Process parameter evaluation and recommendation method based on digital production line cutting profit rate

Abstract

The invention belongs to the field of machining process optimization, and relates to a process parameter evaluation and recommendation method based on a digital production line cutting profit rate. The method comprises the steps that relevant information of machining features is split into a plurality of field features, the field features are matched with corresponding technological parameters, and then technological resources used for machining the field features, part drawing numbers and cutting amount parameters are matched with the corresponding technological parameters; obtaining a parameter which does not depend on a specific process parameter and exists independently as a global variable; storing all the data by adopting a three-level structure of a database, a data table and a field: evaluating the process parameters according to user requirements by adopting a dynamic script mode, utilizing the field stored in the process parameters and combining with the global variable field; and according to an evaluation result, recommending an optimal process parameter.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of machining process optimization, and relates to a process parameter evaluation and recommendation method based on a digital production line cutting profit rate. BACKGROUND

[0002] Optimization of process and process parameters is the most important research and application content of intelligent manufacturing. The advantages and disadvantages of process parameters have a profound and significant impact on machining efficiency, machining cost and machining quality. How to continuously and effectively promote the optimization of process parameters is one of the important ways for manufacturing enterprises to improve technical level, increase profit rate and realize industrial upgrading. The cutting parameter database is a relatively effective platform for realizing storage, query and optimization of cutting parameters. At present, many enterprises, universities and research institutes have established many process parameter databases to realize the optimization of process parameters. However, these methods have the following shortcomings: 1. The generation process of the optimization data is separated from the actual production. Many cutting parameter optimization schemes developed by universities are mainly based on theoretical models and laboratory conditions. The process conditions are far from the actual production application, the reproducibility is poor, and the process is a multi-factor composite process involving materials, tools, clamping, cooling and other conditions. When one of the conditions changes, the usability of the machining parameters needs to be re-evaluated.

[0003] 2. The data lacks evaluation criteria. Many data can only be qualitatively defined as "usable" parameters. When new machining parameters appear, it is difficult to evaluate which is better compared with the existing parameters, and it is also difficult to realize the continuous evolution of the parameters.

[0004] 3. The construction of the process database takes a long time, and it is difficult to link with the performance of the management personnel. Enterprises cannot invest sufficient funds in this aspect, and workers have resistance to improving machining efficiency. It takes a lot of time to collect process data, and cannot get corresponding remuneration. SUMMARY

[0005] The technical problem to be solved by the application is to overcome the shortcomings of the prior art that only process personnel record parameters and only record the parameters themselves, and cannot evaluate the advantages and disadvantages of the parameters, and only record the usability of the parameters. A process parameter evaluation and scoring method based on the evaluation value of the target function is provided. The machining parameters, machine tools, cutting tools, materials and other global variable parameters are used to fuse the cutting parameter optimization matching and recommendation algorithm, and an evaluation mechanism is provided for realizing the self-evolution of the process parameters.

[0006] The technical scheme of the application is A process parameter evaluation and recommendation method based on a digital production line cutting profit rate is provided, which comprises the following steps: The relevant information of the machining feature is divided into several field features, the corresponding process parameters are matched by the field features, and then the process resources, part drawing number, and cutting amount parameters used for the field feature machining are matched with the corresponding process parameters; A parameter that exists independently without depending on a specific process parameter is obtained as a global variable; A three-level structure of a database, a data table, and a field is adopted to store all the data: a plurality of data tables of the database are established according to the relevant information of the machining feature, each data table contains a plurality of data entries of the same field feature, and each data entry records a field feature corresponding process resource field, part drawing number field, cutting amount parameter field, process parameter field, and global variable field; In a dynamic script mode, the fields stored in the process parameters are used, the global variable field is combined, and process parameter evaluation is performed according to user requirements; According to the evaluation result, the optimal process parameter is recommended.

[0007] Further, the process parameters include cutting amount, feed amount, and spindle speed in the machining process; The process resources include part material, tool type, tool size, tool coating information, cooling condition, tool stiffness, machine tool model, machine tool type, machine tool motion axis parameter, machine tool maximum cutting force, maximum torque, and maximum power; The relevant information of the machining feature includes feature type, machining method, feature size, feature position, accuracy requirement, roughness requirement, and clamping stiffness; The global variable includes material parameters, machine tool parameters, and tool parameters involved in the factory; the material parameters include material hardness, strength, thermal conductivity, and machinability; the machine tool parameters include machine tool axis number, maximum feed speed of each axis, speed range, maximum torque, and maximum cutting force; and the tool parameters include tool type, tool tooth number, tool angle, tool life coefficient, and tool clamping method.

