Cutter cutting edge parameter optimization method based on workpiece-cutter performance parameters

Abstract

The invention provides a tool cutting edge parameter optimization method based on workpiece-tool performance parameters, and relates to the technical field of tool cutting edge parameter optimization, and the method specifically comprises the steps: obtaining cutting characteristic parameters and workpiece characteristic parameters under different tool cutting edge parameter combinations; establishing a tool characteristic prediction model, inputting a tool cutting edge parameter, and outputting a cutting characteristic parameter; tool cutting edge parameters are randomly combined under constraint conditions, and a prediction model is input to obtain cutting characteristic parameters; carrying out data processing and correlation analysis on the workpiece combination, and constructing a function relationship among the cutting characteristics, a tool life coefficient and a workpiece surface quality coefficient; and iteratively optimizing individuals of the initial population through a genetic algorithm, and extracting an optimal cutting edge parameter of the cutter. The intelligent level of tool design is remarkably improved, the tool design can be optimized in real time according to the actual machining condition, different machining conditions can be effectively coped with, and the optimal performance of the tool in actual application is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of tool edge parameter optimization, and in particular to a tool edge parameter optimization method based on workpiece-tool performance parameters. Background Art

[0002] In the field of metal cutting, tool cutting performance and workpiece performance are closely related. With the improvement of machining accuracy and efficiency requirements, traditional single-objective tool optimization methods have gradually exposed some shortcomings. Most existing technologies focus on the optimization of tool materials, coating selection or geometric shape design, but these methods often ignore the mutual influence between tool cutting performance and workpiece performance, and fail to consider the joint optimization of tool cutting performance and workpiece performance at the same time. Although some studies have introduced adaptive algorithms or optimization models, most methods still lack dynamic adjustment of tool cutting performance and workpiece performance in the actual machining process, resulting in the inability to effectively improve tool life and workpiece surface quality.

[0003] In the prior art, a method and system for designing the cutting edge of an end mill based on an equiangular spiral is provided in the publication number CN118568886A, which includes the following steps: collecting tool parameters and cutting edge parameters, the tool parameters include tool diameter and tool length, and the cutting edge parameters include cutting edge radius and cutting edge inclination; processing the tool parameters and performing correlation analysis to generate a size influence coefficient that affects the cutting edge size; processing the cutting edge parameters and performing correlation analysis to generate a shape influence coefficient that affects the cutting edge shape. The method collects relevant parameters of the cutting edge, optimizes the parameter values ​​of the equiangular spiral function and the geometric features of the cutting edge, generates a curve of the equiangular spiral function according to the geometric features of the cutting edge of the tool, obtains the geometric shape of the cutting edge of the end mill through the curve of the equiangular spiral function, quickly obtains the optimal cutting edge design scheme, and improves the accuracy and efficiency of cutting edge design.

[0004] However, there are still some deficiencies. As can be seen from the above statements, the existing technology relies on the static parameter analysis of the tool and the workpiece, and lacks an effective two-way feedback mechanism. This results in the inability to adjust the tool design in real time according to the actual processing conditions, and ignores the impact of changes in workpiece characteristics on tool performance during the processing process. This one-way information flow makes the tool design parameter optimization unsatisfactory when facing complex and changeable processing conditions.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide a method for optimizing tool edge parameters based on workpiece-tool performance parameters to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing tool edge parameters based on workpiece-tool performance parameters, the specific steps comprising: S1. Obtain tool cutting characteristic parameters and workpiece characteristic parameters under different tool edge parameter combinations, the tool cutting characteristic parameters include cutting speed, feed rate and cutting depth, the workpiece characteristic parameters include surface temperature, surface hardness and surface roughness, the tool edge parameters include rake angle, back angle, edge radius and chamfer radius, splice the workpiece characteristic parameters and tool edge parameters to construct individuals of the initial population; S2. Construct a tool characteristic prediction model, take the tool edge parameter combination of the initial population individuals as input, and the tool cutting characteristic parameters as the label training model to train the tool characteristic prediction model; S3. Establishing the constraint conditions of the tool edge parameters, under the constraint conditions of the tool edge parameters, randomly combining the tool edge parameters of the initial population individuals, inputting the tool edge parameter combination of the initial population individuals into the tool characteristic prediction model, and obtaining the tool cutting characteristic parameters; S4. Perform data processing and correlation analysis on the workpiece combination of the initial population individuals to obtain the workpiece surface quality coefficient, use the cutting speed, feed rate, cutting depth and the upper and lower limits of the ideal range to obtain the deviation values ​​of the cutting speed, feed rate and cutting depth, and construct a functional relationship between the deviation values ​​of the cutting speed, feed rate and cutting depth and the tool life coefficient, and construct a functional relationship between the workpiece surface quality coefficient, the tool life coefficient and the comprehensive evaluation coefficient. The comprehensive evaluation coefficient is used to comprehensively evaluate the workpiece-tool performance; S5. Taking the maximization of the comprehensive evaluation coefficient as the objective function, the individuals of the initial population are iteratively optimized through the genetic algorithm under the constraints of the tool edge parameters to obtain the optimal individuals, and based on the optimal individuals, the optimal values ​​of the tool edge parameters are extracted.

