Control parameter optimization method and device for metal part polishing

Abstract

The invention provides a control parameter optimization method and device for metal part grinding, and relates to the technical field of metal part grinding, and the method comprises the steps: determining an optimization target of metal part grinding, generating a target optimization effect based on the optimization target, and selecting a target control parameter according to the target optimization effect; adjusting the target control parameter according to the target optimization effect to obtain an optimization control parameter; and metal part polishing control is executed based on the optimized control parameters. According to the method, the technical problems that in the prior art, due to the lack of intelligent adjusting capacity, the mutual relation among multiple control parameters cannot be effectively considered, and achievement of optimization indexes such as the surface roughness and the material removal rate in the polishing process is further affected can be solved; the technical target of collaborative optimization of control parameters is achieved, and the technical effects of improving the surface quality, improving the machining efficiency, reducing energy consumption and prolonging the service life of equipment are achieved.

Description

Technical Field

[0001] This application relates to the field of metal grinding technology, and in particular to a method and apparatus for optimizing control parameters for metal grinding. Background Technology

[0002] In the metal parts processing industry, grinding is widely used to improve the surface quality of metal parts and enhance product performance. However, existing metal parts grinding technologies have certain shortcomings and still face significant challenges.

[0003] Currently, existing grinding control systems lack sufficient intelligent adjustment capabilities during the optimization process. Many traditional equipment and control systems adjust based on a single control parameter, such as grinding pressure or grinding speed, failing to consider the coordinated optimization of multiple control parameters. For example, grinding pressure, feed rate, grinding speed, and other control parameters have complex interactions; adjusting one parameter alone may lead to changes in another, thus affecting the overall processing effect. Existing technologies often ignore the interrelationships between these control parameters, making it difficult to achieve ideal optimization levels for indicators such as surface roughness and material removal rate during the grinding process.

[0004] In summary, the existing technology suffers from a lack of intelligent adjustment capabilities, which leads to the failure to effectively consider the interrelationships between multiple control parameters, further affecting the achievement of optimization indicators such as surface roughness and material removal rate during the grinding process. Summary of the Invention

[0005] The purpose of this application is to provide a method and apparatus for optimizing control parameters in metal grinding, in order to solve the technical problem in the prior art that the lack of intelligent adjustment capability leads to the failure to effectively consider the interrelationship between multiple control parameters, which further affects the achievement of optimization indicators such as surface roughness and material removal rate in the grinding process.

[0006] In view of the above problems, this application provides a method and apparatus for optimizing control parameters for grinding metal parts.

[0007] In a first aspect, this application provides a method for optimizing control parameters for grinding metal parts, implemented by a device for optimizing control parameters for grinding metal parts, comprising: determining an optimization target for grinding metal parts; generating a target optimization effect based on the optimization target; selecting target control parameters according to the target optimization effect; adjusting the target control parameters according to the target optimization effect to obtain optimized control parameters; and performing grinding control on metal parts based on the optimized control parameters.

[0008] Secondly, this application also provides a control parameter optimization device for metal part grinding, used to execute the control parameter optimization method for metal part grinding as described in the first aspect, comprising: a target control parameter acquisition module, used to determine the optimization target for metal part grinding, generate a target optimization effect based on the optimization target, and select target control parameters according to the target optimization effect; an optimized control parameter obtaining module, used to adjust the target control parameters according to the target optimization effect to obtain optimized control parameters; and a grinding control module, used to execute metal part grinding control based on the optimized control parameters.

[0009] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of synergistic optimization of control parameters, it achieves the technical effects of improving surface quality, increasing processing efficiency, reducing energy consumption, and extending equipment service life.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the control parameter optimization method for metal part grinding used in this application; Figure 2 This is a schematic diagram of the control parameter optimization device for metal grinding according to this application.

[0013] Explanation of reference numerals in the attached diagram: Target control parameter acquisition module 11, Optimized control parameter acquisition module 12, Polishing control module 13. Detailed Implementation

[0014] This application provides a method and apparatus for optimizing control parameters in metal grinding, solving the technical problem in existing technologies where the lack of intelligent adjustment capabilities leads to ineffective consideration of the interrelationships between multiple control parameters, further affecting the achievement of optimization indicators such as surface roughness and material removal rate during the grinding process. It achieves the technical goal of synergistic optimization of control parameters, resulting in improved surface quality, increased processing efficiency, reduced energy consumption, and extended equipment lifespan.

