CN115365890A - Method and device for online predicting tool wear value, intelligent terminal and storage medium - Google Patents
Method and device for online predicting tool wear value, intelligent terminal and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
- B23Q17/0957—Detection of tool breakage
Abstract
The invention discloses a method and a device for predicting a cutter wear value on line, an intelligent terminal and a storage medium, wherein the method comprises the following steps: collecting cutting data of a cutter during cutting; obtaining a cutting ratio energy value according to a cutting ratio energy calculation formula based on the cutting data; acquiring a super-parameter value corresponding to the current cutting condition and used for a cutting specific energy model, wherein the step of determining the parameters of the cutting condition comprises the following steps: tool specification, workpiece material and machining mode; based on the cutting data, the over-parameter value, and the cutting ratio energy value, a tool wear value is obtained from a cutting ratio energy model. Compared with the prior art, the method has the advantages that the cutting data of the cutter during cutting are collected, the cutting specific energy is calculated according to the cutting data, and the cutter abrasion value is further calculated according to the cutting specific energy model. The tool wear value can be accurately predicted on line.
Description
Technical Field
The invention relates to the technical field of cutter wear monitoring, in particular to a method and a device for predicting a cutter wear value on line, an intelligent terminal and a storage medium.
Background
With the continuous development of modern production and manufacturing technologies, numerical control machine tools are widely used for machining and manufacturing various workpieces, and in order to improve machining efficiency and ensure machining safety, the state of a cutter needs to be monitored on line, the abrasion state of the cutter is detected in time, the machine tool and the workpiece are protected, and the productivity is improved.
The existing online cutter wear monitoring system compares the machine tool power with a set threshold value in real time to judge cutter wear, only can obtain a fuzzy recognition result of the cutter wear state, and cannot accurately predict a cutter wear value (VB value).
Thus, the prior art is in need of improvement and enhancement.
Disclosure of Invention
The invention mainly aims to provide a method, a device, an intelligent terminal and a storage medium for online prediction of a tool wear value, and aims to solve the problem that the tool wear value cannot be accurately predicted online in the prior art.
In order to achieve the above object, a first aspect of the present invention provides a method for online predicting a tool wear value, the method comprising:
collecting cutting data of a cutter during cutting;
obtaining a cutting ratio energy value according to a cutting ratio energy calculation formula based on the cutting data;
acquiring a super-parameter value corresponding to the current cutting condition and used for a cutting specific energy model, wherein the step of determining the parameters of the cutting condition comprises the following steps: the specification of a cutter, the material of a workpiece and a processing mode;
obtaining a tool wear value from a specific cutting energy model based on the cutting data, the super-parameter value, and the specific cutting energy value.
Optionally, the super-parameter value is a pre-calibrated value, and the method for pre-calibrating the super-parameter value includes:
under the set cutting working condition, grouping according to the process parameters, carrying out a plurality of cutting experiments under each group of process parameters, and obtaining the cutting data and the cutter abrasion value of each cutting experiment, wherein the process parameters comprise: spindle rotation speed, cutting speed and cutting depth;
and obtaining a super-parameter value corresponding to a set cutting working condition according to all the cutting data and the tool wear value.
Optionally, the cutting data includes power data and a real-time cutting volume, and the obtaining of the cutting data and the tool wear value of each cutting experiment includes:
respectively acquiring tool wear values before and after a cutting experiment;
collecting power data during a cutting experiment;
obtaining a real-time cutting volume based on a real-time simulation cutting simulation model;
and calculating the average value of the cutter abrasion value before the cutting experiment and the cutter abrasion value after the cutting experiment to obtain the cutter abrasion value.
Optionally, the obtaining, according to all the cutting data and the tool wear value, a super-parameter value corresponding to a set cutting condition includes:
and substituting all the cutting data and the tool wear value into the cutting specific energy model, and obtaining the super-parameter value according to a least square method.
Optionally, the expression of the specific energy to cut model is: sec = K 0 *(b+VB)+K 1 *n/V real +K 2 /V real Where Sec is specific energy of cutting, VB is the wear value of the tool, n is the rotational speed of the spindle, V real For cutting volume in real time, K 0 、K 1 、K 2 The coefficient to be determined is the specific energy model of cutting; b is a bias term.
