Intelligent optimization method and system for service life of milling and turning tool

Abstract

The invention provides an intelligent optimization method and system for the service life of a cutter for milling and turning machining. The method comprises the steps of obtaining product information of a to-be-machined product, extracting product material characteristic data and precision grade demand data for processing, obtaining a product machinability index, obtaining cutter performance data and cutter work verification data for processing, obtaining a cutter availability index, and obtaining a product machinability index according to the cutter performance data and the cutter work verification data. Processing according to the product machinability index and the tool availability index to determine an adaptive tool, acquiring machining process information, extracting machining process parameters, then implementing machining, acquiring real-time work verification data of the tool, processing to obtain a real-time tool life attenuation evaluation index, comparing the real-time tool life attenuation evaluation index with a preset effective tool life threshold value, and determining the tool life attenuation evaluation index according to the real-time tool life attenuation evaluation index. Adjusting machining process parameters according to a threshold comparison result; according to the method, the adaptive cutter is determined by calculating the machinability index of the product and the availability index of the cutter, and then the real-time cutter life attenuation evaluation index is subjected to threshold comparison, so that intelligent optimization of the cutter life is realized.

Description

Technical Field

[0001] The present application relates to the field of mechanical processing technology, and in particular to a method and system for intelligently optimizing tool life in milling and turning processing. Background Art

[0002] In milling and turning composite processing, the life of the tool directly affects the processing efficiency, cost and processing quality; traditional tool life management often relies on experience to adjust the cutting parameters, and cannot achieve intelligent adjustment according to the specific processing products and changes in the processing process, resulting in premature wear or excessive use of the tool, increasing production costs and production cycles. With the development of intelligent manufacturing technology, there is an urgent need for a method that can intelligently match tools, intelligently adjust based on processing parameters, improve the intelligence level of tool life optimization, and achieve intelligent optimization of tool life.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a method and system for intelligent optimization of tool life in milling and turning processing. The suitable tool can be determined by calculating the product machinability index and the tool availability index, and then the real-time tool life attenuation evaluation index is compared with the threshold, thereby realizing intelligent optimization of tool life.

[0005] The present application also provides a method for intelligently optimizing tool life in milling and turning processing, comprising the following steps:

[0006] Obtain product information of the product to be processed, extract product material characteristic data and precision grade requirement data according to the product information, process the product material characteristic data and precision grade requirement data, and obtain the product processability index;

[0007] Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and process the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number;

[0008] Processing the product machinability index in combination with the tool availability index to obtain tool adaptation coefficients and sort them to determine the adapted tool;

[0009] Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information;

[0010] Processing the product to be processed according to the adapted tool and the processing parameters, obtaining real-time working verification data of the tool, and processing the real-time working verification data to obtain a real-time tool life attenuation evaluation index;

[0011] The real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result.

[0012] Optionally, in the intelligent optimization method for tool life of milling and turning machining described in the present application, the obtaining of product information of the product to be machined, extracting product material characteristic data and precision grade requirement data according to the product information, and processing the product material characteristic data and precision grade requirement data to obtain the product machinability index includes:

[0013] Obtain product information of the product to be processed, and extract product material characteristic data and precision grade requirement data based on the product information. The product material characteristic data includes thermal conductivity data, toughness data, viscosity data and hardness data;

[0014] The thermal conductivity data, toughness data, viscosity data and hardness data as well as the precision grade requirement data are input into a preset product machinability evaluation model for processing to obtain a product machinability index.

[0015] Optionally, in the intelligent optimization method for tool life of milling and turning machining described in the present application, the tool number in the tool magazine and the corresponding tool performance data and tool work verification data are obtained, and the tool performance data and tool work verification data are processed to obtain the tool availability index corresponding to the tool number, including:

[0016] Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data;

[0017] The tool performance data includes hardness data, wear resistance data, impact resistance data and heat resistance data of the tool material;

[0018] Inputting the hardness data, wear resistance data, impact resistance data and heat resistance data into a preset tool life evaluation model for processing to obtain a tool life evaluation index;

[0019] The tool work verification data includes practical time limit data, practical working frequency and product quality grade mean data;

[0020] The practical time limit data, the practical working frequency and the product quality grade mean data and the tool life evaluation index are input into a preset tool availability evaluation model for processing to obtain a tool availability index corresponding to the tool number.

[0021] Optionally, in the intelligent optimization method for tool life of milling and turning machining described in the present application, the processing according to the product machinability index combined with the tool availability index, obtaining the tool adaptation coefficient and sorting them, and determining the adapted tool include:

[0022] Inputting the product machinability index and the tool availability index into a preset tool adaptation evaluation model for processing to obtain a tool adaptation coefficient corresponding to the tool number;

[0023] Sorting the tool adaptation coefficients from large to small;

[0024] The tool number with the largest tool adaptation coefficient is used as the adapted tool.

[0025] Optionally, in the intelligent optimization method for tool life of milling and turning machining described in the present application, the step of obtaining machining process information of the product to be machined and extracting machining process parameters corresponding to each process according to the machining process information includes:

[0026] Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information;

[0027] The processing steps include turning processing steps and milling processing steps;

[0028] The processing parameters include turning processing parameters and milling processing parameters;

[0029] The turning process parameters include turning speed, turning feed rate and back cutting depth;

[0030] The milling process parameters include milling rotation speed, milling feed speed, milling depth and feed per tooth.

[0031] Optionally, in the intelligent optimization method for tool life of milling and turning machining described in the present application, the processing of the product to be processed is performed according to the adapted tool and the machining process parameters, real-time working verification data and machining quality verification data of the tool are obtained, and the real-time working verification data is processed to obtain a real-time tool life attenuation evaluation index, including:

[0032] Processing the product to be processed according to the adapted tool and the processing parameters;

[0033] Obtain real-time working verification data of the tool, including cutting force, vibration amplitude and real-time temperature;

[0034] The cutting force, vibration amplitude and real-time temperature are compared with preset standard cutting force, standard vibration amplitude and standard temperature respectively to obtain a cutting force deviation value, a vibration amplitude deviation value and a real-time temperature deviation value;

[0035] The cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value are input into a preset tool life decay rate evaluation model for processing to obtain a real-time tool life decay evaluation index.

