Welding self-adaptive control method and system for formwork production

Abstract

The invention provides a welding self-adaptive control method and system for formwork production, and relates to the technical field of welding. Formwork components are decomposed, and welding target parameters of the components are analyzed according to functional requirements; performing process parameter optimization by taking welding target parameters as targets to obtain a welding control strategy composed of each decomposition; according to the welding control strategy and the welding target parameters, target influence analysis is conducted, and self-adaptive parameters are determined; welding monitoring equipment is arranged to collect real-time data; and according to the real-time data, welding control strategy self-adaptive adjustment is conducted according to the control adjustment relation between the self-adaptive parameters and the welding target parameters. The technical problems that in the prior art, due to the fact that the welding parameters depend on experience setting, the welding quality consistency is poor, and the process adaptability is insufficient are solved, and the technical effects that the welding parameters are adjusted in a self-adaptive mode, and then the welding quality and the production efficiency are improved are achieved.

Description

Technical Field

[0001] This application relates to the field of welding technology, and particularly to a welding adaptive control method and system for die set production. Background Art

[0002] As an important structural component in industrial manufacturing, the die set is widely used in fields such as automobiles, daily necessities, and medical product equipment. Its welding quality directly affects the accuracy, strength, and service life of the final product.

[0003] Currently, the welding control in die set production mainly relies on manual setting of welding parameters. The operator selects process parameters such as current, voltage, and welding speed based on experience and adjusts them by observing the molten pool and weld seam state during the welding process. This empirical method is difficult to achieve precise adaptation when welding die sets with different materials, thicknesses, and structural forms, resulting in poor consistency of welding quality and prone to weld defects such as pores and cracks, thereby affecting the overall performance of the die set. Secondly, fixed welding parameters are difficult to adapt to different die set structures and process requirements. When the die set structure changes, the parameters need to be readjusted, increasing the uncertainty and debugging cost of production, and also limiting the stability and reliability of the welding process. Summary of the Invention

[0004] This application provides a welding adaptive control method and system for die set production, which solves the technical problems in the prior art that due to the welding parameters depending on empirical setting and being difficult to adapt to different welding condition changes, the welding quality consistency is poor and the process adaptability is insufficient, and achieves the technical effects of adaptively adjusting welding parameters, thereby improving the stability of welding quality, reducing welding defects, and enhancing production efficiency.

[0005] In view of the above problems, on the one hand, this application provides a welding adaptive control method for die set production. The method includes: decomposing the die set composition, parsing the welding target parameters of each component according to the functional requirements of the decomposed composition; taking the welding target parameters as the goal, optimizing the welding process parameters of the decomposed composition to obtain the welding control strategy for each decomposed composition; according to the welding control strategy and the welding target parameters, conducting a target impact analysis to determine the adaptive parameters, and establishing a control adjustment relationship between the adaptive parameters and the welding target parameters; arranging welding monitoring equipment based on the adaptive parameters, and collecting real-time monitoring data through the welding monitoring equipment; according to the real-time monitoring data, adaptively adjusting and controlling the welding control strategy according to the control adjustment relationship.

[0006] On the other hand, the present application also provides a welding adaptive control system for die carrier production. The system includes: a target analysis module for decomposing the die carrier composition and analyzing the welding target parameters of each component according to the functional requirements of the decomposed composition; a parameter optimization module for optimizing the welding process parameters of the decomposed components with the welding target parameters as the goal to obtain the welding control strategies for each decomposed component; an impact analysis module for performing target impact analysis based on the welding control strategies and the welding target parameters to determine the adaptive parameters and establish the control adjustment relationship between the adaptive parameters and the welding target parameters; a welding monitoring module for arranging welding monitoring equipment based on the adaptive parameters and collecting real-time monitoring data through the welding monitoring equipment; and an adaptive adjustment module for adaptively adjusting and controlling the welding control strategy according to the real-time monitoring data in accordance with the control adjustment relationship.

[0007] One or more technical solutions provided in the present application have at least the following beneficial effects:

[0008] By decomposing the die carrier and analyzing the welding target parameters according to the functional requirements of each component, the specific goals to be achieved for the welding of each part can be clarified, providing an accurate guiding direction for subsequent process parameter optimization and control strategy formulation. Guided by the welding target parameters analyzed in the first step, the welding process parameters of the decomposed components are optimized to obtain the welding control strategies for each part. This step can ensure that the optimal parameter combination is adopted in the welding process to achieve the best balance between welding quality and efficiency. By determining the adaptive parameters through target impact analysis, the key factors that have a greater impact on the welding target parameters can be scientifically screened to determine the adaptive parameters, making the subsequent control strategy adjustment more targeted and effective. Establishing the control adjustment relationship between the adaptive parameters and the welding target parameters enables the welding process to be dynamically adjusted according to the actual situation, enhancing the flexibility and adaptability of the welding process. Arranging the welding monitoring equipment based on the determined adaptive parameters can collect the monitoring data during the welding process in real time, providing real-time data support for subsequent adaptive adjustment. According to the real-time monitoring data, the welding control strategy is adaptively adjusted in accordance with the pre-established control adjustment relationship, keeping the welding process in the optimal control state at all times, timely responding to various changes, and ensuring the stability of welding quality and efficiency.

[0009] In summary, through the organic combination of the above steps, the present application forms a complete closed-loop control process, realizes the adaptive parameter adjustment of the welding process, significantly improves the welding quality, welding efficiency, and the stability and reliability of the welding process, making the welding control of die carrier production more scientific, accurate, and efficient.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific implementation manners of this application are given below. Description of the Drawings

[0011] Figure 1 It is a schematic flowchart of the welding adaptive control method for die carrier production provided by an embodiment of this application.

[0012] Figure 2 It is a schematic flowchart of analyzing the welding target parameters of each component according to the decomposed functional requirements in the welding adaptive control method for die carrier production provided by an embodiment of this application.

[0013] Figure 3 It is a schematic structural diagram of the welding adaptive control system for die carrier production provided by an embodiment of this application.

[0014] Description of the reference numerals: target analysis module 10, parameter optimization module 20, influence analysis module 30, welding monitoring module 40, adaptive adjustment module 50. Specific Embodiment

[0015] By providing the welding adaptive control method and system for die carrier production in the embodiments of this application, the technical problem in the prior art that the welding parameters depend on empirical settings and it is difficult to adapt to different welding working condition changes, resulting in poor welding quality consistency and insufficient process adaptability, is solved, and the technical effects of adaptively adjusting the welding parameters, thereby improving the stability of welding quality, reducing welding defects, and enhancing production efficiency are achieved.

[0016] Embodiment 1, as Figure 1 shown, the embodiments of this application provide a welding adaptive control method for die carrier production, and the method includes:

[0017] Step S1: Decompose the die carrier components and analyze the welding target parameters of each component according to the decomposed functional requirements.

