CN117991718B - Multi-axis collaborative state monitoring method and system for horizontal machining center - Google Patents
Multi-axis collaborative state monitoring method and system for horizontal machining center Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 118
- 238000012544 monitoring process Methods 0.000 title claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 334
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- 238000003754 machining Methods 0.000 claims description 71
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- 238000005299 abrasion Methods 0.000 claims description 2
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- 238000002372 labelling Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000003801 milling Methods 0.000 description 3
- 238000000227 grinding Methods 0.000 description 2
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- 238000003062 neural network model Methods 0.000 description 1
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- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/19—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by positioning or contouring control systems, e.g. to control position from one programmed point to another or to control movement along a programmed continuous path
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Abstract
The embodiment of the specification provides a multi-axis collaborative state monitoring method and system for a horizontal machining center, wherein the method comprises the following steps: acquiring processing parameters of a plurality of processing steps in the processing process and a workpiece image after the processing is finished; judging the processing problem of the workpiece based on the workpiece image; determining a problem processing step by a judging model based on the processing problem, processing parameters of a plurality of processing steps, static rigidity of a machine tool of the plurality of processing steps and the degree of tool wear of a workpiece in each processing step; the static stiffness of the machine tool refers to the deformation resistance of the horizontal machining center under the action of static load, and the model is judged to be a machine learning model; and adjusting the processing parameters of the problematic processing step.
Description
Description of the division
The application aims at the application date of 2022, 10 months and 18 days, and the application number is: 202211272513.9A divisional application filed in China is entitled "method and System for Multi-axis cooperative control of horizontal machining center".
Technical Field
The specification relates to the technical field of machining center control, in particular to a multi-axis collaborative state monitoring method and system for a horizontal machining center.
Background
The machining center is a highly-automatic multifunctional numerical control machine tool with a tool magazine and an automatic tool changing device. The horizontal machining center is a machining center with a horizontal main shaft, and is suitable for machining box parts. Compared with a vertical machining center, the horizontal machining center has the advantages that chip removal is easy during machining, and machining is facilitated. However, because the mechanical structure of the horizontal machining center is more complex, when the multi-axis linkage is used for machining, the machining precision can be directly influenced by the degree of cooperation among the multi-axis.
Therefore, it is desirable to provide a method and a system for monitoring multi-axis coordination state of a horizontal machining center, so as to improve coordination degree during multi-axis linkage machining and ensure machining precision of workpieces.
Disclosure of Invention
One or more embodiments of the present disclosure provide a method for monitoring a multi-axis collaborative state of a horizontal machining center, the method comprising: acquiring processing parameters of a plurality of processing steps in the processing process and a workpiece image after the processing is finished; judging the processing problem of the workpiece based on the workpiece image; determining a problem processing step by a judging model based on the processing problem, the processing parameters of the plurality of processing steps, the static rigidity of the machine tool of the plurality of processing steps and the abrasion degree of the cutter when the workpiece is in each processing step; the static stiffness of the machine tool refers to the deformation resistance of the horizontal machining center under the action of static load, and the judgment model is a machine learning model; and adjusting the processing parameters of the problem processing step.
One or more embodiments of the present specification provide a multi-axis collaborative status monitoring system for a horizontal machining center, the system comprising: the acquisition module is configured to acquire processing parameters of a plurality of processing steps in the processing process and processed workpiece images; a first judging module configured to judge a processing problem of a workpiece based on the workpiece image; a second judging module configured to determine a problem processing step by a judging model based on the processing problem, the processing parameters of the plurality of processing steps, the static rigidity of the machine tool of the plurality of processing steps, and the degree of tool wear of the workpiece at each processing step; the static stiffness of the machine tool refers to the deformation resistance of the horizontal machining center under the action of static load, and the judgment model is a machine learning model; and a parameter adjustment module configured to adjust a processing parameter of the problem processing step.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
fig. 1 is a schematic view of an application scenario of a multi-axis cooperative control system of a horizontal machining center according to some embodiments of the present disclosure;
FIG. 2 is an exemplary block diagram of a multi-axis coordinated control system of a horizontal machining center according to some embodiments of the present disclosure;
FIG. 3 is an exemplary flow chart of a method of multi-axis coordinated control of a horizontal machining center according to some embodiments of the present disclosure;
FIG. 4 is an exemplary schematic diagram of a decision model structure shown in accordance with some embodiments of the present disclosure;
FIG. 5 is an exemplary flow chart of adjusting process parameters of a problem processing step according to some embodiments of the present description;
FIG. 6 is an exemplary block diagram of process map structural data shown in accordance with some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic view of an application scenario of a multi-axis cooperative control system of a horizontal machining center according to some embodiments of the present disclosure.