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] Example 1, please refer to the appendix. Figure 1 This application provides a method for optimizing control parameters for grinding metal parts, which is applied to a device for optimizing control parameters for grinding metal parts, and specifically includes the following steps: S1: Determine the optimization target for metal part grinding, generate the target optimization effect based on the optimization target, and select the target control parameters according to the target optimization effect.

[0017] Specifically, defining optimization goals for metal parts grinding means identifying the specific aspects that need improvement in the metal processing process, such as surface quality, processing efficiency, equipment lifespan, or energy consumption. Metal parts refer to various components made of metal materials, such as stainless steel housings, aluminum alloy brackets, or copper connectors, while grinding involves using abrasives or tools to finish metal surfaces to remove burrs, reduce roughness, or improve smoothness. Determining optimization goals typically requires considering production requirements and technical constraints. For example, if the product is used in high-precision machinery, surface quality may be the primary concern, while for mass-produced ordinary parts, processing efficiency may be more important.

[0018] Target optimization effect generation based on optimization objectives refers to setting specific metrics and expected improvement margins after clarifying the optimization objectives. For example, if the optimization objective is to improve surface quality, the optimization effect might be defined as reducing surface roughness to a specific value. If the objective is to improve processing efficiency, the optimization effect might be manifested as increasing the material removal rate from five cubic millimeters per second to eight cubic millimeters per second. Setting target optimization effects needs to be based on historical data, industry standards, or experimental analysis to ensure that the direction of improvement is clear and quantifiable.

[0019] Selecting target control parameters based on the desired optimization effect means determining the adjustable machining variables, such as abrasive grain size, feed rate, grinding pressure, or grinding speed, given the desired optimization effect. The selection of target control parameters must be based on their impact on the optimization effect. For example, if surface quality is the primary concern, abrasive grain size and feed rate may be key parameters, as finer abrasive grains and lower feed rates typically reduce surface roughness. If the goal is to improve machining efficiency, it may be necessary to adjust grinding pressure and grinding speed to accelerate material removal. Different target optimization effects usually require different combinations of control parameters; therefore, the interaction of multiple variables must be comprehensively considered when selecting them.

[0020] S2: Adjust the target control parameters according to the target optimization effect to obtain the optimized control parameters.

[0021] Specifically, the optimization process first requires defining the optimization objective, such as improving processing efficiency, enhancing surface quality, or reducing energy consumption. Then, by analyzing the optimization effect on the objective, it's determined how to adjust the target control parameters to achieve this goal. Target control parameters are variables that can be directly adjusted, such as feed rate, grinding speed, and grinding pressure. The optimization effect is measured by adjusting these parameters.

[0022] S3: Perform metal part grinding control based on the optimized control parameters.

[0023] Specifically, after determining the optimized control parameters, the metal parts are ground according to these parameter settings. The optimized control parameters are obtained by analyzing the target optimization effect and adjusting the target control parameters, aiming to achieve predetermined processing goals, such as minimizing surface roughness, increasing material removal rate, or reducing energy consumption. During the grinding control process, these optimized control parameters serve as operational guidelines, ensuring that the equipment operates within the appropriate parameter range, thereby achieving the best processing results.

[0024] Furthermore, this application also includes: generating an effect influence trend based on the target optimization effect; filtering the effect influence trend to obtain effect influence requirements; selecting a matching target optimization effect through the effect influence requirements; and determining the target control parameters by matching the matching target optimization effect among multiple control parameters.

[0025] Specifically, generating trend analysis based on target optimization results means that after setting optimization goals and defining optimization effects, it is necessary to analyze the various factors affecting the optimization effects and determine their changing trends under different conditions. The target optimization effect refers to the specific improvement desired to be achieved in the metal part grinding process, such as reducing surface roughness or increasing material removal rate. The trend analysis describes how different control parameters affect the optimization effect, but surface roughness may worsen. Analyzing this trend helps to understand the interactions between parameters, thereby enabling more precise optimization of process conditions.

[0026] Screening for impact trends to determine impact requirements refers to identifying the most relevant influencing factors to the optimization objective after analyzing various impact trends, and clarifying their scope of influence. Impact trends may include many variables, but not all variables contribute equally to the optimization objective. For example, when optimizing surface quality, abrasive grain size may be a key factor, while cooling method has a smaller impact; therefore, abrasive grain size can be screened as the primary impact requirement. If the optimization objective is to extend grinding wheel life, grinding speed and cooling method may be more important, while feed rate has a smaller impact. This screening process removes unnecessary variables, making the optimization process more efficient and accurate.

