Control boundary determination method and device, electronic equipment and computer storage medium

Abstract

The invention discloses a control boundary determination method and device, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining an actual machining parameter of a tool in a machining process and a control parameter of the tool, the control parameter being an abnormal value allowing the tool; calling a boundary threshold model based on the processing scene; and inputting the actual processing parameters and the control parameters into a boundary threshold model to obtain a control boundary. Therefore, by inputting the actual processing parameters and the control parameters into the boundary threshold model, the control boundary can be generated, and objectivity, accuracy and generation efficiency of the control boundary can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tool processing monitoring, and in particular to a control boundary determination method, a control boundary determination device, an electronic device and a computer-readable storage medium. Background Art

[0002] When a CNC machine tool processes a workpiece, it is necessary to monitor and control various parameters in the process to ensure processing quality, safe production and normal operation of the equipment. A common monitoring method is to set a control boundary for the processing process to monitor whether the real-time physical quantity of the actual processing exceeds the control boundary.

[0003] At present, the method of setting control boundaries is mainly based on manual experience to directly determine the control boundaries of the workpiece to be processed. This control boundary set by experience will have problems of subjectivity, instability and lag, which may affect the quality and efficiency of processing. Summary of the invention

[0004] The main technical problem solved by the present application is to provide a control boundary determination method, a control boundary determination device, an electronic device and a computer-readable storage medium, which can determine the control boundary according to the boundary threshold model, thereby ensuring the objectivity and accuracy of the control boundary setting.

[0005] In order to solve the above technical problems, a technical solution adopted in the present application is: to provide a method for determining a control boundary, the method comprising: obtaining the actual processing parameters of the tool during the processing and the control parameters of the tool, the control parameters are abnormal values ​​allowed for the tool; based on the processing scenario, calling a boundary threshold model; inputting the actual processing parameters and the control parameters into the boundary threshold model to obtain the control boundary.

[0006] In order to solve the above technical problems, another technical solution adopted in the present application is: to provide a control boundary determination device, the model includes: an acquisition module, used to obtain the actual processing parameters of the tool during the processing and the control parameters of the tool, the control parameters are abnormal values ​​allowed for the tool; a calling module, used to call the boundary threshold model based on the processing scenario; an input module, used to input the actual processing parameters and the control parameters into the boundary threshold model to obtain the control boundary.

[0007] In order to solve the above technical problems, another technical solution adopted in the present application is: to provide an electronic device, including a memory and a processor, the memory storing program instructions, and the processor calling the program instructions from the memory to execute the above control boundary determination method.

[0008] In order to solve the above technical problems, another technical solution adopted in the present application is: providing a computer-readable storage medium including program data stored therein, and the program data is used to implement the above-mentioned control boundary determination method when executed by a processor.

[0009] Compared with the control boundary currently set by experience to monitor the machining process of machine tools, the above scheme has certain subjectivity and inaccuracy. The present application provides a method for determining the control boundary, including: obtaining the actual machining parameters of the tool during the machining process and the control parameters of the tool, the control parameters are the abnormal values ​​that allow the tool; based on the machining scenario, calling the boundary threshold model; inputting the actual machining parameters and the control parameters into the boundary threshold model to obtain the control boundary. Therefore, by inputting the actual machining parameters and the control parameters into the boundary threshold model, the control boundary can be generated, which can improve the objectivity, accuracy and generation efficiency of the control boundary. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0011] Figure 1 It is a flowchart of an exemplary embodiment of the method for determining the control boundary provided by the present application;

[0012] Figure 2 yes Figure 1 A flow chart of an exemplary embodiment of step S130 in the control boundary determination method is shown;

[0013] Figure 3 yes Figure 2 A flow chart of an exemplary embodiment of step S210 in the control boundary determination method is shown;

[0014] Figure 4 yes Figure 3 A flow chart of an exemplary embodiment of step S320 in the control boundary determination method is shown;

[0015] Figure 5 yes Figure 3 A flow chart of an exemplary embodiment of step S330 in the control boundary determination method is shown;

[0016] Figure 6 yes Figure 3 A flow chart of an exemplary embodiment of step S310 in the control boundary determination method is shown;

[0017] Figure 7 yes Figure 2 A flow chart of an exemplary embodiment of step S220 in the control boundary determination method is shown;

[0018] Figure 8 yes Figure 2 A flowchart of another exemplary embodiment of step S220 in the control boundary determination method is shown;

[0019] Fig. 9 yes Figure 2 A flowchart of another exemplary embodiment of step S230 in the control boundary determination method is shown;

[0020] Fig.10 yes Figure 1 A flowchart of another exemplary embodiment of step S120 in the control boundary determination method is shown;

[0021] Fig.11 is a structural schematic diagram of an exemplary embodiment of a control boundary determination device provided by the present application;

[0022] Fig.12 It is a structural schematic diagram of an embodiment of an electronic device provided by the present application;

[0023] Fig.13 It is a structural schematic diagram of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It will be appreciated that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some but not all structures related to the present application are shown in the drawings. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the art without making creative work are within the scope of protection of the present application.

[0025] First of all, it should be noted that the control boundary refers to the boundary for monitoring and controlling key parameters and quality indicators during machine tool processing. In the prior art, the control boundary of machine tool processing is generally set by experienced on-site technicians. This method has certain subjectivity and inaccuracy, and it is easy to set different control boundaries for the same process, which in turn leads to different quality of processed workpieces.