As shown in fig. 1, the application scenario 100 may include a server 110, a network 120, a machining center 130, a data acquisition device 140, and a storage device 150.
In some embodiments, the application scenario 100 may adjust the processing parameters of the problem processing step by implementing the multi-axis cooperative control method disclosed in the present specification. For example, in a typical application scenario, when a problem processing step needs to be determined during processing and processing parameters of the problem processing step are adjusted, the processing parameters of a plurality of processing steps during processing and the processed workpiece image may be acquired by the data acquisition device 140, and then the processing parameters and the workpiece image are sent to the server 110. The server 110 determines a machining problem of the workpiece based on the workpiece image, determines a problem machining step having the machining problem based on the machining problem, and adjusts machining parameters of the problem machining step, so that the machining problem can be found in time in the machining process, and the machining parameters of the problem machining step can be adjusted to ensure machining precision, machining efficiency and quality of the product.
In some embodiments, the server 110 may be used to process information and/or data related to the application scenario 100. For example, the server 110 may determine that a workpiece has a processing problem (e.g., a workpiece is oversized/undersized, etc.) based on the workpiece image. For another example, the server 110 may determine a problem processing step (e.g., cutting, punching, etc.) in which a processing problem exists based on the processing problem. For another example, the server 110 may be used to adjust processing parameters (e.g., tool speed, advance speed, etc.) of the problematic processing step. In some embodiments, the server 110 may be a single server or a group of servers. The server set may be centralized or distributed, may be dedicated, or may be serviced concurrently by other devices or systems. In some embodiments, server 110 may be regional or remote. In some embodiments, server 110 may be implemented on a cloud platform or provided in a virtual manner.
In some embodiments, server 110 may include a processing device. A processing device may process data and/or information obtained from other devices or system components. The processing device may execute program instructions to perform one or more functions described herein based on such data, information, and/or processing results.
In some embodiments, network 120 may be used for the exchange of information and/or data. In some embodiments, one or more components of the application scenario 100 (e.g., the server 110, the machining center 130, the data collection device 140, and the storage device 150) may send information and/or data to other components of the application scenario 100 via the network 120. For example, machining center 130 may obtain the machining parameters of the problem machining step from server 110 via network 120.
Machining center 130 may be a highly automated multi-function numerical control device with tool magazine and automatic tool changing device. In some embodiments, the processing center 130 may obtain processing parameters corresponding to the processing steps output by the server 110, such as a workpiece fixing angle, a tool rotation speed, and the like. The machining center 130 may operate according to the machining steps and machining parameters corresponding to the machining steps to produce a satisfactory product.
In some embodiments, the data acquisition device 140 may be used to acquire data. In some embodiments, data acquisition device 140 may include, but is not limited to, an image acquisition device 140-1, a temperature acquisition device 140-2, a rotational speed acquisition device, and the like. In some embodiments, the image capturing device 140-1 may be used to capture an image of the workpiece after the processing is complete, and the image capturing device 140-1 may include a camera, video camera, or the like. In some embodiments, the temperature acquisition device 140-2 may be used to acquire the temperature of the workpiece during processing, and the temperature acquisition device 140-2 may include a thermometer, an infrared temperature sensor, a thermal resistor, a thermocouple, and the like. In some embodiments, the rotational speed acquisition device may be used to acquire the rotational speed of the tool during machining, may be a rotational speed sensor, or the like.
In some embodiments, storage device 150 may store data and/or instructions. In some embodiments, the storage device 150 may store data acquired from the data acquisition apparatus 140. In some embodiments, the storage device 150 may store information related to the machining center 130. For example, data such as a tool model of the machining center 130. In some embodiments, storage device 150 may store data and/or instructions used by server 110 to perform or use the exemplary methods described herein. In some embodiments, storage device 150 may be implemented on a cloud platform.