[0036] Optionally, in the intelligent optimization method for tool life of milling and turning machining described in the present application, the

[0037] The real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result, including:

[0038] Compare the real-time tool life decay evaluation index with the preset standard tool life decay evaluation index to obtain the relative tool life decay value;

[0039] Comparing the relative value of tool life attenuation with a preset tool effective life threshold;

[0040] If it is less than or equal to the first preset tool effective life threshold, no adjustment is made;

[0041] If it is greater than the first preset tool effective life threshold and less than or equal to the second preset tool effective life threshold, then the optimized machining process parameters are obtained by processing the cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value through a preset machining process adjustment method;

[0042] If it is greater than the second preset tool effective life threshold, the processing is stopped and the early warning response is activated.

[0043] Optionally, the intelligent optimization method for tool life in milling and turning processing described in the present application further includes:

[0044] Acquire processing path data of the product to be processed, including turning processing path data and milling processing path data;

[0045] Extracting special path point data according to the turning process path data and the milling process path data;

[0046] Obtain processing time point data corresponding to special path points and real-time processing time point data of products to be processed;

[0047] Comparing the real-time processing time point data with the processing time point data corresponding to the special path point to obtain the processing time deviation;

[0048] Performing a threshold comparison between the processing time deviation and a preset processing time deviation threshold;

[0049] If it is greater than the preset processing time deviation threshold, processing is performed according to the processing parameters;

[0050] If it is less than or equal to a preset processing time deviation threshold, the processing parameters are automatically adjusted.

[0051] In a second aspect, the present application provides a system for intelligent optimization of tool life for milling and turning machining, the system comprising: a memory and a processor, the memory comprising a program of an intelligent optimization method for tool life for milling and turning machining, and the program of the intelligent optimization method for tool life for milling and turning machining is executed by the processor to implement the following steps:

[0052] Obtain product information of the product to be processed, extract product material characteristic data and precision grade requirement data according to the product information, process the product material characteristic data and precision grade requirement data, and obtain the product processability index;

[0053] Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and process the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number;

[0054] Processing the product machinability index in combination with the tool availability index to obtain tool adaptation coefficients and sort them to determine the adapted tool;

[0055] Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information;

[0056] Processing the product to be processed according to the adapted tool and the processing parameters, obtaining real-time working verification data of the tool, and processing the real-time working verification data to obtain a real-time tool life attenuation evaluation index;

[0057] The real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result.

[0058] Optionally, in the tool life intelligent optimization system for milling and turning machining described in the present application, the step of obtaining product information of the product to be machined, extracting product material characteristic data and precision grade requirement data according to the product information, and performing processing according to the product material characteristic data and precision grade requirement data to obtain the product machinability index includes:

[0059] Obtain product information of the product to be processed, and extract product material characteristic data and precision grade requirement data based on the product information. The product material characteristic data includes thermal conductivity data, toughness data, viscosity data and hardness data;

[0060] The thermal conductivity data, toughness data, viscosity data and hardness data as well as the precision grade requirement data are input into a preset product machinability evaluation model for processing to obtain a product machinability index.

[0061] From the above, it can be seen that the present application provides a method and system for intelligent optimization of tool life in milling and turning processing. By calculating the product machinability index and the tool availability index, the suitable tool is determined, and then the real-time tool life attenuation assessment index is compared with the threshold, thereby realizing the intelligent optimization of tool life.

[0062] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A flow chart of a method for intelligently optimizing tool life in milling and turning provided in an embodiment of the present application;

[0065] Figure 2 A flowchart of obtaining a product machinability index for a method for intelligently optimizing tool life in milling and turning provided in an embodiment of the present application;

[0066] Figure 3 A flowchart of obtaining a tool availability index for a method for intelligently optimizing tool life in milling and turning provided in an embodiment of the present application;

[0067] Figure 4 A flow chart of obtaining a real-time tool life attenuation evaluation index for a method for intelligent optimization of tool life in milling and turning machining provided in an embodiment of the present application. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0069] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0070] Please refer to Figure 1 , Figure 1 The figure is a flow chart of a method for intelligently optimizing tool life in milling and turning machining in some embodiments of the present application. The method for intelligently optimizing tool life in milling and turning machining is used in terminal devices, such as computers, mobile phone terminals, etc. The method for intelligently optimizing tool life in milling and turning machining comprises the following steps:

[0071] S11, obtaining product information of the product to be processed, extracting product material characteristic data and precision grade requirement data according to the product information, processing the product material characteristic data and precision grade requirement data to obtain a product processability index;

[0072] S12, obtaining the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and processing the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number;

[0073] S13, processing the product machinability index in combination with the tool availability index to obtain tool adaptation coefficients and sort them to determine an adapted tool;

[0074] S14, obtaining processing procedure information of the product to be processed, and extracting processing parameters corresponding to each procedure according to the processing procedure information;

[0075] S15, processing the product to be processed according to the adapted tool and the processing process parameters, obtaining real-time working verification data of the tool, and processing the real-time working verification data to obtain a real-time tool life attenuation evaluation index;

[0076] S16, performing a threshold comparison between the real-time tool life attenuation evaluation index and a preset tool effective life threshold, and adjusting the processing parameters corresponding to each processing step according to the threshold comparison result.

[0077] It should be noted that in order to achieve intelligent optimization of tool life, product material characteristic data and precision grade requirement data are extracted and processed according to the product information of the product to be processed to obtain the product machinability index. Multiple tools are stored in the tool magazine. The corresponding tool performance data and tool work verification data are obtained and processed according to the tool number to obtain the tool availability index corresponding to the tool number. The tool adaptation coefficient is obtained and sorted from large to small according to the product machinability index and the tool availability index. The tool with the largest tool adaptation coefficient is selected as the optimal tool for processing the product to be processed. The tool is reasonably selected to ensure that the tool maximizes the cutting performance and extends its life. After determining the suitable tool, the processing parameters corresponding to each process are extracted according to the obtained processing process information, and then processing is carried out. The real-time working verification data of the tool is obtained and processed to obtain the real-time tool life attenuation evaluation index. Finally, the processing parameters corresponding to each processing process are adjusted through threshold comparison to ensure that the suitable tool is supplemented with the optimal processing parameters to ensure the processing quality while optimizing the tool life.

