An articulated shearing system

Abstract

The present application relates to the field of shearing equipment, and particularly relates to a hinged shearing system, which comprises a shearing cutter, a shearing base, a driving component and a controller. The shearing cutter is connected to the base through a hinge, and the base is provided with a plurality of telescopic positioning blocks. The driving component is connected to the shearing cutter and controls the shearing speed of the shearing cutter. The cutting edge of the shearing cutter is divided into a plurality of wear evaluation zones. The controller generates a wear evaluation vector by analyzing the power output curve of the driving component and the position information of the positioning blocks, and calculates the wear value of each wear evaluation zone by using a deep learning model. Through a wear matrix M, if the cumulative sum of the wear values of any column exceeds a preset threshold, an alarm information is sent. The system can effectively monitor the wear condition of the cutter, prompt the staff to replace the cutter or adjust the position of the cut target in time, so as to reduce the energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of shearing equipment, in particular to a hinged shearing system. BACKGROUND

[0002] Shearing process is a basic manufacturing technology widely used in metal processing, textile manufacturing, paper processing and other industries, its main purpose is to cut or separate materials according to predetermined size and shape by applying external force. The process can be divided into several types, including but not limited to mechanical shearing, laser shearing, water jet cutting, etc., each method has its specific application scenario and technical characteristics.

[0003] Mechanical shearing is one of the most traditional shearing methods, which usually uses a pair of relative motion cutters (upper cutter and lower cutter or upper cutter and cutter holder) to realize the cutting of materials. This method is suitable for various forms of materials such as plates and blocks, and is more suitable for shearing materials with regular shapes. At the same time, with the increase of cutting times, and the difference in hardness and size of different sheared materials, the wear degree of the blade parts of the shearing cutter in different areas will be different. In the related art, the wear degree of the blade parts of the shearing cutter cannot be determined in time, which leads to an increase in energy consumption of the shearing system when using the excessively worn area of the blade for shearing. SUMMARY

[0004] In view of the above technical problems, the technical scheme adopted by the present application is as follows:

[0005] According to one aspect of the present application, a hinged shearing system is provided, which comprises a shearing cutter, a shearing base, a driving component and a controller.

[0006] One end of the shearing cutter is hingedly arranged on the shearing base, and a plurality of extendable positioning blocks are arranged on the shearing base in the extension direction of the shearing cutter; the driving component is connected with the shearing cutter and used to drive the shearing cutter to shear at a preset shearing speed; the shearing cutter is divided into a plurality of wear evaluation zones along the extension direction of the blade; the driving component and the controller are communicatively connected.

[0007] The controller is used to execute the following steps:

[0008] According to the power output curve of the driving component, the preset shearing speed and the position information of the positioning block, the power influence curve information corresponding to each wear evaluation zone after this shearing is intercepted from the power output curve; the power influence curve information is the power output curve of the corresponding wear evaluation zone from the time of just contacting the target to be cut to the corresponding time period after this shearing;

[0009] all the power influence curve information corresponding to this shearing is input into a first target deep learning model to generate the wear value corresponding to each wear evaluation zone after this shearing;

[0010] According to the wear value corresponding to each wear evaluation area after each shearing, a wear matrix M corresponding to the current shearing cutter is generated;

[0011] ; wherein a ji is the wear value corresponding to the i-th wear evaluation area after the j-th shearing, z is the total number of wear evaluation areas divided on the shearing cutter, i=1, 2…z; m is the total number of current shearing; j=1, 2…m;

[0012] If the cumulative sum of the wear values in any column of M is greater than a preset threshold, the wear warning information of the column corresponding wear evaluation area is generated.

[0013] The present application has at least one of the following beneficial effects:

[0014] In the present application, the cutting edge of the shearing cutter is divided into a plurality of wear evaluation areas, and the power influence curve information of each wear evaluation area subjected to wear in the shearing operation is formed after each shearing operation. Then, through the trained first target deep learning model, the wear value corresponding to each wear evaluation area after this shearing is generated, and finally the wear matrix M corresponding to the shearing cutter is generated. The cumulative sum of the wear values in each column of M represents the current wear degree of the corresponding wear evaluation area, so that the wear degree of each region of the cutting edge can be judged according to the size of the cumulative value of each column, and the corresponding wear warning information is generated to prompt the worker to replace the shearing cutter in time or change the placement position of the cut target in time. Further, the cutting edge with smaller wear is used for shearing as much as possible to reduce the energy consumption of the shearing system. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0016] Figure 1 A flowchart of the execution steps of the controller in the articulated shearing system provided by the embodiment of the present application is provided.

[0017] Figure 2 A structural schematic diagram of the shearing system provided by the embodiment of the present application when performing shearing operation is provided.

[0018] Figure 3 A flowchart of the execution steps of the controller in the shearing system for rectangular shearing section provided by the embodiment of the present application is provided.