[0008] Furthermore, the workpiece characteristic parameters and tool edge parameters are spliced ​​to construct individuals of the initial population of tool edge parameters. The specific process is as follows: Collect workpiece characteristic parameters, including workpiece surface temperature , workpiece surface hardness and workpiece surface roughness , the workpiece characteristic parameter combination is calibrated as ,and , They represent the workpiece surface temperature, workpiece surface hardness and workpiece surface roughness in the workpiece characteristic parameter combination respectively, and the initial population formed by splicing is calibrated as , and the initial population , is the first Individuals, is the index of the individual in the initial population, and , is the number of individuals in the initial population, ,in, Respectively The surface temperature, surface hardness, surface roughness, rake angle, back angle, cutting edge radius and chamfer radius of each individual workpiece.

[0009] Furthermore, the tool characteristic prediction model is composed of a deep neural network based on a multilayer perceptron, and the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, and the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; The process of training the tool characteristic prediction model is as follows: The rake angle, back angle, cutting edge radius and rounding radius of the initial population individuals are used as input, and the tool cutting characteristic parameters are used as output labels for training. The mean square error is used as the loss function. When the mean square error is When it is within the range, the training of the tool characteristic prediction model is completed.

[0010] Furthermore, the workpiece surface temperature, workpiece surface hardness and workpiece surface roughness are subjected to data processing and correlation analysis to obtain the workpiece surface quality coefficient, based on the following formula: ; in, For the The workpiece surface quality coefficient is used to comprehensively evaluate the influence of workpiece characteristic parameters on workpiece surface quality from three aspects: workpiece surface temperature, workpiece surface hardness and workpiece surface roughness. is the weight coefficient of the workpiece surface temperature, is the weight coefficient of the workpiece surface hardness, is the weight coefficient of the workpiece surface roughness. On the basis of .

[0011] Furthermore, the functional relationship between the deviation values ​​of cutting speed, feed rate, cutting depth and tool life coefficient is constructed as follows: ; ; in, For the The tool life coefficient of each individual tool is used to comprehensively evaluate the influence of tool cutting characteristic parameters on tool life from three levels: cutting speed, feed rate and cutting depth; For the The individual cutting speed deviation value, For the The average cutting speed of each individual is the lower limit of the ideal cutting speed range, is the upper limit of the ideal range of cutting speed, For the The feed deviation value of each individual, For the The average feed amount of each individual, is the lower limit of the ideal feed range, is the upper limit of the ideal feed rate range, For the The individual cutting depth deviation values, For the The average cutting depth of each individual is the lower limit of the ideal cutting depth range, It is the upper limit of the ideal range of cutting depth; is the weight of the cutting speed deviation value in evaluating the influence of tool cutting characteristic parameters on tool life, is the weight of the feed deviation value in evaluating the impact of tool cutting characteristic parameters on tool life, is the weight of the cutting depth deviation in evaluating the influence of tool cutting characteristic parameters on tool life. On the basis of .

[0012] Furthermore, the functional relationship between the workpiece surface quality coefficient, tool life coefficient and comprehensive evaluation coefficient is constructed as follows: ; in, For the The comprehensive evaluation coefficient of each individual is used to combine the workpiece surface quality coefficient and the tool life coefficient to comprehensively evaluate the workpiece-tool performance; and are the weights in the calculation of workpiece surface quality coefficient and tool life coefficient, respectively, and and The specific value of is determined by the hierarchical analysis method.

[0013] Furthermore, the specific process of step S5 is as follows: Iterative optimization is performed on the individuals of the initial population. During the iterative optimization process, the constraints of the tool edge parameters are set, that is, the maximum and minimum values ​​of the rake angle, clearance angle, edge radius and fillet radius are set respectively. Within the constraints of the rake angle, clearance angle, edge radius and fillet radius, the tool edge parameters are iteratively optimized. Specifically, the comprehensive evaluation coefficient Sort from large to small and select the comprehensive evaluation coefficient The individuals in the front row are taken as the parents. Through crossover and mutation operations, the genes of the parent individuals are exchanged, combined and mutated to generate new individuals. The tool cutting characteristic parameters of the newly generated individuals are obtained by using the tool characteristic prediction model, and their comprehensive evaluation coefficients are calculated. The new individuals and the parents are taken as the new population, and the selection, crossover and mutation operations are repeated until the predetermined number of iterations is reached. The individual corresponding to the maximum value of the comprehensive evaluation coefficient is taken as the optimal combination of tool edge parameters, and the individual corresponding to the maximum value of the comprehensive evaluation coefficient is calibrated as , then the optimal tool edge parameter combination is the rake angle , rear angle , cutting edge radius and fillet radius .