[0027] Selecting a matching target optimization effect based on the impact of requirements means that after identifying key impact requirements, it's necessary to ensure they match the optimization effect and support the achievement of the optimization goal. For example, if the identified key impact requirement is to control grinding pressure and feed rate to optimize material removal rate, then the target optimization effect should be to maximize material removal rate while maintaining acceptable surface quality. If the target optimization effect is to reduce surface roughness to below one micrometer, but the impact requirement includes a high feed rate strategy, then the two may not match, requiring adjustment of the feed rate range or selection of other variables for optimization. This process ensures that the optimization strategy is feasible and that all adjustments are moving towards the optimization goal.

[0028] The process involves matching multiple control parameters to determine the target optimization effect. The meaning of the target control parameters is that after matching the target optimization effect, specific control parameters need to be selected, and their optimal combination determined. For example, if the target optimization effect is to improve machining efficiency, and the matching impact requirement is to increase the feed rate and grinding speed, then the target control parameters might be a feed rate set to 8 mm per second and a grinding speed set to 2000 rpm. If the target optimization effect is to reduce grinding wheel wear, and the impact requirement is to lower the grinding temperature and optimize the cooling method, then the target control parameters might be to use wet grinding and increase the coolant flow rate to 50 ml per minute. This step ensures that the optimization process can be implemented and ultimately applied in actual production.

[0029] Furthermore, this application also includes: establishing a control correlation between the target control parameters and the target optimization effect; adjusting the target control parameters with the positive effect of the target optimization effect as the guide to obtain unconstrained optimization control parameters; and constraining the unconstrained optimization control parameters based on the stability of the target control parameters and the target optimization effect to obtain the optimization control parameters.

[0030] Specifically, establishing a control correlation between target control parameters and the target optimization effect means that after determining the optimization objective and control parameters, it is necessary to analyze the relationship between the two and establish a mathematical model or empirical formula to describe this relationship. Target control parameters refer to directly adjustable machining variables, such as feed rate, grinding speed, and grinding pressure, while the target optimization effect is the desired final result achieved by adjusting these parameters, such as reduced surface roughness or increased material removal rate. The control correlation describes how these parameters affect the optimization effect, although surface roughness may worsen. The purpose of establishing this correlation is to find the optimal control strategy to maximize the optimization effect.

[0031] Guided by the positive effects of the target optimization, adjusting the target control parameters to obtain unconstrained optimal control parameters means, after establishing control relationships, prioritizing parameter combinations that bring positive optimization effects and performing optimization calculations without considering actual constraints. The positive effects of the target optimization refer to changes that meet the optimization objectives and bring about production improvements. For example, in pursuing high-efficiency machining, increasing the feed rate is usually beneficial, while decreasing the grinding speed may be detrimental. At this stage, it is necessary to find the combination of control parameters that maximizes the optimization effect through mathematical models or experimental data analysis. For example, if the objective is to improve the material removal rate, and increasing the feed rate can significantly improve the removal rate, then the unconstrained optimal control parameters might be to increase the feed rate from 5 mm / s to 12 mm / s, regardless of the maximum load capacity of the equipment or changes in the quality of the workpiece surface.

[0032] Based on the premise that the target control parameters and the target optimization effect tend to stabilize, constraints are imposed on the unconstrained optimization control parameters to obtain the optimized control parameters. This means that after finding the theoretically optimal parameter combination, realistic constraints need to be introduced, and the parameter range needs to be adjusted to achieve the optimum within a feasible range. The stabilization of the target control parameters and the target optimization effect means that after adjusting the parameters within a certain range, the optimization effect will not improve significantly, indicating that further increasing the feed rate has limited effect. Although the unconstrained optimization control parameters can theoretically achieve the optimal optimization effect, they may exceed the equipment's capabilities or lead to other adverse consequences; therefore, constraints need to be set. Adjusting the parameters under these constraints, the final optimized control parameters obtained are the feasible solution.

[0033] Furthermore, this application also includes: constructing an activity vector based on the target control parameters, constructing a situation vector based on the target optimization effect; determining an active drift direction based on the drift of the situation vector in the situation space; adjusting the activity vector in the activity space guided by the active drift direction to obtain an activity vector adjustment decision, and using the activity vector adjustment decision as the unconstrained optimization control parameter.