[0019] REFERENCE NUMERALS:

[0020] 1, cutting knife; 2, cutting target; 3, cutting base; 4, indicator light; 5, positioning block. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0022] As a possible embodiment of the present application, as shown in the accompanying drawings, a hinged cutting system is also provided, which comprises a cutting knife 1, a cutting base 3, a driving component and a controller. Figure 1

[0023] As shown in the accompanying drawings, one end of the cutting knife 1 is hingedly arranged on the cutting base 3, and a plurality of extendable positioning blocks 5 are arranged on the cutting base 3 along the extension direction of the cutting knife 1. The driving component is connected with the cutting knife 1 for driving the cutting knife 1 to cut at a preset cutting speed. The driving component is a hydraulic driving component, such as a hydraulic cylinder or a hydraulic motor. Such a design makes the driving more stable and powerful, and can meet the required force for cutting different materials. The cutting knife 1 is divided into a plurality of wear evaluation zones along the extension direction of the blade. The cutting base 3 is provided with a plurality of indicator lights 4 along the extension direction of the cutting knife 1, and each wear evaluation zone corresponds to at least one indicator light 4. The driving component and the controller are in communication connection. The cutting knife 1 is divided into a plurality of wear evaluation zones along the extension direction of the blade, so as to accurately evaluate the wear condition of each region. The positions of the indicator lights 4 correspond to the wear evaluation zones, and thus the wear degree of the blade can be explicitly indicated by the color change of the indicator lights 4. The driving component and the controller are in communication connection, and the controller can accurately obtain various data of the driving component. Figure 2 The controller is used to perform the following steps:

[0024] S100: According to the power output curve of the driving component, the preset cutting speed and the position information of the positioning block 5, the power influence curve information corresponding to each wear evaluation zone after this cutting is intercepted from the power output curve. The power influence curve information is the power output curve of the corresponding wear evaluation zone from the time when it just contacts the cutting target 2 to the corresponding period when the cutting ends.

[0025]

[0026] ​​Specifically, when the target is subjected to the shearing operation, the shearing section of the sheared target 2 is rectangular. In the present embodiment, the sheared target object can be a metal material, and of course, the sheared target 2 can also be other block materials other than metal, such as various plates made of high-molecular organic materials, or cloth such as hard felt with a large thickness, and the like.

[0027] In some scenarios where different materials need to be cut, due to the different types of materials and different hardnesses, the degree of wear of the cutting blade by different sheared materials is also different. In the present scenario, the types of sheared materials are complex and varied, and if the hardness information of each type of sheared material needs to be obtained, professional tools need to be used for corresponding detection, which will consume a long time and slow down the processing progress. Therefore, in the present embodiment, the hardness information of each sheared material does not need to be directly measured, but the degree of wear of different wear evaluation regions of the cutting blade after each shearing operation is obtained by means of the power output curve of the driving component.

[0028] Specifically, S100 includes:

[0029] S101: According to the contact time t1 of the first corner of the sheared target 2, the contact time t2 of the second corner of the sheared target 2, and the distance L between the positioning surface of the positioning block 5 and the hinge point of the shearing knife 1 in the power output curve, the initial wear point information W1 and the cut-off wear point information W2 of the shearing knife 1 corresponding to the present shearing are determined. The first corner and the second corner of the sheared target 2 are the upper corners near the positioning block 5 and the upper corners away from the positioning block 5 in the rectangular shearing section when the sheared target 2 is in the shearing position. In the present embodiment, the sheared target 2 is in the shearing position formed by the shearing knife 1 and the shearing base 3, and in the process of cutting in, the shearing knife 1 contacts the first corner and the second corner of the sheared target 2 in turn. If the side near the positioning block 5 is left in the horizontal direction, and the side away from the positioning block 5 is right in the horizontal direction, the first corner and the second corner are the left upper corner and the right upper corner in the rectangular shearing section when the sheared target 2 is in the shearing position.

[0030] ; .

[0031] Wherein, α t1 and α t2 are the included angles between the shearing knife 1 and the shearing base 3 at t1 and t2. L x tan α t1 is the thickness of the sheared target 2.

[0032] In this embodiment, the cutting knife 1 cuts the material to be cut at a constant cutting speed, and the initial position of the cutting knife 1 is fixed. Therefore, once the current motion time of the cutting knife 1 is known, the included angle between the cutting knife 1 blade and the cutting base 3 can be calculated according to the angular velocity of the cutting knife 1 and the corresponding time. Based on this, W1 and W2 can be calculated according to the right triangle formed by the knife blade, the material to be cut, and the cutting base 3, as shown in FIG. 6. Figure 2

[0033] In each cutting process, first, the cutting knife 1 rotates from the initial position to the cutting base 3 at a constant speed. Before contacting the first corner of the target to be cut 2, the driving component only needs to maintain the uniform motion of the cutting knife 1 at a constant power, so the power value at this time is almost a constant value. When the cutting knife 1 contacts the first corner of the target to be cut 2, the output power of the driving component will instantaneously increase, and then the power will have a small amplitude rise. This is because the slight resistance such as the roughness of the metal surface, the oxide layer, etc. needs to be overcome. This section of the curve shows a slight upward slope.