[0018] Specifically, the mold base is a frame structure used to support and fix the mold, usually composed of multiple parts, such as the template system, guiding system, ejection mechanism, cooling system, etc. First, the composition of the mold base is decomposed in detail to clarify the specific composition of each part. Taking the injection mold base as an example, it is decomposed into parts such as the template system, guiding system, ejection mechanism, and cooling system. Among them, the template system includes the A plate (fixed mold base plate), B plate (movable mold base plate), support plate, ejector plate, etc.; the guiding system includes guide pillars and guide bushes; the ejection mechanism includes ejector pins and return rods; the cooling system contains an internal water circuit. Next, for each decomposed component, analyze its functional requirements in actual applications, so as to analyze the welding target parameters of each part. For example, for the template system, its functional requirement is high rigidity, so the welding target parameters include welding strength, welding deformation amount, etc., and it is necessary to ensure that it can withstand an injection pressure and clamping force of 150 - 200 MPa without deformation or damage after welding. For the guiding system, the functional requirement is precise guiding, and the welding target parameters involve the mating accuracy after welding, ensuring that the mating accuracy of the guide pillar and guide bush reaches ±0.01 mm to ensure that the mold closing accuracy ≤ 0.02 mm and avoid flash. The functional requirement of the ejection mechanism is efficient ejection, and the welding target parameters include the welding strength and position accuracy of the ejector pin and return rod to ensure smooth ejection and reset in a thin plate structure (thickness 10 - 30 mm). The functional requirement of the cooling system is efficient cooling, and the welding target parameters are mainly the sealing performance and water pressure test performance after welding, ensuring good sealing of the internal water circuit and being able to withstand a water pressure test of ≥ 0.6 MPa, thereby shortening the molding cycle and improving production efficiency.

[0019] Through the decomposition of the mold base composition and the analysis of functional requirements, the welding target parameters of each component can be clarified, providing an accurate basis for the subsequent optimization of welding process parameters and the formulation of control strategies, ensuring that the welding process can meet the actual application requirements of the mold base and improving the welding quality and product performance.

[0020] Step S2: Taking the welding target parameters as the goal, optimize the welding process parameters for the decomposed components to obtain the welding control strategies for each decomposed component.

[0021] Specifically, welding process parameters are various parameters that need to be controlled during the welding process, such as welding current, voltage, welding speed, wire diameter, welding angle, etc. The welding control strategy is a series of control measures and methods formulated based on the welding target parameters and the optimization results of process parameters, used to guide the welding process to ensure welding quality and efficiency. For example, for different welding positions (flat welding, vertical welding, overhead welding, etc.), different combinations of welding current, voltage, and speed need to be adopted, which is part of the welding control strategy.

[0022] Guided by the welding target parameters parsed in step S1, optimize the welding process parameters for each decomposed component of the die set. Tools such as welding process databases, welding simulation software, and experimental design methods can be used. For example, for the welding of the template system, the target parameters are high rigidity and low welding deformation. The welding stress and deformation under different welding process parameters can be simulated through welding simulation software (such as ANSYS, ABAQUS, etc.) to find the optimal combination of welding current, voltage, and speed. At the same time, combined with experimental design methods (such as orthogonal experiments, response surface methods, etc.), verify and optimize the welding process parameters in actual welding tests. For the welding of the guiding system, the target parameter is precise guiding, that is, the fitting accuracy after welding. The welding deformation can be reduced by precisely controlling the welding heat input and welding sequence, so as to ensure that the fitting accuracy of the guide pillar and guide sleeve reaches ±0.01 mm. In addition, the empirical data in the welding process database can be used and adjusted and optimized in combination with the actual situation to formulate a welding control strategy for each decomposed component, including the specific numerical range of welding parameters, welding sequence, welding method, etc.

[0023] By optimizing the welding process parameters and formulating the welding control strategy, the optimal process parameters and control methods can be provided for the welding of each component of the die set, ensuring that the welding process can achieve the predetermined welding target parameters, improving the welding quality and production efficiency, and reducing welding defects and rework rates.

[0024] Step S3: According to the welding control strategy and the welding target parameters, conduct a target impact analysis, determine the adaptive parameters, and establish a control adjustment relationship between the adaptive parameters and the welding target parameters.

[0025] Specifically, the target impact analysis refers to analyzing the impact relationship between the welding control strategy and the welding target parameters to determine which factors or parameters have a significant impact on the achievement of the welding target. For example, changes in welding current and voltage may have a greater impact on the welding penetration and weld formation, while changes in welding speed mainly affect the welding heat input and welding deformation. Adaptive parameters are parameters that can be automatically adjusted according to real-time monitoring data during the welding process to adapt to changes in the welding environment and working conditions. For example, welding current, voltage, speed, etc. can all be used as adaptive parameters. The control adjustment relationship is the mathematical or logical relationship between the adaptive parameters and the welding target parameters, which is used to guide the adjustment of the adaptive parameters. For example, when the welding penetration is insufficient, the welding current or voltage needs to be increased; when the welding deformation is too large, the welding speed needs to be reduced or the welding sequence needs to be adjusted.

[0026] According to the welding control strategy and welding target parameters formulated in step S2, perform a target impact analysis. Statistical methods such as sensitivity analysis and regression analysis can be used to determine which welding process parameters have a significant impact on the welding target parameters. For example, by analyzing the correlation between parameters such as welding current, voltage, speed and target parameters such as welding penetration, welding deformation, and welding strength, determine the key influencing factors. Then, select appropriate adaptive parameters from these key influencing factors, such as welding current, voltage, speed, etc. Next, establish the control adjustment relationship between the adaptive parameters and the welding target parameters, which can be achieved through methods such as experimental data fitting and mathematical modeling. For example, through a large number of welding tests, collect the welding target parameter data under different welding parameters, use regression analysis to establish a linear or non-linear relationship model between the welding current and the welding penetration, or use machine learning methods such as neural networks to establish a more complex mapping relationship. In addition, existing welding process specifications and expert experience can also be referred to, and adjusted and optimized in combination with the actual situation to ensure the accuracy and reliability of the control adjustment relationship.

[0027] Determine the adaptive parameters through the target impact analysis and establish the control adjustment relationship between them and the welding target parameters, so that the welding parameters can be automatically adjusted according to the real-time monitoring data during the welding process, adapt to the changes in the welding working conditions, further improve the welding quality and production efficiency, and enhance the stability and reliability of the welding process.

[0028] Step S4: Arrange welding monitoring equipment based on the adaptive parameters, and collect real-time monitoring data through the welding monitoring equipment.

[0029] Specifically, the welding monitoring equipment is used to monitor various parameters and states during the welding process, such as welding current sensors, voltage sensors, arc sensors, weld tracking sensors, temperature sensors, etc. These devices can collect real-time data during the welding process and provide a basis for adaptive control. For example, the welding current sensor can monitor the magnitude and change of the welding current in real time, the arc sensor can detect the stability of the welding arc, and the weld tracking sensor can monitor the position and shape of the weld. The real-time monitoring data is the data collected by the welding monitoring equipment in real time during the welding process, including welding current, voltage, welding speed, weld position, temperature, etc. These data can reflect the real-time state and quality of the welding process and provide a basis for adaptive adjustment control.