In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more components of the application scenario 100 (e.g., the server 110, the machining center 130, and the data acquisition apparatus 140). One or more components of the application scenario 100 may access data or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 may be directly connected to or in communication with one or more components of the application scenario 100. In some embodiments, the storage device 150 may be part of the server 110.
It should be noted that the application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of the present application. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the application scenario 100 may also include a database. However, such changes and modifications do not depart from the scope of the present application.
FIG. 2 is an exemplary block diagram of a multi-axis coordinated control system for a horizontal machining center according to some embodiments of the present disclosure.
Some embodiments of the present disclosure provide a multi-axis cooperative control system for a horizontal machining center, as shown in fig. 2, the system 200 includes an acquisition module 210, a first judgment module 220, a second judgment module 230, and a parameter adjustment module 240.
The acquisition module 210 may be configured to acquire processing parameters for a plurality of processing steps during processing and an image of the workpiece after processing is completed. For details on the processing steps, processing parameters, workpiece images, see fig. 3 and its associated description.
The first determining module 220 may be configured to determine, based on the workpiece image, that a machining problem exists with the workpiece. For details of processing problems see fig. 3 and its associated description.
The second determining module 230 may be configured to determine a problem processing step in which a processing problem exists based on the processing problem. For details of the problem processing steps, see fig. 3 and its associated description.
In some embodiments, the second determination module 230 may be configured to determine a problem processing step by determining a model based on the processing problem and processing parameters of the plurality of processing steps. Wherein the judgment model is a machine learning model. For more details on the judgment model, see fig. 4 and its associated description.
The parameter adjustment module 240 may be used to adjust the process parameters of the problematic process steps. For details of the problem processing steps, see fig. 3 and its associated description.
In some embodiments, parameter adjustment module 240 may be configured to construct process map structure data based on process parameters for a plurality of process steps in a process; processing the structure data of the processing diagram based on the parameter adjustment model, and determining modified processing parameters of the problem processing step; the processing parameters of the problem processing step are adjusted based on the modified processing parameters of the problem processing step. The parameter adjustment model is a machine learning model. Details of the parameter tuning model and the modified process parameters may be found in fig. 5 and the associated description thereof, and details of the process map structure data may be found in fig. 5, 6 and the associated description thereof.
It should be understood that the system shown in fig. 2 and its modules may be implemented in a variety of ways.
It should be noted that the above description of the multi-axis cooperative control system of the horizontal machining center and the modules thereof is for convenience of description only, and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the first determination module and the second determination module disclosed in fig. 2 may be different modules in one system, or may be one module to implement the functions of two or more modules. Such variations are within the scope of the present description.
FIG. 3 is an exemplary flow chart of a method of multi-axis coordinated control of a horizontal machining center according to some embodiments of the present disclosure. In some embodiments, the process 300 may be performed by the horizontal machining center multi-axis cooperative control system 200. As shown in fig. 3, the process 300 may include the steps of:
Step 310, acquiring processing parameters of a plurality of processing steps in the processing process and a workpiece image after processing is completed. In some embodiments, step 310 may be performed by the acquisition module 210.
The machining step may refer to a machining operation performed on the workpiece in sequence in a machining process of the workpiece to be machined by the horizontal machining center. Machining operations may include milling, turning, grinding, planing, drilling, and the like. Illustratively, a machining step may be "hole digging in the front face of the workpiece", "milling the left side of the workpiece", and so forth.
The machining parameters may refer to position parameters of each axis of the horizontal machining center during the machining process corresponding to the machining step.
The positional parameters may refer to positional information of the respective axes of the horizontal machining center at a plurality of successive points in time in a certain machining step. The position information may include spatial position information of the respective axes with respect to the origin of coordinates and/or rotational angle information with respect to the initial angle. The time intervals of the plurality of successive time points may be preset, for example, 1 second. For example, if the machining center includes an X axis, a Y axis, a Z axis, an a axis, and a C axis, the content of a certain position parameter of the machining center may be "30 mm X axis, 35mm Y axis, 7mm Z axis, 60 a axis, and 90 ° C axis", which means that the distance between the machining center main axis and the origin of coordinates in the X axis direction is 30mm, the distance between the machining center main axis and the origin of coordinates in the Y axis direction is 35mm, the distance between the machining center main axis and the origin of coordinates in the Z axis direction is-7 mm, the rotation angle around the X axis is 60 °, and the rotation angle around the Z axis is 90 °.