[0008] Further, the process parameters have the following two source modes: 1) Custom process parameters: the database system reads process resource information through a production scheduling task interface when the intelligent production line starts machining by a process issued numerical control program, obtains feature information processed by analyzing the NC code, and stores the feature information as a process parameter in the database; 2) Recommended optimal process parameters: process personnel actively define or cam software automatically identifies part feature information and process resource information in the CAM software, and obtains machining parameter information to combine a process parameter and store the process parameter in the database.

[0009] Further, process parameter evaluation according to user requirements includes: According to the user demand, the objective function is determined, the objective function includes the cutting efficiency evaluation target, that is, the material removal rate is calculated according to the rotational speed, feed, cutting depth and cutting width, and the parameter with higher material removal rate under the same process condition can obtain higher objective function value; the processing cost target, that is, the total cost of various processing elements consumed in processing the feature; the profit rate target function, the equivalent profit rate of the cutting process is calculated according to the cutting parameter, the processing time, the tool consumption speed, the machine tool cost and other parameters in the global variable, and the cutting parameter with higher profit rate is more excellent.

[0010] Further, the objective function of the cutting efficiency evaluation value E is: ; Wherein, is the cutting linear speed; is the cutting depth; f is the feed speed.

[0011] Further, the objective function of the processing cost evaluation index C is: ; Wherein, is the machine tool single-hour use cost; e h is the labor cost rate; e p is the power cost rate; t r is the tool changing time; c t is the tool single-use cost; t m is the processing time; T is the machine tool service life.

[0012] Further, the objective function of the profit rate evaluation index is: ; Wherein, w v is the volume value coefficient.

[0013] Further, the method further comprises: When a process operator adopts a recommended optimal process parameter, the download quantity field of the optimal process parameter is automatically increased by 1, and the score value of the contributor corresponding to the optimal process parameter is increased; The evaluation index score value of the process parameter prepared by each process operator in the same type of data is updated, thereby establishing a value calculation mechanism for technical personnel.

[0014] Further, the method further comprises: The new processing technologist receives a new processing technology, that is, selects specific field features from a plurality of field features of known related information; According to the specific field feature, search for optimal process parameters in the database.

[0015] The beneficial effects of the present application are that the present application provides a process parameter evaluation and buffet-style recommendation method based on digital production line cutting profit rate, which realizes process parameter acquisition through a digital production line system and a functional module embedded in CAM software, and stores it in a database in a structured manner according to the organization structure of fields and digital tables, and predefines parameter evaluation algorithms from the dimensions of processing efficiency, cutting cost, cutting profit rate, etc. to objectively evaluate each process parameter. When process technicians select process parameters for numerical control program compilation, they can select the optimal processing process according to actual needs in combination with the actual processing needs of the digital production line, improve process design efficiency and digital production line processing quality and efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is a hierarchical diagram of the three-level data structure of database, data table, field and record in the embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of a data field of a process parameter and its source in the embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions of the present application will be described in further detail below in combination with specific embodiments, but the protection scope of the present application is not limited to the following description A process parameter evaluation and recommendation method based on digital production line cutting profit rate mainly includes process parameter acquisition, evaluation, sorting recommendation and self-evolution based on a digital production line, and the implementation manner mainly includes the following steps: In a processing process, a certain feature is continuously processed using the same cutting parameters, the part features are divided into a plurality of processing features according to such granularity, the related information of each processing feature is acquired, and a process parameter is defined for each processing feature. The process parameters include cutting amount, feed amount and spindle speed in the processing process. The related information of the processing feature is divided into a plurality of field features, the field features are matched with the process parameters, and then the process resources used for processing the field features, the part drawing number, the cutting amount parameters and the process parameters are matched. The process resources include part material, tool type, tool size, tool coating information, cooling condition, tool stiffness, machine tool model, machine tool type, machine tool motion axis parameters, machine tool maximum cutting force, maximum torque and maximum power, etc.

[0019] Preferably, the relevant information of the machining feature should include the type of the feature, the machining method, the feature size, the feature position, the accuracy requirement, the roughness requirement, the clamping stiffness, etc.