[0034] Cutting-in stage: as the cutting knife gradually penetrates into the metal, the power consumption rapidly increases. This is because a greater force needs to be provided to overcome the elastic deformation and plastic deformation of the metal material. This part of the curve will rapidly rise, forming a relatively steep slope, and reaching the power peak value in the entire process. In this stage, the rapid increase of power is because the area of the metal contacted by the cutting blade gradually increases during the cutting process. At the same time, since the cutting section of the target to be cut 2 in this embodiment is rectangular, when the cutting knife 1 blade contacts the second corner of the target to be cut 2, the area of the target to be cut 2 contacted by the cutting knife 1 blade will no longer increase significantly. Therefore, usually the stage of rapid increase of power will tend to be stable when the cutting knife 1 blade cuts the second corner of the target to be cut 2, and will no longer increase at a steep slope. Therefore, according to the rhythm change characteristics of the power in this stage, using the existing recognition method or manual recognition method, t1 and t2 can be clearly determined from the power output curve.

[0035] Stable cutting stage: once the cutting knife fully penetrates into the metal, it enters the stable cutting stage, and the power consumption may remain at a relatively high stable value or only have a small amplitude fluctuation. This depends on factors such as the hardness and thickness of the material. In this interval, the curve tends to be flat or has a slight fluctuation.

[0036] Cutting end stage: when the cutting is close to completion, the remaining uncut part becomes thin, and the material's resistance to cutting weakens, resulting in a decrease in the required force, which leads to a decrease in power consumption. The curve shows a downward trend in this stage.

[0037] ​S102: Determine the power cut-off time corresponding to each wear evaluation zone of the shear cutter 1 in the current cutting according to W1 and W2. The power cut-off time tn corresponding to the nth wear evaluation zone satisfies the following conditions:

[0038] ; wherein, ΔL n is the distance between the evaluation point of the nth wear evaluation zone and W1; the evaluation point is a preset point used to represent the positioning information of the wear evaluation zone; the evaluation point of the first wear evaluation zone of the shear cutter 1 in the current cutting is the position corresponding to W1. The evaluation point of the last wear evaluation zone is the position corresponding to W2. The evaluation point of the middle wear evaluation zone is the middle point of the wear evaluation zone itself. W is the rotational angular velocity of the shear cutter 1.

[0039] S103: According to the power cut-off time corresponding to the wear evaluation zone, select the power output curve in the section after the corresponding power cut-off time from the power output curve as the power influence curve information corresponding to the wear evaluation zone.

[0040] S200: Input all the power influence curve information corresponding to the current cutting into the first target deep learning model to form a wear evaluation vector to generate the wear value corresponding to each wear evaluation zone after the current cutting.

[0041] Specifically, the power output point column corresponding to each wear evaluation zone is obtained from the power influence curve at a preset interval to form a wear evaluation vector.

[0042] The first target deep learning model can be an LSTM model and / or a CNN model.

[0043] Because the power output curve reflects the actual energy consumption change of the driving component during the cutting process. Specifically, when the shear cutter 1 contacts the target 2 to be cut, due to the influence of factors such as material hardness and thickness, the driving component needs to provide additional power to overcome the resistance, thereby causing a significant change in the power output curve.

[0044] These power change information can reflect the actual stress and wear of different regions of the shear cutter 1 during the cutting process. By intercepting the power influence curve information corresponding to each wear evaluation zone after each cutting, the power output characteristics corresponding to each wear evaluation zone can be matched more accurately. After all the power output characteristics corresponding to all the wear evaluation zones are obtained, a wear evaluation vector is formed, which can more accurately capture the energy consumption characteristics of each wear evaluation zone during the cutting process.

[0045] Further, the wear evaluation vectors are input into a first target deep learning model (such as an LSTM, long short-term memory network model, and / or a CNN, convolutional neural network model). Through learning of a large amount of historical data, the model can identify a complex nonlinear relationship between different power influence curves and wear degrees. Therefore, the model can generate a corresponding wear value according to the power influence curve information of each wear evaluation area in the current shearing operation, and further accurately evaluate the wear degree of each wear evaluation area.

[0046] The LSTM model can effectively capture the dependency relationship in a long time sequence. In the shearing system, the power influence curve information generated by each shearing operation can be regarded as a time sequence data. Through learning of the time sequence data, the LSTM model can identify a complex relationship between different power influence curves and wear degrees, and further generate a corresponding wear value.