[0026] This application provides a method for determining a control boundary, which can be applied to machine tool processing. Specifically, the actual processing parameters of the tool during the processing and the control parameters of the tool are obtained. The control parameters are abnormal values ​​that allow the tool to be used; based on the processing scenario, the boundary threshold model is called; the actual processing parameters and the control parameters are input into the boundary threshold model to obtain the control boundary. In this way, the control boundary can be calculated by modeling, ensuring the objectivity and accuracy of the control boundary.

[0027] The control boundary determination method of this application can be applied to the following industrial Internet scenarios.

[0028] In a possible system architecture of an industrial Internet scenario, it includes a server, an edge device, and a CNC machine tool, wherein the server and the CNC machine tool can communicate directly, and the server can also communicate with the CNC machine tool indirectly through an edge computer. In addition, the server can be an industrial cloud platform, a physical server, or a device of a physical server, wherein the industrial cloud platform can be a public cloud platform or an enterprise's private cloud platform. The physical server can be built using a single physical server or multiple servers to form a server group. The edge device is used to collect information and act as an intermediate medium to transmit communication between the server and the CNC machine tool, wherein a single edge device can correspond to multiple CNC machine tools, and multiple edge devices correspond one by one to a CNC machine tool associated with themselves.

[0029] The execution subject of the control boundary determination method of the present application can directly execute the following embodiments through a CNC machine tool, or can control the CNC machine tool to execute through an edge computer, and the specific details are not limited here.

[0030] The control boundary determination method of the present application is now described in combination with the above-mentioned architecture. It should be understood that this description is only exemplary and the present application is not limited to the implementation method under this description.

[0031] See also Figure 1 , Figure 1 : is a flowchart of an exemplary embodiment of the method for determining a control boundary provided by the present application. Specifically, the method for determining a control boundary of this embodiment may include the following steps:

[0032] S110: Acquire actual machining parameters of the tool during machining and control parameters of the tool, where the control parameters are abnormal values ​​that allow the tool to be processed.

[0033] A tool refers to a tool used for cutting, turning, milling, etc. in machine tool processing. For example, the tool may be a milling cutter, a turning tool, a drill, etc., and a suitable tool may be selected according to the hardness, toughness, and process requirements of the workpiece material being processed.

[0034] The actual processing parameters refer to a series of parameters that control and adjust the tool during the machining process of the machine tool. For example, the actual processing parameters can be determined based on multiple factors such as the material of the workpiece being machined, the performance of the tool, and the machining requirements. For example, the actual processing parameters may include the cutting width, cutting depth, tool feed speed, etc. of the workpiece.

[0035] The control parameter can be the maximum abnormal value that allows the tool to be abnormal. As a possible example, when the cause of the tool abnormality is wear or breakage during the processing, the tool wear value can be quantified, such as 1mm, 2mm, 3mm, etc. During the processing, a certain degree of tool wear does not affect the processing of the machine tool. Therefore, the maximum abnormal value that allows the tool to be abnormal can be set to monitor the machine tool processing process. The maximum abnormal value indicates that the degree of tool wear will affect the machine tool processing.

[0036] The control boundary determination device obtains the actual processing parameters of the input tool during the processing and the control parameters of the tool.

[0037] S120: Based on the processing scenario, a boundary threshold model is called.

[0038] A processing scenario refers to a process method selected according to processing requirements. For example, processing scenarios include milling, turning, and grinding. In different processing scenarios, due to the different processing tools and workpiece process requirements used, the method of confirming the control boundary is also different. To this end, a processing scenario library can be established in advance, in which each processing scenario corresponds to a boundary threshold model.

[0039] The boundary threshold model is a model used to generate a control boundary based on actual processing parameters and control parameters. For example, when the current processing scenario is turning, the control boundary can be the machine tool processing power corresponding to the control parameter; when the current processing scenario is milling, the control boundary can be the machine tool vibration frequency corresponding to the control parameter.

[0040] The control boundary determination device calls the boundary threshold model corresponding to the current processing scenario from a pre-established processing scenario library according to the current processing scenario of the workpiece.

[0041] S130: Input the actual processing parameters and the control parameters into the boundary threshold model to obtain the control boundary.

[0042] The control boundary is the physical quantity of machine tool processing corresponding to the control parameter. The control boundary can include an upper boundary and a lower boundary. In order to ensure the quality and safety of machine tool processing, when the processing physical quantity exceeds the control boundary, the operator or the CNC machine tool can perform correction operations to restore the processing physical quantity to within the control boundary. Among them, the processing physical quantity can be processing power. For example, when the cause of the abnormality is that the machine tool processing physical quantity exceeds the processing power corresponding to the control boundary during the processing, the machine tool processing can be restored to normal by switching the tool.

[0043] For example, in order to better monitor the machine tool processing process, an alarm function can be set. When the machine tool's processing signal exceeds the control boundary, an alarm will be used to warn of the processing abnormality, thereby serving as a warning.

[0044] The control boundary determination device inputs the actual processing parameters and the control parameters into the boundary threshold model, obtains the control boundary, and can control the processing process according to the control boundary. Exemplarily, the input of the actual processing parameters and the control parameters can be realized through the processing code, or can be input by the operator using a touch screen or an external input device.