[0020] Global variable storage function, global variable, namely the parameter that does not depend on a certain cutting parameter and exists independently, the global variable should contain the material parameters involved in the factory, such as material hardness, strength, thermal conductivity, machinability, etc.; machine tool parameters, such as the number of machine tool axes, the maximum feed speed of each axis, the commonly used speed range, the maximum torque, the maximum cutting force, etc.; tool parameters, tool type, tool tooth number, tool angle, tool life coefficient, tool clamping method, etc.

[0021] As shown in Figure 1 The three-level structure of database, data table and field is adopted to store all data, including the global variable, the process parameter item, different data tables are established according to the relevant information of the machining feature, each table contains a plurality of data items, each item contains a plurality of fields, the field names in the same table are the same, each table contains a plurality of records, and each field of each record has a specific value.

[0022] The process parameters have the following two sources: 1 through the process of intelligent production line to start processing, the database system calls the production task interface, reads the process resource information, obtains the feature information processed by analyzing the NC code, and combines it into a process parameter and stores it in the database 2 through the process of CAM software to actively define or cam software to automatically identify part feature information, process resource information, and call processing parameter information to combine a process parameter and store it in the database.

[0023] The dynamic script is adopted, the fields stored in the process parameters are used, the global variable fields are combined, and the above parameters are calculated according to a certain algorithm logic, and finally a function value is obtained. This mechanism is called objective function, and the value of the objective function measures the advantages and disadvantages of the cutting parameters in a certain sense.

[0024] The objective function mainly includes the cutting efficiency evaluation target, that is, the material removal rate is calculated according to the speed, feed, depth and width, and the parameter with higher material removal rate under the same process condition can obtain higher objective function value; the processing cost target, that is, the total cost of various processing elements consumed in the processing of the feature; the profit rate target function, according to the cutting parameters, the processing time, the tool consumption speed, the machine tool cost, and the equivalent profit rate of the cutting process are calculated according to the other parameters in the global variable, and the cutting parameter with higher profit rate is more excellent.

[0025] Example 1 This invention provides a method for evaluating and scoring process parameters based on objective function evaluation values, such as... Figure 2 As shown, it mainly includes the collection, evaluation, ranking and recommendation, and self-evolution of process parameters. Its implementation mainly includes the following steps: Select a feature that is continuously machined using the same cutting parameters during the processing. Based on this granularity, divide the part features into several machining features, obtain relevant information for each machining feature, and define a process parameter for each machining feature. The relevant information of the machining feature is broken down into several fields and defined in the process parameters. Then, the process resources used for machining this feature, the part number, cutting parameters, and other information are stored as fields in the process parameters. The global variable storage function stores parameters that do not depend on any specific cutting parameter. These are mainly parameters of fixed asset elements in the production process, such as the performance parameters of a machine tool, the geometric and performance parameters of a cutting tool, and the cutting performance parameters of a certain material.

[0026] All data is stored using a three-level structure of database, data table, and fields, including the global variables and the cutting parameter entries. Different data tables are created according to the object type described by the data. Each table contains several data entries, and each entry contains several fields. Fields in the same table have the same names. Each table contains several records, and each field in each record has its specific value.

[0027] By employing a dynamic script approach, and utilizing the fields already stored in the cutting parameters, combined with the global variable fields, and following a specific algorithmic logic, these parameters are involved in the calculation, ultimately yielding a function value. This mechanism is called the objective function, and the value of the objective function measures the quality of the cutting parameters in a certain sense.

[0028] The process parameters used in the production process are obtained through the database and the intelligent production line interface, and evaluation values ​​are automatically assigned to them according to the evaluation function.

[0029] When a process engineer creates a new machining process, the system uses feature recognition technology to identify the process feature information and process resource information to be created. Based on this, it searches the database for the most matching process parameters. The search algorithm comprehensively considers the matching degree of various resources and the evaluation function value of the cutting parameters themselves. Parameters with higher matching degree and higher evaluation value will be given priority recommendation.

[0030] Whenever a process parameter is used by a process operator, the download field of the parameter is automatically increased by 1, and the score value of the contributor of the parameter is increased. Furthermore, the evaluation index score value of the process parameter prepared by each process operator in the same type of data is calculated, so that a value computer mechanism for technical personnel is established.

