Processing data processing method and device and application method thereof

Abstract

The invention provides a processing data processing method which comprises the steps that a process data set generated when a target tool machines a target workpiece is acquired, and the process data set comprises process response data and processing quality data; based on the process response data, respectively calculating material performance and tool performance when the target workpiece is machined; quality parameters generated by machining the target workpiece are determined based on the machining quality data; correlating the material performance with the tool performance to obtain a tool workpiece combination; and generating a tool and workpiece combination evaluation database according to the tool and workpiece combinations and the corresponding quality parameters. Material performance and tool performance are calculated based on actual process response data through the process data set of the target tool during machining of the target workpiece, and actual performance evaluation is obtained. Meanwhile, the material performance, the tool performance and the quality parameters are associated to generate a tool and workpiece combination evaluation database, so that a user can determine a tool and workpiece combination which should be selected according to the quality parameters in the process production activity according to the evaluation database.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of industrial Internet and intelligent manufacturing, and in particular to a processing data processing method, an apparatus and an application method thereof. Background Art

[0002] In the mechanical processing industry, some elements will change frequently during use, and the performance of these frequently changed elements will also change. For example, the tool performance of a machining tool changes with use.

[0003] However, based on such frequently changed elements, their performance evaluation often adopts the initial performance evaluation based on the time of purchase. On the one hand, as the equipment or tool is used, this performance evaluation continuously deviates from the actual performance, and it is impossible to accurately describe the actual performance of the tool or equipment, providing little help for actual process production activities. Summary of the Invention

[0004] The present application aims to provide a processing data processing method to establish an association relationship between actual performance and processing quality, so as to provide effective guidance for actual production activities.

[0005] In a first aspect, the embodiments of the present application provide a processing data processing method, including:

[0006] Obtain a process data set generated when a target tool processes a target workpiece, where the process data set includes process response data and processing quality data;

[0007] Based on the process response data, calculate the material performance and tool performance when processing the target workpiece respectively;

[0008] Based on the processing quality data, determine the quality parameters generated when processing the target workpiece;

[0009] Associate the material performance with the tool performance to obtain a tool-workpiece combination;

[0010] Generate a tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameters.

[0011] Optionally, the target workpiece includes a plurality of processing regions, and each processing region has independent quality parameters. The obtaining of the process data set generated when the target tool processes the target workpiece includes:

[0012] Determine a target processing region, where the target processing region is one of the plurality of processing regions;

[0013] Obtain the process response data of the target processing region;

[0014] Then, the steps of calculating the material properties and tool properties during the machining of the target workpiece based on the process response data respectively include:

[0015] Based on the process response data of the target machining area, calculate the material properties and tool properties of the target machining area respectively.

[0016] Optionally, the calculating the material properties of the target machining area based on the process response data of the target machining area includes:

[0017] Based on the process response data of the target machining area, calculate the specific cutting energy per revolution or the specific cutting force per revolution of the target machining area, and the specific cutting energy per revolution and the specific cutting force per revolution are used to represent the material properties.

[0018] Optionally, the calculating the tool properties of the target machining area based on the process response data of the target machining area includes:

[0019] Based on the process response data of the target machining area, calculate the theoretical cutting power or the theoretical cutting force of the target machining area;

[0020] Compare the theoretical cutting power or the theoretical cutting force with the actual spindle cutting power or the actual spindle cutting force in the actual process parameters to obtain the cutting power increment or the cutting force increment, and the cutting power increment and the cutting force increment are used to represent the tool properties.

[0021] Optionally, after calculating the cutting power increment or the cutting force increment, it further includes:

[0022] Determine the number of workpieces machined by the target tool and the corresponding cutting power increment or the cutting force increment of the target tool under different numbers of workpieces;

[0023] Define multiple wear stages according to the number of workpieces machined by the target tool;

[0024] Obtain the wear rate of the target tool according to the number of workpieces of the target tool in different wear stages and the cutting power increment or the cutting force increment.

[0025] Optionally, the tool-workpiece combination evaluation database includes a tool-workpiece combination evaluation diagram;

[0026] The generating the tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameters includes:

[0027] Generate the coordinate axes of the tool-workpiece combination evaluation diagram according to the material properties and the tool properties;

[0028] Generate the quality parameter line segments of the tool-workpiece combination evaluation diagram according to the quality parameters corresponding to the tool-workpiece combination;

[0029] Generate the tool-workpiece combination evaluation diagram according to the quality parameter line segments and the coordinate axes.

[0030] In a second aspect, the present application also provides an application method for processing data. Based on the tool-workpiece combination evaluation database as described in the first aspect, the application method includes:

[0031] Confirm the tool-workpiece combination and the nominal process parameters of the target tool for machining the target workpiece;

[0032] Based on the tool-workpiece combination evaluation database as described in the first aspect, obtain the corresponding nominal process response data;

[0033] Machine based on the nominal process parameters, use the target tool to machine the target workpiece, and obtain the real-time process response data during the machining process;

[0034] Determine the difference between the real-time process response data and the nominal process response data;

[0035] Predict whether the quality parameters after machining meet the conditions based on the difference.