[0045] It can be seen that the control boundary determination method of the embodiment of the present application obtains the actual processing parameters of the tool during the processing and the control parameters of the tool, and the control parameters are the abnormal values ​​allowed for the tool; based on the processing scenario, the boundary threshold model is called; the actual processing parameters and the control parameters are input into the boundary threshold model to obtain the control boundary. Thus, by inputting the actual processing parameters and the control parameters into the boundary threshold model, the control boundary can be generated, which can improve the objectivity, accuracy and generation efficiency of the control boundary.

[0046] Based on the above embodiments, the embodiments of the present application adopt Figure 2 The flowchart details how to obtain the control boundary based on the boundary threshold model. Figure 2 , Figure 2 yes Figure 1 The flowchart of an exemplary embodiment of step S130 in the control boundary determination method is shown. Specifically, step S130 inputs the actual processing parameters and the control parameters into the boundary threshold model to obtain the control boundary, which specifically includes the following steps:

[0047] First of all, it should be noted that the boundary threshold model includes a first sub-model and a second sub-model, wherein the first sub-model may be a physical model, and the second sub-model may be a big data model, such as a neural network model.

[0048] S210: Based on the first sub-model, determine a first machining physical quantity according to actual machining parameters, where the first machining physical quantity is a machining physical quantity when there is no abnormality in the tool.

[0049] The first sub-model is determined by the processing scenario, and different processing sites call different first sub-models. For example, when the processing scenario is turning, the first sub-model corresponding to turning is called, and when the processing scenario is milling, the first sub-model corresponding to milling is called.

[0050] The first processing physical quantity is the result output by the first sub-model according to the actual processing parameters. Exemplarily, the first processing physical quantity may be physical quantities such as machine tool processing power, vibration of the cutting area, and sound of the cutting area. For example, when the processing scenario is turning, the output of the first sub-model may be machine tool processing power, and when the processing scenario is milling, the output of the first sub-model may be vibration of the cutting area.

[0051] The first sub-model of the control boundary determination device performs theoretical calculations based on the input actual processing parameters to obtain the processing physical quantity when the machine tool processing is normal, and determines it as the first processing physical quantity.

[0052] S220: Based on the second sub-model, determine a second machining physical quantity by the control parameter, where the second machining physical quantity is the machining physical quantity when the abnormal value of the tool is the control parameter.

[0053] The second sub-model is different from the first sub-model, and the second sub-model may not be determined by the processing scenario, and different processing scenarios may correspond to the same second sub-model. Exemplarily, after the processing scenario is determined, the first sub-model corresponding to the processing scenario is called, and the second sub-model is called to form a boundary threshold model.

[0054] The second machining physical quantity is the result output by the second sub-model according to the control parameters, indicating the change value of the machining physical quantity of the machine tool relative to the first machining physical quantity when the abnormal value of the tool is the control parameter.

[0055] The control boundary determination device inputs the control parameters into the second sub-model, and the second sub-model determines the second processing physical quantity according to the control parameters.

[0056] S230: Determine a control boundary according to the first processing physical quantity and the second processing physical quantity.

[0057] After the control boundary determination device determines the first processing physical quantity and the second processing physical quantity by the first sub-model and the second sub-model, the control boundary of the machine tool processing can be determined by the first processing physical quantity and the second processing physical quantity.

[0058] It can be seen that the control boundary determination method of the embodiment of the present application is based on the first sub-model, and the first processing physical quantity is determined by the actual processing parameters. The first processing physical quantity is the processing physical quantity when the tool is normal; based on the second sub-model, the second processing physical quantity is determined by the control parameters, and the second processing physical quantity is the processing physical quantity when the tool is abnormal and deviates from the first processing physical quantity; the control boundary is determined based on the first processing physical quantity and the second processing physical quantity. Therefore, the first processing physical quantity and the second processing physical quantity can be determined based on the first sub-model and the second sub-model, which has the characteristics of simple modeling and high computational efficiency.

[0059] Based on the above embodiments, the embodiments of the present application adopt Figure 3 The flowchart explains in detail how to determine the first processing physical quantity based on the actual processing parameters. Figure 3 , Figure 3 yes Figure 2 The flowchart of an exemplary embodiment of step S210 in the control boundary determination method is shown. Specifically, the process of determining the first processing physical quantity from the actual processing parameters in step S210 based on the first sub-model specifically includes the following steps:

[0060] S310: Obtaining expected machining physical quantities when the tool in the first sub-model is normal and preset machining physical quantities of the tool in the first sub-model.

[0061] The expected machining physical quantity refers to the normal machining physical quantity in the actual machining process. For example, the expected machining physical quantity of the tool when there is no abnormality can be obtained by experiment, and the obtained expected machining physical quantity is input into the first sub-model.

[0062] The preset processing physical quantity is a processing physical quantity obtained by theoretical calculation based on actual processing parameters. For example, the preset processing physical quantity is the same when processing the same process.

[0063] The control boundary determination device inputs the expected processing physical quantities and actual processing parameters obtained through experiments into the first sub-model, and the first sub-model obtains the preset processing physical quantities according to the actual processing parameters.

[0064] S320: Determine cutting correction parameters according to the expected processing physical quantity and the preset processing physical quantity.

[0065] The cutting correction parameter is a parameter that improves the accuracy of the output results of the first sub-model. The preset machining physical quantity calculated according to the actual machining parameters is a machining physical quantity obtained by a theoretical calculation method, which may deviate from the machining physical quantity generated in the actual machining process. Therefore, setting the correction coefficient can correct the deviation of the theoretical calculation.