[0034] Specifically, constructing an activity vector based on target control parameters and a situation vector based on target optimization effects means mathematically describing the control parameters and optimization effects in a vector format to analyze their changing trends in space. Target control parameters refer to variables that can be directly adjusted during metal grinding, such as feed rate, grinding speed, and grinding pressure. Constructing an activity vector means arranging these parameters mathematically so that they can be calculated in a multi-dimensional space. The target optimization effect is the desired outcome during grinding, such as reducing surface roughness or increasing material removal rate. Constructing a situation vector means expressing the changes in the optimization effect in vector form, giving it directionality and magnitude in space. For example, if increasing the feed rate significantly improves the material removal rate while having little impact on surface quality, an activity vector can be constructed where the feed rate has a higher weight, while other parameters have lower weights, making the optimization calculation more accurate.

[0035] Determining the positive drift direction based on the drift of the situation vector in the situation space means finding the direction of change that is beneficial to the optimization objective after analyzing the changing trend of the optimization effect. The drift of the situation vector represents the change of the optimization effect under different parameter combinations. For example, increasing the grinding speed may lead to an increase in material removal rate, but also an increase in surface roughness. Therefore, the situation vector will drift in the direction of increasing material removal rate but worsening surface roughness. Determining the positive drift direction means finding the most favorable trend of change under the optimization objective. For example, if the objective is to improve machining efficiency and accept a certain degree of change in surface roughness, the positive drift direction may be the direction of simultaneously increasing the feed rate and grinding speed. If the objective is to improve surface quality without reducing efficiency, it may be necessary to find a direction that balances both, such as moderately increasing the grinding speed while keeping the feed rate constant.

[0036] Guided by the positive drift direction, the active vector is adjusted within the activity space to obtain the active vector adjustment decision. Using this decision as the unconstrained optimization control parameter means that after finding the optimal optimization direction, specific parameter adjustments need to be made within the control parameter space to form a decision scheme. The positive drift direction provides the directionality of optimization, while the activity space refers to the range of all adjustable control parameters. Adjusting the active vector means making adjustments within these parameter ranges to ensure the optimization process proceeds in the optimal direction. For example, if the positive drift direction points to increasing the feed rate and decreasing the grinding speed, the active vector adjustment decision might be to increase the feed rate and decrease the grinding speed. Ultimately, these optimization decisions are used as the unconstrained optimization control parameters, meaning they are treated as theoretically optimal control parameters for further optimization and constraint adjustment.

[0037] Furthermore, this application also includes: extracting a first control parameter and a second control parameter of the target control parameter; determining whether there is a constraint relationship between the first control parameter and the second control parameter; if the determination is yes, and if the first control parameter is the activity vector, then the second control parameter is the situation vector.

[0038] Specifically, extracting the first and second control parameters of the target control parameters means selecting two variables with significant influence from all target control parameters to analyze the relationship between them. Target control parameters refer to directly adjustable machining variables, such as feed rate, grinding speed, and grinding pressure. The first and second control parameters are two key variables selected from these. For example, when optimizing the grinding of metal parts, feed rate and grinding pressure may be two key variables affecting the material removal rate; therefore, they can be extracted as the first and second control parameters. The purpose of extracting these parameters is to further analyze whether there are any interrelationships between them in order to optimize the machining process.

[0039] Determining whether a first control parameter constrains a second control parameter involves analyzing whether these two parameters influence each other and whether a change in one parameter limits or restricts the adjustment range of the other. A constraining relationship means that adjusting one parameter may restrict the other. For example, in metal grinding, increasing the feed rate increases the material removal rate per unit time, but it may also increase the grinding pressure, thus affecting the tool's lifespan. If the feed rate adjustment must be constrained by the grinding pressure—for example, the grinding pressure cannot exceed 0.4 MPa, otherwise it will lead to overheating or increased wear—then a constraining relationship exists between the feed rate and the grinding pressure. This relationship requires special attention during optimization because, if left uncontrolled, it may cause a parameter to exceed an acceptable range, affecting the final processing result.

[0040] If the determination is valid, and the first control parameter is the active vector, then the second control parameter is a situation vector. This means that if the constraint relationship between the first control parameter and the second control parameter is confirmed, and the first control parameter falls within the range of the active vector, then the second control parameter can be classified as a situation vector. The active vector represents parameters that can be directly adjusted, such as feed rate, grinding speed, and grinding pressure, while the situation vector describes the changing trend of the optimization effect, such as material removal rate, surface roughness, and tool life. Therefore, if the feed rate is an active vector, and the grinding pressure it affects determines the tool life, then the grinding pressure can be considered a situation vector because it describes the changing trend of the device during the optimization process. For example, if an increase in feed rate leads to an increase in grinding pressure and ultimately affects a decrease in tool life, then the feed rate is a directly adjustable variable (active vector), while the grinding pressure becomes a variable describing the processing state (situation vector).