[0047] The CNN model performs well in feature extraction. In the shearing system, the power influence curve information can be regarded as a one-dimensional signal. The CNN model can automatically extract key features in the signal through a convolution layer, reduce the data dimension through a pooling layer, and finally generate a wear value.

[0048] In addition to using the LSTM model and the CNN model alone, the CNN model and the LSTM model can also be used in combination. For example, the CNN model is used to preliminarily extract features from the time sequence data to form a continuous sequence with more obvious features, and then the sequence is input into the LSTM model for final prediction.

[0049] In addition, in order to make the evaluation vector more obviously reflect the characteristics of wear, the wear evaluation vector can further include the thickness value of the cut target 2, the shearing speed, and the time length of the power influence curve corresponding to each wear evaluation area.

[0050] Meanwhile, in order to obtain more effective information, the output value of the first target deep learning model can further include hardness information of the cut target 2.

[0051] The form setting of the above evaluation vector and the setting of the final output information can be realized by setting the training data in the corresponding form in the training stage.

[0052] S300: According to the wear value corresponding to each wear evaluation area after each shearing, a wear matrix M corresponding to the current shearing cutter 1 is generated.

[0053] ; wherein a ji is the wear value corresponding to the i-th wear evaluation area after the j-th shearing, z is the total number of wear evaluation areas divided on the shearing cutter, i = 1, 2, …, z; m is the total number of current shearing; and j = 1, 2, …, m.

[0054] S400: If the cumulative sum of the wear values ​​in any column in M ​​is greater than a preset threshold, then generate wear warning information for the wear assessment area corresponding to the column.

[0055] The cumulative sum of the wear values ​​in each column of M represents the current total wear level of the corresponding wear assessment area. Based on the cumulative values ​​of each column, the wear level of each area of ​​the blade can be assessed, and corresponding wear warning information can be generated to prompt the staff to replace the shear blade 1 or reposition the object 2 in a timely manner. This allows the blade in the less worn area to be used for shearing as much as possible, reducing the energy consumption of the shearing system.

[0056] In addition, the controller is also used to perform the following steps:

[0057] S500: If the cumulative sum of the wear values ​​of the wear assessment area corresponding to the column in M ​​is greater than a preset threshold, the indicator light 4 corresponding to the wear assessment area displays a preset warning color.

[0058] In the embodiment, the arrangement of the indicator lights 4 is consistent with the extension direction of the blade. Therefore, when the display color of the indicator lights 4 in a certain area changes to a preset warning color, such as red, it indicates that the degree of wear of the blade corresponding to the area is too great.

[0059] Based on this, the object 2 to be cut can be placed in an area where the indicator light 4 displays a non-warning color. Specifically, in this embodiment, since multiple retractable positioning blocks 5 are provided on the cutter base, the positioning blocks 5 at corresponding positions can be raised according to the different colors displayed by the indicator light 4 to position the object 2 to be cut. This allows the object 2 to be placed in different positions, and the blade with less wear is used as much as possible for cutting, thereby reducing energy consumption during the cutting process.

[0060] As another possible embodiment of the present invention, the controller is further configured to perform the following steps:

[0061] S310: Generate a contact power sequence corresponding to the current shearing operation based on the power output curve corresponding to the time interval [t1, t1+ΔT] in the power output curve. The contact power sequence is composed of multiple power scatter point values ​​in the power output curve corresponding to [t1, t1+ΔT]. For example, the multiple power scatter point values ​​can be obtained from the power output curve according to a preset collection interval. ΔT is a preset time interval. ΔT can be 1 second.

[0062] S320: Input the contact power sequence into the target binary classification model to generate the type of the first corner of the cut target 2 in the current shearing operation. The type of the first corner is a sharp corner or a chamfer.

[0063] When the first corner of the cut target 2 is a sharp corner: the power curve can have a significant transient peak, followed by a rapid decline, and there can be some fluctuations throughout the process. This phenomenon is due to the small contact area at the sharp corner, which leads to an increase in local stress, and thus requires more force to complete the shearing action, and once the cut is made, the local stress will decrease rapidly, and because the material has a strong resistance to shearing, some discontinuities or fluctuations in the power output can be observed.

[0064] When the first corner of the cut target 2 is a flat corner: because the contact area is larger, it will not immediately produce high local stress, and the power curve will tend to rise smoothly to a stable value, and show smaller fluctuations throughout the shearing process. This is because the flat corner provides a larger contact area, allowing the force to be more evenly distributed over a larger area.

[0065] Therefore, based on the power variation characteristics under different shearing conditions, the angle type of the cut target 2 can be accurately identified by analyzing the contact power sequence (such as clustering or classification processing). Generally, the above power variation characteristics are particularly evident within the first few seconds after the first corner of the cut target 2 is contacted by the shearing knife 1. Therefore, according to the power output curve corresponding to [t1, t1+△T], the system can quickly determine the angle type of the cut target 2.