In some embodiments, the processing parameters of the plurality of processing steps in the processing include: the positional parameters of the respective processing axes in each of the plurality of processing steps are represented based on the tensor. I.e. the position parameters of the respective machining axis in each of the plurality of machining steps can be represented on the basis of tensor form. For example, the content of the positional parameters of each machining axis in a certain machining step may be ((20, -15,5, 43, 156), (21, -15.5,4.8, 43, 150), (22, -16,4.6, 45, 150.)) with the represented meaning that at the first point in time of the machining step, the machining center main axis is 20mm in the X-axis direction from the origin of coordinates, -15mm in the Y-axis direction from the origin of coordinates, -5 mm in the Z-axis direction from the origin of coordinates, -the angle of rotation about the X-axis is 43 °, the angle of rotation about the Z-axis is 156 °; at the second point in time of the machining step, the machining center spindle is 21mm in distance from the origin of coordinates in the X-axis direction, 15.5mm in distance from the origin of coordinates in the Y-axis direction, 4.8mm in distance from the origin of coordinates in the Z-axis direction, the angle of rotation about the X-axis is 43, the angle of rotation about the Z-axis is 150, and so on.
In some embodiments of the present disclosure, the processing parameters are characterized in tensor form, and the position and angle data of each axis can be represented at each time point, and the synergy of a plurality of axes can be referred to in subsequent processing.
In some embodiments, the processing parameters may be acquired by position and angle sensors disposed within the acquisition module 210.
The workpiece image may refer to image data of a workpiece being processed by the horizontal machining center.
In some embodiments, the workpiece image may be acquired by a camera device disposed within the acquisition module 210.
Step 320, determining a machining problem of the workpiece based on the workpiece image. In some embodiments, step 320 may be performed by the first determination module 220.
Machining problems may refer to abnormal machining results that occur after a workpiece machining step is completed. For example, a processing problem may be that the surface that should be flat is not flat after the processing step is completed. As another example, the machining problem may be that the depth of the excavated hole does not reach a predetermined depth after the machining step is completed.
In some embodiments, the processing problem may be determined by a problem identification model. The problem recognition model may be a machine learning model, such as a convolutional neural network model, or the like. The problem identification model may determine a machining problem with the workpiece based on processing the image of the workpiece. The input may be a workpiece image and the output may be a machining problem.
In some embodiments, the problem recognition model may be trained from a plurality of labeled training samples. For example, a plurality of training samples with labels may be input into an initial problem identification model, a loss function is constructed from the labels and the results of the initial problem identification model, and parameters of the initial problem identification model are iteratively updated based on the loss function. And when the loss function of the initial problem identification model meets the preset condition, model training is completed, and a trained problem identification model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold value, etc.
In some embodiments, the training sample may include at least a plurality of historical workpiece images. The label may be a machining problem corresponding to the historical workpiece image. Wherein the tag may be obtained based on manual labeling.
Step 330, based on the processing problem, judging a problem processing step in which the processing problem exists. In some embodiments, step 330 may be performed by the second determination module 230.
The problem processing step may refer to a processing step that causes a processing problem to occur. By way of example, a problem machining step may be "hole digging in the center of the front face of the workpiece", and the corresponding machining problem may be "hole digging depth shortage".
In some embodiments, a problem processing step in which a processing problem exists may be determined based on a correspondence between the processing step and the processing problem. For example, a process flow for a workpiece may include the following process steps: and (3) digging a hole in the center of the front surface of the workpiece, vertically milling the left side surface of the workpiece, and grinding the right side surface of the workpiece.
In some embodiments, the problem processing step may be determined by a judgment model based on the processing problem and processing parameters of the plurality of processing steps. The relevant description of the judgment model can be seen in fig. 4 and the relevant description thereof.
Step 340, adjusting the processing parameters of the problem processing step.
In some embodiments, the processing parameters may be adjusted based on the content of the processing problem. For example, if the content of a machining problem is "the hole depth is insufficient", the position parameter value of the axis corresponding to the hole digging direction among the machining parameters may be adjusted.
In some embodiments, the adjusted process parameters may be determined by a parameter adjustment model based on process parameters of a plurality of process steps in the process. The relevant description of the parameter adjustment model can be seen in fig. 5 and the relevant description thereof.