[0041] Furthermore, this application also includes: extracting a third control parameter of the target control parameter; determining whether there is a constraint relationship between the second control parameter and the third control parameter; if the second control parameter is the activity vector, then the third control parameter is the situation vector.

[0042] Specifically, extracting a third control parameter from the target control parameters means selecting a key variable from all target control parameters, in addition to the already determined first and second control parameters, to further analyze its interaction with other parameters. Target control parameters are variables that can be directly adjusted, such as feed rate, grinding speed, and grinding pressure, while the third control parameter is an additional variable that may be affected during the optimization process. For example, when optimizing the grinding of metal parts, if feed rate and grinding pressure are already used as the first and second control parameters, and the grinding speed may be affected by them, then the grinding speed can be extracted as the third control parameter. The purpose of extracting this parameter is to further analyze the interaction between multiple variables during the machining process in order to optimize the control strategy.

[0043] Determining whether a second control parameter constrains a third control parameter involves analyzing whether the second control parameter restricts the adjustment range or trend of the third control parameter. A constraining relationship means that a change in one parameter may affect the adjustable range or final value of another parameter. For example, increasing the grinding pressure (the second control parameter) may affect the grinding speed (the third control parameter) because excessively high grinding pressure may increase the equipment load, thus limiting further increases in the grinding speed. If it is found that when the grinding pressure reaches 0.3 MPa, the grinding speed cannot exceed 2,500 revolutions per minute, otherwise it may cause instability in the machining process, then this indicates a constraining relationship between the grinding pressure and the grinding speed. During optimization, this constraining relationship needs to be analyzed to ensure that all parameter adjustments are within reasonable ranges without causing additional problems.

[0044] If the determination is valid, and the second control parameter is the active vector, then the third control parameter is a situation vector. This means that if the constraint relationship between the second control parameter and the third control parameter is confirmed, and the second control parameter falls within the range of the active vector, then the third control parameter can be classified as a situation vector. The active vector represents parameters that can be directly adjusted, such as feed rate, grinding speed, and grinding pressure, while the situation vector describes the changing trend of the optimization effect, such as material removal rate, surface roughness, and processing stability. Therefore, if the grinding pressure is an active vector, and its changes limit the adjustment range of the grinding speed, then the grinding speed can be considered a situation vector because it is indirectly affected by the adjustment of the grinding pressure.

[0045] Furthermore, this application also includes: determining a situational equilibrium point in the situational space using the situational vector of the second control parameter and the third control parameter; determining an active equilibrium point of the first control parameter and the second control parameter based on the situational equilibrium point, with the active equilibrium point as the active drift direction.

[0046] Specifically, determining the equilibrium point in the situation space using the situation vectors of the second and third control parameters means that during the optimization process, it is necessary to analyze the changing trends of the second and third control parameters and find a key point in the situation space that allows the device to remain stable. The second and third control parameters are variables extracted from the target control parameters. The second control parameter is usually the variable that is directly adjusted, while the third control parameter is the variable that is affected. The situation vector represents the direction and magnitude of these variables' changes, while the situation space contains all possible combinations of states. For example, in the grinding process of metal parts, if the grinding pressure is the second control parameter and the grinding speed is the third control parameter, then the situation vector can describe the changing trend of the grinding speed when the grinding pressure changes. If the grinding pressure is too high, it will cause the grinding speed to decrease, thus affecting the processing quality; if the grinding pressure is too low, it may lead to insufficient grinding. Therefore, in the situation space, it is necessary to find a balance point between the grinding pressure and the grinding speed that ensures both processing efficiency and equipment stability; this point is called the situation equilibrium point.