[0078] Please refer to Figure 2 , Figure 2 The flowchart of the method for intelligent optimization of tool life of milling and turning in some embodiments of the present application is to obtain the product machinability index. According to the embodiment of the present invention, the method of obtaining product information of the product to be processed, extracting product material characteristic data and precision grade requirement data according to the product information, and processing the product material characteristic data and precision grade requirement data to obtain the product machinability index includes:

[0079] S21. Obtain product information of the product to be processed, and extract product material characteristic data and precision grade requirement data according to the product information, wherein the product material characteristic data includes thermal conductivity data, toughness data, viscosity data and hardness data;

[0080] S22, inputting the thermal conductivity data, toughness data, viscosity data and hardness data as well as the precision grade requirement data into a preset product machinability evaluation model for processing to obtain a product machinability index.

[0081] It should be noted that different materials and precision requirements have different processing requirements. According to the product information of the product to be processed, the product material characteristic data and precision grade requirement data including thermal conductivity data, toughness data, viscosity data and hardness data are extracted, and the thermal conductivity data, toughness data, viscosity data, hardness data and precision grade requirement data are input into the preset product processability evaluation model for processing to obtain the product processability index, which is used to evaluate the processing difficulty of the product to be processed;

[0082] The product processability index calculation formula in the product processability evaluation model is:

[0083]

[0084] Among them, k s is the product processability index, r d 、r x 、n x ,y d are thermal conductivity data, toughness data, viscosity data and hardness data, t i is the accuracy level requirement data, γ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the tool life optimization platform).

[0085] Please refer to Figure 3 , Figure 3 The flowchart of the tool availability index of the intelligent optimization method for tool life of milling and turning in some embodiments of the present application is as follows. According to the embodiment of the present invention, the tool number in the tool magazine and the corresponding tool performance data and tool work verification data are obtained, and the tool availability index corresponding to the tool number is obtained according to the processing of the tool performance data and the tool work verification data, including:

[0086] S31, obtaining the tool number in the tool magazine and the corresponding tool performance data and tool work verification data;

[0087] S32, the tool performance data includes hardness data, wear resistance data, impact resistance data and heat resistance data of the tool material;

[0088] S33, inputting the hardness data, wear resistance data, impact resistance data and heat resistance data into a preset tool life evaluation model for processing to obtain a tool life evaluation index;

[0089] S34, the tool work verification data includes practical time limit data, practical working frequency and product quality grade mean data;

[0090] S35, inputting the practical time limit data, the practical working frequency and the product quality grade mean data and the tool life evaluation index into a preset tool availability evaluation model for processing, and obtaining a tool availability index corresponding to the tool number.

[0091] It should be noted that the tool magazine of the milling and turning machining center will store multiple tools, each of which has different materials and usage conditions. In order to maximize the cutting performance of the tool, each tool should be evaluated. By obtaining tool performance data including hardness data, wear resistance data, impact resistance data and heat resistance data of the tool material, the tool performance data is input into the preset tool life evaluation model for processing to obtain the tool life evaluation index;

[0092] The calculation formula of tool life evaluation index in the tool life evaluation model is:

[0093]

[0094] Among them, A s is the tool life evaluation index, n y 、n m 、n c 、n r They are hardness data, wear resistance data, impact resistance data and heat resistance data respectively, and χ1 and χ2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the tool life optimization platform);

[0095] Then obtain tool work verification data including practical time limit data, practical working frequency and product quality grade mean data, wherein the practical time limit data refers to the actual working time of the tool involved in turning or milling, the practical working frequency refers to the ratio of the actual working times of the tool to the total working times of the same type of tools in the tool magazine within a preset time period, in this embodiment, the preset time period is set to 1 day, and the product quality grade mean data refers to the average value of multiple product quality grades processed by the tool; input the practical time limit data, practical working frequency and product quality grade mean data and the tool life evaluation index into the preset tool availability evaluation model for processing, and obtain the tool availability index corresponding to the tool number;

[0096] The calculation formula of the tool availability index corresponding to the tool number in the tool availability evaluation model is:

[0097]

[0098] Among them, D k is the tool availability index, t a 、p a ,t j are respectively the practical time limit data, practical working frequency and product quality grade mean data, A s is the tool life evaluation index, and υ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the tool life optimization platform).

[0099] According to an embodiment of the present invention, the processing according to the product machinability index combined with the tool availability index to obtain tool adaptation coefficients and sort them to determine the adapted tool includes:

[0100] Inputting the product machinability index and the tool availability index into a preset tool adaptation evaluation model for processing to obtain a tool adaptation coefficient corresponding to the tool number;

[0101] Sorting the tool adaptation coefficients from large to small;

[0102] The tool number with the largest tool adaptation coefficient is used as the adapted tool.

[0103] It should be noted that in order to match the optimal tool, the obtained product machinability index and tool availability index are input into the preset tool adaptation evaluation model for processing to obtain the tool adaptation coefficient corresponding to the tool number, wherein the tool adaptation evaluation model is obtained by obtaining the product machinability index and tool availability index of a large number of historical samples and the corresponding tool adaptation coefficient training, and the obtained tool adaptation coefficients are sorted from large to small, and the tool number with the largest tool adaptation coefficient is selected as the optimal suitable tool for processing the product.

[0104] According to an embodiment of the present invention, the step of obtaining processing procedure information of the product to be processed and extracting processing parameters corresponding to each procedure according to the processing procedure information includes:

[0105] Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information;

[0106] The processing steps include turning processing steps and milling processing steps;

[0107] The processing parameters include turning processing parameters and milling processing parameters;

[0108] The turning process parameters include turning speed, turning feed rate and back cutting depth;

[0109] The milling process parameters include milling rotation speed, milling feed speed, milling depth and feed per tooth.