[0030] Select a suitable welding monitoring device and deploy it according to the adaptive parameters determined in step S3. For example, if the adaptive parameters include welding current and voltage, a welding current sensor and a voltage sensor need to be installed to monitor the real-time values of the welding current and voltage respectively; if the adaptive parameters include the weld position, a weld tracking sensor such as a laser vision sensor or a capacitive sensor needs to be installed to monitor the position and shape of the weld in real time. The deployment positions of the monitoring devices should be reasonably arranged according to the welding process and the die carrier structure to ensure that the required monitoring data can be accurately collected. For example, in the welding of the template system, the welding current and voltage sensors can be installed at the output end of the welding power supply, and the weld tracking sensor is installed on the welding torch to move with the welding torch and monitor the weld position in real time.

[0031] By reasonably deploying the welding monitoring devices, various data in the welding process can be collected in real time, providing accurate information support for subsequent adaptive adjustment control, ensuring real-time monitoring and dynamic adjustment of the welding process, further improving the welding quality and production efficiency, and reducing welding defects and rework rates.

[0032] Step S5: According to the real-time monitoring data, perform adaptive adjustment control on the welding control strategy according to the control adjustment relationship.

[0033] Specifically, receive the monitoring data collected in step S4 and perform data analysis and processing according to the control adjustment relationship established in step S3. For example, when it is monitored that the welding current is lower than the set value, calculate the amount of welding current that needs to be increased according to the control adjustment relationship, and send a corresponding control signal to the welding power supply to adjust the welding current. If it is monitored that the weld position deviates, calculate the position and angle that the welding torch needs to be adjusted according to the data of the weld tracking sensor in combination with the control adjustment relationship, and then send a control instruction to the driving device of the welding robot or the welding torch to adjust the position and angle of the welding torch to ensure the accuracy and quality of the weld. In addition, advanced control methods such as PID controllers and fuzzy control algorithms can also be used to more precisely adjust the welding parameters to adapt to complex welding environments and working conditions changes. For example, during the welding process, when the environmental temperature change causes the welding heat input to be unstable, the PID controller can automatically adjust the welding speed and current according to the real-time monitored temperature data to maintain the stability of the welding heat input, thereby ensuring the consistency of the welding quality.

[0034] By performing adaptive adjustment control on the welding control strategy according to the real-time monitoring data, it is possible to timely respond to changes in the welding environment and working conditions, maintain the stability of the welding process and the consistency of the welding quality, further improve the welding efficiency and product quality, reduce manual intervention and debugging time, and realize the automation and intelligence of the welding process.

[0035] Further, in step S1 of the embodiment of the present application, the decomposition of the mold base assembly includes:

[0036] Step S11: Disassemble the structure of the design drawing of the mold base to obtain the component disassembly structure and welding marking points.

[0037] Step S12: Obtain the working process of the injection mold base and analyze the process functions of each component.

[0038] Step S13: Perform function clustering according to the process functions of the respective components to obtain the decomposed composition.

[0039] Specifically, first obtain the complete design drawing of the mold base and view and analyze it using CAD software (such as AutoCAD, SolidWorks). Utilize the exploded view function of the software to decompose the mold base into independent components to obtain the component disassembly structure, that is, the disassembled mold base components and their connection relationships, such as templates, guide pillars, ejection mechanisms, etc. Identify the welding positions and requirements according to the markings and symbols on the drawing and record the welding marking points. For example, in the design drawing of the injection mold base, the welding marking points of the template system (A plate, B plate, support plate, ejector plate) include the welding positions and welding methods (such as butt welding, fillet welding, etc.) between each plate. For the guiding system (guide pillars, guide sleeves), the marking points indicate the welding positions and precision requirements of the guide pillars and guide sleeves (such as the fit precision of ±0.01 mm).

[0040] By referring to injection process materials and equipment manuals and combining with actual production experience, obtain the working process of the injection mold base, that is, the working sequence and cooperation method of each part of the injection mold base during the injection molding process. For example, the working process includes: the mold closing stage, where the template system provides sufficient clamping force and the guiding system ensures the precise alignment of the moving mold and the stationary mold; the injection stage, where the template system resists the injection pressure and the guiding system keeps the mold core stable; the cooling stage, where the cooling system removes the mold heat through water circulation to shorten the molding cycle; the ejection stage, where the ejection mechanism ejects the molded product from the mold. According to the working process, analyze the process functions of each component: the template system provides high-rigidity support during the mold closing and injection stages and resists an injection pressure of 150 - 200 MPa; the guiding system provides precise guidance during the mold closing and opening processes to ensure a mold closing precision of ≤0.02 mm; the cooling system provides efficient cooling during the cooling stage, with a water pressure test ≥0.6 MPa to shorten the molding cycle; the ejection mechanism provides stable ejection force during the ejection stage to ensure the complete demolding of the product.

[0041] According to the process functions of the parsed components, a clustering analysis method is used for function clustering. For example, the A plate, B plate, and support plate with the functions of providing support and resisting injection pressure are classified into the template system; the guide pillars and guide sleeves responsible for core alignment and movement guidance are classified into the guiding system; the ejector pins and return rods used for product demolding are classified into the ejection mechanism; and the built-in water channels responsible for mold cooling are classified into the cooling system. The functions of the components can be quantified and classified with the help of data analysis software (such as Excel, SPSS), and the clustering results can be determined by calculating the function similarity or correlation. For example, in Excel, weights and scores can be set for the functions of each component, and the K-means clustering algorithm can be used for classification. The final decomposition composition includes the template system, guiding system, ejection mechanism, and cooling system. The functions of the components within each part are closely related and jointly complete the overall function of the mold base.

[0042] By decomposing the mold base into several function-related components through function clustering, it provides a reasonable object classification for setting welding target parameters for different decomposition components subsequently, which is conducive to improving the scientificity and rationality of welding parameter setting, thereby improving the welding quality and overall performance of the mold base.

[0043] Further, step S13 includes:

[0044] Step S131: Configure function clustering centers, including the template framework, guiding coefficient, ejection mechanism, and cooling system.

[0045] Step S132: Perform process function clustering according to the function response relationship between each component and the function clustering center, and determine the function categories of each component.

[0046] Step S133: Decompose each component according to the function category to obtain the decomposition composition, and each decomposition composition includes a function label and a welding marking point.

[0047] Specifically, the function clustering center is a core concept set in advance that represents different function types and is used as a reference standard for clustering. According to the function requirements of the injection mold base, four function clustering centers, namely the template framework, guiding system, ejection mechanism, and cooling system, are set.

[0048] The functional response relationship refers to the degree of response of each component to the function represented by the functional clustering center in the work process, that is, to what extent the component undertakes the function represented by the functional clustering center. For each component, analyze its function in the working process of the injection mold base, and then compare the functional relationship between each component and the four functional clustering centers (template frame, guiding system, ejection mechanism, cooling system). For example, for the component of the return rod, it mainly participates in the reset operation of the ejection mechanism during the injection process, so it has a functional response relationship with the functional clustering center of the ejection mechanism, and thus is classified into the functional category of the ejection mechanism. Tools such as functional analysis matrices or correlation diagrams can be used to quantify and visualize the relationship between each component and the functional clustering center, so as to perform functional clustering more accurately.