[0031] Preferably, the process parameters have the following two source modes: 1. Through the numerical control program issued by the process to start processing in the intelligent production line, the database system calls the production task interface, reads the process resource information, obtains the processed feature information by analyzing the NC code, and combines it into a process parameter and stores it in the database. 2. Through the process operator actively defining or cam software automatically identifying part feature information, process resource information, and calling processing parameter information to combine a process parameter and store it in the database.

[0032] Preferably, the process resources include part material, tool type, tool size, tool coating information, cooling condition, tool stiffness, machine tool model, machine tool type, machine tool motion axis parameters, machine tool maximum cutting force, maximum torque, maximum power, etc.

[0033] Preferably, the related information of the machining feature should include the type of the feature, the machining method, the feature size, the feature position, the accuracy requirement, the roughness requirement, the clamping stiffness, etc.

[0034] Preferably, the global variables should include material parameters related to the factory, such as material hardness, strength, thermal conductivity, machinability, etc.; machine tool parameters, such as the number of machine tool axes, the maximum feed speed of each axis, the commonly used speed range, the maximum torque, the maximum cutting force, etc.; tool parameters, such as tool type, tool tooth number, tool angle, tool life coefficient, tool clamping method, etc.

[0035] Preferably, the objective function mainly includes the cutting efficiency evaluation target, i.e. the material removal rate is calculated according to the speed, feed, depth of cut and width of cut, and the parameter with higher material removal rate under the same process condition can obtain higher objective function value; the processing cost target, i.e. the total cost of various processing elements consumed in the processing of the feature; the profit margin target function, which calculates the equivalent profit margin of the cutting process according to the cutting parameters, processing time, tool consumption speed, machine tool cost, and other parameters in the global variables, and the cutting parameter with higher profit margin is better.

[0036] The following example further illustrates the process parameter data evaluation and scoring method, and the protection scope of the present patent is not limited to the calculation method in the following example.

[0037] The object attributes in the following examples are only partially shown, and only attribute values related to process data evaluation are listed. Attribute values not related to process evaluation, such as the number of axes of a machine tool, are not listed here.

[0038] Example global variables include machine tool information (Table 1), tool information (Table 2), and other global variables (Table 3). Evaluation algorithm examples include three evaluation algorithms shown in Table 4.

[0039] Table 1 Machine Tool Information Table

[0040] Table 2 Tool Information Table

[0041] Table 3 Other Global Variables

[0042] Table 4 Evaluation Algorithm Table

[0043] Example cutting parameter and feature information is shown in Table 5.

[0044] Table 5 Cutting Parameter Table

[0045] The evaluation process for cutting parameters is as follows: when two cutting parameters in Table 5 are entered into the system, three evaluation functions in Table 4 are automatically called to calculate their evaluation values. For the cutting efficiency evaluation index, all three variables in the formula are present in the cutting parameters, so direct calculation is possible. The cutting efficiency evaluation value for cutting parameter 1 is:

[0046] The cutting efficiency evaluation value for cutting parameter 2 is:

[0047] The calculated evaluation values are stored in the corresponding evaluation value fields of cutting parameter 1 and cutting parameter 2, as shown in Table 6.

[0048] Table 6 Cutting Parameter Evaluation Value - Cutting Efficiency Evaluation Index

[0049] For the machining cost evaluation index, since the formula contains values not directly present in the cutting parameters, these values can be queried according to the tools and machine tools in the cutting parameters. Thus, through database connection query function, the augmented cutting parameter table as shown in Table 7 is obtained.

[0050] Table 7 Extended cutting parameter table

[0051] First, according to the life formula of two tools, the tool life under the corresponding parameters is calculated; The tool life of tool 1 under parameter 1 is: ; The tool life of tool 2 under parameter 2 is: ; Then the labor cost rate in the global variable is called , the power cost rate , which is substituted into the cost evaluation formula to obtain the machining cost evaluation index of two cutting parameters.

[0052] The machining cost evaluation index of cutting parameter 1 is: ; The machining cost evaluation index of cutting parameter 2 is: ; And store it in the corresponding evaluation value field of cutting parameter 1 and cutting parameter 2, as shown in Table 8.

[0053] Table 8 Cutting parameter evaluation value—cutting efficiency evaluation index

[0054] Similarly, for the profit rate evaluation index, there is The profit rate evaluation index of cutting parameter 1 is: ; The profit rate evaluation index of cutting parameter 2 is: ; And store it in the corresponding evaluation value field of cutting parameter 1 and cutting parameter 2, as shown in Table 9.