[0047] Determining the active equilibrium point of the first and second control parameters based on the situational equilibrium point means that after finding the situational equilibrium point, it is necessary to trace back to the relationship between the first and second control parameters and determine their optimal matching point, i.e., the active equilibrium point. The first control parameter is the initially adjusted variable in the device, and the second control parameter is affected by it, further influencing the third control parameter. Therefore, after finding the situational equilibrium point, it is necessary to trace back to the adjustment range of the first control parameter. For example, if the feed rate is the first control parameter and the grinding pressure is the second control parameter, and the situational equilibrium point has determined that the grinding pressure should be maintained to ensure stable grinding speed, then it is necessary to analyze how the feed rate should be adjusted to stabilize the grinding pressure at this level. Through analysis, it may be found that the grinding pressure is most stable when the feed rate is maintained at a certain value. Therefore, the combination of feed rate and grinding pressure values ​​is the active equilibrium point. At the same time, this active equilibrium point can serve as an active drift direction for optimization adjustments, meaning that in subsequent optimization processes, parameters should be adjusted along this direction first to ensure device stability and improve optimization results.

[0048] Furthermore, this application also includes: generating a situation vector synchronization decision based on the control correlation and the activity vector adjustment decision, wherein the activity space is used as the adjustment boundary to constrain the activity vector adjustment decision, and the situation space is used as the synchronization boundary to constrain the situation vector synchronization decision; selecting a priority stopping decision from the critical activity vector adjustment decision obtained based on the activity space constraining the activity vector adjustment decision and the critical situation vector synchronization decision obtained based on the situation space constraining the situation vector synchronization decision, to obtain a priority stopping decision; and obtaining the adjustment optimization effect and the optimized control parameters by linking the priority stopping decision with the sequential stopping decisions according to the control correlation.

[0049] Specifically, the optimization process requires comprehensive consideration of the interrelationships between different control parameters, combined with the adjustment decisions of the activity vector, to generate synchronous decisions for the situation vector. Control correlations describe the influence relationships between different control parameters, such as the interaction between feed rate, grinding pressure, and grinding speed. The activity vector refers to parameters that can be directly adjusted, such as feed rate and grinding pressure, while the situation vector refers to the changing trend of the device's optimization effect, such as material removal rate and surface roughness. The activity space refers to the adjustable range of the activity vector, while the situation space refers to the reasonable range of variation of the situation vector. Adjustments to the feed rate must be constrained by the activity space, and corresponding changes in the material removal rate must also be constrained by the situation space to ensure the stability of the entire device.

[0050] During the adjustment process, it is necessary to identify which parameters are approaching their limits and which adjustments must be stopped first to prevent the device from exceeding its stable range. Critical activity vector adjustment decision refers to the situation where parameter adjustments within the activity space approach the boundary of their adjustable range. Critical situation vector synchronization decision refers to the situation where the device's optimization effect approaches its allowable range. If two parameters are simultaneously approaching their boundaries, it is necessary to prioritize determining which parameter's adjustment should be stopped first; this is the priority stop decision. For example, if the material removal rate is close to its upper limit when the feed rate increases to 7.5 mm / s, while the grinding pressure remains within a reasonable range, then further increases in the feed rate should be stopped first to avoid exceeding processing quality requirements.

[0051] After determining the priority stop point, it is necessary to further adjust other parameters to ensure that all control parameters remain within a reasonable range and ultimately achieve the optimization goal. Sequential stop point decisions refer to adjusting each variable step by step according to the relationships between parameters. For example, if the feed rate has reached the priority stop point, but the grinding pressure can still be adjusted, it may be necessary to appropriately reduce the grinding pressure to further optimize the grinding speed and material removal rate without affecting the machining quality. Ultimately, this adjustment will match the optimized control parameters with the target optimization effect. Table 1 shows the optimization control parameter adjustment decisions and optimization effects of a recent grinding of a metal part.

[0052] Table 1: Optimization Parameter Adjustment Decisions and Optimization Results for the Most Recent Grinding of a Metal Part Control parameters Activity space constraints situation space constraints Critical Activity Vector Adjustment Decision Critical situation vector synchronization decision Prioritize stopping decision Stop decision in sequence Adjustment and optimization effects Optimize control parameters feed rate 5-10 mm / s 5-8 mm / s 7 mm / s 6 mm / s 7 mm / s 6 mm / s Optimize surface roughness 7 mm / s Grinding speed 2500-3000 RPM 2400-2800 RPM 2700 rpm 2600 rpm 2700 rpm 2600 rpm Improve removal rate 2700 rpm Grinding pressure 0.2-0.4 MPa 0.3-0.35 MPa 0.3 MPa 0.32 MPa 0.3 MPa 0.32 MPa Improve efficiency 0.3 MPa Furthermore, this application also includes: the activity space is constructed based on the adjustment limit of the target control parameter.