[0036] In a third aspect, the present application also provides a processing data processing device, including:

[0037] A process data acquisition module, configured to acquire a process data set generated when a target tool machines a target workpiece, where the process data set includes process response data and machining quality data;

[0038] A performance calculation module, configured to calculate the material performance and the tool performance respectively when machining the target workpiece based on the process response data;

[0039] A quality parameter acquisition module, configured to determine the quality parameters generated when machining the target workpiece based on the machining quality data;

[0040] A performance association combination module, configured to associate the material performance with the tool performance to obtain a tool-workpiece combination;

[0041] An evaluation generation module, configured to generate a tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameters.

[0042] Fourth aspect, the present application further provides an electronic device, including a memory and a processor, the memory being connected to the processor, the processor being configured to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the electronic device implements the method as described in the first aspect or the third aspect.

[0043] Fifth aspect, the present application further provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and when the program instructions are executed by a processor, the processor is caused to execute the method as described in the first aspect or the third aspect.

[0044] The embodiments of the present application can achieve the following technical effects: Based on the process data set when the target tool processes the target workpiece, calculate the material performance and tool performance based on the actual process response data to obtain a real performance evaluation. At the same time, correlate the material performance and tool performance to obtain a tool-workpiece combination, and then generate a tool-workpiece combination evaluation database through the tool-workpiece combination and its corresponding quality parameters, enabling the user to determine the tool and workpiece combination to be selected for the target quality parameters in the process production activities according to the evaluation database. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0046] Figure 1 It is a diagram of an application scenario of a processing data processing method provided by an embodiment of the present application;

[0047] Figure 2 It is a schematic flowchart of a processing data processing method provided by an embodiment of the present application;

[0048] Figure 3 It is a schematic flowchart of a method for calculating the material performance of machining the target machining area provided by an embodiment of the present application;

[0049] Figure 4 It is a schematic flowchart of a method for calculating the tool performance of machining the target machining area provided by an embodiment of the present application;

[0050] Figure 5 It is a schematic flowchart of a method for generating a tool-workpiece combination evaluation diagram according to the tool-workpiece combination and the corresponding quality parameters provided by an embodiment of the present application;

[0051] Figure 6 Tool-workpiece combination evaluation diagram provided by an embodiment of the present application;

[0052] Figure 7 Schematic flowchart of the application method of the processing data provided by an embodiment of the present application;

[0053] Figure 8 Schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0054] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts fall within the protection scope of the present application.

[0055] It should be noted that if there is no conflict, the various features in the embodiments of the present application can be combined with each other and all fall within the protection scope of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart. Furthermore, the terms "first", "second", "third", etc. used in the present application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0056] First, to facilitate the introduction of the processing data processing method provided by the embodiments of the present application, the application environment of the method provided by the embodiments of the present application will be introduced.

[0057] Please refer to Figure 1 , Figure 1 which is an application scenario diagram of the processing data processing method provided by an embodiment of the present application. This application scenario includes a processing device 10, a monitoring system 20, and a target workpiece 30. Among them, the processing device 10 is used to process the assembled target workpiece 30 according to a preset program algorithm, and the monitoring system 20 is used to collect and analyze the process data of the processing device 10.

[0058] The processing device 10 includes a machine tool 11, a tool 12 and a fixture disposed on the machine tool 11. Among them, the fixture is used to clamp the workpiece 30 to fix the workpiece 30 at the processing position of the machine tool 11, and the tool 12 is connected to the driving spindle of the machine tool 11, and the driving spindle is used to drive the tool 12 to perform the processing operation on the workpiece 30.

[0059] The monitoring system 20 is used to monitor the process data during the machining of the workpiece 30 by the machining device 10, and after analyzing the process data, summarize it to obtain a process data set.

[0060] Specifically, the process data can be divided into 6 categories:

[0061] One is the shape feature variables of the workpiece 30, including but not limited to: length, width, angle, diameter, etc., or the ratio or product of the above parameters, such as the aspect ratio, depth-width ratio, depth-diameter ratio, etc.

[0062] The second is the process parameters for driving the spindle, including but not limited to: the actual spindle speed, the actual feed rate, the actual cutting width, the actual cutting depth, etc., or the ratio or product of the above parameters, such as the feed per revolution, the material removal rate, etc.

[0063] The third is the process response data, including but not limited to: the actual spindle power generated during the machining process, the actual vibration of the components, etc.

[0064] The fourth is the material properties of the workpiece 30, including but not limited to: the machinability of the material, the specific cutting energy of the material, etc.

[0065] The fifth is the characteristics of the tool 12, including but not limited to: the sharpness of the tool 12, the wear degree of the tool 12, etc.

[0066] The sixth is the machining quality data, including but not limited to: dimensional accuracy, form and position accuracy, surface roughness, burn degree, vibration pattern degree, etc.

[0067] In some embodiments, the monitoring system 20 is specifically a digital twin system, which is used to control the machine tool 11 for machining.

[0068] Specifically, the digital twin system can, based on the existing machine tool accuracy database and process database, form a process strategy based on feature recognition technology after optimization and verification, including but not limited to: the machine tool 11 selection strategy (i.e., how to select a suitable machine tool 11); the fixture design strategy (i.e., how to select the fixture setting position and how to determine the clamping posture); the machining method strategy (i.e., how to select the process method, the tool path, the machining step sequence, and the corresponding tool 12); the process parameter determination strategy (i.e., how to determine process parameters such as the nominal spindle speed, the nominal feed rate, the nominal cutting depth, the nominal cutting width, etc.); the target control strategy (i.e., how to control the target through the spindle power and how to control the target through the vibration of the components).