[0110] It should be noted that when the milling and turning machining center processes products, it needs to implement it according to the prepared machining process. First, it obtains the machining process information and extracts the machining process parameters corresponding to each process. The corresponding turning machining process parameters include turning speed, turning feed rate and back cutting amount. The milling machining process parameters include milling speed, milling feed speed, milling depth and feed per tooth.

[0111] Please refer to Figure 4 , Figure 4 The flowchart of the method for intelligently optimizing tool life of milling and turning machining in some embodiments of the present application is to obtain a real-time tool life decay evaluation index. According to the embodiment of the present invention, the method of processing the product to be processed according to the adapted tool and the machining process parameters, obtaining the real-time working verification data and machining quality verification data of the tool, and processing the real-time working verification data to obtain the real-time tool life decay evaluation index includes:

[0112] S41, processing the product to be processed according to the adapted tool and the processing parameters;

[0113] S42, obtaining real-time working verification data of the tool, including cutting force, vibration amplitude and real-time temperature;

[0114] S43, comparing the cutting force, vibration amplitude and real-time temperature with preset standard cutting force, standard vibration amplitude and standard temperature respectively, to obtain a cutting force deviation value, a vibration amplitude deviation value and a real-time temperature deviation value;

[0115] S44, inputting the cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value into a preset tool life decay rate evaluation model for processing to obtain a real-time tool life decay evaluation index.

[0116] It should be noted that if the tool is affected by unreasonable cutting force, temperature and other factors during the product processing, the life decay will be aggravated. By obtaining real-time working verification data including cutting force, vibration amplitude and real-time temperature, and comparing them with the preset standard cutting force, standard vibration amplitude and standard temperature respectively, the cutting force deviation value, vibration amplitude deviation value and real-time temperature deviation value are obtained, wherein the cutting force deviation value is the difference between the cutting force and the standard cutting force divided by the standard cutting force, the vibration amplitude deviation value is the difference between the vibration amplitude and the standard vibration amplitude divided by the standard vibration amplitude, and the real-time temperature deviation value is the difference between the real-time temperature and the standard temperature divided by the standard temperature. After pre-processing, negative values ​​and zero values ​​are eliminated, and then the data is input into the preset tool life decay rate evaluation model for processing, so as to obtain the real-time tool life decay evaluation index.

[0117] The calculation formula of the real-time tool life decay evaluation index in the tool life decay rate evaluation model is:

[0118]

[0119] Among them, A m is the real-time tool life decay evaluation index, q i 、z d ,t i are cutting force deviation, vibration amplitude deviation and real-time temperature deviation respectively. ρ1, ρ2, ρ3, τ1, τ2 and τ3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the tool life optimization platform).

[0120] According to an embodiment of the present invention, the real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result, including:

[0121] Compare the real-time tool life decay evaluation index with the preset standard tool life decay evaluation index to obtain the relative tool life decay value;

[0122] Comparing the relative value of tool life attenuation with a preset tool effective life threshold;

[0123] If it is less than or equal to the first preset tool effective life threshold, no adjustment is made;

[0124] If it is greater than the first preset tool effective life threshold and less than or equal to the second preset tool effective life threshold, then the optimized machining process parameters are obtained by processing the cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value through a preset machining process adjustment method;

[0125] If it is greater than the second preset tool effective life threshold, the processing is stopped and the early warning response is activated.

[0126] It should be noted that the obtained real-time tool life decay evaluation index is compared with the preset standard tool life decay evaluation index to obtain the relative value of tool life decay. For example, the real-time tool life decay evaluation index is 8, and the preset standard tool life decay evaluation index is 10, then 8 / 10=0.8 is the relative value of tool life decay, and then the relative value of tool life decay is compared with the preset tool effective life threshold, wherein the tool effective life threshold refers to the maximum evaluation value allowed for tool life decay. In this embodiment, the tool effective life threshold includes a first tool effective life threshold and a second tool effective life threshold, and the first tool effective life threshold is less than the second tool effective life threshold. The tool has an effective life threshold, and the effective tool life threshold is set to (0, 0.5], (0.5, 0.8], and (0.8, 1], which respectively correspond to no adjustment, optimization of machining process parameters, and stopping machining and activating early warning response. For example, if the relative value of tool life attenuation obtained is 0.4, no adjustment is made. If the relative value of tool life attenuation obtained is 0.6, the optimized machining process parameters are obtained through the preset machining process adjustment method according to the cutting force deviation value, the vibration amplitude deviation value, and the real-time temperature deviation value. The preset machining process adjustment method is provided by the tool life optimization platform. If the relative value of tool life attenuation obtained is 0.9, the machining is stopped and the early warning response is activated.

[0127] According to an embodiment of the present invention, it also includes:

[0128] Acquire processing path data of the product to be processed, including turning processing path data and milling processing path data;

[0129] Extracting special path point data according to the turning process path data and the milling process path data;

[0130] Obtain processing time point data corresponding to special path points and real-time processing time point data of products to be processed;

[0131] Comparing the real-time processing time point data with the processing time point data corresponding to the special path point to obtain the processing time deviation;

[0132] Performing a threshold comparison between the processing time deviation and a preset processing time deviation threshold;

[0133] If it is greater than the preset processing time deviation threshold, processing is performed according to the processing parameters;

[0134] If it is less than or equal to a preset processing time deviation threshold, the processing parameters are automatically adjusted.