[0066] After obtaining the expected processing physical quantity and the preset processing physical quantity, the first sub-model in the control boundary determination device determines the cutting correction parameter according to the expected processing physical quantity and the preset processing physical quantity.

[0067] S330: Correcting the actual processing parameters input into the first sub-model based on the cutting correction parameters to obtain the first processing physical quantity output by the first sub-model.

[0068] The first sub-model in the control boundary determination device obtains the preset processing physical quantity according to the actual processing parameters, and determines the cutting correction parameter according to the expected processing physical quantity and the preset processing physical quantity. In practical applications, the cutting correction parameters of the same process are the same. Therefore, when processing the same process, the first processing physical quantity output by the first sub-model can be directly obtained through the actual processing parameters and the above-mentioned cutting correction parameters.

[0069] It can be seen that the control boundary determination method of the embodiment of the present application obtains the expected processing physical quantity when the tool is normal in the first sub-model and the preset processing physical quantity of the tool in the first sub-model; determines the cutting correction parameter according to the expected processing physical quantity and the preset processing physical quantity; and corrects the actual processing parameter input into the first sub-model based on the cutting correction parameter to obtain the first processing physical quantity output by the first sub-model. This can improve the accuracy of the first processing physical quantity.

[0070] Based on the above embodiments, the embodiments of the present application adopt Figure 4 The flowchart explains in detail how to determine the cutting correction parameters, see Figure 4 , Figure 4 yes Figure 3 The flowchart of an exemplary embodiment of step S320 in the control boundary determination method is shown. Specifically, the process of determining the cutting correction parameter according to the desired processing physical quantity and the preset processing physical quantity in step S320 specifically includes the following steps:

[0071] S410: Calculate the ratio between the expected processing physical quantity and the preset processing physical quantity.

[0072] The first sub-model of the control boundary determination device obtains the expected processing physical quantity and the preset processing physical quantity and calculates the ratio between the expected processing physical quantity and the preset processing physical quantity.

[0073] S420: Use the ratio as a cutting correction parameter.

[0074] The control boundary determination device uses the ratio between the expected processing physical quantity and the preset processing physical quantity as the cutting correction parameter.

[0075] It can be seen that the control boundary determination method of the embodiment of the present application calculates the ratio between the expected processing physical quantity and the preset processing physical quantity, and uses the ratio as a cutting correction parameter. This can make the deviation between the first processing physical quantity obtained by the first sub-model and the expected processing physical quantity smaller, and closer to the actual processing.

[0076] Based on the above embodiments, the embodiments of the present application adopt Figure 5 The flowchart explains in detail how to calculate the first processing physical quantity, please refer to Figure 5 , Figure 5 yes Figure 3 The flowchart of an exemplary embodiment of step S330 in the control boundary determination method is shown. Specifically, step S330 corrects the actual processing parameters input into the first sub-model based on the cutting correction parameters, and the process of obtaining the first processing physical quantity output by the first sub-model specifically includes the following steps:

[0077] First of all, it should be noted that this embodiment takes turning as an example. In turning, the actual processing parameters include the cutting width, cutting depth, linear speed and material strength of the workpiece during the tool processing.

[0078] S510: Calculate the first product of cutting width, cutting depth, line speed and material strength.

[0079] Cutting width refers to the width of the cut made by the tool on the workpiece; cutting depth refers to the cutting depth of the tool perpendicular to the surface of the workpiece.

[0080] Linear speed refers to the speed at which the cutting edge of a tool moves relative to the surface of a workpiece during machining. For example, the linear speed can be calculated by the product of the diameter of the rotating body and the rotational speed.

[0081] Material strength refers to the ability of the material of the processed workpiece to resist deformation and damage.

[0082] The control boundary determination device obtains the cutting width, cutting depth, linear speed and material strength from the actual processing parameters, and calculates the first product between the cutting width, cutting depth, linear speed and material strength.

[0083] S520: Calculate a second product between the first product of the preset multiple and the cutting correction parameter.

[0084] Exemplarily, the preset multiple may be cutting efficiency, where cutting efficiency refers to the ability to remove material at a faster speed during a cutting process.

[0085] The control boundary determination device uses the product of the first product, the preset multiple and the cutting correction parameter as the second product.

[0086] S530: Use the second product as the first processing physical quantity output by the first sub-model.

[0087] The control boundary determination device calculates the second product according to the cutting width, cutting depth, linear speed, material strength, preset multiple and cutting correction parameters, and uses the second product as the first processing physical quantity output by the first sub-model.

[0088] It can be seen that the control boundary determination method of the embodiment of the present application calculates the first product between the cutting width, cutting depth, linear speed and material strength; and calculates the second product between the first product of the preset multiple and the cutting correction parameter. Thus, the first processing physical quantity can be calculated in real time through the actual processing parameters.

[0089] Based on the above embodiments, the embodiments of the present application adopt Figure 6 The flowchart explains in detail how to calculate the preset processing physical quantities, please refer to Figure 6 , Figure 6 yes Figure 3 The flowchart of an exemplary embodiment of step S310 in the control boundary determination method is shown. Specifically, the process of obtaining the preset processing physical quantity of the tool in the first sub-model in step S310 specifically includes the following steps:

[0090] First of all, it should be noted that this embodiment also takes turning as an example. In turning, the actual processing parameters include the cutting width, cutting depth, linear speed and material strength of the workpiece during the tool processing process.