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention significantly improves the intelligent level of tool design by establishing a two-way feedback relationship between workpiece characteristic parameters and tool cutting characteristic parameters. This mechanism not only enables tool design to be optimized in real time according to actual processing conditions, but also effectively responds to different processing conditions to ensure the best performance of the tool in actual applications. At the same time, with the help of an intelligent tool characteristic prediction model, it is possible to more accurately predict the cutting performance of the tool under specific workpieces and processing conditions, thereby reducing unnecessary trial and error costs and improving the accuracy of the design stage. In addition, the comprehensive evaluation of tool and workpiece performance is committed to achieving higher resource utilization efficiency in the processing process, reducing energy consumption and material costs, and thus improving production efficiency and product quality. The use of genetic algorithms for optimization can quickly explore the global optimal solution, which is especially suitable for complex multi-dimensional parameter spaces, avoiding the local optimal solution trap that may occur in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Embodiment 1: See also Figure 1 , the present invention provides a technical solution: A method for optimizing tool edge parameters based on workpiece-tool performance parameters, the specific steps comprising: S1. Obtain tool cutting characteristic parameters and workpiece characteristic parameters under different tool edge parameter combinations, the tool cutting characteristic parameters include cutting speed, feed rate and cutting depth, the workpiece characteristic parameters include surface temperature, surface hardness and surface roughness, the tool edge parameters include rake angle, back angle, edge radius and chamfer radius, splice the workpiece characteristic parameters and tool edge parameters to construct individuals of the initial population; S2. Construct a tool characteristic prediction model, take the tool edge parameter combination of the initial population individuals as input, and the tool cutting characteristic parameters as the label training model to train the tool characteristic prediction model; S3. Establishing the constraint conditions of the tool edge parameters, under the constraint conditions of the tool edge parameters, randomly combining the tool edge parameters of the initial population individuals, inputting the tool edge parameter combination of the initial population individuals into the tool characteristic prediction model, and obtaining the tool cutting characteristic parameters; S4. Perform data processing and correlation analysis on the workpiece combination of the initial population individuals to obtain the workpiece surface quality coefficient, use the cutting speed, feed rate, cutting depth and the upper and lower limits of the ideal range to obtain the deviation values ​​of the cutting speed, feed rate and cutting depth, and construct a functional relationship between the deviation values ​​of the cutting speed, feed rate and cutting depth and the tool life coefficient, and construct a functional relationship between the workpiece surface quality coefficient, the tool life coefficient and the comprehensive evaluation coefficient. The comprehensive evaluation coefficient is used to comprehensively evaluate the workpiece-tool performance; S5. Taking the maximization of the comprehensive evaluation coefficient as the objective function, the individuals of the initial population are iteratively optimized through the genetic algorithm under the constraints of the tool edge parameters to obtain the optimal individuals, and based on the optimal individuals, the optimal values ​​of the tool edge parameters are extracted.

[0019] Based on the above embodiments, the rake angle refers to the angle between the cutting edge of the tool and the rake face of the tool. The larger the rake angle, the lower the cutting force and cutting temperature of the tool during cutting, and the better the cutting performance of the workpiece material. The back angle refers to the angle between the cutting edge of the tool and the back face of the tool. The back angle affects the cutting strength and wear resistance of the tool. A larger back angle is beneficial to reduce the friction between the tool and the workpiece. The cutting edge radius refers to the smoothness of the cutting edge of the tool, which affects the stress distribution generated during the cutting process. An appropriate cutting edge radius can improve the durability and cutting quality of the tool. The fillet radius refers to the radius of the transition part of the tool edge. Similar to the edge radius, it also helps to reduce cutting forces and wear.

[0020] The design of the rake angle and back angle affects the heat and friction generated during cutting. A larger rake angle tends to reduce cutting temperature, which helps improve tool life and workpiece quality.

[0021] The cutting characteristics of the tool (such as cutting speed and feed rate) are affected by the edge parameters, which in turn affect the surface hardness of the workpiece. Reasonable cutting parameters can avoid excessive wear and hardness loss.

[0022] Cutting edge parameters, especially cutting edge radius and rounding radius, have a direct impact on the surface smoothness of the workpiece. A smaller radius may lead to finer cutting particles and increase surface roughness, while proper rounding treatment can improve surface quality.

[0023] The settings of the rake angle and clearance angle will affect the cutting efficiency and applicable cutting speed of the tool. A larger rake angle can usually increase the cutting speed, while a suitable clearance angle can maintain better tool stability.

[0024] The combination of rake angle and back angle affects the feed rate of the tool during cutting. An appropriate rake angle helps to reduce cutting resistance, thereby increasing the feed rate and improving production efficiency.

[0025] The design of the cutting edge radius and fillet radius of the tool will affect the selection of cutting depth. A larger cutting edge radius may increase the cutting depth capability, but may also lead to an increase in the surface roughness of the workpiece.

[0026] On the basis of the above-mentioned embodiment, the equipment and method for collecting the workpiece surface temperature, workpiece surface hardness and workpiece surface roughness are as follows: Aim the infrared thermometer at the area where the tool contacts the workpiece and record the surface temperature of the workpiece.