In some embodiments of the present disclosure, the efficiency and accuracy of determining the problem processing step may be improved by determining the problem processing step and adjusting the processing parameters of the problem processing step through the model, and the adjusted processing parameters may be made to meet the production needs.
It should be noted that the above description of the process 300 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description. For example, the processing parameters of the problem processing step are acquired after the problem processing step is determined.
Fig. 4 is an exemplary schematic diagram of a decision model structure shown in accordance with some embodiments of the present description.
In some embodiments, the second determination module 230 may determine a problem processing step by the determination model 420 based on the processing problem and the processing parameters of the plurality of processing steps. The decision model 420 is a machine learning model.
As shown in FIG. 4, the decision model 420 may determine a problem processing step 430 based on processing the processing problem 410-1 and the processing parameters 410-2 of the plurality of processing steps. The inputs to the decision model 420 may include the process problem 410-1 and the process parameters 410-2 for the plurality of process steps, and the output may be the problem process step 430.
In some embodiments, the input to the decision model 420 may also include the machine tool static stiffness 410-3 for multiple processing steps. The static stiffness of a machine tool may refer to the ability of a horizontal machining center to resist deformation under static load. The static stiffness of the machine tool can be calculated by the following formula (1):
Wherein j represents the static rigidity of the machine tool; f represents a static load force applied to the machining center; x represents the machine tool deformation amount when the static load force applied by the machining center is F; k represents a proportionality coefficient, is a constant, and can be preset.
In some embodiments, the input to the decision model 420 may also include the tool wear level 410-4 of the workpiece at each processing step. The degree of tool wear can be calculated by the following formula (2):
Wherein m represents the degree of tool wear; t represents the total time length that the tool has been used when performing a certain machining step; t 0 represents the theoretical useful life of the tool.
In some embodiments of the present disclosure, by introducing the static stiffness of the machine tool as an input to the model, the accuracy of the model output results may be improved.
In some embodiments of the present disclosure, the tool wear level is introduced as an input to the model based on other inputs, so that accuracy of the model output result may be further improved.
In some embodiments, the decision model 420 may be trained from a plurality of labeled training samples. For example, a plurality of training samples with labels may be input into the initial judgment model, a loss function is constructed through the labels and the results of the initial judgment model, and parameters of the initial judgment model are iteratively updated based on the loss function. And when the loss function of the initial judgment model meets the preset condition, model training is completed, and a trained judgment model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold value, etc.
In some embodiments, the training sample may include at least a plurality of historical data. The historical data may include processing parameters of the historical processing problem and its corresponding plurality of historical processing steps. The label may be a question processing step corresponding to a historical processing question. Wherein the tag may be obtained based on manual labeling.
In some embodiments, when the number of the historical data is insufficient, the processing parameter data of multiple steps of the multiple processing procedures without processing problems can be used as training original data, the values of part of the processing parameters in each set of training original data are modified, and the processing problems generated when the workpiece is processed by using the modified processing parameters are manually marked and respectively used as training samples and labels of the training judgment model 420. Wherein the magnitude of modification of the process parameter may exceed a noise threshold of the process parameter.
The noise threshold may refer to a range of variation of a process parameter that does not create a process problem during processing. For example, if the content of the machining parameters of a machining step that does not cause a machining problem is ((20, -15,5,0,0), (21, -15,5,0,0) & gt. & gt (30, -15,5,0,0)), and the noise threshold value of the X-axis is 0.1, the machining parameters can be modified to ((21, -15,5,0,0), (22, -15,5,0,0) & gt (31, -15,5,0,0)), and the corresponding machining problem is set to "the X-axis direction hole is bored too deeply". A related description of the processing parameters can be found in fig. 3.
In some embodiments, the processing parameters of multiple processing steps may be modified simultaneously while the processing parameters are modified, while multiple processing issues are set accordingly. For example, if the contents of the processing parameters of the plurality of processing steps that do not cause the processing problem include ((20,-15,5,0,0),(21,-15,5,0,0)......(30,-15,5,0,0)),............,((30,-15,5,0,0),(30,-15,6,0,0)......(30,-15,15,0,0)),X axis and Z axis noise threshold values of 0.1mm, then the processing parameters may be modified to ((21,-15,5,0,0),(22,-15,5,0,0)......(31,-15,5,0,0)),............,((30,-15,4,0,0),(30,-15,5,0,0)......(30,-15,14,0,0)), and the corresponding processing problem may be set to "the X axis direction hole is too deep and the Z axis direction hole is too shallow".