[0066] S330: According to the power output curve corresponding to the time interval [t1, t2] of the power output curve, the hardness value of the cut target 2 is generated.

[0067] S330: includes:

[0068] S331: According to the power increase slope of the power output curve corresponding to [t1, t2], the hardness value of the cut target 2 is generated.

[0069] Specifically, during the shearing process, the change in the power output curve can reflect the characteristics of the material. When the shearing knife 1 cuts into the cut target 2, due to the different hardness of the material, the driving component needs to provide additional power to overcome the resistance, resulting in a significant change in the power output curve. Because the power increase slope directly reflects the degree of resistance of the material to the shearing force, it can be used to evaluate the hardness of the material. By analyzing the power increase slope of the power output curve within the [t1, t2] time interval, the hardness value of the cut target 2 can be inferred. For example, a machine learning model can be used to generate the hardness value.

[0070] S340: According to the hardness value of the cut target 2 and the type of the first corner, the wear adjustment coefficient of the first wear evaluation zone of the shearing knife 1 in the current shearing operation is obtained from the pre-set mapping table. The wear adjustment coefficient corresponding to the sharp corner is greater than the wear adjustment coefficient corresponding to the chamfer and is greater than one.

[0071] Since the sharp corner will have local stress concentration when shearing, the blade of the shear 1 will bear greater impact and wear. Therefore, when setting the wear adjustment coefficient in the mapping table, the wear value of the sharp corner needs to be further increased. Therefore, the wear adjustment coefficient corresponding to the sharp corner will be greater than the wear adjustment coefficient corresponding to the chamfer. For example, the wear adjustment coefficient corresponding to the sharp corner can be set to 1.3, and the wear adjustment coefficient corresponding to the chamfer is 1.

[0072] S350: According to the wear adjustment coefficient, the wear value of the first wear evaluation zone of the shear 1 in the current shearing operation is adjusted.

[0073] The adjusted wear value of the first wear evaluation zone is substituted into the corresponding position of the wear matrix M for updating. In this way, the actual wear condition of the first contact position between the shear 1 and the cut target 2 under different cut target 2 conditions can be more accurately reflected. For example, if the first corner of the cut target 2 in the current shearing operation is a sharp corner, the wear value adjusted by the wear adjustment coefficient will be relatively large, and after updating the wear matrix M, the wear value sum corresponding to this column will be more quickly close to or exceed the preset threshold, prompting the system to issue a wear warning message more timely.

[0074] In addition to adjusting the wear value according to the corner type and hardness value of the cut target 2, the material properties of the cut target 2 can also be considered. Different materials have different ways and degrees of wear on the shear 1 during shearing. For example, materials with brittle texture can cause the shear 1 to have a chipped blade phenomenon, while materials with strong toughness can cause the shear 1 to wear more evenly but to a greater extent. For different materials, the preset mapping table can be further refined to set a specific wear adjustment coefficient calculation method for each material, thereby further optimizing the evaluation of the wear degree of the shear 1.

[0075] As another possible embodiment of the present application, as shown in Figure 3 A shearing system for a rectangular shearing section is also provided, which has the same hardware structure as the shearing system disclosed in the above embodiments.

[0076] The specific difference is that in the present embodiment, the controller is further used to perform the following steps:

[0077] S410: If there is a local fluctuation greater than a threshold in the power output curve of the driving component corresponding to the cutting-in stage, the time corresponding to the maximum power in the local fluctuation is taken as the fluctuation determination time. The cutting-in stage is the period from the contact time of the shear 1 with the first corner of the cut target 2 (i.e. t1) to the contact time of the shear 1 with the second corner of the cut target 2 (i.e. t2).

[0078] S420: According to the fluctuation determination time, the contact time t1 of the first corner of the shear cutter 1 and the cut target 2, the distance L between the positioning surface of the positioning block 5 and the hinge point of the shear cutter 1, and the preset shear speed, the cut-in stop position of the shear cutter 1 at the fluctuation determination time is determined.

[0079] Specifically, at the fluctuation determination time T p , the cut-in stop position W P of the shear cutter 1 satisfies the following condition:

[0080] Wherein, a t1 is the included angle between the shear cutter 1 and the shear base 3 at t1. W is the rotational angular velocity of the shear cutter 1.

[0081] Generally, in the cut-in stage, the shear cutter is in contact with the upper plane of the cut target 2 and performs cutting. In this stage, each new position cut is similar to ice breaking operation, and generally the hardness of the position of the cut target 2 currently contacted by the shear cutter 1 will affect the power consumed when the shear cutter cuts the position. Therefore, the hardness information of the cutting position can also be clearly reflected in the power curve. If the hardness of the current cutting position is significantly higher than that of other positions, there may be a local fluctuation in the power output curve. Of course, sometimes the local fluctuation in the power curve may also be caused by abnormal data collection.