The invention provides a device for predicting the wear value of a tool on line in a second aspect, wherein the device comprises:
the cutting data module is used for collecting cutting data when the cutter cuts;
the cutting specific energy calculation module is used for obtaining a cutting specific energy value according to a cutting specific energy calculation formula based on the cutting data;
the super-parameter module is used for acquiring a super-parameter value corresponding to the current cutting working condition and used for the cutting specific energy model, and the determining of the parameters of the cutting working condition comprises the following steps: tool specification, workpiece material and machining mode;
and the tool wear value module is used for obtaining a tool wear value according to a cutting specific energy model based on the cutting data, the super-parameter value and the cutting specific energy value.
Optionally, the super-parameter value is a pre-calibrated value, and the system further includes a calibration module, configured to group the super-parameter values according to the process parameters under a set cutting condition, perform multiple cutting experiments under each group of process parameters, and obtain cutting data and a tool wear value of each cutting experiment, where the process parameters include: spindle rotation speed, cutting speed and cutting depth; and obtaining a super-parameter value corresponding to a set cutting working condition according to all the cutting data and the tool wear value.
Optionally, the calibration module includes a calibration unit, and the calibration unit is configured to obtain tool wear values before and after a cutting experiment respectively; collecting power data during a cutting experiment; obtaining a real-time cutting volume based on the real-time simulation cutting simulation model; and calculating the average value of the cutter abrasion value before the cutting experiment and the cutter abrasion value after the cutting experiment to obtain the cutter abrasion value.
A third aspect of the present invention provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and a program stored in the memory and executable on the processor for predicting a tool wear value online, and the program for predicting a tool wear value online implements any one of the steps of the method for predicting a tool wear value online when executed by the processor.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a program for on-line tool wear value prediction, which, when executed by a processor, implements any of the steps of the method for on-line tool wear value prediction.
Therefore, the cutting specific energy is calculated according to the cutting data by collecting the cutting data of the cutter during cutting, and the cutter abrasion value is further calculated according to the cutting specific energy model. Compared with the prior art, the method not only realizes the online monitoring of the cutter abrasion, but also can accurately predict the cutter abrasion value in real time.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for predicting a wear value of a tool on line according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for pre-calibrating the value of the hyper-parameter in the embodiment of FIG. 1;
FIG. 3 is a schematic structural diagram of an apparatus for on-line predicting a wear value of a tool according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when 8230that is," or "once" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted depending on the context to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings of the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
The existing cutter wear online monitoring system only can obtain a fuzzy recognition result of the cutter wear state due to comparison of machine tool power and a set threshold range, and cannot accurately predict a cutter wear value; and the existing tool wear online monitoring system needs to preset a processing sample and a threshold section corresponding to the processing sample in advance, so that the system is only suitable for batch production, when a processing workpiece is different from the processing sample, the processing sample needs to be obtained again and the threshold section needs to be calibrated, and the system is not suitable for monitoring a single product.
Aiming at the technical problem, the invention provides a method for predicting the wear value of a cutter on line, which can accurately predict the current VB value (the wear value of the cutter) of the cutter according to a cutting specific energy model after collecting cutting data on line during cutting processing by establishing the cutting specific energy model and calibrating the over-parameter values of the cutting specific energy model under various cutting working conditions in advance. Because the predicted cutter wear value is unhooked from the processing sample, the processing sample does not need to be set, and the cutter wear online prediction during cutting of a single product can be realized.
Exemplary method
The present embodiment takes a numerically controlled milling machine as an example to describe the method for online predicting the wear value of the tool of the present invention. The method of the present invention is also applicable to predicting tool wear in cutting processes such as turning, grinding, and drilling.
As shown in fig. 1, the method specifically comprises the following steps:
step S100: collecting cutting data of a cutter during cutting;
specifically, the cutting data of the cutter during cutting is collected through a machine tool sensor, and the cutting data comprises: spindle no-load power, cutting material power, real-time cutting volume, cutting depth, effective cutting width (back-cut amount), feeding speed and the like.