[0091] S610: Determine the cutting cross-sectional area according to the cutting width and cutting depth.

[0092] Cutting width refers to the width of the cut made by the tool on the workpiece; cutting depth refers to the cutting depth of the tool perpendicular to the surface of the workpiece.

[0093] The cutting cross-sectional area refers to the cross-sectional area of ​​the workpiece material per turn of the tool during turning. For example, the cutting cross-sectional area can be obtained by the product of the cutting width and the cutting depth.

[0094] The control boundary determination device calculates the product between the cutting width and the cutting depth to determine the cutting cross-sectional area of ​​the machine tool processing.

[0095] S620: Determine the main cutting resistance based on material strength and cutting cross-sectional area.

[0096] The main cutting resistance refers to the cutting force in the main motion direction, that is, the force resisting the material from being removed when the tool cuts into the material. For example, the main cutting resistance can be obtained by calculating the product between the material strength and the cutting cross-sectional area.

[0097] The control boundary determination device calculates the product of material strength and cutting cross-sectional area to determine the main cutting resistance of machine tool processing.

[0098] S630: Determine the preset processing physical quantity according to the linear speed and the main cutting resistance.

[0099] The control boundary determination device can obtain the preset processing physical quantity by calculating the product of the linear speed, the main cutting resistance and the cutting efficiency.

[0100] It can be seen that the control boundary determination method of the embodiment of the present application determines the cutting cross-sectional area according to the cutting width and cutting depth; determines the main cutting resistance according to the material strength and the cutting cross-sectional area; and determines the preset processing physical quantity according to the linear speed and the main cutting resistance. In this way, the preset processing physical quantity can be obtained in real time according to the actual processing parameters of the current tool.

[0101] Based on the above embodiments, the embodiments of the present application adopt Figure 7 The flowchart explains in detail how to obtain the second processing physical quantity, please refer to Figure 7 , Figure 7 yes Figure 2 The flowchart of an exemplary embodiment of step S220 in the control boundary determination method is shown. Specifically, the process of determining the second processing physical quantity by the control parameter based on the second sub-model in step S220 specifically includes the following steps:

[0102] S710: Constructing a first relationship in the second sub-model according to the historical abnormal values ​​of the tool and the tool processing physical quantities corresponding to each historical abnormal value, wherein the first relationship refers to the corresponding relationship between the historical abnormal values ​​of the tool and the tool processing physical quantities corresponding to each historical abnormal value.

[0103] The historical abnormal value refers to the abnormal value of the tool obtained through experiments. For example, the historical abnormal value of the tool can be the wear degree of the tool, for example, the historical abnormal value can be the wear of the tool by 1 mm, 2 mm, and 3 mm.

[0104] The tool processing physical quantity refers to the tool processing physical quantity corresponding to the abnormal value of the tool.

[0105] The first relationship refers to the relationship between the tool's historical abnormal values ​​and the tool's physical quantities corresponding to each historical abnormal value. As an example, the first processing physical quantity of the tool processing is determined to be 1kw through the first sub-model. When the tool's abnormal value is 1mm, the corresponding tool processing physical quantity is 1.2kw. When the tool's abnormal value is 2mm, the corresponding tool processing physical quantity is 1.4kw. When the tool's abnormal value is 3mm, the corresponding tool processing physical quantity is 1.6kw. Through the relationship between the historical abnormal values ​​and the tool's physical quantities corresponding to each historical abnormal value, it can be found that for every increase of 1mm in wear value, the tool processing physical quantity will increase by 0.2kw accordingly. It is concluded that the second relationship between the tool's historical abnormal values ​​and the processing physical quantities corresponding to each historical abnormal value relative to the change value of the first processing physical quantity is y=0.2x, where y represents the change value of the processing physical quantity corresponding to each historical abnormal value relative to the first processing physical quantity, and x represents the historical abnormal value.

[0106] The second sub-model in the control boundary determination device constructs a first relationship based on the historical abnormal values ​​of the tool and the tool processing physical quantities corresponding to each historical abnormal value, and obtains the change law of the tool processing physical quantities corresponding to each historical abnormal value from the first relationship, thereby obtaining the relationship between the historical abnormal values ​​of the tool and the processing physical quantities corresponding to each historical abnormal value relative to the change value of the first processing physical quantity.

[0107] S730: Determine, according to the control parameter, from the first relationship of the second sub-model a second machining physical quantity when the abnormal value of the tool is the control parameter.

[0108] The control parameter may be the maximum abnormal value of the tool allowed to be abnormal. The second sub-model of the control boundary determination device obtains the input control parameter and determines the second processing physical quantity according to the input control parameter. As a possible example, according to the first relationship, the relationship between the historical abnormal value of the tool and the change value of the tool processing physical quantity corresponding to each historical abnormal value relative to the first processing physical quantity is y=0.2x. If the input control parameter is 5mm, it can be concluded that the second processing physical quantity is 1kw.

[0109] The control boundary determination device obtains the control parameter, and determines the second machining physical quantity when the abnormal value of the tool is the control parameter from the first relationship of the second sub-model according to the control parameter.