[0082] At the same time, in this process, the corresponding wear evaluation area of the shear cutter 1 when the next cut-in stop position (as shown in S420) can be calculated according to the time and the shear speed of the shear cutter 1. Therefore, the wear value condition of the cutting position corresponding to the local fluctuation area can be more accurately determined in M, and the foundation for generating the corresponding comparative power curve according to the historical wear value is also laid for the subsequent.

[0083] S430: According to the historical wear values corresponding to the plurality of wear evaluation areas adjacent to the wear evaluation area to which the cut-in stop position belongs in the wear matrix M corresponding to the shear cutter 1 and the hardness information of the cut target 2, a power curve prediction vector is formed. The power curve prediction vector is used to represent the characteristics affecting the power output of the driving part in this cutting.

[0084] In this step, the historical wear values corresponding to three wear evaluation areas adjacent to the wear evaluation area before and after the wear evaluation area can be selected.

[0085] S440: input the power curve prediction vector into the second target deep learning model to generate a comparison power output curve. The comparison power output curve is the predicted power output curve corresponding to the local fluctuation interval of the fluctuation determination time in this shearing. The length of the specific predicted power output curve can be determined according to actual needs, such as the power output curve within a preset time period after the fluctuation determination time, or the power output curve within a preset time period before the fluctuation determination time, or the power output curve within a preset time period before and after the fluctuation determination time. These time periods all include the fluctuation determination time.

[0086] The second target deep learning model can be an LSTM model and / or a CNN model. The training method of the second target deep learning model can refer to the training process of the first target deep learning model. Since both processes are existing contents, they will not be described here.

[0087] Due to the current local fluctuation, which may be caused by data acquisition anomalies, the wear value of each wear evaluation area corresponding to this shearing obtained in M may have abnormal conditions.

[0088] In order to more accurately predict the power curve of the local fluctuation region in the current solution process, the wear value obtained from M is no longer selected as the wear value result of this shearing. Generally, wear is a gradual accumulation process, so the historical wear data of the corresponding region can more accurately reflect the wear degree of the corresponding region on the shearing knife 1 before this shearing.

[0089] Then, combined with the hardness information of the cut target 2, the fluctuation characteristics of the power output curve in the current solution process can be more clearly reflected, thereby ensuring the accuracy of the comparison power curve predicted by the second target learning model.

[0090] In this embodiment, the hardness information of the cut target 2 can be directly obtained, i.e., the hardness information output by the first target deep learning model. In addition, since the local fluctuation condition is rare, the hardness information can also be obtained by manual measurement.

[0091] S450: if the similarity between the comparison power output curve and the power output curve of the local fluctuation region is less than the similarity threshold, generate power acquisition data error information.

[0092] The similarity between the comparison power output curve and the power output curve of the local fluctuation region is obtained according to the following steps:

[0093] S451: align the two curves according to the maximum output power value in the two curves.

[0094] S452: take the preset length interval adjacent to the maximum output power value as the comparison interval.

[0095] S453: Calculate the Euclidean distance between the two curves in the comparison interval as the similarity of the two curves.

[0096] S460: If the similarity of the comparison power output curve and the power output curve of the local fluctuation region is greater than the similarity threshold, generate power collection data verification pass information.

[0097] In this embodiment, the comparison power output curve is generated by using the multiple columns of historical wear values corresponding to the local fluctuation region in the wear matrix M and the hardness information of the cut target 2. Since the input data is assisted by the multiple columns of historical wear values and the hardness information of the cut target 2, the power output curve in the current cutting process can be more accurately predicted. Then, the similarity of the predicted power output curve (i.e., the comparison power output curve) and the power output curve of the local fluctuation region is compared, and it is finally determined whether the power output curve of the local fluctuation region is caused by data collection sensor abnormality. By re-verifying the data, the accuracy of determining the wear degree of the cutting edge part can be improved.

[0098] When the power collection data error information is generated, the system can automatically trigger further processing procedures. For example, prompting the operator to check the power collection equipment to see if there is a hardware failure, such as whether the sensor connection is loose, whether the data transmission line is damaged, etc. At the same time, the system can record the detailed information of the error, including the error time, the cutting task parameters (such as the material, hardness, angle type of the cut target 2) at the time of the error, so as to analyze the fault reason subsequently.

[0099] If the power collection data verification pass information is generated, the system can continue the normal cutting process, and store the power data collected this time and various analysis results (such as wear value, hardness value, etc.) obtained according to the data as historical data. These historical data can be used to optimize the subsequent prediction model, such as further training the first target deep learning model and the second target deep learning model, so that the prediction result is more accurate.

