[0081] The fifth step is cooling. Only after the molded plastic product has cooled and solidified to a certain rigidity can it be released from the mold and prevent deformation due to external forces. Cooling time accounts for approximately 70% to 80% of the entire molding cycle. A well-designed cooling system can significantly shorten molding time, improve injection molding productivity, and reduce costs. An improperly designed cooling system will prolong molding time and increase costs; uneven cooling can further cause warping of the plastic product.

[0082] Step 6: Open the mold.

[0083] The seventh step is demolding. Demolding is the final step in the injection molding cycle. Although the product has been cold-formed, demolding still has a significant impact on its quality. Improper demolding methods can result in uneven force during demolding, causing defects such as deformation during ejection. There are two main demolding methods: ejector pin demolding and stripper plate demolding.

[0084] Each step in the entire injection molding cycle consumes a certain amount of energy. The entire process is characterized by high energy consumption and low energy efficiency. Multiple energy conversions occur, and non-molding actions consume a high proportion of energy, leading to significant energy waste. Therefore, establishing energy consumption limits for injection molding processes is a key measure to strengthen energy management, monitoring, and carbon reduction practices during the production of injection molded products. However, the entire injection molding machine system consumes numerous energy sources, with complex energy consumption patterns, making it difficult to obtain energy consumption data. Existing energy consumption limit formulation methods mostly start with the inherent specific energy of the process and then determine it by setting a corresponding scaling factor based on the service life of the processing equipment and the company's processing needs. This approach is highly subjective, and the resulting energy consumption limits have limited applicability. Furthermore, the energy consumption limit formulation process fails to consider the random fluctuations in energy consumption during the production process, resulting in low accuracy and an inability to truly control the energy consumption of the injection molding process.

[0085] In the field of industrial control, Statistical Process Control (SPC) is the use of statistical methods to monitor, analyze and control a process during a specific process change. Nowadays, SPC technology is very mature, easy to use, and widely applicable to a variety of different processes, such as the processing and manufacturing processes of electronic chips, batteries and other products. In order to solve the problems of difficulty in obtaining energy consumption during the injection molding process, low accuracy of the energy consumption limit formulation method, and limited applicability of the formulated energy consumption limit, the present application provides a method for quickly formulating injection molding energy consumption limits that combines SPC technology and random sampling methods. First, an energy consumption model for the normal operation of a single injection molding cycle is constructed, that is, a working energy consumption model and its objective function. Based on the model establishment, all parameters of the normal working energy consumption within a single cycle are determined, and process parameters that have a greater impact on the normal working energy consumption of a single cycle are selected. Then, combined with the probability statistical distribution method and the Monte Carlo method, the working energy consumption sample value within a single cycle is obtained through random vector samples. Finally, the energy consumption limit for injection molding equipment processing is formulated using SPC technology and control charts.

[0086] Reference Figure 1 As shown, an embodiment of the present application will be described and elaborated below on a method for quickly formulating an injection molding energy consumption limit. The method may include but is not limited to the following steps.

[0087] S100, calculating the total working energy consumption of a single processing cycle according to the injection molding process, and determining a working energy consumption model and all working energy consumption parameters.

[0088] In this step, based on existing research and the seven injection molding processes of injection molding equipment, a normal operating energy consumption model for a single injection molding cycle was determined. In this embodiment, the ejector is ejected using the ejector's advance and retraction method. Therefore, when designing the normal operating energy consumption model, the energy consumption of the ejector process is calculated using the pressure measured by the hydraulic cylinder during the ejector's advance and retraction process, along with processing information.

[0089] S200 , performing sensitivity analysis on all working energy consumption parameters in sequence to determine the process parameters that have a greater impact on normal working energy consumption among all working energy consumption parameters.

[0090] In this step, based on the established model, all parameters for the normal energy consumption of a single injection molding cycle are determined, and a sensitivity analysis is performed on these parameters. Throughout the entire injection molding cycle, all parameters for normal energy consumption will vary to a certain extent, which in turn affects the magnitude of the variation in normal energy consumption per cycle, i.e., the objective function value of the energy consumption model. Based on the sensitivity, the process parameters with the greatest impact on normal energy consumption per cycle are determined.

[0091] S300, establishing a probability statistical distribution model based on process parameters, generating random vector samples that obey the probability statistical distribution model through the Monte Carlo method, and substituting the random vector samples into the work energy consumption model to obtain work energy consumption sample values ​​of N single processing cycles;

[0092] S400 , drawing a control chart based on the working energy consumption sample values ​​of the N single processing cycles, and determining the normal working energy consumption limit of the single processing cycle through the control chart.

[0093] In this step, SPC technology is used to formulate energy consumption limits for injection molding equipment using control charts to determine the energy consumption limits for injection molding equipment. The sampling plan of the control chart collects samples according to the Monte Carlo method mentioned above, and then calculates the energy consumption limit according to the calculation formula of the control limit.