In the embodiment, a set of simulation cutting simulation model capable of running cutting G codes is set up on a monitoring system terminal in advance. The monitoring system terminal is connected and communicated with the CNC machine tool through the Ethernet, and a CNC bottom function library is called to obtain machine tool processing data (such as spindle rotating speed, absolute coordinates, feeding speed, tool number and the like) in real time. Simulating cutting simulation according to the data to obtain real-time cutting volume V real 。
Step S200: based on the cutting data, obtaining a cutting ratio energy value according to a cutting ratio energy calculation formula;
specifically, the specific cutting energy refers to the cutting energy required for removing a unit volume of material, and can reflect the mapping relationship between the cutting energy consumption and the real-time cutting volume.
The specific energy of cut is calculated as Sec = Q/V real . Wherein Sec is specific energy for cutting, Q is machine tool power in cutting stage, and V real The volume is cut in real time. The machine power in the cutting phase is divided into a cutting material power P cutting And main shaft no-load power P idle Namely: q = P cutting +P idle 。
And substituting the machine tool power and the real-time cutting volume into a cutting specific energy calculation formula according to the collected cutting data to calculate the cutting specific energy value.
Step S300: acquiring a super-parameter value corresponding to the current cutting condition and used for a cutting specific energy model, wherein the step of determining the parameters of the cutting condition comprises the following steps: tool specification, workpiece material and machining mode;
step S400: and obtaining a tool wear value according to the cutting specific energy model based on the cutting data, the super-parameter value and the cutting specific energy value.
Specifically, the specific cutting energy calculation formula is further exercised to obtain a specific cutting energy model. Power P of material to be cut cutting ＝K 0 *V real * (b + VB), spindle no-load power P idle ＝K 1 *n+K 2 Substituting the formula into a numerical control milling machine cutting specific energy calculation formula to obtain:
Sec＝(P cutting +P idle )/V real
＝[K 0 *V real *(b+VB)+K 1 *n+K 2 ]/V real
＝K 0 *(b+VB)+K 1 *n/V real +K 2 /V real 。
obtaining a specific energy of cut model, wherein the specific expression is as follows: sec = K 0 *(b+VB)+K 1 *n/V real +K 2 /V real Wherein Sec is specific energy for cutting, VB is the abrasion value of the cutter, n is the main shaft rotating speed of the numerical control milling machine, V real For real-time cutting of volume, K 0 、K 1 、K 2 The cutting specific energy model is an over-parameter; b is a bias term. The bias term is introduced to ensure that the multiplier K 0 * (b + VB) is not 0. In the present embodiment, the bias term b =1.
From the above expression of the specific energy to cut model, K is obtained 0 、K 1 、K 2 After the super-parameter value, obtaining the cutting ratio energy value Sec according to the step S200, and cutting the volume V according to the real-time cutting volume in the cutting data real And the tool wear value VB can be directly calculated through the cutting specific energy model.
Wherein, the super parameter value of the cutting specific energy model is in one-to-one correspondence with the cutting condition, and the parameters for determining the cutting condition comprise: when the specification of the tool, the material of the workpiece and the processing mode, namely the value of any one or more of the three items, are changed, the cutting condition is changed. The method can store all the super parameter values corresponding to the common cutting conditions in a memory of a terminal for operating the method, acquire the specification of the tool, the material of the workpiece and the processing mode from a programmable controller of a numerical control machine tool during cutting processing, and then match the corresponding super parameter values in the memory according to the data; or training the network model in advance, inputting the data corresponding to the cutting working condition into the network model, and obtaining the super-parameter value output by the network model.
In summary, in the present embodiment, the cutting data of the tool during cutting is collected, the specific cutting energy is calculated according to the cutting data, and the tool wear value is further calculated in real time according to the specific cutting energy model. The online monitoring of the tool wear is realized, and the tool wear value can be accurately estimated in real time.
Since the specific energy cutting model is a nonlinear regression model, in order to accelerate the efficiency of predicting the wear value of the tool in real time, in an embodiment, the above-mentioned over-parameter value in step S300 is a pre-calibrated value, and the method for pre-calibrating the over-parameter value is shown in fig. 2, and specifically includes the following steps:
step S310: under the set cutting working condition, grouping according to the process parameters, carrying out a plurality of cutting experiments under each group of process parameters, and obtaining the cutting data cutter abrasion value of each cutting experiment, wherein the process parameters comprise: spindle rotation speed, cutting speed and cutting depth;
specifically, each of the commonly used cutting conditions is calibrated. And under each set cutting condition, grouping according to the rotating speed of the main shaft, the cutting speed and the cutting depth, and carrying out multiple cutting experiments on each group. Multiple sets of data are obtained, each set of data comprising multiple experimental data.