[0049] According to the determined functional categories of each component, combine the components belonging to the same functional category together to form a decomposition composition. Then assign corresponding functional labels to each decomposition composition and mark the welding marking points. For example, combine components such as plate A, plate B, and support plate classified into the functional category of the template frame into the template frame decomposition composition, mark its functional label as "template frame", and record the welding marking points of each component; combine the ejector pins and bushings classified into the functional category of the guiding coefficient into the guiding coefficient decomposition composition, mark the functional label as "guiding coefficient", and record the welding marking points; similarly, form the decomposition compositions of the ejection mechanism and the cooling system, mark the corresponding functional labels respectively and record the welding marking points. The assembly and annotation functions of CAD software can be used to assist this process to ensure the accuracy and integrity of the decomposition composition.

[0050] By analyzing the functional response relationship between each component and the functional clustering center, the components are accurately classified into the corresponding functional categories, and a decomposition composition with clear functional labels and welding marking points is obtained, which provides a clear structure and basis for the subsequent analysis of welding target parameters and optimization of process parameters, helps to improve the pertinence and efficiency of the welding process, and ensures that the welding quality meets the functional requirements of the mold base.

[0051] Furthermore, as Figure 2 shown, in step S1, analyze the welding target parameters of each composition according to the functional requirements of the decomposition composition, including:

[0052] Step S14: Construct a working simulation model of the injection mold base, and the working simulation model incorporates the three-dimensional model and material properties of the mold base and defines boundary conditions and load data.

[0053] Step S15: Through the working simulation model, conduct an injection working process simulation, and monitor the working status and force data of each decomposition composition in the working process.

[0054] Step S16: Analyze the forward functional conditions according to the working state, and analyze the reverse compensation according to the force data to obtain the welding target parameters of each component.

[0055] Specifically, a working simulation model is a computer model used to simulate the behavior and performance of an injection mold base under actual working conditions, and can predict the forces, deformations, temperature distributions, etc. of the mold base during the injection process. Boundary conditions are the constraint conditions set in the simulation, such as the injection pressure, holding pressure, and mold opening speed of the injection molding machine. Load data are the various forces that the mold base may be subjected to during the injection process, such as the extrusion force of the liquid plastic and the self-weight of the mold. Use professional injection molding simulation software (such as Autodesk Moldflow, SolidWorks Simulation) to build a working simulation model of the injection mold base. First, according to the design drawings of the mold base, create a 3D model of the mold base in the software, including all components such as the moving template, fixed template, core, cavity, cooling system, etc. Then, assign corresponding material properties to each component in the model. For example, the elastic modulus and thermal conductivity of the mold base material, the fluidity and thermal expansion coefficient of the plastic raw material, etc. Next, define the boundary conditions and load data, such as process parameters such as the injection pressure (150 - 200 MPa), holding pressure, and mold opening speed of the injection molding machine, as well as the extrusion force of the liquid plastic and the self-weight of the mold base that the mold base may be subjected to during the injection process. Through these settings, a simulation model that can accurately reflect the actual working conditions of the mold base is established.

[0056] Use the constructed working simulation model to simulate each stage of the injection mold base during the injection process, including mold closing, injection, holding pressure, cooling, mold opening, ejection, etc. By setting simulation parameters such as injection time, holding pressure time, cooling time, etc., run the simulation. During the simulation, monitor the working state and force data of each decomposed component. Among them, the working state is the operation of each decomposed component in the injection work process, such as position, speed, temperature, etc.; the force data is the magnitude and direction of various forces received by each decomposed component during the injection process, such as injection pressure, holding pressure, and thermal stress of the cooling water channel. For example, monitor the deformation of the template system during the injection stage, the centering and movement smoothness of the guiding system during mold closing and mold opening, the force received by the ejection mechanism during the ejection stage, and the temperature distribution of the cooling system during the cooling stage. The monitoring tools and data recording functions in the simulation software can be used to obtain detailed data of each decomposed component at different stages.

[0057] According to the working states of each decomposed component in the injection molding process, perform forward functional condition analysis to analyze the functional conditions it needs to meet, such as high rigidity, precise guidance, efficient cooling, etc., so as to determine relevant welding target parameters, such as welding strength, welding position accuracy, etc. For example, the template system needs to withstand an injection pressure of 150 - 200 MPa during the injection stage, so it needs to have high rigidity and strength to resist deformation and damage caused by the pressure. Set the core welding target parameters as: flatness error ≤ 0.03 mm / m 2 ; residual stress ≤ 100 MPa; weld strength ≥ 90% of the base material; the guiding system needs to maintain precise centering during the mold closing and opening processes, so it requires micron-level accuracy and wear resistance. Set the core welding target parameters as: guide post perpendicularity ≤ 0.01 mm / m; heat affected zone (HAZ) width ≤ 0.3 mm; surface hardness ≥ HRC55; the cooling system needs to efficiently remove heat during the cooling stage, so it requires good sealing and corrosion resistance. Set the core welding target parameters as: penetration depth ≥ 1.1 times the plate thickness; leakage rate ≤ 1x10 -9 Pam 2 / s; salt spray corrosion resistance ≥ 500 h. Then, according to the forces and stresses received by each decomposed component during the injection molding process, perform reverse compensation analysis to analyze the compensation measures that need to be carried out, such as structural strengthening, dimensional adjustment, etc., so as to make adjustments based on the previously determined welding target parameters, and generate the final welding target parameters for each component to meet the functional requirements. For example, if excessive deformation occurs in the template system during the simulation, it is necessary to compensate by increasing the welding strength or adjusting the welding sequence to reduce the deformation; if a centering deviation occurs in the guiding system during the mold closing process, it is necessary to compensate by precisely controlling the welding position and dimensions to ensure the fitting accuracy. Finally, combining the results of the forward functional condition analysis and the reverse compensation analysis, determine the welding target parameters for each component, such as the welding strength requirements for the template system, the welding accuracy requirements for the guiding system, the welding sealing requirements for the cooling system, etc.

[0058] By constructing a working simulation model of the injection mold base to simulate the injection molding process, complete the forward functional condition analysis and reverse compensation analysis, comprehensively consider the actual requirements and force conditions of each decomposed component in the injection molding process, and accurately determine the welding target parameters for each component, providing a scientific basis for the subsequent optimization of welding process parameters and the formulation of control strategies, and ensuring that the welding process can meet the functional requirements and quality requirements of the mold base.

[0059] Furthermore, step S2 of the embodiment of the present application includes:

[0060] Step S21: Based on the function tags, extract the component structures of each decomposed component, and identify the welding distribution of the component structures according to the welding marking points.

[0061] Step S22: Aiming at maximizing the welding target parameters, perform an optimization search for the welding method, welding path, and welding parameters according to the component structure and welding distribution formed by the decomposition. When the search target is reached, obtain the welding control strategies for each decomposed component, where the search target includes the number of searches and the target parameter threshold.

[0062] Specifically, according to the function labels, extract the component structure of each decomposed component from the design drawing or 3D model. For example, for the decomposed components of the template frame function category, extract the 3D models and material properties of components such as Plate A, Plate B, and support plates. Then, determine the welding distribution of each component structure according to the identified welding marking points. For example, the butt welding position between Plate A and Plate B, and the fillet welding positions of the support plate with Plate A and Plate B.