[0094] In one embodiment of the present application, S100 of the present application will be further described and elaborated below. S100 may include but is not limited to the following steps.

[0095] S110 defines the injection molding process as being divided into seven sub-processes: mold closing, plasticizing, filling, pressure holding, cooling, mold opening, and ejector advance and retreat.

[0096] S120 , respectively calculating the energy consumption E1 of the mold closing and opening process, the energy consumption E2 of the ejector advance and retreat process, the energy consumption E3 of the plasticizing process, the energy consumption E4 of the injection process, the energy consumption E6 of the pressure holding process, and the energy consumption E6 of the cooling process within a single processing cycle.

[0097] S130, calculate the total working energy consumption E in a single processing cycle through energy consumption E1, E2, ..., E6 s , through the total working energy consumption E s To express the objective function of the working energy consumption model of a single processing cycle.

[0098] In this specific embodiment, combined with existing research and based on the injection molding process of injection molding equipment, the injection molding process includes mold closing, plasticizing, filling, pressure holding, cooling, mold opening, and ejector advance and retreat. Each process in this process consumes a certain amount of energy. Therefore, in order to more intuitively illustrate the components of energy consumption, the energy consumption calculation formula for a single injection molding cycle is expressed as:

[0099] E s =E1+E2+E3+E4+E5+E6

[0100] Among them, E sIndicates the total energy consumed in a molding cycle, that is, the total working energy consumption. E1 represents the energy required for the mold opening and closing stages, E2 represents the energy required for the demolding stage, E3 represents the energy required for the plasticizing stage, E4 represents the energy required for the injection stage, E5 represents the energy required for the pressure holding stage, and E6 represents the energy required for the cooling stage.

[0101] It can be understood that the above-mentioned formula for calculating the energy consumption of a single injection molding cycle is also the objective function of the working energy consumption model of a single processing cycle.

[0102] S140: Determine all working parameters according to the working energy consumption model.

[0103] Furthermore, the implementation steps of S120 include the following steps:

[0104] Regarding the energy consumption required for the mold opening and closing stages in a single processing cycle, that is, the energy consumption of the mold closing and opening process, this application calculates the theoretical energy consumption of the process through the pressure information and processing information of the hydraulic cylinder of the injection molding machine during the mold opening and closing process, and then determines the energy consumption E1 of the mold closing and opening process in a single processing cycle. E1 can be expressed as:

[0105] E1=∫P1A1dS

[0106] In this specific embodiment, the energy consumption of the mold opening and closing processes is considered as the energy consumption of a single process. In the above formula: During the mold closing and opening processes: the liquid pressure in the hydraulic cylinder is represented by P1, and the difference between the piston area and the piston rod area of ​​the hydraulic cylinder is represented by A1. S is the stroke of the piston rod.

[0107] Furthermore, regarding the energy consumption required for the demoulding stage in a single processing cycle, this application calculates the theoretical energy consumption of the process by using the pressure information and processing information of the hydraulic cylinder of the injection molding machine during the demoulding process, and then determines the energy consumption E2 of the demoulding process in a single processing cycle. It should be noted that in this specific embodiment, demoulding is achieved by the advance and retreat of the ejector pin. E2 can be expressed as:

[0108] E2=∫P2A2dS

[0109] In the above formula: During the demolding process, the liquid pressure in the hydraulic cylinder is represented by P2, and the difference between the piston area and the piston rod area of ​​the hydraulic cylinder is represented by A2. S is the stroke of the piston rod.

[0110] Furthermore, for the energy consumption required for the plasticization stage in a single processing cycle, this application calculates the theoretical energy consumption of the process through the information of the processed material and the processing process during the plasticization process, and then determines the energy consumption E3 of the plasticization process within the processing cycle. E3 can be expressed as:

[0111] E3=cmΔT+M Tωt3+P3A1s

[0112] In the above formula: the specific heat capacity of the processed material is represented by c, and M is the mass of the processed material. During the plasticizing process: the temperature difference between the temperature and the reference temperature is represented by ΔT, and the screw drive torque is represented by M. T The screw angular velocity is represented by ω; t3 is the time required for the plasticizing process; P3 is the back pressure of the plasticizing process, and s is the distance the screw retreats after plasticizing.

[0113] Furthermore, regarding the energy consumption required for the injection molding stage in a single processing cycle, the present application calculates the theoretical energy consumption of the process by using the filling pressure information and processing melt information during the injection molding process, and then determines the energy consumption E4 of the injection molding process in a single processing cycle. E4 can be expressed as:

[0114] E4=∫P4Q4dt1 In the above formula: During the injection molding process: the filling pressure of the injection molding machine is represented by P4, the volume flow rate of the melt is represented by Q4, and the time required for this process is represented by t1.