[0069] It should be understood that the above is only one of the multiple embodiments provided by this application. Those skilled in the art can configure the hardware architecture and software design of the monitoring system 20 according to the processing signals required by specific business needs, and the specific composition of the monitoring system 20 is not limited herein. For example, if the configured processing signal is a power signal, the monitoring system 20 can be configured with a current sensor and a voltage sensor. If the configured processing signal is a vibration signal, the monitoring system 20 can be configured with a vibration sensor, and the vibration sensor can be installed in the processing area.

[0070] Based on the above application scenarios, the processing data processing method provided by the embodiments of this application will be specifically introduced below.

[0071] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of the processing data processing method provided by an embodiment of this application, and it includes:

[0072] S21. Obtain a process data set generated when a target tool processes a target workpiece, where the process data set includes process response data and machining quality data.

[0073] In this step, the target workpiece is the workpiece to be processed corresponding to the process data collected by the monitoring system, and the target tool is the machining tool corresponding to the process data collected by the monitoring system.

[0074] It can be understood that the target workpiece or target tool does not refer to a specific workpiece or tool, but a workpiece or tool that has a specific connection with a certain process data collected. For example, when tool A1 processes workpiece B1 to obtain process data C1, for process data C1, tool A1 is the target tool and workpiece B1 is the target workpiece. And for the process data C2 obtained by tool A1 processing workpiece B2, tool A1 is still the target tool and workpiece B2 is the target workpiece. That is, the determination of the target tool and the target workpiece depends on the tool and workpiece corresponding to the process data.

[0075] In this step, the process data set is a set constructed from multiple sets of process data. For example, the process data set includes process data {C1, C2, C3,..., C n}. Specifically, the process data set includes at least two types of data: process response data and machining quality data.

[0076] In this step, the process response data is defined as the data actually generated when the processing device processes under the set nominal process parameters. For example, the cutting power signal driving the spindle, the vibration signal driving the spindle, the three-way cutting force on the target workpiece, the sound signal, etc. The process response data is used to calculate the material properties of the target workpiece and the tool properties of the target tool.

[0077] In this step, the machining quality data is defined as the quality parameters of the target workpiece, such as the dimensional accuracy, geometric accuracy, surface roughness, degree of burning, degree of vibration marks, etc. of the workpiece. In the embodiment of the present application, the machining quality data is one of the evaluation bases for evaluating the target workpiece and the target tool, and is used to establish a connection with the corresponding target workpiece, target tool and their combinations, etc., so as to reasonably evaluate the corresponding target workpiece, target tool and their combinations.

[0078] S22. Based on the process response data, calculate the material properties and tool properties when machining the target workpiece respectively.

[0079] In this step, the material property is a material characteristic parameter used to describe the target workpiece.

[0080] In this embodiment, the material property may refer to the machinability of the target workpiece, that is, a quantitative parameter index indicating whether the material is easily removed by the tool. It can be understood that whether the material is easily removed by the tool depends not only on the machinability of the material itself, but also on the performance of the tool during machining. Therefore, the brand of the tool, the degree of wear of the tool, the type of the tool, etc. will all affect the machinability.

[0081] When using machinability to horizontally compare the cutting properties of materials of different brands or grades, it is necessary to use the cutting property parameters generated by a new tool of the same brand and structure for comparison. Specifically, in the embodiment of the present application, machinability is represented by the specific cutting energy per revolution or the specific cutting force per revolution parameter.

[0082] In this step, the tool property is a performance characteristic parameter used to describe the target tool. In the embodiment of the present application, the tool property may refer to the wear degree of the target tool, that is, a quantitative parameter index of the cutting ability of the tool at different usage times. It can be understood that there are differences in the cutting ability of tools of different brands, different materials or different structures at the same usage time. Therefore, the tool property can be used to longitudinally compare the cutting ability of tools of the same brand and the same structure at different usage times.

[0083] Specifically, in the embodiment of the present application, the tool wear degree can be represented by the spindle cutting power after removing the influence of the material property.

[0084] In this step, both the material property of the target workpiece and the tool property of the target tool are calculated based on the process response data. The specific calculation method will be described in detail later and will not be elaborated here.

[0085] S23. Determine the quality parameters generated when machining the target workpiece based on the machining quality data.

[0086] In this step, the quality parameter is a parameter used to describe the machining quality of the target workpiece. Essentially, it belongs to the same type of data as the machining quality data, and the quality parameter is qualitatively described through classification based on the machining quality data. Specifically, the machining quality data is a parameter specific to a certain process data. It can be understood that converting the machining quality data into quality data in this step is equivalent to changing the parameter from belonging to the process data to using it as the final evaluation criterion. For example, the quality parameter can be dimensional accuracy, form and position accuracy, surface roughness, degree of burn, degree of vibration marks, etc.

[0087] S24. Correlate the material property of the workpiece with the tool property to obtain a tool-workpiece combination.

[0088] In this step, the target workpiece has material properties, and the target tool correspondingly has tool properties. The tool-workpiece combination is obtained by correlating the material properties of the target workpiece with the tool properties of the target tool. In the prior art, since the suppliers of workpieces and the suppliers of tools are usually independent, the material properties of the workpieces provided are independent of the tool properties of the tools. However, in actual production activities, the workpiece must obviously be used in cooperation with the tool, and the machining quality of the workpiece depends on the material state and the tool state of the workpiece. For a parts factory, when machining the same type of workpiece, the workpiece generally remains unchanged, so the correlation between the tool and the quality in the same machining scenario is particularly important. Therefore, in the embodiments of this application, the material properties and tool properties obtained through actual detection are correlated with each other to obtain a tool-workpiece combination regarding the target workpiece and the target tool.