[0135] It should be noted that when implementing milling and turning processing, the tool is processed according to the preset path. Special path nodes such as corners have a greater impact on the tool life. Therefore, special path point data is extracted according to the processing path data, and the processing time point data corresponding to the special path point and the real-time processing time point data of the product to be processed are obtained. Then, the processing time deviation is obtained. The larger the processing time deviation, the farther away from the special path point. At this time, there is no need to adjust the processing parameters. Otherwise, the processing parameters are automatically adjusted. For example, the processing time point data corresponding to the special path point is 100 seconds after the tool is started, and the real-time processing time point data is adjusted. The processing time point data is 90 seconds, then 100-90=10 seconds is the processing time deviation. The obtained processing time deviation is compared with the preset processing time deviation threshold. In this embodiment, the processing time deviation threshold is set to 5 seconds. For example, when the obtained processing time deviation is 5 seconds, the processing parameters are automatically adjusted, such as reducing the turning feed amount from the original 6mm feed per time to 3mm per time, and cutting in two times. When the obtained processing time deviation is 6 seconds, the processing is performed according to the originally set processing parameters, which ensures the processing efficiency and minimizes the degree of tool life attenuation.

[0136] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0137] Obtaining the average surface temperature of the workpiece to be processed within a preset time period;

[0138] Comparing the average surface temperature of the workpiece to be processed with a preset processing temperature control threshold;

[0139] If it is less than the preset processing temperature control threshold, the cutting fluid flow rate will not be adjusted;

[0140] If it is greater than or equal to the preset processing temperature control threshold, the real-time temperature difference is obtained, and the increase in cutting fluid flow is obtained by processing the real-time temperature difference through the preset cutting fluid flow optimization method.

[0141] It should be noted that during milling and turning processing, the surface of the workpiece is subjected to the cutting force, which will cause the temperature of the workpiece to rise sharply, and the temperature will be transmitted to the tool. Therefore, the surface temperature of the workpiece should be strictly controlled, and the reasonable use of cutting fluid plays an important role in ensuring temperature control. First, the average surface temperature of the workpiece to be processed within a preset time period is obtained, and the threshold is compared with the preset processing temperature control threshold. In this embodiment, the processing temperature control threshold is set to 40°C. For example, the average surface temperature of the workpiece to be processed is 30°C, indicating that the temperature is normal, and the cutting fluid flow rate is not adjusted. If the average surface temperature of the workpiece to be processed is 45°C, indicating that the current cutting fluid is not enough to cool down, the increase in cutting fluid flow rate is obtained through the preset cutting fluid flow optimization method according to the real-time temperature difference, wherein the preset cutting fluid flow optimization method is provided by the tool life optimization platform.

[0142] The present invention also discloses a tool life intelligent optimization system for milling and turning machining, comprising a memory and a processor, wherein the memory comprises a tool life intelligent optimization method program for milling and turning machining, and when the tool life intelligent optimization method program for milling and turning machining is executed by the processor, the following steps are implemented:

[0143] Obtain product information of the product to be processed, extract product material characteristic data and precision grade requirement data according to the product information, process the product material characteristic data and precision grade requirement data, and obtain the product processability index;

[0144] Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and process the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number;

[0145] Processing the product machinability index in combination with the tool availability index to obtain tool adaptation coefficients and sort them to determine the adapted tool;

[0146] Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information;

[0147] Processing the product to be processed according to the adapted tool and the processing parameters, obtaining real-time working verification data of the tool, and processing the real-time working verification data to obtain a real-time tool life attenuation evaluation index;

[0148] The real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result.

[0149] It should be noted that in order to achieve intelligent optimization of tool life, product material characteristic data and precision grade requirement data are extracted and processed according to the product information of the product to be processed to obtain the product machinability index. Multiple tools are stored in the tool magazine. The corresponding tool performance data and tool work verification data are obtained and processed according to the tool number to obtain the tool availability index corresponding to the tool number. The tool adaptation coefficient is obtained and sorted from large to small according to the product machinability index and the tool availability index. The tool with the largest tool adaptation coefficient is selected as the optimal tool for processing the product to be processed. The tool is reasonably selected to ensure that the tool maximizes the cutting performance and extends its life. After determining the suitable tool, the processing parameters corresponding to each process are extracted according to the obtained processing process information, and then processing is carried out. The real-time working verification data of the tool is obtained and processed to obtain the real-time tool life attenuation evaluation index. Finally, the processing parameters corresponding to each processing process are adjusted through threshold comparison to ensure that the suitable tool is supplemented with the optimal processing parameters to ensure the processing quality while optimizing the tool life.

[0150] According to an embodiment of the present invention, the step of obtaining product information of a product to be processed, extracting product material characteristic data and precision level requirement data according to the product information, and performing processing according to the product material characteristic data and precision level requirement data to obtain a product processability index includes:

[0151] Obtain product information of the product to be processed, and extract product material characteristic data and precision grade requirement data based on the product information. The product material characteristic data includes thermal conductivity data, toughness data, viscosity data and hardness data;

[0152] The thermal conductivity data, toughness data, viscosity data and hardness data as well as the precision grade requirement data are input into a preset product machinability evaluation model for processing to obtain a product machinability index.

[0153] It should be noted that different materials and precision requirements have different processing requirements. According to the product information of the product to be processed, the product material characteristic data and precision grade requirement data including thermal conductivity data, toughness data, viscosity data and hardness data are extracted, and the thermal conductivity data, toughness data, viscosity data, hardness data and precision grade requirement data are input into the preset product processability evaluation model for processing to obtain the product processability index, which is used to evaluate the processing difficulty of the product to be processed;

[0154] The product processability index calculation formula in the product processability evaluation model is:

[0155]

[0156] Among them, k s is the product processability index, rd 、r x 、n x ,y d are thermal conductivity data, toughness data, viscosity data and hardness data, t i is the accuracy level requirement data, γ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the tool life optimization platform).

[0157] According to an embodiment of the present invention, the step of obtaining the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and performing processing according to the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number includes:

[0158] Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data;

[0159] The tool performance data includes hardness data, wear resistance data, impact resistance data and heat resistance data of the tool material;

[0160] Inputting the hardness data, wear resistance data, impact resistance data and heat resistance data into a preset tool life evaluation model for processing to obtain a tool life evaluation index;

[0161] The tool work verification data includes practical time limit data, practical working frequency and product quality grade mean data;

[0162] The practical time limit data, the practical working frequency and the product quality grade mean data and the tool life evaluation index are input into a preset tool availability evaluation model for processing to obtain a tool availability index corresponding to the tool number.