[0063] Aiming at maximizing the welding target parameters, that is, by optimizing the welding process parameters to make the welding target parameters reach the best state, such as the highest welding strength, the smallest welding deformation, and the highest welding precision, etc. According to the component structure and welding distribution formed by the decomposition, perform an optimization search for the welding method, welding path, and welding parameters. Among them, the search target, that is, the termination condition of the optimization search, includes that the number of searches reaches the set value or the target parameter reaches the set threshold. First, select a suitable welding method. For example, for the thick plate welding of the template frame, select gas shielded welding; for the precision welding of the guiding system, select laser welding. Then, plan the welding path. For example, for complex welding distributions, adopt segmented welding or symmetric welding paths to reduce welding stress and deformation. Next, set the initial range of welding parameters, such as welding current, voltage, speed, etc., and use optimization algorithms (such as genetic algorithms, simulated annealing algorithms, etc.) for the optimization search. For example, use the genetic algorithm to search for the parameter combination that optimizes the welding target parameters (such as welding strength, welding deformation amount, etc.) within the set welding parameter range through multiple generations of crossover, mutation, and selection operations. During the search process, continuously evaluate the welding target parameter values under the current welding parameter combination. When the search target (such as the number of searches reaches 100 times or the welding target parameter reaches the set threshold) is reached, stop the search and obtain the welding control strategies for each decomposed component, including the optimal welding method, welding path, and welding parameters.

[0064] Through the optimization search aiming at maximizing the welding target parameters, the optimal welding method, welding path, and welding parameters are determined for each decomposed component, forming specific welding control strategies, which not only improve the welding quality and efficiency, but also reduce welding defects and rework rates, ensuring the performance and reliability of the die set.

[0065] Furthermore, step S22 includes:

[0066] Step S221: Establish a welding process database, including preset welding methods, welding path planning strategies, welding parameter ranges, and welding effects, where the welding effects include welding stress distribution, welding deformation amount, welding heat input amount, welding strength, and weld penetration depth.

[0067] Step S222: Taking the welding target parameters as the goal, establish an objective evaluation function based on the influence relationships between the welding method, welding path, welding parameters, and welding target parameters, where the influence relationship between the welding method and the welding target parameters is represented by the welding stress distribution, welding deformation amount, welding heat input amount, welding strength, and weld penetration depth.

[0068] Step S223: Use the objective evaluation function to evaluate the welding control strategies for the welding method, welding path, and welding parameters, and search for the welding control strategy with the best evaluation value as the optimization result.

[0069] Specifically, establish a comprehensive welding process database covering different welding methods, path planning strategies, parameter ranges, and their corresponding welding effects. Among them, the welding method refers to the welding technology adopted, such as manual arc welding, gas shielded welding, laser welding, etc.; the welding path planning strategy refers to the method of planning the trajectory and sequence of the welding torch movement during the welding process, such as segmented symmetric welding method, skip welding, backstep welding, etc.; the welding parameter range refers to the possible value ranges of each parameter during the welding process, such as welding current, voltage, speed, etc.; the welding effect is the quality and performance index of the weld after welding, including welding stress distribution, welding deformation amount, welding heat input amount, welding strength, weld penetration depth, etc. Exemplarily, for the welding of the template frame, the database may include methods such as segmented symmetric welding method, pre-set anti-deformation technology, dynamic heat input control, etc., and their parameter ranges and effect data. For the welding of the guiding system, it may include methods such as high-energy beam welding technology, precision fixture and positioning system, etc., and their parameter ranges and effect data. These data can be obtained through experiments, literature review, and accumulation of practical production experience. A database management system (such as MySQL, Excel) can be used to organize and store these data for convenient subsequent query and call. By establishing the welding process database, rich reference data are provided for the subsequent optimization of welding process parameters, ensuring the scientificity and practicality of the optimization process and improving the reliability of the welding control strategy.

[0070] Aiming at maximizing the welding target parameters, based on the data in the welding process database, an influence relationship model of welding methods, welding paths, welding parameters and welding target parameters is established. Among them, the influence relationship between the welding method and the welding target parameters is represented by the welding stress distribution, welding deformation amount, welding heat input amount, welding strength, and weld penetration. For example, for the welding of the template frame, the relationship functions between welding current, voltage, speed and welding strength, welding deformation amount can be established, and these functions are fitted through experimental data or simulation results. For the welding of the guiding system, the relationship functions between laser power, welding speed and welding accuracy, welding strength can be established. These relationship functions can be expressed as linear or non-linear equations. Finally, these relationship functions are integrated into an objective evaluation function to evaluate different welding control strategies. For example, the optimization goal is to maximize the welding quality (such as strength, penetration), while reducing the deformation amount and stress concentration. The objective function can be expressed as: J = w1S + w2D + w3H + w4T, where: S represents the weld strength (the larger the better); D is the deformation amount (the smaller the better); H is the penetration (needs to be greater than the threshold); T represents the heat input amount (needs to be moderate); w1, w2, w3, w4 are weight coefficients, which are adjusted through experiments or optimization algorithms. The objective evaluation function provides a quantitative standard for evaluating welding control strategies. Through this function, different welding methods, paths and parameters can be converted into a comparable evaluation value, so as to be able to evaluate various possible welding control strategies more scientifically and accurately, which is conducive to finding the optimal welding control strategy.

[0071] Using the established objective evaluation function, evaluate the welding control strategies composed of various possible welding methods, welding paths, and welding parameter combinations. For example, for the welding of the template frame, try different segmented symmetric welding sequences, different combinations of welding current and voltage, calculate the welding stress distribution, welding deformation amount, welding heat input amount, welding strength, and weld penetration under each combination, and evaluate its advantages and disadvantages through the objective evaluation function. For the welding of the guiding system, different combinations of laser power and welding speed can be tried. Optimization algorithms (such as genetic algorithms, simulated annealing algorithms) can be used to search in the welding process database to find the welding control strategy with the best evaluation value. For example, using the genetic algorithm, through multiple generations of crossover, mutation and selection operations, gradually approach the optimal solution. Finally, take the welding control strategy with the best evaluation value as the optimization result, including the optimal welding method, path and welding parameters.

[0072] Further, step S3 of the embodiment of the present application includes:

[0073] Step S31: Analyze the adjustable parameters of the welding control strategy to obtain the adjustable parameters and the adjustment range.

[0074] Step S32: Establish the regulation response relationship between the adjustable parameters and the welding target parameters.

[0075] Step S33: Obtain the regulation benefit data according to the regulation range and the regulation response relationship.

[0076] Step S34: Screen the regulation response relationship and the regulation benefit data according to the preset adaptive screening rules to determine the adaptive parameters.

[0077] Specifically, the adjustable parameters are the parameters that can be adjusted and controlled during the welding process, such as welding current, voltage, speed, segment length, interlayer temperature, etc. Analyze the welding method, welding path, and welding parameters in the welding control strategy to identify the adjustable parameters and the allowable change range (regulation range) of these adjustable parameters during the welding process. For example, for the welding of the template frame, the adjustable parameters include welding current (I), welding speed (v), segment length (L), interlayer temperature (T), etc. Determine the regulation range of these parameters by referring to the welding process specifications and actual production experience. For example, the regulation range of the welding current is from 150A to 250A, and the regulation range of the welding speed is from 0.5m / min to 2.0m / min. Parameter analysis tools (such as Excel, MATLAB) can be used to organize and analyze these adjustable parameters and their ranges. By analyzing the adjustable parameters and their regulation ranges in the welding control strategy, it provides a basis for the subsequent establishment of the regulation response relationship and the screening of adaptive parameters, ensuring the flexibility and controllability of the welding process.