In some embodiments of the present disclosure, since there may be fewer processing problems occurring in the actual production process, resulting in insufficient training samples, the model training efficiency and the accuracy of the output of the trained model may be improved by modifying the normal processing parameters to obtain sufficient training sample data.
In some embodiments of the present disclosure, the problem processing step is determined by the model, which not only realizes accurate judgment of the problem processing step, but also improves the determination efficiency and saves the time cost.
FIG. 5 is an exemplary flow chart for adjusting process parameters of a problem processing step according to some embodiments of the present description. In some embodiments, the process 500 may be performed by the parameter adjustment module 240. As shown in fig. 5, the process 500 may include the steps of:
Step 510, constructing process map structure data based on the process parameters of the plurality of process steps in the process.
In some embodiments, the process map structure data may be a data structure comprised of nodes and edges, the edges connecting the nodes, the nodes and edges may have attributes. Details of the process map structure data are described in connection with fig. 6.
In some embodiments, the process map structure data may be constructed based on process parameters of a plurality of process steps in a process of the horizontal processing center.
FIG. 6 is an exemplary block diagram of process map structural data shown in accordance with some embodiments of the present description.
In some embodiments, nodes that process the graph structure data may include a first node and a second node. Wherein the first node may be a processing step node; the second node may be a transitional processing step node. The processing step nodes may be nodes corresponding to processing steps, and the relevant description of the processing steps may be referred to in fig. 3. The transitional processing step node may be a node that gathers data information of processing parameters of the same processing step of each processing center. A machining center may refer to a collection of machining steps based on a plurality of machining steps that produce a product.
In some embodiments, edges of the process map structure data may include a first edge and a second edge. The first edge can be used for connecting two first nodes corresponding to adjacent processing steps of the same processing center, the first edge is a unidirectional edge, and the first nodes corresponding to the processing steps with the previous time sequence point to the first nodes corresponding to the processing steps with the subsequent time sequence; the second edge is used for connecting the first node and the second node corresponding to the same processing step, the second edge is a unidirectional edge, and the unidirectional edge pointing to the second node from the first node.
For example, as shown in fig. 6, the tooling pattern structure data 600 may include a first node A1, a first node A2, a first node A3, a first node C4, a second node 1, a second node 2, a second node 3, and a second node 4, a first edge a, a first edge b, a first edge i, a second edge a, a second edge b, a second edge l.
In some embodiments, the first node may correspond to a processing step. Wherein, the first nodes A1, A2, A3 and A4 respectively correspond to 4 processing steps of the processing procedure by using the horizontal processing center A. The first nodes B1, B2, B3 and B4 correspond to 4 processing steps of the processing using the horizontal processing center B, respectively. The first nodes C1, C2, C3 and C4 correspond to 4 processing steps of the processing using the horizontal processing center C, respectively. The horizontal machining center A, B, C is a same machining center, and the machining process is a machining process for machining the same workpiece. The attribute of the first node may reflect the relevant characteristics of the processing step. For example, the attribute of the first node may be a processing parameter corresponding to the processing step. A related description of the processing parameters may be found in fig. 3 and related description thereof.
In some embodiments, the second node may also correspond to a processing step. For example, the second node 2 may refer to the 2 nd processing step of a process using a horizontal processing center. The attribute of the second node may reflect the relevant characteristics of the processing step. For example, the attribute of the second node may be a mean value of processing parameters corresponding to processing steps in which processing problems do not occur in each processing center. Illustratively, if the 3 rd processing step of the horizontal processing center a is a problem processing step, the attribute of the second node 3 is the average of the attributes of the first node B3 and the first node C3, and the attribute of the second node 1 is the average of the attributes of the first node A3, the first node B3 and the first node C3. The attribute determining methods of the second node 2 and the second node 4 refer to the second node 1.
In some embodiments, the attribute of the second node may be a weighted average of processing parameters corresponding to processing steps for which processing problems do not occur at each processing center. The weight value of the process parameter corresponding to each process step may be inversely related to the number of times the process center has a process problem at that process step.