And when each cutting experiment is carried out, the bottom of the cutter is shot by an industrial camera, and the cutter abrasion value before the cutting experiment and the cutter abrasion value after the cutting experiment are obtained according to a machine vision algorithm. In order to balance the robustness of the model, after each single cutting, calculating the average value of the cutter wear value before the cutting experiment and the cutter wear value after the cutting experiment as the cutter wear value of the current cutting experiment; and obtaining the no-load power and the cutting material power in the cutting experiment through a Hall sensor, and obtaining the real-time cutting volume through a simulation cutting simulation model.
Step S320: and obtaining a super-parameter value corresponding to the set cutting working condition according to all the cutting data and the tool wear value.
Specifically, cutting data and tool wear values of all cutting experiments under each set cutting condition are substituted into a cutting specific energy model, and a super-parameter value corresponding to the set cutting condition is obtained according to a least square method.
The cutting data of each cutting experiment under the set cutting condition is substituted into a cutting specific energy calculation formula to obtain the cutting specific energy value of each cutting experiment. All cutting experiments under the set cutting working conditionTool wear value VB and real-time cutting volume V real Substituting the main shaft rotating speed n and the cutting specific energy value Sec into the cutting specific energy model Sec = K0 (1 + VB) + K1 + n/V real +K2/V real And obtaining an overdetermined equation set, solving out overdimension values K0, K1 and K2 according to a least square method, and obtaining the overdimension values corresponding to the set cutting working condition.
In one embodiment, after the calibration result of the super-parameter value is obtained, when the super-parameter value is stored, the cutting data when the super-parameter is calibrated under the set cutting condition is also stored. When the specification, cutting material and processing mode of the cutter in the cutting working condition are changed to form a new cutting working condition, namely, the over-parameter value corresponding to the new cutting working condition is not calibrated in advance. In order to realize the online prediction of the tool wear under a new cutting condition, the cutting condition which is most similar to the new cutting condition is firstly obtained, then the super-parameter value corresponding to the most similar cutting condition and the cutting data during the super-parameter calibration are obtained, and the super-parameter value is updated by using a transfer learning strategy according to the cutting data under the current cutting condition, the super-parameter value corresponding to the most similar cutting condition and the cutting data during the super-parameter calibration, so that the updated super-parameter value can be suitable for the new cutting condition, and the method for online predicting the tool wear value has self-adaptability.
In this embodiment, the process parameter sets in the cutting experiment when calibrating the super parameter values are shown in the following table.
And each group is subjected to 40 to 60 cutting experiments, so that the obtained super-parameter value is higher in robustness, and an accurate tool wear value is obtained according to the super-parameter value.
Exemplary device
As shown in fig. 3, an embodiment of the present invention further provides an apparatus for online predicting a tool wear value, corresponding to the method for online predicting a tool wear value, where the apparatus for online predicting a tool wear value includes:
the cutting data module 600 is used for collecting cutting data of the cutter during cutting;
a cutting specific energy calculation module 610 for obtaining a cutting specific energy value according to a cutting specific energy calculation formula based on the cutting data;
a hyper-parameter module 620, configured to obtain a hyper-parameter value for a specific cutting energy model corresponding to a current cutting condition, where determining a parameter of the cutting condition includes: tool specification, workpiece material and machining mode;
a tool wear value module 630, configured to obtain a tool wear value according to a cutting specific energy model based on the cutting data, the over-parameter value, and the cutting specific energy value.
Optionally, the super-parameter value is a pre-calibrated value, and the system further includes a calibration module, configured to group the super-parameter values according to the process parameters under a set cutting condition, perform multiple cutting experiments under each group of process parameters, and obtain cutting data and a tool wear value of each cutting experiment, where the process parameters include: spindle rotation speed, cutting speed and cutting depth; and obtaining a super-parameter value corresponding to a set cutting working condition according to all the cutting data and the tool wear value.