[0115] Furthermore, regarding the energy consumption required for the holding phase in a single processing cycle, this application calculates the theoretical energy consumption of the process using the holding pressure information and the processing melt information during the holding process, and then determines the energy consumption E5 of the holding process in a single processing cycle. E5 can be expressed as:

[0116] E5=∫P5Q5dt2

[0117] In the above formula: During the holding process: the holding pressure is represented by P5, the volume flow rate of the melt is represented by Q5, and the time required for this process is represented by t2.

[0118] Furthermore, the present application calculates the theoretical energy consumption of the cooling process by the output work of the water pump of the injection molding machine during the cooling stage, and then determines the energy consumption E6 of the cooling process within a single processing cycle.

[0119] In this specific embodiment, the cooling water pump in the injection molding machine's cooling system consumes relatively little energy. In actual applications, the energy consumption during the cooling process is difficult to derive theoretically. Therefore, during the cooling process, it is assumed that the energy consumption of the cooling process is consistent with the output power of the cooling water pump. That is, the energy consumption E6 of the cooling process is represented by the output power of the cooling water pump during the cooling process. E6 can be expressed as:

[0120] E6=P6Q6t4

[0121] In the above formula: During the cooling process: the water pump output pressure is represented by P6, the water pump output flow is represented by Q6, and the time required for the process is represented by t4.

[0122] Furthermore, in S130, through the above energy consumption analysis of the seven sub-processes in the injection molding process, the following injection molding cycle energy consumption calculation formula can be obtained, that is, the objective function of the working energy consumption model:

[0123] E s =∫P1A1dS+∫P2A2ds+cmΔT+M T ωt3+P3A1s+∫P4Q4dt1+∫P5Q5dt2+P6Q6t4

[0124] Furthermore, in S140, through the above energy consumption analysis of the seven sub-processes in the injection molding process, all working energy consumption parameters x1, x2, ..., x2 in a single injection molding cycle can be determined. 22 All working energy consumption parameters are shown in Table 1 below.

[0125] Table 1 All working energy consumption parameters and their codes in a single processing cycle

[0126]

[0127] From Table 1 we can see that:

[0128] x1=P1, x2=A1, x3=S, x4=P2, x5=A2, x6=c, x7=m, x8=ΔT, x9=M T , x 10 =ω,x 11 =t3,x 12 =P3,x 13 =s,x 14 =P4,x 15 =Q4,x 16 =t1,x 17 =P5x 18 =Q5,x 19 =t2,x 20 =P6,x 21 =Q6,x 22 =t4.

[0129] Reference Figure 2 As shown, an embodiment of the present application, S200 will be further described and elaborated below. In this specific embodiment, according to the influence of the working energy consumption parameter on the normal working energy consumption of a single injection molding cycle, a sensitivity analysis is performed on each working energy consumption parameter, and the relative importance of different working energy consumption parameters is obtained by analyzing the results. The purpose of performing a sensitivity analysis on all parameters is to screen out the process parameters that have a greater impact on the normal working energy consumption of a single processing cycle, and then establish the distribution probability of all process parameters as the basis for subsequent Monte Carlo sampling. S200 may include but is not limited to the following steps.

[0130] S210, according to the preset sensitivity function, calculate the working energy consumption parameter x i Sensitive function value; i = 1, 2, 3, ..., 22;

[0131] S220, comparing the sensitivity function values ​​of all working energy consumption parameters, and reordering all working energy consumption parameters into x according to the sensitivity function values. j ′,j=1,2,3,...,22;

[0132] S230, summarizing the working energy consumption parameters x1′, x2′, ..., x1′ whose sensitive function values ​​are greater than the sensitive threshold m ′, m<22, x1′, x2′, …, x m ′, m<22 is the process parameter that has a greater impact on the energy consumption of normal operation.

[0133] In this specific embodiment, the larger the sensitivity function value, the greater the impact of the corresponding parameter on the model's objective function. Based on this principle, the sensitivity function values ​​of all parameters calculated in S210 are compared, and the corresponding parameters are reordered from high to low based on the sensitivity function value. Finally, several parameters with high sensitivity are summarized and regarded as process parameters with the greatest impact on normal operation energy consumption.

[0134] Furthermore, S210 specifically includes the following steps:

[0135] First, determine the value range of all working energy consumption parameters. Specifically:

[0136] According to the working energy consumption model determined in S100 and the codes of the parameters in Table 1, the expression of the target parameter of the working energy consumption model is determined:

[0137] E S =∫x1x2dx3+∫x4x5dx3+x6x7x8+x9x 10 x 11 +x 12 x2x 13 +∫x 14 x 15 dx 16 +∫x 17 x 18 dx 19 +x 20 x 21 x 22

[0138]

[0139] in, and x iis the corresponding parameter x i The upper and lower limits of each working energy consumption parameter x in a single processing cycle are determined based on prior knowledge under the three working conditions of manufacturing thin-walled products, ordinary products and thick-walled products. i of and x i value.