[0110] It can be seen that the control boundary determination method of the embodiment of the present application constructs a first relationship in the second sub-model based on the historical abnormal values ​​of the tool and the tool processing physical quantity corresponding to each historical abnormal value. The first relationship refers to the corresponding relationship between each historical abnormal value of the tool and the tool processing physical quantity corresponding to each historical abnormal value; the second processing physical quantity when the abnormal value of the tool is the control parameter is determined from the first relationship of the second sub-model according to the control parameter. In this way, the change law of the tool processing physical quantity corresponding to the tool abnormal value can be obtained, and then the second processing physical quantity corresponding to each abnormal value can be obtained according to the change law, which improves the calculation efficiency of the second processing physical quantity and reduces the difficulty of operation.

[0111] Based on the above embodiments, the embodiments of the present application adopt Figure 8 The flowchart explains in detail how to use the neural network model to obtain the second processing physical quantity. Figure 8 , Figure 8 yes Figure 2 The flowchart of another exemplary embodiment of step S220 in the control boundary determination method is shown. Specifically, step S220 is based on the second sub-model, and the process of determining the second processing physical quantity by the control parameter specifically includes the following steps:

[0112] S810: Input the acquired abnormal value samples of the tool and the tool processing physical quantity samples corresponding to each abnormal value sample into the second sub-model to obtain the tool processing physical quantity output by the second sub-model.

[0113] The abnormal value sample refers to the abnormal value of the tool obtained by debugging or experiment. For example, taking the wear of the tool as an abnormality, the abnormal value of the tool can be wear of 1mm, 2mm, and 3mm.

[0114] Tool processing physical quantity refers to the change value of the processing physical quantity obtained by monitoring the processing process when the tool is processed under various abnormal values. For example, taking tool wear as an abnormality, when the abnormal value of the tool is 1mm wear, the corresponding tool processing physical quantity is 2kw, when the abnormal value of the tool is 2mm wear, the corresponding tool processing physical quantity is 4kw, when the abnormal value of the tool is 3mm wear, the corresponding tool processing physical quantity is 6kw, and so on.

[0115] In another possible example, the abnormal value sample of the tool can also be an abnormal signal, that is, a waveform diagram. The abnormal processing signal of the tool in a certain processing scenario can be input into the second sub-model for training, thereby directly generating upper and lower control boundaries based on the output results of the first sub-model.

[0116] The control boundary determination device inputs the acquired abnormal value samples of the tool and the tool processing physical quantity samples corresponding to each abnormal value sample into the second sub-model to obtain the tool processing physical quantity output by the second sub-model.

[0117] S820: Calculate the loss function between the tool processing physical quantity output by the second sub-model and the tool processing physical quantity sample.

[0118] The loss function is a function that measures the difference between the tool processing physical quantity output by the second sub-model and the tool processing physical quantity. Exemplarily, it can be calculated by a mean square error loss function, a cross entropy loss function, etc.

[0119] After obtaining the tool processing physical quantity output by the second sub-model, the control boundary determination device calculates the loss function between the tool processing physical quantity output by the second sub-model and the tool processing physical quantity sample.

[0120] S830: Train the second sub-model with the goal of reducing the loss value of the loss function to obtain a trained second sub-model.

[0121] The smaller the loss function is, the closer the tool processing physical quantity output by the second sub-model is to the tool processing physical quantity, and the output result of the second sub-model is more accurate. The loss function can be reduced by updating the model parameters to complete the training of the second sub-model.

[0122] The control boundary determination device continuously updates the parameters of the second sub-model with the goal of reducing the loss value of the loss function until the loss function drops to an acceptable level, thereby obtaining a trained second sub-model.

[0123] S840: Input the control parameters into the trained second sub-model, obtain the tool processing physical quantity corresponding to the control parameters output by the second sub-model, and use the tool processing physical quantity corresponding to the control parameters as the second processing physical quantity.

[0124] The trained second sub-model can determine the tool processing physical quantity corresponding to the input tool abnormality value based on the input tool abnormality value. The control boundary determination device inputs the control parameter into the trained second sub-model. The second sub-model outputs the corresponding tool processing physical quantity based on the control parameter, and determines the tool processing physical quantity as the second processing physical quantity.

[0125] It can be seen that the control boundary determination method of the embodiment of the present application inputs the acquired outlier value samples of the tool and the tool processing physical quantity samples corresponding to each outlier sample into the second sub-model to obtain the tool processing physical quantity output by the second sub-model; calculates the loss function between the tool processing physical quantity output by the second sub-model and the tool processing physical quantity sample; trains the second sub-model with the goal of reducing the loss value of the loss function to obtain a trained second sub-model; inputs the control parameters into the trained second sub-model to obtain the tool processing physical quantity corresponding to the control parameters output by the second sub-model, and uses the tool processing physical quantity corresponding to the control parameters as the second processing physical quantity. The above method can improve the accuracy and generation efficiency of the second processing physical quantity.

[0126] Based on the above embodiments, the embodiments of the present application adopt Fig. 9 The flowchart details how to determine the control boundary based on the first processing physical quantity and the second processing physical quantity. Fig. 9 , Fig. 9 yes Figure 2 The flowchart of another exemplary embodiment of step S230 in the control boundary determination method is shown. Specifically, the process of step S230 determining the control boundary according to the first processing physical quantity and the second processing physical quantity specifically includes the following steps:

[0127] S910: Calculate the physical quantity sum between the first processing physical quantity and the second processing physical quantity.