[0055] Table 9 Cutting parameter evaluation value—profit rate evaluation index .

[0057] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

[0058] It should be understood that the present disclosure is not limited to the precise structures as herein described and illustrated in the drawings, and that various modifications and changes can be made without departing from its scope. The scope of the present disclosure is limited only by the claims that follow.

Claims

1. A method for evaluating and recommending process parameters based on the cutting profit margin of a digital production line, characterized in that, include: The relevant information of the machining feature is broken down into several field features. The field features are matched with the corresponding process parameters. Then, the process resources, part number, and cutting parameters used for machining the field feature are matched with the corresponding process parameters. Obtain parameters that exist independently of any specific process parameter as global variables; A three-level structure of database, data table, and fields is used to store all the above data: multiple data tables are established in the database according to the relevant information of the processing features. Each data table contains several data entries for the same field feature. Each data entry records the corresponding process resource field, the part drawing number field, the cutting parameter field, the process parameter field, and the global variable field. Using a dynamic script approach, the process parameters are evaluated based on user requirements by utilizing the fields already stored in the process parameters and combining them with the global variable fields. Based on the evaluation results, the optimal process parameters are recommended.

2. The method according to claim 1, characterized in that, Process parameters include cutting amount, feed rate, and spindle speed during the machining process; Process resources include part materials, tool type, tool size, tool coating information, cooling conditions, tool stiffness, machine tool model, machine tool type, parameters of each motion axis of the machine tool, maximum cutting force, maximum torque, and maximum power of the machine tool; Information related to machining features includes the feature type, machining method, feature size, feature location, accuracy requirements, roughness requirements, and clamping rigidity; The global variables include material parameters, machine tool parameters, and tool parameters involved in the factory. Material parameters include material hardness, strength, thermal conductivity, and machinability. Machine tool parameters include the number of machine tool axes, maximum feed rate of each axis, speed range, maximum torque, and maximum cutting force. Tool parameters include tool type, number of tool teeth, tool angle, tool life coefficient, and tool clamping method.

3. The method according to claim 1, characterized in that, Process parameters can be obtained from the following two sources: 1) Custom process parameters: The processing is started on the intelligent production line through the CNC program issued by the process. The database system calls the production scheduling task interface, reads the process resource information, obtains the processing feature information by parsing the NC code, and combines it into a process parameter and stores it in the database. 2) Recommended optimal process parameters: The process engineer actively defines the process parameters in the CAM software or the CAM software automatically identifies the part feature information and process resource information, and retrieves the processing parameter information to combine them into a process parameter and store it in the database.

4. The method according to claim 2, characterized in that, Evaluate process parameters based on user requirements, including: Based on user requirements, the objective function is determined. The objective function includes a cutting efficiency evaluation objective, which is to calculate the material removal rate based on the spindle speed, feed rate, depth of cut, and cutting width. Under the same process conditions, parameters with higher material removal rates will obtain higher objective function values. The machining cost objective is the total cost of various machining elements consumed in performing this feature machining. The profit margin objective function is to calculate the equivalent profit margin of this cutting process based on cutting parameters, machining time, tool consumption speed, machine tool cost, and other parameters in the global variables. Cutting parameters with higher profit margins are more excellent.

5. The method according to claim 4, characterized in that, The objective function for the cutting efficiency evaluation value E is: ; in, It is the cutting linear velocity; It is the depth of cut; f It refers to the feed rate.

6. The method according to claim 4, characterized in that, Objective function of processing cost evaluation index C: ; in, Cost per hour of machine tool use; e h The labor cost rate; e p For energy cost ratio; t r This is the tool change time; c t Cost per use of the cutting tool; t m Processing time; T For machine tool life.

7. The method according to claim 5, characterized in that, Profit margin evaluation indicators Objective function: ; in, w v It is the volume value coefficient.

8. The method according to claim 1, characterized in that, The method further includes: Whenever a process engineer adopts a recommended optimal process parameter, the download count field for that optimal process parameter is automatically incremented by 1, and the score of the contributor to that optimal process parameter will increase. The evaluation index scores of the process parameters compiled by each process engineer were updated in the same type of data, thereby establishing a value calculation mechanism for technical personnel.

9. The method according to claim 1, characterized in that, The method further includes: Receive new processing technology from process engineers, that is, select specific field features from several field features of known relevant information; Based on specific field characteristics, search the database for optimal process parameters.

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