[0027] Using a Rockwell hardness tester, place the sample on the test platform of the Rockwell hardness tester, apply the initial load, maintain it for a period of time, then apply the measuring load and read the surface hardness value of the workpiece.

[0028] Using a surface roughness meter, place the surface roughness meter on the surface of the workpiece and measure along the surface. The instrument will automatically collect surface profile data and calculate the surface roughness of various workpieces.

[0029] The equipment and method for collecting cutting speed, feed rate, cutting depth, rake angle, back angle, cutting edge radius and fillet radius are as follows: Use a tachometer to measure the actual speed of the tool's rotating axis and calculate the cutting speed (V=π×D×n, where D is the tool diameter and n is the speed).

[0030] On CNC machines, the feed rate is monitored and recorded by the numerical control system.

[0031] On CNC machines, use an electronic depth gauge or vernier caliper to measure the distance between the tool and the workpiece surface and record the actual cutting depth.

[0032] Use an optical microscope to observe and measure the rake angle of the tool edge.

[0033] Fix the tool on the measuring table and use an optical microscope to measure the tool's clearance angle.

[0034] Place the tool under an optical microscope to observe the cutting edge curve and use a vernier caliper to measure the cutting edge radius.

[0035] Place the tool under an optical microscope and measure the radius of the rounded portion of the tool edge.

[0036] On the basis of the above embodiment, after collecting the workpiece surface temperature, workpiece surface hardness, workpiece surface roughness, cutting speed, feed rate, cutting depth, front angle, back angle, cutting edge radius and chamfer radius, the above parameters are respectively subjected to maximum-minimum normalization processing, and then the normalized data is used for subsequent analysis and processing, so that in the subsequent analysis and processing process, various data are analyzed and processed under the same dimension, avoiding the problem of some data being neglected due to different dimensions.

[0037] Among them, infrared thermometers, Rockwell hardness testers, surface roughness meters, tachometers, CNC machine tools, electronic depth gauges, and optical microscopes can all use models of existing equipment and are not limited here.

[0038] There are multiple groups (such as 3 groups) of workpiece surface temperature, workpiece surface hardness and workpiece surface roughness, cutting speed, feed rate, cutting depth, rake angle, back angle, cutting edge radius and fillet radius. The same data measured multiple times are averaged, and the final average is used as the corresponding data in the workpiece material characteristic parameters, tool cutting characteristic parameters and tool cutting edge parameters, so as to avoid accidental errors in taking individual points.

[0039] On the basis of the above embodiment, the workpiece characteristic parameters and the tool edge parameters are spliced ​​to construct individuals of the initial population of tool edge parameters. The specific process is as follows: Collect workpiece characteristic parameters, including workpiece surface temperature , workpiece surface hardness and workpiece surface roughness , the workpiece characteristic parameter combination is calibrated as ,and , Respectively represent the workpiece surface temperature, workpiece surface hardness and workpiece surface roughness in the workpiece combination, and the initial population formed by splicing is calibrated as , and the initial population , is the first Individuals, is the index of the individual in the initial population, and , is the number of individuals in the initial population, ,in, Respectively The surface temperature, surface hardness, surface roughness, rake angle, back angle, cutting edge radius and chamfer radius of each individual workpiece.

[0040] On the basis of the above embodiment, the tool characteristic prediction model is composed of a deep neural network based on a multilayer perceptron, and the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, and the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; In this embodiment, the input features of the deep learning network of the multilayer perceptron include four features: rake angle, back angle, cutting edge radius and rounding radius.

[0041] The structure of the deep learning network of multi-layer perceptron is: Input layer: receives input of 4 features; The first hidden layer has 64 neurons and uses ReLU as the activation function. The second hidden layer has 32 neurons and also uses the ReLU activation function. The third hidden layer has 16 neurons and uses the ReLU activation function. Output layer: has a single neuron, tool cutting characteristic parameters.

[0042] The process of training the tool characteristic prediction model is as follows: The rake angle, back angle, cutting edge radius and rounding radius of the initial population individuals are used as input, and the tool cutting characteristic parameters are used as output labels for training. The mean square error is used as the loss function. When the mean square error is When it is within the range, the training of the tool characteristic prediction model is completed.

[0043] Based on the above embodiments, the correlations between the surface temperature, surface hardness and surface roughness and the surface quality of the workpiece are as follows: When the tool works at high temperature, the wear rate tends to accelerate, resulting in unstable cutting, thus affecting the surface quality of the workpiece. Therefore, the surface temperature of the workpiece and the surface quality of the workpiece are positively correlated.

[0044] Within a certain range, materials with higher surface hardness are generally better able to resist wear and deformation and have better surface quality. Therefore, there is a positive correlation between the surface hardness of a workpiece and its surface quality.