[0140] Then, calculate the sensitivity function value of each working energy consumption parameter. Specifically including:

[0141] When calculating the i-th working energy consumption parameter x i When the sensitive function value is i Within the corresponding value range, a value is randomly selected as the working energy consumption parameter x i The new value of But the new value Not equal to parameter x i At the same time, other working energy consumption parameters take the benchmark values.

[0142] Calculate the value of the objective function of the working energy consumption model in the current single processing cycle, and record the current objective function value as the current function value E si .

[0143] According to E si And working energy consumption parameter x i The new value of Calculate the working energy consumption parameter x by the following formula i Sensitive function value S k (x i ):

[0144]

[0145] In the above formula: ΔE si =E s ′-E si , E s ′ represents the value of the objective function of the working energy consumption model in a single processing cycle when all working energy consumption parameters are taken as the benchmark value, which is recorded as the benchmark objective function value. si Represents the benchmark objective function value E s ′ and the current objective function value E si difference. x′ i Represents the working energy consumption parameter x i The reference value of Δx i Represents x i The reference value x′ i With its new value The difference. krepresents a set of dimensionless non-negative real numbers. S k The larger the value, the higher the current function value E si For this work, the energy consumption parameter x i The more sensitive, the working energy consumption parameter x i For the current function value E si The greater the impact.

[0146] After that, when the i-th working energy consumption parameter x is calculated i When the sensitive function value of is obtained, let i=i+1 and repeat the above steps until the calculation of the sensitive function values ​​of all working energy consumption parameters is completed.

[0147] The implementation process of S200 is described below with an example.

[0148] First, based on prior knowledge, determine all working energy consumption parameters x i , the value range of i=1, 2, 3, ..., 22.

[0149] Then, choose the first parameter x i , i = 1. In x i , x=1 corresponds to a new value within the parameter range required by the standard. It should be noted that the new value is not equal to x i At the same time, except for x i Other parameters except are taken as reference values ​​and remain unchanged. Then, calculate x i Sensitive function value S k (x i ). Analyze the selected parameter x i Sensitivity to the objective function of the model, sensitivity is expressed by S k (x i ) to indicate.

[0150] Repeat the above steps, selecting a new parameter x each time i , and control except x i The other parameters except are taken as baseline values ​​and remain unchanged until the sensitive function values ​​of all parameters are calculated.

[0151] For example, after calculating the sensitive function value of the first working energy consumption parameter x1, the sensitive function value of the second working energy consumption parameter x2 is calculated; after calculating the sensitive function value of the second working energy consumption parameter x2, the sensitive function value of the third working energy consumption parameter x3 is calculated... and so on, until the 22nd working energy consumption parameter x is calculated. 22After obtaining the sensitive function values ​​of all working energy consumption parameters, the corresponding parameters are reordered from high to low according to the sensitive function values. Finally, several parameters with high sensitivity are summarized and regarded as process parameters x1′, x2′, …, x1′ that have a greater impact on normal working energy consumption. m ′, m<22.

[0152] This application utilizes a sensitivity analysis method to identify the combined impact of parameters across the entire parameter space on the results. This allows us to identify process parameters that significantly impact normal operating energy consumption within a single processing cycle, facilitating subsequent analysis of injection molding equipment processes that exceed energy consumption limits. Furthermore, by focusing on parameters with high energy sensitivity, we can more quickly quantify the energy consumption of the molding process.

[0153] Based on the above embodiment, a single processing cycle contains many sub-processes. To obtain the normal operating energy consumption value within a processing cycle, it is necessary to measure multiple parameters of each sub-process, calculate the operating energy consumption value based on these parameters, and set the energy consumption limit. This method is inefficient, and the cycle time for obtaining the normal operating energy consumption value is long and the steps are cumbersome. To improve the efficiency of obtaining normal operating energy consumption values, the present invention utilizes Monte Carlo sampling. By extracting m highly sensitive parameters, the small number of measured samples used in the aforementioned sensitivity analysis is used to simulate a large number of samples, thereby obtaining the operating energy consumption sample values ​​of several single processing cycles.

[0154] Reference Figure 3 As shown, Figure 3 The flowchart of the method for generating random vector samples provided by an embodiment of the present application is shown. In one embodiment of the present application, S300 will be further described and elaborated below. S300 may include but is not limited to the following steps.

[0155] S310, make reasonable assumptions.

[0156] In this specific embodiment, reasonable assumptions are: (1) the m strongly sensitive parameters are independent random variables; (2) within a single processing cycle, the working energy consumption of each sub-process conforms to the constant working energy consumption model; (3) when performing sensitivity analysis, the injection molding equipment processing process is a normal working process without any abnormalities.

[0157] S320, establishing a probability statistical distribution model of process parameters.

[0158] In this step, the working energy consumption parameters x1′, x2′, ..., x3′ with high sensitivity analyzed in S200 are m ′, m<22, combining the above three reasonable assumptions, construct m probability statistical distribution models P1, P2, P3, ..., P m . Probability and statistical distribution models P1, P2, P3, ..., Pm is the distribution function of the corresponding process parameter.