[0100] At the same time, considering that there may be various interference factors affecting the accuracy of the power collection data in the actual production environment, such as electromagnetic interference, environmental temperature change, etc., some anti-interference measures can be researched and adopted. For example, shielding devices are added to the power collection sensor to reduce the influence of electromagnetic interference on the data; temperature adjusting devices are installed around the equipment to maintain the relative stability of the working environment temperature of the collection equipment, thereby ensuring the reliability of the power collection data.

[0101] Moreover, although individual steps of the methods in the present disclosure are described in a particular order in the figures, this is not required or implied as to the order of execution of the steps, nor is it required that all of the steps be executed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into a single step, a single step can be broken into multiple steps, etc.

[0102] From the above description of the embodiments, those skilled in the art will easily understand that the example embodiments described herein can be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, etc.) or a network, and includes a number of instructions to make a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

[0103] In the example embodiments of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0104] Those skilled in the art can understand that each aspect of the present disclosure can be implemented as a system, a method or a program product. Therefore, each aspect of the present disclosure can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0105] The electronic device according to this embodiment of the present disclosure. The electronic device is merely an example, and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0106] The electronic device is in the form of a general computing device. The components of the electronic device can include, but are not limited to, the at least one processor described above, the at least one storage described above, and a bus connecting different system components (including storage and processor).

[0107] The storage stores program code that can be executed by the processor, so that the processor executes the steps according to various example embodiments of the present disclosure described in the "example method" section of the present specification.

[0108] The storage can include a readable medium in the form of a volatile storage, such as a random access memory (RAM) and / or a cache memory, and can further include a read-only memory (ROM).

[0109] The storage can also include a program / utility, having a set of program modules that include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof, can include implementation of a networking environment.

[0110] The bus can represent one or more of several types of bus structures, including a storage bus or

[0111] The electronic device can also communicate with one or more external devices such as a keyboard or a pointing device, through an I / O interface. Additionally, the electronic device can communicate with one or more devices that enable a user to interact with the electronic device, and / or one or more devices (e.g., a router, a modem, a server, etc.) that enable the electronic device to communicate with one or more other computing devices. Such communication can occur via an I / O interface. Still yet, the electronic device can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or the Internet) through a network adapter. It should be appreciated that the network adapter can also be utilized to enable the electronic device to communicate with other electronic devices or devices of the ultimate system provider. The communication can occur via the bus. The electronic device can also include an interface to one or more devices that enable a user to interact with the electronic device. User interaction can occur, for example, in connection with the display of information to the user on the display of the electronic device or interaction with input devices such as a keyboard or a pointing device. The electronic device and the various modules interact with one another in performing processes of the subject disclosure.

[0112] Those skilled in the art will readily understand that the example embodiments described herein can be implemented by software and / or by hardware coupled with software, as described above. Thus, the techniques of embodiments of the present disclosure can be embodied in a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, or the like) or on a network, and includes a number of instructions for enabling a computing device (such as a personal computer, a server, a terminal device, or a network device, etc.) to execute the methods according to the embodiments of the present disclosure.

[0113] In the example embodiments of the present disclosure, a computer readable storage medium is also provided, which stores a program product capable of implementing the methods described above. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing a terminal device to execute the steps described in the “Example Method” section above according to various example embodiments of the present disclosure when the program product is run on the terminal device.

[0114] A program product can take any combination of one or more computer-readable media. The computer-readable media can be a computer-readable storage medium or a computer-readable signal medium. The computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0115] The computer-readable signal medium can include a computer-readable storage medium that is propagated as a carrier wave in a baseband or propagated as part of a propagated data signal in a carrier, such as a propagated signal. The propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium that is not a computer-readable storage medium and that can communicate, propagate, or transport programming for use by or in connection with an instruction execution system, apparatus, or device.

[0116] The program code embodied on the computer-readable media can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0117] Program code used to practice the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider. The present application can also be practiced in

[0118] In addition, the above-described flowcharts are merely illustrative of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to limit the present application. It is readily understood that the processes shown in the above-described flowcharts do not indicate or limit the time sequence of the processes. In addition, it is readily understood that the processes can be executed synchronously or asynchronously, for example, in a plurality of modules.

[0119] It should be noted that, although several modules or units of the devices for action execution are mentioned in the above detailed description, the division into such modules or units is not mandatory. Indeed, according to an embodiment of the present disclosure, the features and functionalities of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functionalities of one of the above-described modules or units can be further divided into several modules or units.