Optionally, the calibration module includes a calibration unit, and the calibration unit is configured to obtain tool wear values before and after a cutting experiment respectively; collecting power data during a cutting experiment; obtaining a real-time cutting volume based on the real-time simulation cutting simulation model; and calculating an average value according to the cutter wear value before the cutting experiment and the cutter wear value after the cutting experiment to obtain the cutter wear value.
Specifically, in this embodiment, the specific functions of each module of the apparatus for predicting a wear value of a tool on line may refer to the corresponding descriptions in the method for predicting a wear value of a tool on line, and are not described herein again.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a schematic block diagram thereof may be as shown in fig. 4. The intelligent terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. Wherein, the processor of the intelligent terminal is used for providing calculation and control capability. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a program for online prediction of a tool wear value. The internal memory provides an environment for an operating system in the nonvolatile storage medium and the running of a program for online predicting a tool wear value. The network interface of the intelligent terminal is used for being connected and communicated with an external terminal through a network. The program for online predicting the tool wear value realizes the steps of any one of the above methods for online predicting the tool wear value when being executed by a processor. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.
It will be understood by those skilled in the art that the block diagram shown in fig. 4 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the intelligent terminal to which the solution of the present invention is applied, and a specific intelligent terminal may include more or less components than those shown in the figure, or combine some components, or have a different arrangement of components.
In one embodiment, an intelligent terminal is provided, where the intelligent terminal includes a memory, a processor, and a program stored on the memory and executable on the processor for predicting a tool wear value online, and the program for predicting a tool wear value online performs the following operations when executed by the processor:
collecting cutting data of a cutter during cutting;
obtaining a cutting ratio energy value according to a cutting ratio energy calculation formula based on the cutting data;
acquiring a super-parameter value corresponding to the current cutting condition and used for a cutting specific energy model, wherein the step of determining the parameters of the cutting condition comprises the following steps: tool specification, workpiece material and machining mode;
obtaining a tool wear value from a specific cutting energy model based on the cutting data, the super-parameter value, and the specific cutting energy value.
Optionally, the super-parameter value is a pre-calibrated value, and the method for pre-calibrating the super-parameter value includes:
under the set cutting working condition, grouping according to the process parameters, carrying out a plurality of cutting experiments under each group of process parameters, and obtaining the cutting data and the cutter abrasion value of each cutting experiment, wherein the process parameters comprise: spindle speed, cutting depth;
and obtaining a super-parameter value corresponding to a set cutting working condition according to all the cutting data and the tool wear value.
Optionally, the cutting data includes power data and a real-time cutting volume, and the obtaining of the cutting data and the tool wear value of each cutting experiment includes:
respectively acquiring tool wear values before and after a cutting experiment;
collecting power data during a cutting experiment;
obtaining a real-time cutting volume based on a real-time simulation cutting simulation model;
and calculating the average value of the cutter abrasion value before the cutting experiment and the cutter abrasion value after the cutting experiment to obtain the cutter abrasion value.
Optionally, the obtaining, according to all the cutting data and the tool wear value, a super-parameter value corresponding to a set cutting condition includes:
and substituting all the cutting data and the tool wear value into the cutting specific energy model, and obtaining the super-parameter value according to a least square method.
Optionally, the expression of the specific energy to cut model is: sec = K 0 *(b+VB)+K 1 *n/V real +K 2 /V real Where Sec is specific energy of cutting, VB is the wear value of the tool, n is the rotational speed of the spindle, V real For cutting volume in real time, K 0 、K 1 、K 2 The coefficient to be determined is the specific energy model of cutting; b is a bias term.
The embodiment of the invention also provides a computer-readable storage medium, wherein a program for predicting the tool wear value on line is stored on the computer-readable storage medium, and when the program for predicting the tool wear value on line is executed by a processor, the steps of any one of the methods for predicting the tool wear value on line provided by the embodiment of the invention are realized.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present invention. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the above modules or units is only one logical division, and the actual implementation may be implemented by another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The integrated modules/units described above may be stored in a computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the embodiments of the method when the computer program is executed by a processor. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. A method for online prediction of tool wear values, the method comprising:
collecting cutting data of a cutter during cutting;
based on the cutting data, obtaining a cutting ratio energy value according to a cutting ratio energy calculation formula;
acquiring a super-parameter value corresponding to the current cutting condition and used for a cutting specific energy model, wherein the step of determining the parameters of the cutting condition comprises the following steps: the specification of a cutter, the material of a workpiece and a processing mode;
obtaining a tool wear value from a specific cutting energy model based on the cutting data, the super-parameter value, and the specific cutting energy value.