[0159] S330, determine the sampling plan, combine the Monte Carlo method to generate the probability statistical distribution model P1, P2, P3, ..., P m A random vector sample x i (j) , where i represents the number of the random vector sample, i=1, 2, 3, ..., N; j represents the number of the process parameter, j=1, 2, 3, ..., m.

[0160] In this step, in order to ensure the reliability of the sample, the sampling scheme is designed as follows: n1 random samplings are performed to form n1 sampling subgroups. In each random sampling, each process parameter x1′, x2′, …, x is generated through m probability statistical distribution models. m ′, n2 random vector samples x corresponding to m<22 i (j) Where N = n1 × n2. After the sampling plan is determined, the probability statistical distribution model P1, P2, P3, ..., P established in the previous step is used. m And a certain sampling scheme, generate several random vector samples x by Monte Carlo method i (j) , and then get n1 groups of random vector samples, each group includes n2 random vector samples. It should be noted that x i (j) Obey the probability statistical distribution model P1, P2, P3, ..., P m .

[0161] In this specific embodiment, since the control diagram of the subsequent S400 is selected Control chart, and The sample size of the control chart is required to be no less than 10, so the m process parameters x1′, x2′, …, x m ′, the sample capacity of m<22 is 10, that is, n2=10. A total of 40 sampling subgroups are obtained through random sampling, that is, n1=40. Then the value of N is 40×10, and a total of 400×m random vector samples are extracted.

[0162] In other embodiments of the present application, the values ​​of n1, n2, and N may be other values. This application does not impose any specific limitation on this.

[0163] S340, the random vector sample x i (j) Substitute the energy consumption values ​​into the working energy consumption model to obtain N working energy consumption sample values ​​of a single processing cycle.

[0164] In this step, the N random vector samples x collected in the previous step are i (j) Substitute them into the working energy consumption model respectively, and according to the objective function E s The working energy consumption sample value is calculated using the formula.

[0165] This application uses the Monte Carlo method to sample data, effectively solving the long and cumbersome process of obtaining the energy consumption value for a single normal operation cycle. Simultaneously, a large number of samples are simulated using the small number of measured samples from the aforementioned sensitivity analysis, providing a data basis for setting energy consumption limits.

[0166] Based on the above embodiments, most of the existing methods for formulating energy consumption limits for injection molding equipment start from the inherent specific energy of the processing process, and then determine it by setting the corresponding scaling factor based on the service life of the processing equipment, the processing needs of the enterprise, etc. This method is highly subjective and will lead to a certain amount of energy waste. In addition, the application of traditional SPC in injection molding is to draw control charts for the quality characteristics of the finished product, thereby achieving quality control. A control chart refers to a chart designed with scientific methods to measure and record the quality of the process for control management. The selection of the control chart determines the control effect of SPC to a certain extent. There are many types of control charts, including Xbar-R chart (mean-range chart), Xbar-S chart (mean-standard deviation chart), P chart (defective product rate chart), np chart (defective product number chart), etc.

[0167] In order to further improve the accuracy of the energy consumption limit, this application applies quality monitoring to energy consumption monitoring, uses the control limits of the control chart to formulate the energy consumption limit of the injection molding equipment processing, and realizes the use of statistical methods to quickly formulate energy consumption limits with low consumption, high efficiency, and more scientific and reasonable. Figure 4 As shown, Figure 4 The following is a flowchart of the process of formulating the normal working energy consumption limit provided by the embodiment of the present application. In one embodiment of the present application, S400 will be further described and elaborated. The control chart is used as an SPC control chart to formulate energy consumption limits. The S400 may include but is not limited to the following steps.

[0168] S410: Calculate and obtain energy consumption sampling data through N working energy consumption sample values.

[0169] It should be noted that the energy consumption sampling data includes: N working energy consumption sample values ​​a k , working energy consumption sample value a k Average value and standard deviation s i . Where: i = 1, 2, 3, ..., N, k = 1, 2, 3, ..., N.

[0170] In this specific embodiment, the energy consumption sampling data is shown in Table 2 below.

[0171] Table 2 Energy consumption sampling data recording format

[0172]

[0173] Among them, a k , k=1, 2, 3, ..., N is N energy consumption sample data calculated according to the working energy consumption model. In this specific embodiment, k=1, 2, 3, ..., 400. The working energy consumption sample value is equivalent to the energy consumption data.

[0174] In Table 2 above, each sampling subgroup has a corresponding sample number. In this specific embodiment, the sample numbers are 1 to 40. Each sampling subgroup has a corresponding working energy consumption sample value a k and its average value and standard deviation s i In this specific embodiment, since each sampling subgroup includes n2 random vector samples, n2=10, each sampling subgroup corresponds to ten working energy consumption sample values ​​a. k and its average value And standard deviation si. For example, for the sampling subgroup with sample number 1, the working energy consumption sample values ​​include a1, a2, ..., a 10 ; For the sampling subgroup with sample number 2, the working energy consumption sample values ​​include a 11 、a 12 ,…,a 20 ; ...; and so on, the last group is the sampling subgroup with sample number 40, and the working energy consumption sample values ​​include a 391 ,…,a 400 .