[0163] It should be noted that the tool magazine of the milling and turning machining center will store multiple tools, each of which has different materials and usage conditions. In order to maximize the cutting performance of the tool, each tool should be evaluated. By obtaining tool performance data including hardness data, wear resistance data, impact resistance data and heat resistance data of the tool material, the tool performance data is input into the preset tool life evaluation model for processing to obtain the tool life evaluation index;

[0164] The calculation formula of tool life evaluation index in the tool life evaluation model is:

[0165]

[0166] Among them, A s is the tool life evaluation index, n y 、n m 、n c 、n rThey are hardness data, wear resistance data, impact resistance data and heat resistance data respectively, and χ1 and χ2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the tool life optimization platform);

[0167] Then obtain tool work verification data including practical time limit data, practical working frequency and product quality grade mean data, wherein the practical time limit data refers to the actual working time of the tool involved in turning or milling, the practical working frequency refers to the ratio of the actual working times of the tool to the total working times of the same type of tools in the tool magazine within a preset time period, in this embodiment, the preset time period is set to 1 day, and the product quality grade mean data refers to the average value of multiple product quality grades processed by the tool; input the practical time limit data, practical working frequency and product quality grade mean data and the tool life evaluation index into the preset tool availability evaluation model for processing, and obtain the tool availability index corresponding to the tool number;

[0168] The calculation formula of the tool availability index corresponding to the tool number in the tool availability evaluation model is:

[0169]

[0170] Among them, D k is the tool availability index, t a 、p a ,t j are respectively the practical time limit data, practical working frequency and product quality grade mean data, A s is the tool life evaluation index, and υ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the tool life optimization platform).

[0171] According to an embodiment of the present invention, the process of processing the product machinability index in combination with the tool availability index to obtain tool adaptation coefficients and sorting them to determine the adapted tool includes:

[0172] Inputting the product machinability index and the tool availability index into a preset tool adaptation evaluation model for processing to obtain a tool adaptation coefficient corresponding to the tool number;

[0173] Sorting the tool adaptation coefficients from large to small;

[0174] The tool number with the largest tool adaptation coefficient is used as the adapted tool.

[0175] It should be noted that in order to match the optimal tool, the obtained product machinability index and tool availability index are input into the preset tool adaptation evaluation model for processing to obtain the tool adaptation coefficient corresponding to the tool number, wherein the tool adaptation evaluation model is obtained by obtaining the product machinability index and tool availability index of a large number of historical samples and the corresponding tool adaptation coefficient training, and the obtained tool adaptation coefficients are sorted from large to small, and the tool number with the largest tool adaptation coefficient is selected as the optimal suitable tool for processing the product.

[0176] According to an embodiment of the present invention, the step of obtaining processing procedure information of the product to be processed and extracting processing parameters corresponding to each procedure according to the processing procedure information includes:

[0177] Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information;

[0178] The processing steps include turning processing steps and milling processing steps;

[0179] The processing parameters include turning processing parameters and milling processing parameters;

[0180] The turning process parameters include turning speed, turning feed rate and back cutting depth;

[0181] The milling process parameters include milling rotation speed, milling feed speed, milling depth and feed per tooth.

[0182] It should be noted that when the milling and turning machining center processes products, it needs to implement it according to the prepared machining process. First, it obtains the machining process information and extracts the machining process parameters corresponding to each process. The corresponding turning machining process parameters include turning speed, turning feed rate and back cutting amount. The milling machining process parameters include milling speed, milling feed speed, milling depth and feed per tooth.

[0183] According to an embodiment of the present invention, the processing of the product to be processed is performed according to the adapted tool and the processing process parameters, real-time working verification data and processing quality verification data of the tool are obtained, and the real-time working verification data is processed to obtain a real-time tool life attenuation evaluation index, including:

[0184] Processing the product to be processed according to the adapted tool and the processing parameters;

[0185] Obtain real-time working verification data of the tool, including cutting force, vibration amplitude and real-time temperature;

[0186] The cutting force, vibration amplitude and real-time temperature are compared with preset standard cutting force, standard vibration amplitude and standard temperature respectively to obtain a cutting force deviation value, a vibration amplitude deviation value and a real-time temperature deviation value;

[0187] The cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value are input into a preset tool life decay rate evaluation model for processing to obtain a real-time tool life decay evaluation index.

[0188] It should be noted that if the tool is affected by unreasonable cutting force, temperature and other factors during the product processing, the life decay will be aggravated. By obtaining real-time working verification data including cutting force, vibration amplitude and real-time temperature, and comparing them with the preset standard cutting force, standard vibration amplitude and standard temperature respectively, the cutting force deviation value, vibration amplitude deviation value and real-time temperature deviation value are obtained, wherein the cutting force deviation value is the difference between the cutting force and the standard cutting force divided by the standard cutting force, the vibration amplitude deviation value is the difference between the vibration amplitude and the standard vibration amplitude divided by the standard vibration amplitude, and the real-time temperature deviation value is the difference between the real-time temperature and the standard temperature divided by the standard temperature. After pre-processing, negative values ​​and zero values ​​are eliminated, and then the data is input into the preset tool life decay rate evaluation model for processing, so as to obtain the real-time tool life decay evaluation index.

[0189] The calculation formula of the real-time tool life decay evaluation index in the tool life decay rate evaluation model is:

[0190]

[0191] Among them, A m is the real-time tool life decay evaluation index, q i 、z d ,t i are cutting force deviation, vibration amplitude deviation and real-time temperature deviation respectively. ρ1, ρ2, ρ3, τ1, τ2 and τ3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the tool life optimization platform).

[0192] According to an embodiment of the present invention, the real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result, including:

[0193] Compare the real-time tool life decay evaluation index with the preset standard tool life decay evaluation index to obtain the relative tool life decay value;

[0194] Comparing the relative value of tool life attenuation with a preset tool effective life threshold;

[0195] If it is less than or equal to the first preset tool effective life threshold, no adjustment is made;

[0196] If it is greater than the first preset tool effective life threshold and less than or equal to the second preset tool effective life threshold, then the optimized machining process parameters are obtained by processing the cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value through a preset machining process adjustment method;

[0197] If it is greater than the second preset tool effective life threshold, the processing is stopped and the early warning response is activated.