[0089] Specifically, the user only needs to determine the tool-workpiece combination to determine the corresponding material properties and tool properties. For example, when the process data C1 is obtained by the target tool A1 machining the target workpiece B1, the tool property D1 and the material property D2 of the workpiece under the conditions of A1 and B1 can be determined from the process data C1. When the target tool A2 machines the target workpiece B2, there are correspondingly the tool property D3 and the material property D4 of the workpiece. The user only needs to determine the tool-workpiece combination (i.e., determine the target tool and the target workpiece) to infer and obtain the real-time tool properties and material properties, and decouple the tool properties and the material properties of the process response data through different indicators, so as to evaluate the tool properties and material properties more accurately.

[0090] S25. Generate a tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameter.

[0091] In this step, the tool-workpiece combination evaluation database is an evaluation set used to evaluate the tool-workpiece combination. The evaluation can be represented in various forms. It is understandable that the evaluation drawing is the most intuitive visual presentation means, and those skilled in the art can convert the evaluation drawing into other forms such as tables, formulas, and text descriptions according to needs, and this application does not make any limitations in this regard.

[0092] Specifically, the tool-workpiece combination evaluation database associates the tool-workpiece combination with its corresponding quality parameters, enabling users to predict the quality parameters obtained by machining based on the tool-workpiece combination, or to determine the tool-workpiece combination to be selected according to the required quality parameters.

[0093] For example, if the user needs to machine the target workpiece to a surface roughness level of Ra1.6, it is found according to the tool-workpiece combination evaluation database that if the target workpiece is B3, then the target tool A4 or A6 should be selected. Another example is that if the user determines to use the target tool A3 to machine the target workpiece B4, it is speculated according to the tool-workpiece combination evaluation database that the surface roughness of the final target workpiece is Ra3.2 level.

[0094] In summary, in the embodiment of this application, through the process data set of the target tool when machining the target workpiece, the material performance and tool performance are calculated based on the actual process response data to obtain a real performance evaluation. At the same time, the material performance and tool performance are correlated with each other to obtain a tool-workpiece combination, and then a tool-workpiece combination evaluation database is generated through the tool-workpiece combination and its corresponding quality parameters, enabling users to determine the tool and workpiece combination to be selected for the target quality parameters in the process production activities according to the evaluation database.

[0095] Next, the method for calculating the material performance of the target workpiece in the embodiment of this application will be specifically introduced.

[0096] First, the target workpiece includes multiple machining areas. For example, for a cylindrical part, the two end faces have different surface roughness requirements. One end face is ground in one process, and this end face belongs to one machining area, and the other end face is ground in another process, and the other end face belongs to another machining area.

[0097] It can be understood that because the machining targets are different, the same end face in different processes also belongs to different machining areas, and different machining areas with different machining targets at different positions also belong to different machining areas.

[0098] Therefore, different machining areas are defined as areas where the process parameters are stable and consistent during the cutting of the workpiece by the tool. Stable and consistent means that within the same machining area, the machining process experienced by each small unit is stable enough, and relevant parameters such as shape feature variables, process parameters, process responses, material properties, and tool properties are considered unchanged. That is, it is necessary to calculate the material properties of each machining area. Specifically, when calculating the material properties of a specific machining area, this machining area is called the target machining area, and the target machining area can be any one of the machining areas. It can be understood that the concept of machining area is also applicable when calculating tool performance, and will not be elaborated further hereinafter.

[0099] Since different machining areas belong to different machining steps, the quality of machining for each machining area can be predicted after obtaining the data. For example, for machining area Z1, since the same machining process is used and the relevant parameters are stable and consistent, the same quality parameters can be used to describe machining area Z1. For example, the roughness of machining area Z1 is at the Ra6.3 level, that is, for each small position point in machining area Z1, its roughness is regarded as the Ra6.3 level. For the adjacent machining area Z2, its roughness may be the same as that of Z1 or different. For example, the roughness is at the Ra1.6 level, that is, the roughness of each position point in machining area Z2 is regarded as the Ra1.6 level.

[0100] Please refer to Figure 3 , Figure 3 which is a schematic flowchart of a method for calculating the material properties of machining the target machining area provided by an embodiment of the present application, specifically including:

[0101] S31. Based on the process response data of the target machining area, determine the nominal process parameters and actual process parameters of the target machining area.

[0102] S32. Based on the nominal process parameters of the target machining area, calculate the nominal material removal rate per revolution of the target machining area.

[0103] S33. According to the nominal material removal rate per revolution and the actual process parameters, calculate the specific cutting energy per revolution or the specific cutting force per revolution of the target machining area, and the specific cutting energy per revolution and the specific cutting force per revolution are used to represent the material properties.

[0104] In S31, the nominal process parameters are the machining parameters designed to achieve the machining target, and the machining parameters can machine the workpiece into a preset shape. For example, the nominal process parameters include parameters such as coordinates, nominal cutting depth, nominal cutting width, nominal spindle speed, and nominal feed rate.

[0105] The actual process parameters are the parameters obtained by the monitoring system reading the real-time information of the numerical control system.