[0045] The higher the surface roughness, the greater the surface unevenness, which will directly affect the surface quality of the workpiece, reducing its smoothness and overall aesthetics. Therefore, the surface roughness of the workpiece and the surface quality of the workpiece are negatively correlated.

[0046] According to the correlation between the workpiece surface temperature, workpiece surface hardness, workpiece surface roughness and the workpiece surface quality, the workpiece surface temperature, workpiece surface hardness and workpiece surface roughness are subjected to data processing and correlation analysis to obtain the workpiece surface quality coefficient, based on the following formula: ; in, For the The workpiece surface quality coefficient of each individual workpiece is used to comprehensively evaluate the influence of workpiece characteristic parameters on workpiece surface quality from three levels: workpiece surface temperature, workpiece surface hardness and workpiece surface roughness. The larger the workpiece surface quality coefficient, the better the workpiece surface quality. is the weight coefficient of the workpiece surface temperature, is the weight coefficient of the workpiece surface hardness, is the weight coefficient of workpiece surface roughness; The reasons for setting the above function form to express the functional relationship between the workpiece surface quality coefficient and the workpiece surface temperature, workpiece surface hardness and workpiece surface roughness are as follows: First, the formula puts the weighted sum of surface temperature and surface hardness in the numerator, so that the positive impact of temperature and hardness on surface quality can be fully reflected. At the same time, surface roughness is in the denominator, indicating its negative impact on surface quality. Overall, this design enables the quality coefficient to comprehensively evaluate the surface quality of the workpiece.

[0047] Second, due to The larger it is, the better the surface quality of the workpiece. Therefore, this function form makes the calculation result of the quality coefficient have a clear physical meaning. High temperature and hardness values ​​(after weight adjustment) help to improve the quality coefficient, while higher roughness will reduce its value.

[0048] Third, by introducing the weight coefficient ( ), the importance of each factor in the workpiece surface quality assessment can be flexibly adjusted according to different application scenarios or specific needs, making the assessment process more accurate and adaptable to different workpiece characteristics.

[0049] In many processing processes, surface temperature directly affects the physical properties of materials, such as plasticity, toughness and fluidity. Higher temperatures tend to help materials form and reduce defects, thereby improving surface quality. Especially in metal processing and heat treatment processes, temperature changes have a particularly significant impact on surface quality. Therefore, the weight coefficient maximum; Surface hardness affects the wear resistance, scratch resistance and fatigue resistance of materials, and usually plays an important role in surface quality assessment. However, compared with temperature, the influence of hardness is relatively small, because the improvement of hardness is usually related to a specific processing process, and not all processing processes can significantly improve hardness. Therefore, the weight coefficient Less than the weight coefficient ; Surface roughness is usually regarded as a negative factor affecting surface quality. Excessive roughness will lead to increased friction, reduced fatigue life and the appearance of surface defects. Although the importance of roughness to the final surface quality cannot be ignored, its weight coefficient in the comprehensive evaluation is usually small, because good temperature and hardness management can offset the negative impact of roughness to a certain extent. Therefore, the weight coefficient Minimum.

[0050] In summary, the weight coefficient of the workpiece surface temperature is greater than the weight coefficient of the workpiece surface hardness, and the weight coefficient of the workpiece surface hardness is greater than the weight coefficient of the workpiece surface roughness. On the basis of .

[0051] As an implementation method, The value range is an open interval of 0.4-0.5. The value range is an open interval of 0.3-0.4. The value range is an open interval of 0.2-0.3. The specific value is set by the technicians according to the actual situation and is not limited here.

[0052] On the basis of the above embodiment, a functional relationship between the deviation value of cutting speed, feed rate, cutting depth and tool life coefficient is constructed, and the formula is as follows: ; ; in, For the The individual cutting speed deviation value is used to measure the degree of deviation of the cutting speed from the ideal range. The smaller the cutting speed deviation value, the more ideal the cutting speed, and then the smaller the tool life coefficient, the longer the tool life. For the The average cutting speed of each individual is the lower limit of the ideal cutting speed range, It is the upper limit of the ideal range of cutting speed; For the The feed deviation value of each individual is used to measure the degree to which the feed amount deviates from the ideal range. The smaller the feed deviation value, the more ideal the feed amount, and then the smaller the tool life coefficient, the longer the tool life. For the The average feed amount of each individual, is the lower limit of the ideal feed range, is the upper limit of the ideal feed rate range; For the The individual cutting depth deviation value is used to measure the degree of deviation of the cutting depth from the ideal range. The smaller the cutting depth deviation value, the more ideal the cutting depth, and then the smaller the tool life coefficient, the longer the tool life. For the The average cutting depth of each individual is the lower limit of the ideal cutting depth range, It is the upper limit of the ideal range of cutting depth; It should be noted that the lower and upper limits of the ideal interval of cutting speed, the lower and upper limits of the ideal interval of feed rate, and the lower and upper limits of the ideal interval of cutting depth can be obtained from the technical manual of tool processing; in, For the The tool life coefficient of each individual tool is used to comprehensively evaluate the influence of tool cutting characteristic parameters on tool life from three levels: cutting speed, feed rate and cutting depth. The smaller the tool life coefficient, the longer the tool life. The reasons for setting the above function form to express the functional relationship between the tool life coefficient and the deviation value of cutting speed, feed rate and cutting depth are as follows: First, cutting is a complex physical process involving the interaction of multiple variables. In the cutting process, cutting speed, feed rate and cutting depth are key parameters that affect tool performance and life. Therefore, it is crucial to quantify how deviations in these parameters affect tool life.