[0078] Use sensitivity analysis (such as the Sobol index) and orthogonal experimental design (DOE), combined with simulation and experimental data, to establish the regulation response relationship between the adjustable parameters and the welding target parameters. For example, for the welding of the template frame, the target parameter is the flatness error Δ, and the adjustable parameters include welding current (I), welding speed (v), segment length (L), interlayer temperature (T). Through sensitivity analysis, calculate the contribution degree of each parameter to the flatness error. Suppose the contribution degree of the welding current is 40%, the welding speed is 30%, the segment length is 20%, and the interlayer temperature is 10%. This indicates that the welding current and the welding speed are the main control parameters of the flatness. Mathematical models between these parameters and the flatness error can be established using methods such as regression analysis or neural networks. For example, the flatness error Δ can be expressed as: Δ = aI + bv + cL + dT + e, where a, b, c, d, e are coefficients obtained by fitting experimental data. By establishing the regulation response relationship between the adjustable parameters and the welding target parameters, it quantifies the influence of each parameter on the target parameter, provides a scientific basis for the subsequent screening of adaptive parameters and the establishment of the control adjustment relationship, and improves the controllability and precision of the welding process.

[0079] According to the determined regulation range and regulation response relationship, calculate the regulation benefits when adjusting the adjustable parameters within the regulation range. For example, for the welding of a template frame, the regulation range of the welding current is from 150 A to 250 A, and the regulation range of the welding speed is from 0.5 m / min to 2.0 m / min. Through the regulation response relationship model, calculate the improvement degree of the flatness error Δ when adjusting the welding current and speed within these ranges. For example, when the welding current increases from 150 A to 250 A, the flatness error Δ decreases by 20%; when the welding speed increases from 0.5 m / min to 2.0 m / min, the flatness error Δ decreases by 15%. These data of the improvement degree are the regulation benefit data. Virtual experiments can be carried out using simulation software (such as ANSYS, ABAQUS), or data can be collected in actual welding tests to obtain the regulation benefit data. By calculating the regulation benefit data, the improvement effect of adjusting the adjustable parameters on the welding target parameters within the regulation range is quantified, providing data support for subsequent adaptive parameter screening, helping to select the optimal adaptive parameters, and improving the welding quality and efficiency.

[0080] According to the preset adaptive screening rules, screen the regulation response relationship and the regulation benefit data to determine the adaptive parameters. For example, set the following screening rules: High sensitivity: Select process parameters with a weight greater than 20% on the target parameters. Controllability: The parameter needs to be able to be monitored in real time and adjusted dynamically (such as current, speed). Engineering feasibility: The adjustment does not significantly increase the cost or complexity. Taking the welding of the template frame as an example, according to the sensitivity analysis results, the contribution degree of the welding current (I) is 40%, the contribution degree of the welding speed (v) is 30%, the contribution degree of the segment length (L) is 20%, and the contribution degree of the interlayer temperature (T) is 10%. According to screening rule 1, the influence weights of the welding current and the welding speed are greater than 20%, meeting the requirements; according to screening rule 2, the welding current and the welding speed can be monitored in real time and adjusted dynamically through the welding power source and the control system; according to screening rule 3, adjusting the welding current and speed will not significantly increase the cost or complexity. Therefore, the welding current and the welding speed are determined as the adaptive parameters. By screening the regulation response relationship and the regulation benefit data according to the preset adaptive screening rules, the optimal adaptive parameters are determined, ensuring that the adaptive control of the welding process is both effective and feasible, improving the welding quality and production efficiency, and reducing the cost and complexity.

[0081] Furthermore, the adjustable parameters described in step S31 include: direct adjustable parameters, indirect adjustable parameters; when the adjustable parameter is a direct adjustable parameter, the regulation range is the numerical regulation range of the direct adjustable parameter; when the adjustable parameter is an indirect adjustable parameter, the regulation range is the conversion relationship expression with the direct control parameter in the welding control strategy, where the indirect adjustable parameter is a parameter that needs to affect through the synergistic effect of the direct control parameter.

[0082] Specifically, the adjustable parameters include direct adjustable parameters and indirect adjustable parameters. Among them, the direct adjustable parameters are process parameters that can be directly set or adjusted by the equipment, such as welding current, welding speed, laser power, wire feeding speed, argon gas flow rate, etc. The indirect adjustable parameters are derived parameters that cannot be directly set and need to be affected by the synergistic effect of direct parameters. Such as heat input, cooling rate, width of heat affected zone (HAZ), weld pool depth, etc.

[0083] Extract the parameters that can be directly set or adjusted from the welding control strategy to determine the direct adjustable parameters, such as welding current, welding speed, laser power, etc. For example, check the control panel of the welding equipment. There are knobs or digital input areas on the control panel dedicated to adjusting parameters such as welding current (I) and welding speed (v). Then, according to the welding process specifications and the material characteristics of the welded parts, determine the numerical adjustment range of each direct adjustable parameter.

[0084] Next, determine the derived parameters that need to be affected by the synergistic effect of direct parameters, that is, indirect adjustable parameters, such as heat input, cooling rate, etc. Through experimental data and theoretical models, establish the mathematical relationship between the indirect adjustable parameters and the direct adjustable parameters. For example, for heat input, according to the law of conservation of energy and the heat transfer principle of the welding process, derive the conversion relationship expression between it and welding current, welding voltage, and welding speed: Q = I×U / V, where I is the welding current, U is the welding voltage, and V is the welding speed. Integrate the direct adjustable parameters and their numerical adjustment ranges, as well as the indirect adjustable parameters and their conversion relationship expressions into the analysis results of the adjustable parameters.

[0085] Detailed analysis of the adjustable parameters and their adjustment ranges in the welding control strategy clarifies the classification and adjustment methods of direct adjustable parameters and indirect adjustable parameters, provides a basis for the subsequent establishment of the adjustment response relationship and the screening of adaptive parameters, ensures the flexibility and controllability of the welding process, and improves the welding quality and production efficiency.

[0086] Furthermore, step S32 includes:

[0087] Step S321: Calculate the contribution degree of the adjustable parameters to the welding target parameters through orthogonal experiments.

[0088] Step S322: Convert the contribution degree into a response weight, and establish the adjustment response relationship between the adjustable parameters and the welding target parameters according to the response weight.

[0089] Specifically, the orthogonal experiment is an experimental design method for studying multiple factors and multiple levels. By reasonably arranging the experimental factors and levels, the experimental points are evenly distributed and representative within the experimental range, enabling more comprehensive experimental information to be obtained with fewer experimental times, so as to analyze the influence of each factor (here referring to adjustable parameters) on the target (here referring to welding target parameters). The contribution degree represents the degree of influence of the adjustable parameters on the welding target parameters.