In some embodiments of the present disclosure, the attribute of the second node is determined by a weighted average method, and the weighted weight value is related to the number of times that the processing center has a problem, so that the attribute of the second node is more practical, and the processing parameters determined based on the graph structure are better adapted to the production needs.
In some embodiments, the edge attribute of the first edge may be a time interval between start times of processing steps corresponding to two first nodes to which the first edge is connected. For example, as shown in fig. 6, if the starting time of the processing steps corresponding to the first node A1 is 10:05:20, and the starting time of the processing steps corresponding to the first node A2 is 10:06:30, the attribute of the first edge a is 70s.
In some embodiments, the edge attribute of the second edge may be a total number of times a processing problem occurs during the history processing for the processing step corresponding to the first node to which the second edge is connected. For example, the second side f has an attribute of 5, and represents a total of 5 times the second machining step of the horizontal machining center B is the number of problematic machining steps. In some embodiments, the process map structure data may be updated periodically or after each machining center completes a complete product process.
Step 520, determining modified processing parameters of the problem processing step based on the processing of the processing map structure data by the parameter adjustment model.
The modified process parameters may refer to process parameter data for subsequent adjustment of the process parameters of the problematic process step. The method for determining the problem processing steps can be seen from the relevant description of fig. 4.
In some embodiments, processing parameters of the same processing steps corresponding to processing of the same workpiece by other identical processing centers may be used as modified processing parameters.
In some embodiments, the modified process parameters of the problem process step may be determined by a parameter tuning model.
The parameter tuning model may determine modified process parameters for the problem process step based on processing the process map structure data. The parameter adjustment model may be a machine learning model, such as a graph neural network model, or the like.
The input of the parameter tuning model may be the process map structure data and the output may be the modified process parameters of the problem process step. Specifically, the output of the model may be determined based on the output of the first node corresponding to the problem processing step.
In some embodiments, the parameter tuning model may be trained from a plurality of labeled training samples. For example, a plurality of training samples with labels may be input into an initial parameter adjustment model, a loss function is constructed from the labels and the results of the initial parameter adjustment model, and parameters of the initial parameter adjustment model are iteratively updated based on the loss function. And when the loss function of the initial parameter adjustment model meets the preset condition, model training is completed, and a trained parameter adjustment model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold value, etc.
In some embodiments, the training sample may include at least a plurality of historical process map structure data determined based on historical process steps and their corresponding historical process parameters. The label may be a modified process parameter of the problem process step corresponding to the historical process map structure data. Wherein the tag may be obtained based on manual labeling.
Step 530, adjusting the process parameters of the question process step based on the modified process parameters of the question process step.
In some embodiments, the modified process parameters output by the parameter adjustment model may be used as new process parameters for the problem process step and the subsequent process may be performed based on the new process parameters.
In some embodiments of the present disclosure, based on the structural data of the build graph, and determining new processing parameters of the problem processing step by the model, the adaptability of the adjusted processing parameters is enhanced, problems remaining after adjustment are avoided, and time and labor costs are saved.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (6)
1. A method for monitoring multi-axis collaborative state of a horizontal machining center, the method comprising:
acquiring processing parameters of a plurality of processing steps in the processing process and a workpiece image after the processing is finished;
judging the processing problem of the workpiece based on the workpiece image;
Determining a problem processing step by a judging model based on the processing problem, the processing parameters of the plurality of processing steps, the static rigidity of the machine tool of the plurality of processing steps and the abrasion degree of the cutter when the workpiece is in each processing step; the static stiffness of the machine tool refers to the deformation resistance of the horizontal machining center under the action of static load, and the judgment model is a machine learning model;
adjusting the processing parameters of the problem processing step; the adjusting of the processing parameters of the problem processing step includes:
Constructing processing diagram structure data based on the processing parameters of the plurality of processing steps in the processing process; the processing graph structure data comprises node data and edge data, wherein the node data comprises at least one of a first node and a second node, and the edge data comprises at least one of a first edge and a second edge; the first edge is used for connecting two first nodes corresponding to adjacent processing steps of the same processing center, and the first edge is a unidirectional edge; the second edge is used for connecting the first node and the second node corresponding to the same processing step, and the second edge is a unidirectional edge;
The first node at least comprises a processing step node, and the attribute of the first node at least comprises processing parameters corresponding to the processing step; the second node at least comprises a transition processing step node, and the attribute of the second node at least comprises a weighted average value of processing parameters corresponding to processing steps without the processing problem, and the weight value of the processing parameter corresponding to each processing step is inversely related to the times of the processing center to the processing problem in the processing step;
the edge attribute of the first edge at least comprises a time interval between starting time of processing steps corresponding to the two first nodes connected with the first edge, and the edge attribute of the second edge at least comprises total times of processing problems occurring in the history processing of the processing steps corresponding to the first nodes connected with the second edge;
Determining modified processing parameters of the problem processing step based on processing of the processing map structure data by a parameter adjustment model; the parameter adjustment model is a machine learning model;
And adjusting the processing parameters based on the modified processing parameters.