[0053] Second, each cutting parameter (cutting speed, feed rate, cutting depth) has an ideal range, usually provided by the tool manufacturer. These ideal values ​​are fully tested and verified to ensure optimal cutting performance and tool life.

[0054] By defining the deviation value ( ), the gap between the actual parameters and the ideal parameters can be quantified, and the calculation method of the deviation value (piecewise function) can reflect the performance impact in different situations.

[0055] Third, the deviation of cutting speed, feed rate and cutting depth will directly affect the friction and temperature between the tool and the workpiece, thereby affecting the wear rate of the tool. For example, too high a cutting speed may cause the tool to overheat and shorten its life.

[0056] Ideal cutting parameters can improve cutting efficiency, reduce processing time and energy consumption. An increase in deviation values ​​usually means a decrease in processing efficiency, which affects production costs and tool life.

[0057] Fourth, the tool life coefficient is obtained by weighted summation of the deviation values ​​( ), a comprehensive evaluation of the effects of cutting speed, feed rate and cutting depth on tool life can be achieved. This functional relationship allows the quantification of the specific impact of each deviation on tool life.

[0058] By adjusting the weight coefficient ( ), can be optimized and adjusted according to different materials, tools and processing conditions to adapt to various processing environments.

[0059] In the formula, are preset proportional factors. Specifically, it is the weight of the cutting speed deviation value in evaluating the influence of the tool cutting characteristic parameters on the tool life. Specifically, it is the weight of the feed deviation value in evaluating the impact of tool cutting characteristic parameters on tool life. Specifically, it is the weight of the cutting depth deviation value in evaluating the influence of the tool cutting characteristic parameters on the tool life; The higher the cutting speed deviation value, the greater the friction between the tool and the workpiece and the heat generated by cutting. High temperature will cause the tool material to soften and wear faster. The feed rate directly affects the amount of cutting per unit time. Too high a feed rate will increase the cutting force. Although a high feed rate deviation value will cause accelerated tool wear, its impact is usually reflected through the applied cutting force and the physical bearing capacity of the tool. It is not as direct and rapid as cutting speed in causing heat generation and changes in material properties. Therefore, when evaluating tool life, cutting speed is usually given a higher weight. .

[0060] The feed rate directly determines the amount of material removed by the tool per revolution. Too high a feed rate deviation will significantly increase the cutting force, leading to accelerated tool wear. An increase in the cutting depth deviation will lead to a greater cutting load, which in turn affects the durability and service life of the tool.

[0061] The feed rate directly determines the amount of material removed per tool revolution. Therefore, in actual processing, adjusting the feed rate can quickly affect the tool's cutting load and material removal efficiency. Excessive feed rate deviation will immediately cause the tool to bear greater cutting forces and accelerate wear. Although the cutting depth also has an important influence on the tool load, its effect often interacts with the cutting speed, material properties and other parameters of the cutting process, which means that the impact of the cutting depth is not as direct and obvious as the feed rate.

[0062] Therefore, although both feed rate and cutting depth have an important influence on tool life, the directness and sensitivity of feed rate in affecting tool wear and life make its influence considered to be more significant. Greater than .

[0063] In summary, the weight coefficient of the cutting speed deviation value is greater than the weight coefficient of the feed deviation value, and the weight coefficient of the feed deviation value is greater than the weight coefficient of the cutting depth deviation value. On the basis of .

[0064] As an implementation method, The value range is an open interval of 0.4-0.5. The value range is an open interval of 0.3-0.4. The value range is an open interval of 0.2-0.3. The specific value is set by the technicians according to the actual situation and is not limited here.