[0090] Take the adjustable parameters as experimental factors, and select appropriate numerical ranges within their adjustment ranges as the number of levels. For example, set the welding current to three levels: 150A, 200A, and 250A; set the welding speed to three levels: 0.5m / min, 1.25m / min, and 2.0m / min. Select an appropriate orthogonal table to arrange the experiment according to the number of factors and levels. Conduct welding experiments according to the orthogonal experiment plan, and record the welding target parameter values under each experimental condition. Calculate the contribution degree of each adjustable parameter to the welding target parameter through statistical methods such as variance analysis. Orthogonal experiment design software (such as the orthogonal design module in SPSS, etc.) can be used to arrange the experiment, measuring instruments (such as a strain gauge to measure the welding deformation and a tensile testing machine to measure the welding strength, etc.) to measure the welding target parameters, and statistical analysis software (such as the data analysis tool in Excel, SPSS, etc.) to process the data and calculate the contribution degree.

[0091] The response weight is a value obtained by converting the contribution degree, which is used to represent the relative importance weight of the adjustable parameters when adjusting the welding target parameters. The regulation response relationship is a relationship that describes the relationship between the adjustable parameters and the welding target parameters, and the quantification degree of this relationship is reflected by the response weight. After normalizing the calculated contribution degree, it is converted into a response weight. For example, if the contribution degree of the welding current is 40%, the contribution degree of the welding speed is 30%, the contribution degree of the segment length is 20%, and the contribution degree of the interlayer temperature is 10%, these contribution degrees can be normalized to obtain the response weights. Assuming the total contribution degree is 100%, then the response weight of the welding current is 0.4, the response weight of the welding speed is 0.3, the response weight of the segment length is 0.2, and the response weight of the interlayer temperature is 0.1. Then, based on these response weights, combined with experimental data or simulation results, establish the regulation response relationship between the adjustable parameters and the welding target parameters. For example, if the welding target parameter is the welding strength, and the adjustable parameters are the welding current (I), welding speed (v), segment length (L), and interlayer temperature (T), a simple linear regulation response relationship model can be established: Welding strength = 0.4×I + 0.3×v + 0.2×L + 0.1×T.

[0092] By converting the contribution degree into a response weight and establishing a regulation response relationship, the influence relationship of adjustable parameters on welding target parameters can be clarified in a quantitative manner. This relationship can be used to predict the change trend of welding target parameters when the adjustable parameters are changed, providing a theoretical basis for optimizing the welding control strategy.

[0093] In summary, the welding adaptive control method for die set production provided by the embodiments of the present application has the following beneficial effects:

[0094] Through the organic combination of the above steps, the embodiments of the present application form a complete closed-loop control process, realizing the automation, intelligence, and adaptive control of the welding process. Its technical effects are mainly reflected in the following aspects: through accurate target parameter analysis, systematic process parameter optimization, and real-time adaptive adjustment control, welding defects are effectively reduced, and the consistency and reliability of welding quality are significantly improved; through the automatic control of the welding process, manual intervention and debugging time are reduced, and the optimized welding parameters make the welding process smoother and more efficient, greatly improving production efficiency; based on the adaptive parameters determined by the target impact analysis and the corresponding control adjustment relationship, the welding control system can better adapt to different die set compositions and working conditions changes, enhancing the adaptability of the welding process.

[0095] Overall, the embodiments of the present application realize the adaptive parameter adjustment of the welding process, significantly improving the welding quality, welding efficiency, and the stability and reliability of the welding process, making the welding control of die set production more scientific, accurate, and efficient.

[0096] Embodiment 2, as Figure 3 shown, based on the same inventive concept as in the foregoing Embodiment 1, the embodiments of the present application provide a welding adaptive control system for die set production, and the system includes:

[0097] A target analysis module 10, configured to decompose the die set composition and analyze the welding target parameters of each component according to the functional requirements of the decomposed composition.

[0098] A parameter optimization module 20, configured to optimize the welding process parameters of the decomposed composition with the welding target parameters as the goal, and obtain the welding control strategy for each decomposed composition.

[0099] An influence analysis module 30, configured to perform target impact analysis according to the welding control strategy and the welding target parameters, determine the adaptive parameters, and establish the control adjustment relationship between the adaptive parameters and the welding target parameters.

[0100] A welding monitoring module 40, configured to deploy welding monitoring devices based on the adaptive parameters and collect real-time monitoring data through the welding monitoring devices.

[0101] The adaptive adjustment module 50 is used to adaptively adjust and control the welding control strategy according to the real-time monitoring data and the control adjustment relationship.

[0102] Furthermore, the target analysis module 10 of the embodiment of the present application is further used to perform the following steps:

[0103] Disassemble the structure of the design drawing of the mold base to obtain the component disassembly structure and welding marking points; obtain the working process of the injection mold base and analyze the process functions of each component; perform function clustering according to the process functions of each component to obtain the decomposition composition.

[0104] Furthermore, the target analysis module 10 of the embodiment of the present application is further used to perform the following steps:

[0105] Configure function clustering centers, including a template framework, a guiding coefficient, an ejection mechanism, and a cooling system; perform process function clustering according to the function response relationship between each component and the function clustering center to determine the function categories of each component; decompose each component according to the function categories to obtain the decomposition composition, and each decomposition composition includes a function label and a welding marking point.

[0106] Furthermore, the target analysis module 10 of the embodiment of the present application is further used to perform the following steps:

[0107] Build a working simulation model of the injection mold base, where the working simulation model internally sets the three-dimensional model and material properties of the mold base and defines boundary conditions and load data; simulate the injection working process through the working simulation model, monitor the working states and force data of each decomposition composition in the working process; perform forward function condition analysis according to the working states and perform reverse compensation analysis according to the force data to obtain the welding target parameters of each composition.

[0108] Furthermore, the parameter optimization module 20 of the embodiment of the present application is further used to perform the following steps:

[0109] Based on the function labels, extract the component structures of each decomposition composition and identify the welding distribution of the component structures according to the welding marking points; with the maximization of the welding target parameters as the goal, perform optimization searches for welding methods, welding paths, and welding parameters according to the component structures and welding distributions of the decomposition compositions, and obtain the welding control strategies of each decomposition composition when the search target is reached, where the search target includes the number of search times and the target parameter threshold.

[0110] Furthermore, the parameter optimization module 20 of the embodiment of the present application is further used to perform the following steps:

[0111] Establish a welding process database, including preset welding methods, welding path planning strategies, welding parameter ranges, and welding effects, where the welding effects include welding stress distribution, welding deformation amount, welding heat input amount, welding strength, and weld penetration depth; taking the welding target parameters as the goal, establish a target evaluation function based on the influence relationships between the welding method, welding path, welding parameters, and welding target parameters, where the influence relationship between the welding method and the welding target parameters is represented by the welding stress distribution, welding deformation amount, welding heat input amount, welding strength, and weld penetration depth; use the target evaluation function to evaluate the welding control strategies for the welding method, welding path, and welding parameters, and search for the welding control strategy with the best evaluation value as the optimization result.