[0175] S420: Draw a control charts, Control charts include s charts (standard deviation charts) and Graph (mean graph), calculate s graph and The control limits of the figure are used to determine the normal working energy consumption limit of a single processing cycle.

[0176] Furthermore, if the actual total working energy consumption of the system E s Beyond S chart and If the control limit of the figure is exceeded, it means that the energy consumption of the system during the injection molding process exceeds the energy consumption limit; if the actual total working energy consumption of the system E s None of them exceeded the s figure and If the control limits of the graph are exceeded, it means that the energy consumption of the system during the injection molding process exceeds the energy consumption limit.

[0177] As an optional embodiment, after obtaining After checking the control limits of the graph and the S graph, first look at the S graph and compare the actual total working energy consumption of the system with the control limits of the S graph. If the actual total working energy consumption of the system does not exceed the upper and lower control limits of the S graph, it means that the overall energy consumption output of the system is in a stable state. Then, look at the X graph and compare the actual total working energy consumption of the system with If there is an abnormal point in the total working energy consumption that falls within the control limit of the graph If the total working energy consumption is outside the control limit of the graph, it means that the overall energy consumption output is unstable. If the output is outside the control limit of the graph, it means that the overall energy consumption output of the system is in a stable state.

[0178] Optionally, the overall energy consumption output of the system is calculated by a working energy consumption model.

[0179] Further, The calculation formulas for the center line and upper and lower control limits of the s chart in the control chart are as follows:

[0180]

[0181]

[0182]

[0183] In the above formula: CL s Indicates the center line of the s-diagram. UCL s and LCL s They represent the upper and lower control limits of the s-chart respectively. represents the average value of the standard deviation of n1 groups of samples. In this specific embodiment, n1 = 40. B4 and B3 represent parameters related to sample size, i.e., constants. In this application, B4 and B3 can be obtained from a conventional control chart parameter table.

[0184] Further, Control chart The calculation formulas for the center line and upper and lower control limits of the graph are as follows:

[0185]

[0186]

[0187]

[0188] In the above formula: express The center line of the figure, and Respectively Upper and lower control limits for the chart. represents the average value of the n1 group sample means, and A3 represents a parameter related to the sample size, that is, a constant. In this application, A3 can be obtained from a conventional control chart parameter table.

[0189] This specific embodiment uses statistical process control technology, combined with The control limits of the control chart define the energy consumption limit of the injection molding equipment, which essentially takes into account the energy consumption fluctuation characteristics of the system: the fluctuation within the control limit can be considered that the energy consumption is within a reasonable range. If the control limit of the control chart is If the control limits of the control chart are exceeded, the injection molding process exceeds the energy consumption limit.

[0190] This application applies to injection molding technology. Leveraging the concept of control limits in SPC technology, combined with injection molding energy consumption models, sensitivity analysis, and Monte Carlo sampling, it implements energy consumption limit calculation. Compared to existing technologies, the method provided in this application is more objective, taking into account the random fluctuations in energy consumption during the manufacturing process, improving the applicability of energy consumption limits and enabling more efficient, scientific, and rational rapid formulation of energy consumption limits.

[0191] In addition, this application also provides a system for quickly formulating energy consumption limits for injection molding, including:

[0192] The model building unit is used to calculate the total working energy consumption of a single processing cycle according to the injection molding process, and to determine the working energy consumption model and all working energy consumption parameters;

[0193] The sensitivity analysis unit is used to perform sensitivity analysis on all working energy consumption parameters in turn and determine the process parameters that have the greatest impact on normal working energy consumption among all working energy consumption parameters;

[0194] The sample generation unit is used to establish a probability statistical distribution model based on process parameters and generate random vector samples that obey the probability statistical distribution model through the Monte Carlo method;

[0195] The first calculation unit is used to substitute the random vector sample into the working energy consumption model to obtain the working energy consumption sample values ​​of N single processing cycles;

[0196] A drawing unit is used to draw a control chart based on the working energy consumption sample values ​​of N single processing cycles;

[0197] The second calculation unit is used to determine the normal working energy consumption limit of a single processing cycle through a control chart.

[0198] At the same time, the present application also provides a storage medium, which stores processor-executable instructions. When the processor executes the processor-executable instructions, the processor-executable instructions are used to execute a method for quickly formulating injection molding energy consumption limits.

[0199] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0200] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0202] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0203] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0204] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk.