[0198] It should be noted that the obtained real-time tool life decay evaluation index is compared with the preset standard tool life decay evaluation index to obtain the relative value of tool life decay. For example, the real-time tool life decay evaluation index is 8, and the preset standard tool life decay evaluation index is 10, then 8 / 10=0.8 is the relative value of tool life decay, and then the relative value of tool life decay is compared with the preset tool effective life threshold, wherein the tool effective life threshold refers to the maximum evaluation value allowed for tool life decay. In this embodiment, the tool effective life threshold includes a first tool effective life threshold and a second tool effective life threshold, and the first tool effective life threshold is less than the second tool effective life threshold. The tool has an effective life threshold, and the effective tool life threshold is set to (0, 0.5], (0.5, 0.8], and (0.8, 1], which respectively correspond to no adjustment, optimization of machining process parameters, and stopping machining and activating early warning response. For example, if the relative value of tool life attenuation obtained is 0.4, no adjustment is made. If the relative value of tool life attenuation obtained is 0.6, the optimized machining process parameters are obtained through the preset machining process adjustment method according to the cutting force deviation value, the vibration amplitude deviation value, and the real-time temperature deviation value. The preset machining process adjustment method is provided by the tool life optimization platform. If the relative value of tool life attenuation obtained is 0.9, the machining is stopped and the early warning response is activated.

[0199] According to an embodiment of the present invention, it also includes:

[0200] Acquire processing path data of the product to be processed, including turning processing path data and milling processing path data;

[0201] Extracting special path point data according to the turning process path data and the milling process path data;

[0202] Obtain processing time point data corresponding to special path points and real-time processing time point data of products to be processed;

[0203] Comparing the real-time processing time point data with the processing time point data corresponding to the special path point to obtain the processing time deviation;

[0204] Performing a threshold comparison between the processing time deviation and a preset processing time deviation threshold;

[0205] If it is greater than the preset processing time deviation threshold, processing is performed according to the processing parameters;

[0206] If it is less than or equal to a preset processing time deviation threshold, the processing parameters are automatically adjusted.

[0207] It should be noted that when implementing milling and turning processing, the tool is processed according to the preset path. Special path nodes such as corners have a greater impact on the tool life. Therefore, special path point data is extracted according to the processing path data, and the processing time point data corresponding to the special path point and the real-time processing time point data of the product to be processed are obtained. Then, the processing time deviation is obtained. The larger the processing time deviation, the farther away from the special path point. At this time, there is no need to adjust the processing parameters. Otherwise, the processing parameters are automatically adjusted. For example, the processing time point data corresponding to the special path point is 100 seconds after the tool is started, and the real-time processing time point data is adjusted. The processing time point data is 90 seconds, then 100-90=10 seconds is the processing time deviation. The obtained processing time deviation is compared with the preset processing time deviation threshold. In this embodiment, the processing time deviation threshold is set to 5 seconds. For example, when the obtained processing time deviation is 5 seconds, the processing parameters are automatically adjusted, such as reducing the turning feed amount from the original 6mm feed per time to 3mm per time, and cutting in two times. When the obtained processing time deviation is 6 seconds, the processing is performed according to the originally set processing parameters, which ensures the processing efficiency and minimizes the degree of tool life attenuation.

[0208] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0209] Obtaining the average surface temperature of the workpiece to be processed within a preset time period;

[0210] Comparing the average surface temperature of the workpiece to be processed with a preset processing temperature control threshold;

[0211] If it is less than the preset processing temperature control threshold, the cutting fluid flow rate will not be adjusted;

[0212] If it is greater than or equal to the preset processing temperature control threshold, the real-time temperature difference is obtained, and the increase in cutting fluid flow is obtained by processing the real-time temperature difference through the preset cutting fluid flow optimization method.

[0213] It should be noted that during milling and turning processing, the surface of the workpiece is subjected to the cutting force, which will cause the temperature of the workpiece to rise sharply, and the temperature will be transmitted to the tool. Therefore, the surface temperature of the workpiece should be strictly controlled, and the reasonable use of cutting fluid plays an important role in ensuring temperature control. First, the average surface temperature of the workpiece to be processed within a preset time period is obtained, and the threshold is compared with the preset processing temperature control threshold. In this embodiment, the processing temperature control threshold is set to 40°C. For example, the average surface temperature of the workpiece to be processed is 30°C, indicating that the temperature is normal, and the cutting fluid flow rate is not adjusted. If the average surface temperature of the workpiece to be processed is 45°C, indicating that the current cutting fluid is not enough to cool down, the increase in cutting fluid flow rate is obtained through the preset cutting fluid flow optimization method according to the real-time temperature difference, wherein the preset cutting fluid flow optimization method is provided by the tool life optimization platform.

[0214] The present invention discloses an intelligent optimization method and system for tool life in milling and turning processing, which determines the suitable tool by calculating the product machinability index and the tool availability index, and then compares the threshold of the real-time tool life attenuation evaluation index, thereby realizing intelligent optimization of tool life.

[0215] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0216] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they 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 present embodiment.

[0217] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0218] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0219] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An intelligent optimization method for tool life in milling and turning, characterized in that: The following steps are involved: Obtain product information of the product to be processed, extract product material characteristic data and precision grade requirement data according to the product information, process the product material characteristic data and precision grade requirement data, and obtain the product processability index; Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and process the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number; Processing the product machinability index in combination with the tool availability index to obtain tool adaptation coefficients and sort them to determine the adapted tool; Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information; Processing the product to be processed according to the adapted tool and the processing parameters, obtaining real-time working verification data of the tool, and processing the real-time working verification data to obtain a real-time tool life attenuation evaluation index; The real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result.