[0106] For example, the actual process parameters include parameters such as the actual spindle cutting power, the actual spindle cutting force, the actual spindle speed override, and the actual feed rate override. It should be particularly noted that the reference tool corresponding to the nominal process parameters is the target tool in the reference state. The reference state is usually the brand-new state of the tool, that is, an unused tool. In the reference state, based on the calculation of the cutting amount by the process personnel, the machining parameters given are the nominal process parameters. It can be understood that the material properties detected for the workpiece under different states of the same tool may be different. For example, when machining a workpiece with a newly sharpened tool, the workpiece is relatively easy to be cut; while if machining the workpiece with an old and dull tool, the detection may consider the workpiece difficult to be cut. Therefore, in order to eliminate the influence of the tool state on the material properties of the workpiece, when detecting the material properties, a tool in the reference state is uniformly used for machining and detection, which can improve the credibility of the obtained material properties.

[0107] In S32, the nominal material removal rate per revolution is defined as the material removal rate of the target machining area when the driving spindle rotates one revolution. Specifically, the nominal material removal rate per revolution can be calculated according to the following exemplary relational expression:

[0108] MRRPR = f1(coord, ap, aw, n, v f )

[0109] where MRRPR is the nominal material removal rate per revolution, ap is the nominal cutting depth, aw is the nominal cutting width, n is the nominal spindle speed, v f is the nominal feed rate, and f1 is the calculation function. The calculation function f1 for each specific parameter can be determined with reference to the common knowledge in the art, or the calculation function f1 can be adaptively adjusted according to the actual situation, and no limitation is made thereto.

[0110] In S33, the specific cutting energy per revolution is defined as the cutting energy required to machine the target machining area when the driving spindle rotates one revolution. The specific cutting force per revolution is defined as the cutting force required to machine the target machining area when the driving spindle rotates one revolution. Both the specific cutting energy per revolution and the specific cutting force per revolution can be used to represent the material properties of the target machining area. Specifically, the specific cutting energy per revolution and the specific cutting force per revolution can be calculated according to the following exemplary relational expression:

[0111] ECPR ~ FCPR = f2(MRRPR, P m , F c , spindle_override, feedrate_override)

[0112] where ECPR is the specific cutting energy per revolution, FCPR is the specific cutting force per revolution, P mis the actual spindle cutting power, F c is the actual spindle cutting force, spindle_override is the actual spindle speed override, feedrate_override is the actual feed rate override, f2 is a calculation function, and ECPR and FCPR can be converted to each other. The calculation function f2 for each specific parameter can be determined with reference to the common general knowledge in the art, or can be adaptively adjusted according to the actual situation for the calculation function f2, and no limitation is imposed thereon.

[0113] The following specifically introduces the method for calculating the tool performance of the target tool in the embodiments of the present application.

[0114] Please refer to Figure 4 , Figure 4 which is a schematic flowchart of the method for calculating the tool performance for machining the target machining area provided by an embodiment of the present application, specifically including:

[0115] S41. Based on the process response data of the target machining area, determine the nominal process parameters and the actual process parameters of the target machining area.

[0116] S42. Based on the nominal process parameters and the actual process parameters of the target machining area, calculate the specific cutting energy per revolution or the specific cutting force per revolution of the target machining area.

[0117] S43. According to the specific cutting energy per revolution or the specific cutting force per revolution, calculate the theoretical cutting power or the theoretical cutting force of the target machining area.

[0118] S44. According to the comparison between the theoretical cutting power or the theoretical cutting force and the actual spindle cutting power or the actual spindle cutting force in the actual process parameters, calculate the cutting power increment or the cutting force increment, and the cutting power increment and the cutting force increment are used to represent the tool performance.

[0119] In S41 and S42, the principle of the steps is the same as the principle of calculating the material performance above, and the specific implementation details can refer to steps S31, S32 and S33, which will not be elaborated here.

[0120] In S43, as described above, the specific cutting energy per revolution and the specific cutting force per revolution are parameters of the target tool in the reference state. Therefore, the theoretical cutting power and the theoretical cutting force are also the cutting power and the cutting force for machining the target area with the target tool in the reference state. The formulas for specifically calculating the theoretical cutting power and the theoretical cutting force according to the specific cutting energy per revolution and the specific cutting force per revolution are common general knowledge in the art and will not be introduced in detail here.

[0121] In S44, the actual process parameters include the actual spindle cutting power and the actual spindle cutting force. By comparing the theoretical cutting power with the actual spindle cutting power, or by comparing the theoretical cutting force with the actual spindle cutting force, the cutting power increment or the cutting force increment can be calculated. For example, if the theoretical cutting power is 5 kw and the actual cutting power is 8 kw, then the calculated cutting power increment is 8 - 5 = 3 kw. Another example, if the theoretical cutting force is 600 N and the actual cutting power is 500 N, then the calculated cutting force increment is 500 - 600 = -100 N. Both the cutting power increment and the cutting force increment can be used to represent the wear degree of the tool, that is, the tool performance.

[0122] In some embodiments, after calculating the tool performance, the wear rate of the tool can also be calculated. Specifically, it includes the following steps:

[0123] S45. Determine the number of workpieces processed by the target tool and the corresponding cutting power increment or cutting force increment of the target tool under different numbers of workpieces processed.

[0124] S46. Define multiple wear stages according to the number of workpieces processed by the target tool.

[0125] S47. Calculate the wear rate of the target tool according to the number of workpieces processed by the target tool under different wear stages and the cutting power increment or cutting force increment.