[0205] The step numbers in the above method embodiment are only provided for the convenience of explanation and do not limit the order of the steps. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

Claims

1. A method for quickly formulating energy consumption limits for injection molding, characterized in that: The steps include: According to the injection molding process, calculate the total working energy consumption of a single processing cycle, determine the working energy consumption model and all working energy consumption parameters; Performing sensitivity analysis on all the working energy consumption parameters in turn to determine the process parameters that have the greatest impact on normal working energy consumption among all the working energy consumption parameters; Establishing a probability statistical distribution model based on the process parameters, generating random vector samples that obey the probability statistical distribution model through the Monte Carlo method, and substituting the random vector samples into the working energy consumption model to obtain working energy consumption sample values ​​of N single processing cycles; Draw a control chart based on the working energy consumption sample values ​​of the N single processing cycles, and determine the normal working energy consumption limit of the single processing cycle through the control chart; The step of drawing a control chart based on the working energy consumption sample values ​​of the N single processing cycles and determining the normal working energy consumption limit of a single processing cycle through the control chart includes: Calculating energy consumption sampling data through the working energy consumption sample values ​​of the N single processing cycles; The energy consumption sampling data includes N working energy consumption sample values. , working energy consumption sample value Average value and standard deviation , , ; According to the energy consumption sample data, draw Control chart, the Control charts include s charts and Graph, calculate s graph and control limits of the graph, and determining the normal operating energy consumption limit of a single processing cycle based on the control limits; Among them, the The control chart satisfies the following formula: , , ; , , ; in, is the center line of the s graph, and are the upper and lower control limits of the s chart, for The center line of the figure, and They are Upper and lower control limits of the chart; for The mean of the group sample standard deviations, for The average of the group sample means, 、 and is a constant.

2. A method for quickly formulating energy consumption limits for injection molding according to claim 1, characterized in that: According to the injection molding process, the total working energy consumption of a single processing cycle is calculated, and the working energy consumption model and all working energy consumption parameters are determined, including: The injection molding process is defined as consisting of seven sub-processes: mold closing, plasticizing, filling, pressure holding, cooling, mold opening, and ejector advance and retreat; Calculate the energy consumption of mold closing and mold opening processes in a single processing cycle , Energy consumption during ejector advance and retreat , Energy consumption of plasticizing process , Energy consumption of injection molding process , Energy consumption during pressure holding process and cooling process energy consumption ; Through energy consumption , calculate the total working energy consumption in a single injection cycle , through the total working energy consumption To express the objective function of the working energy consumption model of a single injection cycle; Among them, the total working energy consumption Satisfies the following formula: ； All working energy consumption parameters are determined according to the working energy consumption model.

3. A method for quickly formulating energy consumption limits for injection molding according to claim 2, characterized in that: The energy consumption of mold closing and mold opening processes is calculated separately within a single processing cycle. , Energy consumption during ejector advance and retreat , Energy consumption of plasticizing process , Energy consumption of injection molding process , Energy consumption during pressure holding process and cooling process energy consumption ,include: The energy consumption of the mold closing and opening process in a single processing cycle is determined by the pressure information of the hydraulic cylinder of the injection molding machine during the mold closing and opening process. ; satisfy: ; in, Indicates the liquid pressure in the hydraulic cylinder during mold closing and opening; It represents the difference between the piston area and the piston rod area of ​​the hydraulic cylinder during mold closing and mold opening. S represents the stroke of the piston rod. Determine the energy consumption of the demoulding process within a single processing cycle through the pressure information of the hydraulic cylinder during the demoulding process ; satisfy: ; in, Indicates the liquid pressure in the hydraulic cylinder during the demoulding process; It represents the difference between the piston area and the piston rod area of ​​the hydraulic cylinder during the demoulding process; S represents the stroke of the piston rod; Determine the energy consumption of the plasticizing process within a single processing cycle based on the information of the processed material during the plasticizing process ; satisfy: ; Where c represents the specific heat capacity of the processed material, m represents the mass of the processed material, Indicates the temperature difference between the temperature during plasticization and the reference temperature, Indicates the screw drive torque, represents the screw angular velocity, Indicates the time required for the plasticization process, It represents the back pressure during the plasticizing process, and s represents the distance the screw retreats after plasticizing; Determine the energy consumption of the injection molding process within a single processing cycle through the filling pressure information and processing melt information during the injection molding process ; satisfy: ; in, Indicates the filling pressure of the injection molding machine during the injection molding process. Indicates the volume flow rate of the melt during the injection molding process, Indicates the time required for the injection molding process; Determine the energy consumption of the holding process within a single processing cycle through the pressure information and processing melt information during the holding process ; satisfy: ; in, Indicates the holding pressure during the holding process. Indicates the volume flow rate of the melt during the holding process, Indicates the time required for the pressure holding process; Determine the energy consumption of the cooling process within a single processing cycle by using the output power of the injection molding machine's water pump during the cooling process ; satisfy: ; in, Indicates the water pump output pressure during the cooling process. Indicates the output flow of the water pump during the cooling process, Indicates the time required for the cooling process.