[0128] The control boundary determination device obtains a first processing physical quantity output by the first sub-model and a second processing physical quantity output by the second sub-model, and calculates a physical quantity sum between the first processing physical quantity and the second processing physical quantity.

[0129] S920: Use physical quantities and as control boundaries.

[0130] Since the second machining physical quantity is the change value corresponding to when the abnormal value of the tool is the control parameter, the sum of the physical quantities between the first machining physical quantity and the second machining physical quantity can be expressed as the tool machining physical quantity corresponding to when the abnormal value of the tool is the control parameter. The sum of the physical quantities is used as the control boundary, which means that the machining physical quantity of the tool during actual machining cannot exceed the tool machining physical quantity corresponding to when the abnormal value of the tool is the control parameter.

[0131] The control boundary determination device uses the physical quantity sum between the first processing physical quantity and the second processing physical quantity as the control boundary to monitor whether the tool processing physical quantity exceeds the maximum abnormal value allowed for the tool during the processing.

[0132] It can be seen that the control boundary determination method of the embodiment of the present application calculates the sum of the physical quantities between the first processing physical quantity and the second processing physical quantity, and uses the sum of the physical quantities as the control boundary. Therefore, the control boundary can be directly determined by the sum of the two, thereby simplifying the method of determining the control boundary.

[0133] Based on the above embodiments, the embodiments of the present application adopt Fig.10 The flowchart details how to determine the control boundary based on the first processing physical quantity and the second processing physical quantity. Fig.10 , Fig.10 yes Figure 1 The flowchart of another exemplary embodiment of step S120 in the control boundary determination method is shown. Specifically, step S120, based on the processing scenario, calls the boundary threshold model and specifically includes the following steps:

[0134] S1010: calling a boundary threshold model based on a processing scene classification model, wherein the processing scene classification model is trained with the processing scene as a label and the boundary threshold model corresponding to the processing scene as an input.

[0135] The processing scenario classification model is used to call the corresponding boundary threshold model according to different processing scenarios.

[0136] Machine tool processing will select different processing scenarios according to different process requirements. For example, the processing scenarios may include milling, turning, and grinding, etc. Therefore, these processing scenarios and the boundary threshold models corresponding to the processing scenarios can be used as inputs of the processing scenario classification model, and the processing scenario classification model is trained with the processing scenarios as labels to obtain the processing scenario classification model after training.

[0137] After the training is completed, the processing scene classification model can obtain the corresponding boundary threshold model through the processing scene.

[0138] The control boundary determination device inputs the processing scene and the boundary threshold model corresponding to the processing scene into the processing scene classification model for training, obtains the trained processing scene classification model, and calls the boundary threshold model based on the trained processing scene classification model.

[0139] It can be seen that the control boundary determination method of the embodiment of the present application calls the boundary threshold model based on the processing scene classification model. The processing scene classification model uses the processing scene as a label and is trained with the boundary threshold model corresponding to the processing scene as input. Thus, the boundary threshold model corresponding to the current processing scene is obtained through the processing scene classification model, which can improve the adaptability and flexibility of the boundary threshold model.

[0140] See also Fig.11 , Fig.11It is a structural diagram of an exemplary embodiment of the control boundary determination device provided by the present application. The control boundary determination device 11 includes an acquisition module 1101, a calling module 1102, and an input module 1103; the acquisition module 1101 is used to obtain the actual processing parameters of the tool during the processing and the control parameters of the tool, and the control parameters are abnormal values ​​that allow the tool; the calling module 1102 is used to call the boundary threshold model based on the processing scenario; the input module 1103 is used to input the actual processing parameters and the control parameters into the boundary threshold model to obtain the control boundary.

[0141] In the above scheme, the control boundary determination device 11 of the embodiment of the present application obtains the actual processing parameters of the tool during the processing and the control parameters of the tool, and the control parameters are the abnormal values ​​allowed for the tool; based on the processing scenario, the boundary threshold model is called; the actual processing parameters and the control parameters are input into the boundary threshold model to obtain the control boundary. Thus, by inputting the actual processing parameters and the control parameters into the boundary threshold model, the control boundary can be generated, which can improve the objectivity, accuracy and generation efficiency of the control boundary.

[0142] Among them, the functions of each module can be found in the implementation example of the control boundary determination method, which will not be repeated here.

[0143] In order to implement the control boundary determination method of the above embodiment, this application proposes another electronic device, which is specifically referred to as Fig.12 , Fig.12 It is a structural schematic diagram of an embodiment of an electronic device provided by the present application.

[0144] The electronic device 12 includes a memory 1201 and a processor 1202 , wherein the memory 1201 and the processor 1202 are coupled.

[0145] The memory 1201 is used to store program data, and the processor 1202 is used to execute the program data to implement the control boundary determination method of the above embodiment.

[0146] In this embodiment, the processor 1202 may also be referred to as a CPU (Central Processing Unit). The processor 1202 may be an integrated circuit chip having signal processing capabilities. The processor 1202 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor 1202 may also be any conventional processor, etc.

[0147] The present application also provides a computer readable storage medium 13, such as Fig.13As shown, the computer-readable storage medium 13 is used to store program data 1301. When the program data 1301 is executed by the processor, it is used to implement the control boundary determination method in the method embodiment of the present application.