[0126] In S45, the process data set records the number of workpieces that the target tool has processed before this processing, that is, the number of workpieces processed. At the same time, the process data set also correspondingly records the cutting power increment or cutting force increment of the target tool at different numbers of workpieces processed, that is, the tool performance. For example, for the target tool A1, it can be determined that the number of workpieces it has processed is 300, and it can be determined that the tool performance of the target tool A1 in the initial state is D1, the tool performance when processing 100 workpieces is D2, the tool performance when processing 200 workpieces is D3, the tool performance when processing 300 workpieces is D4... and so on.

[0127] In S46, the wear rate of the target tool is obviously different under different wear conditions. For example, when the target tool is just starting to be used, obvious wear will occur after a relatively long period of use; while when the target tool already shows a certain degree of wear, after a relatively short period of use, its wear will significantly further intensify. Specifically, in some embodiments, the target tool is divided into 3 wear stages: the initial wear stage, the normal wear stage, and the rapid wear stage. It can be understood that in other embodiments, the target tool can be defined as having more or fewer wear stages, and those skilled in the art can make adaptive adjustments according to the actual situation.

[0128] In S47, for the target tool at different wear stages, the variation relationships of the cutting power increment or the cutting force increment with the number of processed workpieces are different. Therefore, in some embodiments, the user can calculate the first-order difference parameter and the second-order difference parameter of the cutting power increment or the cutting force increment with respect to the number of processed workpieces, and construct a wear rate equation based on the first-order difference parameter and the second-order difference parameter. For example, an example of the wear rate equation is as follows:

[0129] △P w = a * n 2 + b * n + c

[0130] where △P w is the cutting power increment, a is the second-order difference parameter, b is the first-order difference parameter, c is a constant, and n is the number of processed workpieces. It can be understood that at different wear stages, the fitted wear rate equations are different, and thus the critical processed workpiece values of different wear rate equations can be determined. To determine the cumulative number of processed workpieces in the initial wear stage of the specific tool as n1, the cumulative number of processed workpieces in the normal wear stage of the tool as n2, and the cumulative number of processed workpieces in the rapid wear stage of the tool as n3, so as to evaluate the wear rate of the target tool in each stage.

[0131] Next, a method for generating a tool-workpiece combination evaluation database provided by an embodiment of the present application will be introduced.

[0132] Please refer to Figure 5 , Figure 5 which is a schematic flowchart of a method for generating a tool-workpiece combination evaluation diagram according to the tool-workpiece combination and the corresponding quality parameters provided by an embodiment of the present application, specifically including:

[0133] S51. Generate the coordinate axes of the tool-workpiece combination evaluation diagram according to the material properties and the tool properties.

[0134] S52. Generate the quality parameter line segment of the tool-workpiece combination evaluation diagram according to the quality parameters corresponding to the tool-workpiece combination.

[0135] S53. Generate the tool-workpiece combination evaluation diagram according to the quality parameter line segment and the coordinate axes.

[0136] In S51, the coordinate axes include a vertical horizontal axis and a vertical axis, where the material properties and the tool properties respectively correspond to one coordinate axis. For example, please refer to Figure 6 , Figure 6This is an evaluation diagram of a tool-workpiece combination provided by an embodiment of the present application. Here, the horizontal axis represents tool performance, and the vertical axis represents material performance. For point P1, its corresponding abscissa is D1 and its ordinate is E2. Thus, it can be determined that the tool performance corresponding to point P1 is D1 and the material performance is E2, that is, point P can represent a specific tool-workpiece combination. It can be understood that in some other embodiments, the horizontal axis can represent material performance and the vertical axis can represent tool performance.

[0137] In S52, different tool-workpiece combinations may have different quality parameters. Specifically, first, according to the determined limit conditions corresponding to the quality parameters after quantization and classification, the position points {P1, P2, P3,... P n} of the tool-workpiece combination are determined, and then these position points are connected to form a line segment L1, and this line segment L1 is the quality parameter line segment. For example, the quality parameter is surface roughness. It is detected and determined that the roughness of the A machining area on the surface of the target workpiece is at the Ra1.6 level, the B area is at the Ra3.2 level, and the C area is at the Ra6.3 level. According to the actual coordinate ranges of the three machining areas A, B, and C, the machining area numbers, the material performance indexes and tool performance indexes at the time of machining are obtained, so as to further subdivide the distribution of the tool-material performance combinations that reach each level of surface roughness under this condition. Please refer to Figure 6 again. The area to the left of line segment L1 is the tool-workpiece combination corresponding to the surface roughness Ra1.6 level; the area between line segment L1 and line segment L2 is the tool-workpiece combination corresponding to the Ra3.2 level, and the area to the right of line segment L2 is the tool-workpiece combination corresponding to the surface roughness Ra6.3 level.

[0138] The embodiment of the present application also provides an application method for machining data. Please refer to Figure 7 Figure 7 This is a schematic flowchart of the application method for machining data provided by an embodiment of the present application, specifically including:

[0139] S71. Confirm the tool-workpiece combination and the nominal process parameters of the tool-workpiece combination.

[0140] S72. Obtain the corresponding nominal process response data according to the tool-workpiece combination evaluation database.

[0141] S73. Based on the nominal process parameters for machining, use the target tool to machine the target workpiece, and obtain the real-time process response data during the machining process.