[0053] Specifically, the workspace is defined by determining the maximum and minimum adjustable ranges for each control parameter. These control parameters are key variables affecting the optimization of the equipment, including feed rate, grinding speed, and grinding pressure. In actual production, each control parameter has its physical and technical limitations. For example, feed rate may be limited by the maximum load capacity of the equipment, and grinding pressure may be limited by the bearing capacity of the grinding wheel or belt. Therefore, the workspace is the acceptable range of parameter adjustment based on these adjustment limits.

[0054] In summary, the control parameter optimization method for metal grinding provided in this application has the following technical effects: by achieving the technical goal of synergistic optimization of control parameters, it can improve surface quality, increase processing efficiency, reduce energy consumption, and extend equipment service life.

[0055] Example 2: Based on the same inventive concept as the control parameter optimization method for metal part grinding in the foregoing examples, this application also provides a control parameter optimization device for metal part grinding. Please refer to the appendix. Figure 2 The system includes: a target control parameter acquisition module 11, used to determine the optimization target for metal part grinding, generate a target optimization effect based on the optimization target, and select target control parameters according to the target optimization effect; an optimization control parameter obtaining module 12, used to adjust the target control parameters according to the target optimization effect to obtain optimization control parameters; and a grinding control module 13, used to execute metal part grinding control based on the optimization control parameters.

[0056] Furthermore, the control parameter optimization device for metal part grinding is also used for: generating an effect influence trend based on the target optimization effect; filtering the effect influence trend to obtain effect influence requirements; selecting a matching target optimization effect through the effect influence requirements; and matching and determining the target control parameters by matching the matching target optimization effect among multiple control parameters.

[0057] Furthermore, the control parameter optimization device for metal part grinding is also used to: establish a control correlation between the target control parameter and the target optimization effect; adjust the target control parameter with the positive effect of the target optimization effect as the guide to obtain unconstrained optimization control parameters; and constrain the unconstrained optimization control parameters based on the stability of the target control parameter and the target optimization effect to obtain the optimized control parameters.

[0058] Furthermore, the control parameter optimization device for metal part grinding is also used for: constructing an activity vector based on the target control parameters, constructing a situation vector based on the target optimization effect; determining an active drift direction based on the drift of the situation vector in the situation space; adjusting the activity vector in the activity space guided by the active drift direction to obtain an activity vector adjustment decision, and using the activity vector adjustment decision as the unconstrained optimization control parameter.

[0059] Furthermore, the control parameter optimization device for metal part grinding is also used to: extract the first control parameter and the second control parameter of the target control parameter; determine whether there is a constraint relationship between the first control parameter and the second control parameter; if the determination is yes, if the first control parameter is the activity vector, then the second control parameter is the situation vector.

[0060] Furthermore, the control parameter optimization device for metal part grinding is also used to: extract a third control parameter of the target control parameter; determine whether there is a constraint relationship between the second control parameter and the third control parameter; if the second control parameter is the activity vector, then the third control parameter is the situation vector.

[0061] Furthermore, the control parameter optimization device for metal part grinding is also used to: determine a situational equilibrium point in the situational space by using the situational vector of the second control parameter and the third control parameter; determine an active equilibrium point of the first control parameter and the second control parameter based on the situational equilibrium point, and use the active equilibrium point as the active drift direction.

[0062] Furthermore, the control parameter optimization device for metal part grinding is also used to: generate a situation vector synchronization decision based on the control correlation and the activity vector adjustment decision, wherein the activity space is used as the adjustment boundary to constrain the activity vector adjustment decision, and the situation space is used as the synchronization boundary to constrain the situation vector synchronization decision; select a priority stop decision for the critical activity vector adjustment decision obtained based on the activity space constraining the activity vector adjustment decision, and the critical situation vector synchronization decision obtained based on the situation space constraining the situation vector synchronization decision, to obtain a priority stop decision; and obtain the adjustment optimization effect and the optimized control parameters by linking the priority stop decision with the sequential stop decision based on the control correlation.

[0063] Furthermore, the control parameter optimization device for metal part grinding is also used for: constructing the activity space based on the adjustment limit of the target control parameter.

[0064] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The control parameter optimization method and specific examples for metal part grinding in the foregoing embodiment one are also applicable to the control parameter optimization device for metal part grinding in this embodiment. Through the foregoing detailed description of the control parameter optimization method for metal part grinding, those skilled in the art can clearly understand the control parameter optimization device for metal part grinding in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0065] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0066] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing control parameters for grinding metal parts, characterized in that, include: Determine the optimization objective for grinding metal parts, generate the target optimization effect based on the optimization objective, and select the target control parameters according to the target optimization effect; The target control parameters are adjusted according to the target optimization effect to obtain the optimized control parameters; The metal part grinding control is performed based on the optimized control parameters.