[0065] On the basis of the above embodiment, a functional relationship between the workpiece surface quality coefficient, the tool life coefficient and the comprehensive evaluation coefficient is constructed, and the formula is as follows: ; in, For the The individual comprehensive evaluation coefficient is used to combine the workpiece surface quality coefficient and the tool life coefficient to comprehensively evaluate the workpiece-tool performance. The larger the comprehensive evaluation coefficient, the better the workpiece-tool performance, that is, the better the workpiece surface quality and the longer the tool life. It should be noted that the workpiece surface quality coefficient The larger the size, the better the workpiece surface quality and the tool life coefficient The larger the value, the shorter the tool life. Therefore, the comprehensive evaluation coefficient and workpiece surface quality factor Positive correlation, comprehensive evaluation coefficient and tool life factor There is a negative correlation, so the calculation formula of the comprehensive evaluation coefficient in the above form is set; In the formula, and are the weights in the calculation of workpiece surface quality coefficient and tool life coefficient, respectively, and and The specific value of is determined by the hierarchical analysis method, and the specific logic is as follows: The two indicators of workpiece surface quality coefficient and tool life coefficient are marked, and the relative importance between them is determined by the nine-scale method to construct a judgment matrix, in which the index of the workpiece surface quality coefficient is marked as 1 and the index of the tool life coefficient is marked as 2. The constructed judgment matrix for: ; in, , denotes the index of the coefficient, and , , indicating that the index is The importance of the coefficient of index v to the comprehensive evaluation coefficient is The specific value is determined by relevant experts using a 1-9 scoring method. Indicates that the index is The coefficient of is extremely important for the comprehensive evaluation coefficient compared to the coefficient with index v. Indicates that the index is The coefficient of is extremely unimportant to the comprehensive evaluation coefficient compared to the coefficient with index v; Each element value in the judgment matrix is ​​divided by the sum of its columns to obtain a normalized judgment matrix. The mean of the element values ​​in each row of the normalized judgment matrix is ​​calculated, and the mean of the element values ​​in the first row is used as the proportional coefficient of the workpiece surface quality coefficient, and the mean of the element values ​​in the second row is used as the proportional coefficient of the tool life coefficient. With the constraint that the sum of the scaled values ​​is equal to 1, the two proportional coefficients are scaled in equal proportion, and the values ​​obtained after scaling are used as the weights of the corresponding coefficients.

[0066] Based on the above embodiment, the specific process of step S5 is as follows: Iterative optimization is performed on the individuals of the initial population. During the iterative optimization process, the constraints of the tool edge parameters are set, that is, the maximum and minimum values ​​of the rake angle, clearance angle, edge radius and fillet radius are set respectively. Within the constraints of the rake angle, clearance angle, edge radius and fillet radius, the tool edge parameters are iteratively optimized. Specifically, the comprehensive evaluation coefficient Sort from large to small and select the comprehensive evaluation coefficient The individuals in the front rank are regarded as the parents, and the front rank refers to the individuals in the comprehensive evaluation coefficient. The first 50% of individuals are exchanged, combined and mutated by crossover and mutation operations, and new individuals are generated. The tool cutting characteristic parameters of the newly generated individuals are obtained by using the tool characteristic prediction model, and their comprehensive evaluation coefficients are calculated. The new individuals and the parents are used as the new population, and the selection, crossover and mutation operations are repeated until the predetermined number of iterations is reached. The individual corresponding to the maximum value of the comprehensive evaluation coefficient is taken as the optimal combination of tool edge parameters, and the individual corresponding to the maximum value of the comprehensive evaluation coefficient is calibrated as , then the optimal tool edge parameter combination is the rake angle , rear angle , cutting edge radius and fillet radius .

[0067] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0068] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0069] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for optimizing tool edge parameters based on workpiece-tool performance parameters, characterized in that; The specific steps include: S1. Obtain tool cutting characteristic parameters and workpiece characteristic parameters under different tool edge parameter combinations, the tool cutting characteristic parameters include cutting speed, feed rate and cutting depth, the workpiece characteristic parameters include surface temperature, surface hardness and surface roughness, the tool edge parameters include rake angle, back angle, edge radius and chamfer radius, splice the workpiece characteristic parameters and tool edge parameters to construct individuals of the initial population; S2. Construct a tool characteristic prediction model, take the tool edge parameter combination of the initial population individuals as input, and the tool cutting characteristic parameters as the label training model to train the tool characteristic prediction model; S3. Establishing the constraint conditions of the tool edge parameters, under the constraint conditions of the tool edge parameters, randomly combining the tool edge parameters of the initial population individuals, inputting the tool edge parameter combination of the initial population individuals into the tool characteristic prediction model, and obtaining the tool cutting characteristic parameters; S4. Perform data processing and correlation analysis on the workpiece combination of the initial population individuals to obtain the workpiece surface quality coefficient, use the cutting speed, feed rate, cutting depth and the upper and lower limits of the ideal range to obtain the deviation values ​​of the cutting speed, feed rate and cutting depth, and construct a functional relationship between the deviation values ​​of the cutting speed, feed rate and cutting depth and the tool life coefficient, and construct a functional relationship between the workpiece surface quality coefficient, the tool life coefficient and the comprehensive evaluation coefficient. The comprehensive evaluation coefficient is used to comprehensively evaluate the workpiece-tool performance; S5. Taking the maximization of the comprehensive evaluation coefficient as the objective function, the individuals of the initial population are iteratively optimized through the genetic algorithm under the constraints of the tool edge parameters to obtain the optimal individuals, and based on the optimal individuals, the optimal values ​​of the tool edge parameters are extracted.