2. The method of on-line prediction of tool wear values as set forth in claim 1, wherein the hyper-parameter value is a pre-calibrated value, the method of pre-calibrating the hyper-parameter value comprising:
under the set cutting condition, grouping according to the technological parameters, carrying out multiple cutting experiments under each group of technological parameters, and obtaining the cutting data and the cutter abrasion value of each cutting experiment, wherein the technological parameters comprise: spindle speed, cutting depth;
and obtaining a super-parameter value corresponding to a set cutting working condition according to all the cutting data and the tool wear value.
3. The method for online prediction of tool wear values according to claim 2, wherein the cutting data comprises power data and real-time cutting volume, and the obtaining of cutting data and tool wear values for each cutting experiment comprises:
respectively acquiring the tool wear values before and after a cutting experiment;
collecting power data during a cutting experiment;
obtaining a real-time cutting volume based on the real-time simulation cutting simulation model;
and calculating the average value of the cutter abrasion value before the cutting experiment and the cutter abrasion value after the cutting experiment to obtain the cutter abrasion value.
4. The method for predicting the tool wear value on line according to claim 2, wherein the obtaining of the over-parameter value corresponding to the set cutting condition according to all the cutting data and the tool wear value comprises:
and substituting all the cutting data and the tool wear value into the cutting specific energy model, and obtaining the super-parameter value according to a least square method.
5. The method for on-line predicting tool wear values of claim 1, wherein the specific energy to cut model is expressed as: sec = K 0 *(b+VB)+K 1 *n/V real +K 2 /V real Where Sec is specific energy for cutting, VB is the wear value of the tool, n is the spindle speed, V real For cutting volume in real time, K 0 、K 1 、K 2 The coefficient to be determined is the specific energy model of cutting; b is a bias term.
6. Device for on-line prediction of tool wear values, characterized in that it comprises:
the cutting data module is used for acquiring cutting data of the cutter during cutting;
the cutting specific energy calculation module is used for obtaining a cutting specific energy value according to a cutting specific energy calculation formula based on the cutting data;
the super-parameter module is used for acquiring a super-parameter value corresponding to the current cutting working condition and used for the cutting specific energy model, and the determining of the parameters of the cutting working condition comprises the following steps: tool specification, workpiece material and machining mode;
and the tool wear value module is used for obtaining a tool wear value according to a cutting specific energy model based on the cutting data, the super-parameter value and the cutting specific energy value.
7. The apparatus for on-line predicting wear value of tool as claimed in claim 6, wherein the over-parameter value is a pre-calibrated value, further comprising a calibration module for grouping according to process parameters under a set cutting condition, performing a plurality of cutting experiments under each group of process parameters, and obtaining cutting data and tool wear value of each cutting experiment, the process parameters comprising: spindle speed, cutting depth; and obtaining a super-parameter value corresponding to a set cutting working condition according to all the cutting data and the tool wear value.
8. The device for on-line prediction of tool wear values according to claim 7, wherein the calibration module comprises a calibration unit for respectively obtaining tool wear values before and after a cutting experiment; collecting power data during a cutting experiment; obtaining a real-time cutting volume based on a real-time simulation cutting simulation model; and calculating the average value of the cutter abrasion value before the cutting experiment and the cutter abrasion value after the cutting experiment to obtain the cutter abrasion value.
9. An intelligent terminal, characterized in that the intelligent terminal comprises a memory, a processor and a program for online predicting tool wear values stored on the memory and executable on the processor, wherein the program for online predicting tool wear values, when executed by the processor, implements the steps of the method for online predicting tool wear values according to any one of claims 1-5.
10. Computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a program of online prediction of tool wear values, which when executed by a processor implements the steps of the method of online prediction of tool wear values according to any of claims 1-5.
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