2. The method of claim 1, wherein the processing parameters of the plurality of processing steps comprise: a positional parameter of each machining shaft in each of the plurality of machining steps based on the tensor representation.
3. The method of claim 1, wherein the judgment model is obtained by training an initial judgment model based on training data, the training data being obtained based on historical data, the historical data including historical processing parameters and corresponding historical problem steps used when the processing problem occurred historically; when the number of the historical data is insufficient, modifying the processing parameters of at least one processing step of the plurality of processing steps in each group of training original data, and manually marking at least one processing problem generated when the workpiece is processed by using the modified processing parameters, wherein the processing problems are respectively used as training samples and labels for training the judging model; the training raw data is the processing parameters of the plurality of processing steps of the plurality of sets of the processing procedure for which the processing problem does not occur.
4. A multi-axis collaborative state monitoring system for a horizontal machining center, the system comprising:
The acquisition module is configured to acquire processing parameters of a plurality of processing steps in the processing process and processed workpiece images;
A first judging module configured to judge a processing problem of a workpiece based on the workpiece image;
A second judging module configured to determine a problem processing step by a judging model based on the processing problem, the processing parameters of the plurality of processing steps, the static rigidity of the machine tool of the plurality of processing steps, and the degree of tool wear of the workpiece at each processing step; the static stiffness of the machine tool refers to the deformation resistance of the horizontal machining center under the action of static load, and the judgment model is a machine learning model;
A parameter adjustment module configured to adjust a processing parameter of the problem processing step; the parameter adjustment module is further configured to:
Constructing processing diagram structure data based on the processing parameters of the plurality of processing steps in the processing process; the processing graph structure data comprises node data and edge data, wherein the node data comprises at least one of a first node and a second node, and the edge data comprises at least one of a first edge and a second edge; the first edge is used for connecting two first nodes corresponding to adjacent processing steps of the same processing center, and the first edge is a unidirectional edge; the second edge is used for connecting the first node and the second node corresponding to the same processing step, and the second edge is a unidirectional edge;
The first node at least comprises a processing step node, and the attribute of the first node at least comprises processing parameters corresponding to the processing step; the second node at least comprises a transition processing step node, and the attribute of the second node at least comprises a weighted average value of processing parameters corresponding to processing steps without the processing problem, and the weight value of the processing parameter corresponding to each processing step is inversely related to the times of the processing center to the processing problem in the processing step;
the edge attribute of the first edge at least comprises a time interval between starting time of processing steps corresponding to the two first nodes connected with the first edge, and the edge attribute of the second edge at least comprises total times of processing problems occurring in the history processing of the processing steps corresponding to the first nodes connected with the second edge;
Determining modified processing parameters of the problem processing step based on processing of the processing map structure data by a parameter adjustment model; the parameter adjustment model is a machine learning model;
And adjusting the processing parameters based on the modified processing parameters.
5. The system of claim 4, wherein the processing parameters of the plurality of processing steps comprise: a positional parameter of each machining shaft in each of the plurality of machining steps based on the tensor representation.
6. The system of claim 4, wherein the judgment model is obtained by training an initial judgment model based on training data, the training data being obtained based on historical data, the historical data including historical processing parameters and corresponding historical problem steps used in the historical occurrence of the processing problem; when the number of the historical data is insufficient, modifying the processing parameters of at least one processing step of the plurality of processing steps in each group of training original data, and manually marking at least one processing problem generated when the workpiece is processed by using the modified processing parameters, wherein the processing problems are respectively used as training samples and labels for training the judging model; the training raw data is the processing parameters of the plurality of processing steps of the plurality of sets of the processing procedure for which the processing problem does not occur.
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