2. The method for optimizing tool edge parameters based on workpiece-tool performance parameters according to claim 1, characterized in that: The workpiece characteristic parameters and tool edge parameters are spliced ​​to construct individuals of the initial population of tool edge parameters. The specific process is as follows: Collect workpiece characteristic parameters, including workpiece surface temperature , workpiece surface hardness and workpiece surface roughness , the workpiece characteristic parameter combination is calibrated as ,and , They represent the workpiece surface temperature, workpiece surface hardness and workpiece surface roughness in the workpiece characteristic parameter combination respectively, and the initial population formed by splicing is calibrated as , and the initial population , is the first Individuals, is the index of the individual in the initial population, and , is the number of individuals in the initial population, ,in, Respectively The surface temperature, surface hardness, surface roughness, rake angle, back angle, cutting edge radius and chamfer radius of each individual workpiece.

3. The method for optimizing tool edge parameters based on workpiece-tool performance parameters according to claim 1, characterized in that: The tool characteristic prediction model is composed of a deep neural network based on a multilayer perceptron, wherein the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, wherein the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; The process of training the tool characteristic prediction model is as follows: The rake angle, back angle, cutting edge radius and rounding radius of the initial population individuals are used as input, and the tool cutting characteristic parameters are used as output labels for training. The mean square error is used as the loss function. When the mean square error is When it is within the range, the training of the tool characteristic prediction model is completed.

4. The method for optimizing tool edge parameters based on workpiece-tool performance parameters according to claim 2, characterized in that: The workpiece surface temperature, workpiece surface hardness and workpiece surface roughness are processed and correlated to obtain the workpiece surface quality coefficient based on the following formula: ； in, For the The workpiece surface quality coefficient is used to comprehensively evaluate the influence of workpiece characteristic parameters on workpiece surface quality from three aspects: workpiece surface temperature, workpiece surface hardness and workpiece surface roughness. is the weight coefficient of the workpiece surface temperature, is the weight coefficient of the workpiece surface hardness, is the weight coefficient of the workpiece surface roughness. On the basis of .

5. The method for optimizing tool edge parameters based on workpiece-tool performance parameters according to claim 4, characterized in that: Construct a functional relationship between the deviation value of cutting speed, feed rate, cutting depth and tool life coefficient, the formula is as follows: ； ； in, For the The tool life coefficient of each individual tool is used to comprehensively evaluate the influence of tool cutting characteristic parameters on tool life from three levels: cutting speed, feed rate and cutting depth; For the The individual cutting speed deviation value, For the The average cutting speed of each individual is the lower limit of the ideal cutting speed range, is the upper limit of the ideal range of cutting speed, For the The feed deviation value of each individual, For the The average feed amount of each individual, is the lower limit of the ideal feed range, is the upper limit of the ideal feed rate range, For the The individual cutting depth deviation values, For the The average cutting depth of each individual is the lower limit of the ideal cutting depth range, It is the upper limit of the ideal range of cutting depth; is the weight of the cutting speed deviation value in evaluating the influence of tool cutting characteristic parameters on tool life, is the weight of the feed deviation value in evaluating the impact of tool cutting characteristic parameters on tool life, is the weight of the cutting depth deviation in evaluating the influence of tool cutting characteristic parameters on tool life. On the basis of .

6. The method for optimizing tool edge parameters based on workpiece-tool performance parameters according to claim 5, characterized in that: The functional relationship between the workpiece surface quality coefficient, tool life coefficient and comprehensive evaluation coefficient is constructed as follows: ； in, For the The comprehensive evaluation coefficient of each individual is used to combine the workpiece surface quality coefficient and the tool life coefficient to comprehensively evaluate the workpiece-tool performance; and are the weights in the calculation of workpiece surface quality coefficient and tool life coefficient, respectively, and and The specific value of is determined by the hierarchical analysis method.

7. The method for optimizing tool edge parameters based on workpiece-tool performance parameters according to claim 6, characterized in that: The specific process of step S5 is as follows: Iterative optimization is performed on the individuals of the initial population. During the iterative optimization process, the constraints of the tool edge parameters are set, that is, the maximum and minimum values ​​of the rake angle, clearance angle, edge radius and fillet radius are set respectively. Within the constraints of the rake angle, clearance angle, edge radius and fillet radius, the tool edge parameters are iteratively optimized. Specifically, the comprehensive evaluation coefficient Sort from large to small and select the comprehensive evaluation coefficient The individuals in the front row are taken as the parents. Through crossover and mutation operations, the genes of the parent individuals are exchanged, combined and mutated to generate new individuals. The tool cutting characteristic parameters of the newly generated individuals are obtained by using the tool characteristic prediction model, and their comprehensive evaluation coefficients are calculated. The new individuals and the parents are taken as the new population, and the selection, crossover and mutation operations are repeated until the predetermined number of iterations is reached. The individual corresponding to the maximum value of the comprehensive evaluation coefficient is taken as the optimal combination of tool edge parameters, and the individual corresponding to the maximum value of the comprehensive evaluation coefficient is calibrated as , then the optimal tool edge parameter combination is the rake angle , rear angle , cutting edge radius and fillet radius .

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