2. The intelligent optimization method for tool life in milling and turning according to claim 1 is characterized in that: The step of obtaining product information of the product to be processed, extracting product material characteristic data and precision grade requirement data according to the product information, and performing processing according to the product material characteristic data and precision grade requirement data to obtain the product processability index includes: Obtain product information of the product to be processed, and extract product material characteristic data and precision grade requirement data based on the product information. The product material characteristic data includes thermal conductivity data, toughness data, viscosity data and hardness data; The thermal conductivity data, toughness data, viscosity data and hardness data as well as the precision grade requirement data are input into a preset product machinability evaluation model for processing to obtain a product machinability index.

3. The intelligent optimization method for tool life in milling and turning according to claim 2 is characterized in that: The obtaining of the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and processing the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number includes: Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data; The tool performance data includes hardness data, wear resistance data, impact resistance data and heat resistance data of the tool material; Inputting the hardness data, wear resistance data, impact resistance data and heat resistance data into a preset tool life evaluation model for processing to obtain a tool life evaluation index; The tool work verification data includes practical time limit data, practical working frequency and product quality grade mean data; The practical time limit data, the practical working frequency and the product quality grade mean data and the tool life evaluation index are input into a preset tool availability evaluation model for processing to obtain a tool availability index corresponding to the tool number.

4. The intelligent optimization method for tool life in milling and turning according to claim 3 is characterized in that: The processing according to the product machinability index combined with the tool availability index to obtain tool adaptation coefficients and sort them to determine the adapted tool includes: Inputting the product machinability index and the tool availability index into a preset tool adaptation evaluation model for processing to obtain a tool adaptation coefficient corresponding to the tool number; Sorting the tool adaptation coefficients from large to small; The tool number with the largest tool adaptation coefficient is used as the adapted tool.

5. The intelligent optimization method for tool life in milling and turning according to claim 4 is characterized in that: The step of obtaining processing procedure information of the product to be processed and extracting processing parameters corresponding to each procedure according to the processing procedure information includes: Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information; The processing steps include turning processing steps and milling processing steps; The processing parameters include turning processing parameters and milling processing parameters; The turning process parameters include turning speed, turning feed rate and back cutting depth; The milling process parameters include milling rotation speed, milling feed speed, milling depth and feed per tooth.

6. The intelligent optimization method for tool life in milling and turning according to claim 5 is characterized in that: The method includes: processing the product to be processed according to the adapted tool and the processing process parameters, obtaining real-time working verification data and processing quality verification data of the tool, and processing the real-time working verification data to obtain a real-time tool life attenuation evaluation index, including: Processing the product to be processed according to the adapted tool and the processing parameters; Obtain real-time working verification data of the tool, including cutting force, vibration amplitude and real-time temperature; The cutting force, vibration amplitude and real-time temperature are compared with preset standard cutting force, standard vibration amplitude and standard temperature respectively to obtain a cutting force deviation value, a vibration amplitude deviation value and a real-time temperature deviation value; The cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value are input into a preset tool life decay rate evaluation model for processing to obtain a real-time tool life decay evaluation index.

7. The intelligent optimization method for tool life in milling and turning according to claim 6 is characterized in that: The real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result, including: Compare the real-time tool life decay evaluation index with the preset standard tool life decay evaluation index to obtain the relative tool life decay value; Comparing the relative value of tool life attenuation with a preset tool effective life threshold; If it is less than or equal to the first preset tool effective life threshold, no adjustment is made; If it is greater than the first preset tool effective life threshold and less than or equal to the second preset tool effective life threshold, then the optimized machining process parameters are obtained by processing the cutting force deviation value, the vibration amplitude deviation value and the real-time temperature deviation value through a preset machining process adjustment method; If it is greater than the second preset tool effective life threshold, the processing is stopped and the early warning response is activated.

8. The intelligent optimization method for tool life in milling and turning according to claim 7 is characterized in that: Also includes: Acquire processing path data of the product to be processed, including turning processing path data and milling processing path data; Extracting special path point data according to the turning process path data and the milling process path data; Obtain processing time point data corresponding to special path points and real-time processing time point data of products to be processed; Comparing the real-time processing time point data with the processing time point data corresponding to the special path point to obtain the processing time deviation; Performing a threshold comparison between the processing time deviation and a preset processing time deviation threshold; If it is greater than the preset processing time deviation threshold, processing is performed according to the processing parameters; If it is less than or equal to a preset processing time deviation threshold, the processing parameters are automatically adjusted.

9. A tool life optimization system for a five-axis milling and turning composite machining center, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a program of a tool life intelligent optimization method for milling and turning machining, and the program of the tool life intelligent optimization method for milling and turning machining is executed by the processor to implement the following steps: Obtain product information of the product to be processed, extract product material characteristic data and precision grade requirement data according to the product information, process the product material characteristic data and precision grade requirement data, and obtain the product processability index; Obtain the tool number in the tool magazine and the corresponding tool performance data and tool work verification data, and process the tool performance data and tool work verification data to obtain the tool availability index corresponding to the tool number; Processing the product machinability index in combination with the tool availability index to obtain tool adaptation coefficients and sort them to determine the adapted tool; Obtain processing procedure information of the product to be processed, and extract processing parameters corresponding to each procedure according to the processing procedure information; Processing the product to be processed according to the adapted tool and the processing parameters, obtaining real-time working verification data of the tool, and processing the real-time working verification data to obtain a real-time tool life attenuation evaluation index; The real-time tool life attenuation evaluation index is compared with a preset tool effective life threshold, and the processing parameters corresponding to each processing step are adjusted according to the threshold comparison result.

10. The tool life optimization system for a five-axis milling and turning machining center according to claim 9, characterized in that: The step of obtaining product information of the product to be processed, extracting product material characteristic data and precision grade requirement data according to the product information, and performing processing according to the product material characteristic data and precision grade requirement data to obtain the product processability index includes: Obtain product information of the product to be processed, and extract product material characteristic data and precision grade requirement data based on the product information. The product material characteristic data includes thermal conductivity data, toughness data, viscosity data and hardness data; The thermal conductivity data, toughness data, viscosity data and hardness data as well as the precision grade requirement data are input into a preset product machinability evaluation model for processing to obtain a product machinability index.

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