[0142] S74. Determine the difference between the real-time process response data and the nominal process response data.

[0143] S75. Based on the difference, predict whether the quality parameters after machining meet the conditions. ​

[0144] In S71, the tool-workpiece combination is known, and accordingly, the nominal process parameters of the tool-workpiece combination can also be queried and obtained. Specifically, the nominal process parameters can be obtained according to the monitoring system, can also be obtained according to the established evaluation database of the tool-workpiece combination, or can also be directly set by the user himself.

[0145] In S72, according to the tool-workpiece combination and in combination with the evaluation database of the tool-workpiece combination provided in the present application, the nominal process parameters and the theoretical quality parameters corresponding to the specific tool-workpiece combination can be determined.

[0146] In S73, the real-time process response data during the machining process can be directly obtained through the monitoring system.

[0147] In S74, the difference between the real-time process response data and the nominal process response data can specifically be obtained by comparing the values of the two. The deviation ratio is the ratio of the real-time process response data to the nominal process response data. The process response parameters include various types of parameters, and specifically which parameter or which combination of several parameters to select can be adjusted according to the actual situation. For example, the theoretical process response data includes parameters u1, u2, u3, and u4, and the actual process response data includes parameters z1, z2, z3, and z4, then the corresponding deviation ratio can be DR i = z i / u i , where i = 1, 2, 3, 4.

[0148] In S74, the actual process response data can be calculated according to the corresponding deviation ratio and the theoretical process response data, and then the actual quality parameters of the target workpiece can also be predicted according to the deviation ratio and the theoretical quality parameters. Specifically, first, a large number of theoretical process response data, actual process response data, deviation ratios, theoretical quality parameters, and actual quality parameters are collected to construct a training sample, and then a quality parameter prediction model is constructed through a machine learning algorithm. Thus, based on the given tool-workpiece combination and the deviation ratio, the deviation ratio of the quality parameters can be obtained, then the theoretical quality parameters are determined according to the tool-workpiece combination and in combination with the tool material evaluation diagram, and finally the predicted quality parameters are calculated according to the theoretical quality parameters and the deviation ratio of the quality parameters, so as to replace part of the inspection of the real quality parameters and reduce the workload of quality inspection of the target workpiece.

[0149] In summary, the machining data processing method provided by the embodiments of the present application quantifies and calculates the characteristics of the tool and workpiece material during machining based on the process response data collected during the actual machining process, and at the same time establishes a connection with the actual quality parameters. First, since the evaluation method uses the actual process response for calculation, the tool performance obtained by calculation changes with machining variables such as actual process parameters and workpiece materials. For a specific user, since they produce specific products, the general material is one or several. Therefore, when the user type is determined, the database established based on this evaluation method can accurately predict the quality parameters and provide a more direct and accurate reference for subsequent similar machining. Second, by calculating the wear rate of the tool performance, the user can establish a dynamic tool performance index database, thereby avoiding the phenomenon that the tool characteristics gradually deviate from the actual situation during the use process. Third, since the evaluation method synchronously collects the quality parameters as the labels for performance evaluation, the user can thus judge whether the tool-workpiece combination under this performance index is reasonable. Furthermore, a calibration relationship of "process response-quality parameter" can be established based on this, and the quality parameters can be directly calculated from the process response, so as to predict the quality parameters in the first time during the machining process. Finally, the machining data processing method provided by the present application calculates the machining performance of the tool or material by using the unified process response data and the unified calculation rules, making it have the generality for horizontal comparison. The user can form an evaluation and management system for different tools and materials based on this, thereby avoiding the problems of performance data disorder and missing caused by the limitation of information sources.

[0150] It should be noted that in the above various embodiments, there is not necessarily a certain sequence between the above steps. Those of ordinary skill in the art can understand according to the description of the embodiments of the present application that in different embodiments, the above steps can have different execution sequences, that is, they can be executed in parallel or exchanged, etc.

[0151] In some embodiments, the present application further provides a processing data processing device. The processing data processing device may be a software module, and the software module includes a number of instructions stored in a memory. The processor can access the memory and call the instructions for execution to complete the processing data processing method described in each of the above embodiments. In some embodiments, the processing data processing device may also be built by hardware devices. For example, the processing data processing device may be built by one or more than two chips, and each chip can work in coordination with each other to complete the processing data processing method described in each of the above embodiments. For another example, the processing data processing device may also be built by various logic devices, such as built by a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine), or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0152] Specifically, the processing data processing device includes a process data acquisition module for acquiring a process data set generated when a target tool processes a target workpiece, where the process data set includes process response data and processing quality data; a performance calculation module for calculating the material performance and tool performance when processing the target workpiece based on the process response data; a quality parameter acquisition module for determining quality parameters generated when processing the target workpiece based on the processing quality data; a performance association combination module for associating the material performance with the tool performance to obtain a tool-workpiece combination; and an evaluation generation module for generating a tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameters.

[0153] It should be noted that the above processing data processing device can execute the processing data processing method provided by the embodiments of the present application and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in the embodiments of the processing data processing device, reference can be made to the processing data processing method provided by the embodiments of the present application.

[0154] See Figure 8 , Figure 8 FIG. is a schematic structural diagram of a computer device provided by an embodiment of the present application. The computer device 80 includes one or more processors 81 and a memory 82. The memory 82 is connected to one or more processors 81, for example, connected to the processor 81 through a bus.