4. A method for quickly formulating energy consumption limits for injection molding according to claim 3, characterized in that: The determining of all working energy consumption parameters according to the working energy consumption model includes: According to the objective function of the working energy consumption model , determine the working energy consumption parameters ;in, Expressed as: ; in, .

5. The method for quickly formulating energy consumption limits for injection molding according to claim 1, characterized in that: The sensitivity analysis of all the working energy consumption parameters is performed in sequence to determine the process parameters that have a greater impact on the normal working energy consumption among all the working energy consumption parameters, including: According to the preset sensitive function, calculate the working energy consumption parameters in sequence The sensitive function value of ; Compare the sensitivity function values ​​of all working energy consumption parameters and re-sort all working energy consumption parameters according to the sensitivity function values. , ; Summarize the working energy consumption parameters whose sensitive function value is greater than the sensitive threshold ,Will As a process parameter that has a greater impact on normal working energy consumption.

6. A method for quickly establishing an injection molding energy consumption limit according to claim 5, characterized in that: According to the preset sensitive function, the working energy consumption parameters are calculated in sequence Sensitive function values ​​include: Determine all working energy consumption parameters The value range of ; When calculating the i-th working energy consumption parameter When the sensitive function value of the working energy consumption parameter Within the corresponding value range, randomly select a value as the working energy consumption parameter The new value of , other working energy consumption parameters take the benchmark value; among them, Not equal to The baseline value; Calculate the value of the objective function of the working energy consumption model in the current single processing cycle, and record the current objective function value as the current function value ; according to and , calculate working energy consumption parameters Sensitive function value of : ， ， ； in: It represents the value of the objective function of the working energy consumption model in a single processing cycle when all working energy consumption parameters take the baseline value; Indicates working energy consumption parameters The baseline value; is a set of dimensionless non-negative real numbers; The larger the right The more sensitive, the right The greater the impact; After calculating the i-th working energy consumption parameter When the sensitive function value of is obtained, let i=i+1 and repeat the above steps until the calculation of the sensitive function values ​​of all working energy consumption parameters is completed.

7. The method for quickly formulating energy consumption limits for injection molding according to claim 1, characterized in that: The method of establishing a probability statistical distribution model based on the process parameters, generating random vector samples that obey the probability statistical distribution model through the Monte Carlo method, and substituting the random vector samples into the working energy consumption model to obtain working energy consumption sample values ​​of N single processing cycles includes: Establish m probability statistical distribution models as a distribution function of said process parameter; Determine the sampling plan and combine it with the Monte Carlo method to generate a probability statistical distribution model A random vector sample of ; Where i represents the number of the random vector sample, ,j represents the number of the process parameter, ; The sampling plan is to Random sampling, consisting of In a single random sampling process, m probability statistical distribution models are used to generate the corresponding random vector samples; where ; The random vector sample Substitute it into the working energy consumption model to obtain working energy consumption sample values ​​of N single processing cycles.

8. A system for quickly setting energy consumption limits for injection molding, characterized in that: include: A model building unit is used to calculate the total working energy consumption of a single processing cycle according to the injection molding process, and to determine the working energy consumption model and all working energy consumption parameters; A sensitivity analysis unit is used to perform sensitivity analysis on all the working energy consumption parameters in sequence to determine the process parameters that have a greater impact on normal working energy consumption among all the working energy consumption parameters; a sample generating unit, configured to establish a probability statistical distribution model based on the process parameters, and generate random vector samples that obey the probability statistical distribution model by using a Monte Carlo method; A first calculation unit is used to substitute the random vector sample into the working energy consumption model to obtain working energy consumption sample values ​​of N single processing cycles; A drawing unit, configured to draw a control chart based on the working energy consumption sample values ​​of the N single processing cycles; The second calculation unit is used to determine the normal working energy consumption limit of a single processing cycle through a control chart; The step of drawing a control chart based on the working energy consumption sample values ​​of the N single processing cycles and determining the normal working energy consumption limit of a single processing cycle through the control chart includes: Calculating energy consumption sampling data through the working energy consumption sample values ​​of the N single processing cycles; The energy consumption sampling data includes N working energy consumption sample values. , working energy consumption sample value Average value and standard deviation , , ; According to the energy consumption sample data, draw Control chart, the Control charts include s charts and Graph, calculate s graph and control limits of the graph, and determining the normal operating energy consumption limit of a single processing cycle based on the control limits; Among them, the The control chart satisfies the following formula: , , ; , , ; in, is the center line of the s graph, and are the upper and lower control limits of the s chart, for The center line of the figure, and They are Upper and lower control limits of the chart; for The mean of the group sample standard deviations, for The average of the group sample means, 、 and is a constant.

9. A storage medium storing instructions executable by a processor, characterized in that: The processor-executable instructions, when executed by the processor, are used to execute a method for quickly formulating an injection molding energy consumption limit as described in any one of claims 1-7.

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