[0148] The method involved in the embodiment of the method for determining the control boundary of the present application, when implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a device, such as a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0149] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for determining a control boundary, It is characterized in that The method comprises: Acquire actual machining parameters of the tool during machining and control parameters of the tool, wherein the control parameters are abnormal values ​​that allow the tool; Based on the processing scenario, the boundary threshold model is called; The actual processing parameters and the control parameters are input into the boundary threshold model to obtain the control boundary.

2. The method for determining the control boundary according to claim 1, It is characterized in that The boundary threshold model includes a first sub-model and a second sub-model; The step of inputting the actual processing parameters and the control parameters into the boundary threshold model to obtain the control boundary includes: Based on the first sub-model, determining a first machining physical quantity from the actual machining parameters, the first machining physical quantity being a machining physical quantity when the tool is normal; Based on the second sub-model, determining the second machining physical quantity by the control parameter, the second machining physical quantity being the machining physical quantity when the abnormal value of the tool is the control parameter; The control boundary is determined according to the first processing physical quantity and the second processing physical quantity.

3. The method for determining the control boundary according to claim 2, It is characterized in that The step of determining the first processing physical quantity from the actual processing parameters based on the first sub-model includes: Acquire the expected machining physical quantity of the tool in the first sub-model when there is no abnormality and the preset machining physical quantity of the tool in the first sub-model; Determining a cutting correction parameter according to the desired processing physical quantity and the preset processing physical quantity; The actual processing parameters input into the first sub-model are corrected based on the cutting correction parameters to obtain the first processing physical quantity output by the first sub-model.

4. The method for determining the control boundary according to claim 3, It is characterized in that The step of determining the cutting correction parameter according to the desired processing physical quantity and the preset processing physical quantity comprises: Calculating a ratio between the desired processing physical quantity and the preset processing physical quantity; The ratio is used as the cutting correction parameter.

5. The method for determining the control boundary according to claim 3, It is characterized in that The actual processing parameters include the cutting width, cutting depth, linear speed and material strength of the workpiece during the tool processing; The step of correcting the actual processing parameters input into the first sub-model based on the cutting correction parameters to obtain the first processing physical quantity output by the first sub-model includes: Calculating a first product among the cutting width, the cutting depth, the linear speed and the material strength; Calculating a second product between the first product of the preset multiple and the cutting correction parameter; The second product is used as the first processing physical quantity output by the first sub-model.

6. The method for determining the control boundary according to claim 3, It is characterized in that The actual processing parameters include: cutting width, cutting depth, linear speed and material strength of the processing material during the tool processing process; the step of obtaining the preset processing physical quantity of the tool in the first sub-model includes: Determine the cutting cross-sectional area according to the cutting width and cutting depth; Determining the main cutting resistance according to the material strength and the cutting cross-sectional area; The preset machining physical quantity is determined according to the linear speed and the main cutting resistance.

7. The method for determining a control boundary according to claim 2, It is characterized in that The step of determining the second processing physical quantity by the control parameter based on the second sub-model includes: Constructing a first relationship in the second sub-model according to the historical abnormal values ​​of the tool and the tool processing physical quantities corresponding to each historical abnormal value, wherein the first relationship refers to the corresponding relationship between the historical abnormal values ​​of the tool and the tool processing physical quantities corresponding to each historical abnormal value; A second machining physical quantity when the abnormal value of the tool is the control parameter is determined from the first relationship of the second sub-model according to the control parameter.

8. The method for determining a control boundary according to claim 2, It is characterized in that The step of determining the second processing physical quantity by the control parameter based on the second sub-model includes: Inputting the acquired abnormal value samples of the tool and the tool processing physical quantity samples corresponding to each abnormal value sample into the second sub-model to obtain the tool processing physical quantity output by the second sub-model; Calculating a loss function between a tool processing physical quantity output by the second sub-model and a sample of the tool processing physical quantity; Training the second sub-model with the goal of reducing the loss value of the loss function to obtain a trained second sub-model; The control parameters are input into the trained second sub-model to obtain the tool processing physical quantity corresponding to the control parameters output by the second sub-model, and the tool processing physical quantity corresponding to the control parameters is used as the second processing physical quantity.

9. The method for determining a control boundary according to claim 2, It is characterized in that The step of determining the control boundary according to the first processing physical quantity and the second processing physical quantity comprises: calculating a physical quantity sum between the first processing physical quantity and the second processing physical quantity; The physical quantities and are used as the control boundaries.

10. The method for determining a control boundary according to claim 1, It is characterized in that The step of calling the boundary threshold model based on the processing scenario includes: The boundary threshold model is called based on a processing scene classification model, wherein the processing scene classification model is trained by taking the processing scene as a label and taking the boundary threshold model corresponding to the processing scene as an input.

11. A control boundary determination device, It is characterized in that The device comprises: An acquisition module, used for acquiring actual processing parameters of the tool during the processing and control parameters of the tool, wherein the control parameters are abnormal values ​​that allow the tool; A calling module is used to call the boundary threshold model based on the processing scenario; An input module is used to input the actual processing parameters and the control parameters into the boundary threshold model to obtain the control boundary.

12. An electronic device, It is characterized in that include: A memory and a processor, wherein the memory stores program instructions, and the processor retrieves the program instructions from the memory to execute the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, It is characterized in that include: Program data is stored, and when the program data is executed by a processor, it is used to implement the method according to any one of claims 1 to 10.

Images