[0112] Further, the impact analysis module 30 of the present application embodiment is further configured to perform the following steps:

[0113] Analyze the adjustable parameters of the welding control strategy to obtain the adjustable parameters and their adjustment ranges; establish the adjustment response relationship between the adjustable parameters and the welding target parameters; obtain the adjustment benefit data according to the adjustment range and the adjustment response relationship; screen the adjustment response relationship and the adjustment benefit data according to a preset adaptive screening rule to determine the adaptive parameters.

[0114] Further, the adjustable parameters include: direct adjustable parameters and indirect adjustable parameters; when the adjustable parameter is a direct adjustable parameter, the adjustment range is the numerical adjustment range of the direct adjustable parameter; when the adjustable parameter is an indirect adjustable parameter, the adjustment range is the conversion relationship expression with the direct control parameter in the welding control strategy, where the indirect adjustable parameter is a parameter that needs to affect the welding target parameter through the synergistic effect of the direct control parameter.

[0115] Further, the impact analysis module 30 of the present application embodiment is further configured to perform the following steps:

[0116] Through orthogonal experiments, calculate the contribution degree of the adjustable parameters to the welding target parameters; convert the contribution degree into a response weight, and establish the adjustment response relationship between the adjustable parameters and the welding target parameters according to the response weight.

[0117] Through the foregoing detailed description of the welding adaptive control method for die set production in this specification, those skilled in the art can clearly know the welding adaptive control system for die set production in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, refer to the description in the method part.

[0118] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A welding adaptive control method for mold frame production, characterized in that: include: Decompose the mold frame components and analyze the welding target parameters of each component according to the functional requirements of the decomposed components; Taking the welding target parameters as the target, optimizing the welding process parameters of the decomposed components to obtain the welding control strategy of each decomposed component; According to the welding control strategy and the welding target parameters, target influence analysis is performed to determine adaptive parameters, and a control and adjustment relationship between the adaptive parameters and the welding target parameters is established; Arrange welding monitoring equipment based on the adaptive parameters, and collect real-time monitoring data through the welding monitoring equipment; Based on the real-time monitoring data, the welding control strategy is adaptively adjusted and controlled according to the control regulation relationship.

2. The welding adaptive control method for mold frame production according to claim 1 is characterized in that: The mold base is decomposed into: Disassemble the design drawings of the mold frame to obtain the component disassembly structure and welding marking points; Obtain the workflow of the injection mold frame and analyze the process functions of each component; Functional clustering is performed according to the process functions of the components to obtain decomposition components.

3. The welding adaptive control method for mold frame production according to claim 2 is characterized in that: Functional clustering is performed according to the process functions of each component, including: Configure the functional cluster center, including template frame, guide coefficient, ejection mechanism, and cooling system; According to the functional response relationship between each component and the functional cluster center, process function clustering is performed to determine the functional category of each component; Each component is decomposed according to the functional category to obtain the decomposed components, each decomposed component includes a functional label and a welding mark point.

4. The welding adaptive control method for mold frame production according to claim 2, characterized in that: Analyze the welding target parameters of each component according to the functional requirements of the decomposed components, including: Constructing a working simulation model of an injection mold frame, wherein the working simulation model has a built-in three-dimensional model and material properties of the mold frame and defines boundary conditions and load data; The injection molding workflow is simulated by the working simulation model, and the working status and force data of each decomposed component in the workflow are monitored; A forward functional condition analysis is performed according to the working state, and a reverse compensation analysis is performed according to the force data to obtain welding target parameters of each component.

5. The welding adaptive control method for mold frame production according to claim 4, characterized in that: Taking the welding target parameters as the target, optimizing the welding process parameters of the decomposed components to obtain the welding control strategy of each decomposed component includes: Extracting the component structures of each decomposed component based on the functional labels, and identifying the welding distribution of the component structures according to the welding marking points; With the goal of maximizing the welding target parameters, the welding method, welding path, and welding parameter optimization search are performed according to the component structure and welding distribution of the decomposed components. When the search target is reached, the welding control strategy of each decomposed component is obtained, wherein the search target includes the number of searches and the target parameter threshold.

6. The welding adaptive control method for mold frame production according to claim 5, characterized in that: Taking the maximization of the welding target parameters as the goal, the welding method, welding path, and welding parameter optimization search are performed according to the decomposed component structure and welding distribution, including: Establish a welding process database, including preset welding methods, welding path planning strategies, welding parameter ranges, and welding effects, where welding effects include welding stress distribution, welding deformation, welding heat input, welding strength, and weld penetration; Taking the welding target parameters as the target, establishing a target evaluation function based on the welding method, welding path, the influence relationship between the welding parameters and the welding target parameters, wherein the influence relationship between the welding method and the welding target parameters is represented by the welding stress distribution, welding deformation, welding heat input, welding strength, and weld penetration; The target evaluation function is used to evaluate the welding control strategy of the welding method, welding path and welding parameters, and the welding control strategy with the best evaluation value is searched as the optimization result.

7. The welding adaptive control method for mold frame production according to claim 1, characterized in that: According to the welding control strategy and the welding target parameters, target influence analysis is performed to determine adaptive parameters, including: Analyzing the adjustable parameters of the welding control strategy to obtain the adjustable parameters and the adjustable range; Establishing a control response relationship between the adjustable parameter and the welding target parameter; Obtaining regulation benefit data according to the regulation range and the regulation response relationship; The control response relationship and the control benefit data are screened according to preset adaptive screening rules to determine the adaptive parameters.

8. The welding adaptive control method for mold frame production according to claim 7, characterized in that: The adjustable parameters include: direct adjustment parameters and indirect adjustment parameters; When the adjustable parameter is a directly adjustable parameter, the adjustable range is the numerical adjustable range of the directly adjustable parameter; When the adjustable parameter is an indirect adjustable parameter, the adjustable range is an expression of a conversion relationship with a direct adjustable parameter in a welding control strategy, wherein the indirect adjustable parameter is a parameter that needs to be influenced by the synergistic effect of the direct adjustable parameter.

9. The welding adaptive control method for mold frame production according to claim 7, characterized in that: Establishing a control response relationship between the adjustable parameter and the welding target parameter includes: By means of orthogonal experiments, the contribution of adjustable parameters to the welding target parameters is calculated; The contribution degree is converted into a response weight, and a control response relationship between the adjustable parameter and the welding target parameter is established according to the response weight.

10. A welding adaptive control system for mold frame production, characterized in that: The system is used to execute the welding adaptive control method for mold frame production according to any one of claims 1 to 9, comprising: The target analysis module is used to decompose the mold frame components and analyze the welding target parameters of each component according to the functional requirements of the decomposed components; A parameter optimization module, used to optimize the welding process parameters of the decomposed components with the welding target parameters as the target, and obtain the welding control strategy of each decomposed component; An influence analysis module, used to perform target influence analysis according to the welding control strategy and the welding target parameters, determine adaptive parameters, and establish a control and adjustment relationship between the adaptive parameters and the welding target parameters; A welding monitoring module, used for deploying welding monitoring equipment based on the adaptive parameters and collecting real-time monitoring data through the welding monitoring equipment; The adaptive adjustment module is used to perform adaptive adjustment control of the welding control strategy according to the real-time monitoring data and the control regulation relationship.

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