2. The method for optimizing control parameters for grinding metal parts as described in claim 1, characterized in that, Select target control parameters based on the target optimization effect, including: The trend of the effect of the optimization effect based on the aforementioned target is generated; The desired impact of an effect is obtained by filtering the trends of the effects. The aforementioned effects influence the target optimization effect of demand selection and matching; The target control parameters are obtained by matching and determining the target optimization effect among multiple control parameters.

3. The method for optimizing control parameters for grinding metal parts as described in claim 1, characterized in that, Adjusting the target control parameters based on the target optimization effect yields optimized control parameters, including: Establish the control correlation between the target control parameters and the target optimization effect; Guided by the positive effect of the target optimization, the target control parameters are adjusted to obtain unconstrained optimization control parameters; Based on the fact that the target control parameters and the target optimization effect tend to stabilize, the unconstrained optimization control parameters are constrained to obtain the optimized control parameters.

4. The method for optimizing control parameters for grinding metal parts as described in claim 3, characterized in that, Guided by the positive effect of the target optimization, the target control parameters are adjusted to obtain unconstrained optimization control parameters, including: An activity vector is constructed based on the target control parameters, and a situation vector is constructed based on the target optimization effect. The positive drift direction is determined based on the drift of the situation vector in the situation space; Guided by the positive drift direction, the activity vector is adjusted within the activity space to obtain the activity vector adjustment decision, which is then used as the unconstrained optimal control parameter.

5. The method for optimizing control parameters for grinding metal parts as described in claim 4, characterized in that, An activity vector is constructed based on the target control parameters, and a situation vector is constructed based on the target optimization effect, including: Extract the first control parameter and the second control parameter of the target control parameter; Determine whether there is a constraint relationship between the first control parameter and the second control parameter; If the determination is true, and if the first control parameter is the activity vector, then the second control parameter is the situation vector.

6. The method for optimizing control parameters for grinding metal parts as described in claim 5, characterized in that, The method further includes constructing an activity vector based on the target control parameters and a situation vector based on the target optimization effect, and also includes: Extract the third control parameter of the target control parameter; Determine whether there is a constraint relationship between the second control parameter and the third control parameter; If the determination is true, and if the second control parameter is the activity vector, then the third control parameter is the situation vector.

7. The method for optimizing control parameters for grinding metal parts as described in claim 6, characterized in that, Based on the drift of the situation vector in the situation space, the positive drift direction is determined, including: The situation equilibrium point is determined in the situation space by the situation vector of the second control parameter and the third control parameter; The active equilibrium point of the first control parameter and the second control parameter is determined based on the state equilibrium point, and the active equilibrium point is taken as the active drift direction.

8. The method for optimizing control parameters for grinding metal parts as described in claim 4, characterized in that, Based on the stabilization of the target control parameters and the target optimization effect, constraints are applied to the unconstrained optimization control parameters to obtain the optimization control parameters, including: Based on the control correlation, a situation vector synchronization decision is generated by combining the activity vector adjustment decision, wherein the activity space is used as the adjustment boundary to constrain the activity vector adjustment decision, and the situation space is used as the synchronization boundary to constrain the situation vector synchronization decision; Priority stopping decision is selected from the critical activity vector adjustment decision obtained based on the activity space constraint of the activity vector adjustment decision and the critical situation vector synchronization decision obtained based on the situation space constraint of the situation vector synchronization decision to obtain the priority stopping decision. Based on the control correlation, the adjustment and optimization effect and the optimized control parameters are obtained by linking the priority stop decision with the sequential stop decision.

9. The method for optimizing control parameters for grinding metal parts as described in claim 4, characterized in that, The activity space is constructed based on the adjustment limits of the target control parameters.

10. A control parameter optimization device for grinding metal parts, characterized in that, The steps for implementing the control parameter optimization method for metal part grinding according to any one of claims 1 to 9 include: The target control parameter acquisition module is used to determine the optimization target for metal part grinding, generate the target optimization effect based on the optimization target, and select the target control parameter according to the target optimization effect; The optimization control parameter acquisition module is used to adjust the target control parameters according to the target optimization effect to obtain the optimized control parameters; A grinding control module is used to perform grinding control on metal parts based on the optimized control parameters.

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