[0155] The processor 81 is configured to support the computer device 80 in performing the corresponding functions in the methods in the above method embodiments. The processor 81 may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The above hardware chip may be an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0156] The memory 82 is used to store program codes, etc. The memory 82 may include a volatile memory (VM), such as a random access memory (RAM); the memory 82 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the memory 82 may further include a combination of the above types of memories.

[0157] The memory 82 can be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as the program instructions / modules corresponding to the processing data processing method in the embodiments of the present application. The processor 81 executes various functional applications and data processing of the processing data processing method and the processing data processing device by running the non-volatile software programs, instructions, and modules stored in the memory 82, that is, implements the functions of each module or unit of the processing data processing method and the processing data processing device provided in the above method embodiments.

[0158] The memory 82 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the processing data processing device and the like. In some embodiments, the memory 82 may optionally include a memory 82 remotely provided with respect to the processor 81, and these remote memories 82 may be connected to the processing data processing device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] The one or more modules are stored in the memory 82 and, when executed by the one or more processors 81, execute the processing data processing method in any of the above method embodiments. For example, the method steps described in the above method embodiments are executed to implement the functions of the modules described in the above device embodiments.

[0160] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, where the computer program includes program instructions, and the program instructions, when executed by a computer, cause the computer to execute the method as described in the foregoing embodiments.

[0161] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the above method embodiments. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0162] The foregoing disclosure is only for the preferred embodiments of the present application, and of course, it cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A method for processing data, characterized in that, Including: Obtain a process data set generated when a target tool processes a target workpiece, where the process data set includes process response data and machining quality data; Based on the process response data, calculate the material properties and tool properties during machining of the target workpiece respectively; Based on the machining quality data, determine the quality parameters generated during machining of the target workpiece; Associate the material properties with the tool properties to obtain a tool-workpiece combination; Generate a tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameters.

2. The method according to claim 1, characterized in that The target workpiece includes multiple machining areas, and each machining area has independent quality parameters. The obtaining of the process data set generated when the target tool processes the target workpiece includes: Determine a target machining area, where the target machining area is one of the multiple machining areas; Obtain the process response data of the target machining area; Then, the step of calculating the material properties and tool properties during machining of the target workpiece respectively based on the process response data includes: Based on the process response data of the target machining area, calculate the material properties and tool properties during machining of the target machining area respectively.

3. The method according to claim 2, wherein The calculating of the material properties during machining of the target machining area based on the process response data of the target machining area includes: Based on the process response data of the target machining area, calculate the specific cutting energy per revolution or the specific cutting force per revolution of the target machining area, and the specific cutting energy per revolution and the specific cutting force per revolution are used to represent the material properties.

4. The method according to claim 2, wherein The calculating of the tool properties during machining of the target machining area based on the process response data of the target machining area includes: Based on the process response data of the target machining area, calculate the theoretical cutting power or the theoretical cutting force of the target machining area; Compare the theoretical cutting power or the theoretical cutting force with the actual spindle cutting power or the actual spindle cutting force in the actual process parameters to obtain a cutting power increment or a cutting force increment, and the cutting power increment and the cutting force increment are used to represent the tool properties.

5. The method according to claim 4, characterized in that, After calculating the cutting power increment or the cutting force increment, it further includes: Determine the number of workpieces processed by the target tool and the corresponding cutting power increment or cutting force increment of the target tool under different numbers of workpieces processed; Define multiple wear stages according to the number of workpieces processed by the target tool; Obtain the wear rate of the target tool according to the number of workpieces processed by the target tool in different wear stages and the cutting power increment or the cutting force increment.

6. The method according to claim 1, characterized in that, The tool-workpiece combination evaluation database includes a tool-workpiece combination evaluation graph; The generating of the tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameters includes: Generate the coordinate axes of the tool-workpiece combination evaluation graph according to the material properties and the tool properties; Generate the quality parameter line segment of the tool-workpiece combination evaluation graph according to the quality parameters corresponding to the tool-workpiece combination; Generate the tool-workpiece combination evaluation graph according to the quality parameter line segment and the coordinate axes.

7. An application method for processing data, characterized in that, The application method includes: Confirm the tool-workpiece combination and the nominal process parameters for the target tool to machine the target workpiece; Based on the tool-workpiece combination evaluation database according to any one of claims 1 to 6, obtain the corresponding nominal process response data; Machine based on the nominal process parameters, machine the target workpiece with the target tool, and obtain the real-time process response data during machining; Determine the difference between the real-time process response data and the nominal process response data; Predict whether the quality parameters after machining meet the conditions based on the difference.

8. A processing data processing device, characterized in that, Comprising: A process data acquisition module, configured to acquire a process data set generated when the target tool machines the target workpiece, the process data set including process response data and machining quality data; A performance calculation module, configured to calculate the material performance and the tool performance respectively when machining the target workpiece based on the process response data; A quality parameter acquisition module, configured to determine the quality parameters generated when machining the target workpiece based on the machining quality data; A performance association combination module, configured to associate the material performance with the tool performance to obtain a tool-workpiece combination; An evaluation generation module, configured to generate a tool-workpiece combination evaluation database according to the tool-workpiece combination and the corresponding quality parameters.

9. An electronic device, characterized in that, Comprising a memory and a processor, the memory is connected to the processor, and the processor is configured to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the electronic device implements the method according to any one of claims 1 to 6 or the method according to claim 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 6 or the method according to claim 7.

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