[0120] The above merely shows the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any changes or replacements within the technical scope disclosed by the present application can be easily conceived by those skilled in the art, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An articulated shearing system, characterized in that: The system includes: a shearing knife, a shearing base, a driving component and a controller; One end of the shearing blade is hingedly connected to the shearing base, and a plurality of retractable positioning blocks are arranged on the shearing base at intervals along the extension direction of the shearing blade; the driving component is connected to the shearing blade, and is used to drive the shearing blade to shear according to a preset shearing speed; the shearing blade is divided into a plurality of wear evaluation areas along the extension direction of the blade; the driving component is in communication with the controller; The controller is used to perform the following steps: Based on the power output curve of the driving component, the preset shearing speed, and the position information of the positioning block, the power impact curve information corresponding to each wear evaluation area after the current shearing is intercepted from the power output curve; the power impact curve information is the power output curve of the corresponding wear evaluation area from the time it just contacts the cut target to the end of the current shearing; All the power impact curve information corresponding to this shearing is converted into a wear judgment vector and input into the first target deep learning model to generate the wear value corresponding to each wear evaluation area after this shearing; Generate a wear matrix M corresponding to the current shearing knife according to the wear value corresponding to each wear evaluation area after each shearing; ; Among them, a ji is the wear value corresponding to the i-th wear evaluation zone after the j-th shearing, z is the total number of wear evaluation zones divided on the shearing knife, i=1, 2…z; m is the total number of current shearing times; j=1, 2…m; If the cumulative sum of the wear values ​​of any column in M ​​is greater than a preset threshold, then a wear warning message for the wear evaluation area corresponding to the column is generated; The shearing cross section of the cut object is a rectangle; According to the power output curve of the driving component, the preset shearing speed and the position information of the positioning block, the power impact curve information corresponding to each wear evaluation area after the shearing is intercepted from the power output curve; including: Based on the contact time t1 of the shearing blade and the first corner of the cut object, the contact time t2 of the second corner, and the distance L between the positioning surface of the positioning block and the hinge point of the shearing blade in the power output curve, the initial wear point information W1 and the end wear point information W2 of the shearing blade corresponding to the current shearing are determined; the first corner and the second corner of the cut object are the upper corners of the side close to the positioning block and the upper corners of the side away from the positioning block in the rectangular shearing section when the cut object is in the shearing station, respectively; ； ； Among them, α t1 and α t2 are the angles between the shear blade and the shear base at t1 and t2 respectively; Based on W1 and W2, determine the power interception time corresponding to each wear evaluation area of ​​the shear blade during this shearing process; wherein, the power interception time tn corresponding to the nth wear evaluation area satisfies the following conditions: ; Among them, △L n is the distance between the evaluation point of the nth wear evaluation area and W1; the evaluation point is a preset point used to represent the positioning information of the wear evaluation area; W is the rotational angular velocity of the shearing knife; In this shearing, the evaluation point of the first wear evaluation zone where the shear knife is worn is the position corresponding to W1; the evaluation point of the last wear evaluation zone is the position corresponding to W2; the evaluation point of the middle wear evaluation zone is the middle point of the wear evaluation zone itself.

2. An articulated shearing system according to claim 1, characterized in that: After determining the power interception time corresponding to each wear evaluation area of ​​the shearing blade that is worn during this shearing, the controller is further configured to perform the following steps: According to the power interception time corresponding to the wear evaluation area, the power output curve in the section after the corresponding power interception time is selected from the power output curve as the power impact curve information corresponding to the wear evaluation area.

3. An articulated shearing system according to claim 2, characterized in that: After obtaining the power impact curve information corresponding to the wear evaluation area, the controller is further configured to perform the following steps: According to preset intervals, a power output point sequence corresponding to each wear evaluation area is obtained from the corresponding power influence curve to form a wear evaluation vector.

4. An articulated shearing system according to claim 1, characterized in that: The first target deep learning model is an LSTM model and / or a CNN model.

5. The articulated shearing system according to claim 1, characterized in that: The shear base is provided with a plurality of indicator lights along the extending direction of the shear blade, and each wear evaluation area corresponds to at least one indicator light; The controller is further configured to perform the following steps: If the cumulative sum of the wear values ​​of the corresponding column in the wear evaluation area in M ​​is greater than a preset threshold, the indicator light corresponding to the wear evaluation area displays a preset alarm color.

6. The articulated shearing system according to claim 1, characterized in that: The driving component is a hydraulic driving component.

7. The articulated shearing system according to claim 1, characterized in that: The controller is further configured to perform the following steps: Generate a contact power sequence corresponding to the current shearing operation based on the power output curve corresponding to the time interval [t1, t1+△T] in the power output curve; the contact power sequence is composed of multiple power scatter point values ​​in the power output curve corresponding to [t1, t1+△T]; △T is a preset time interval; Inputting the contact power sequence into a target binary classification model to generate the type of the first corner of the target to be cut in the current shearing operation; the type of the first corner is a sharp corner or a chamfer; Generate a hardness value of the cut target according to the power output curve corresponding to the time interval [t1, t2] in the power output curve; According to the hardness value of the object being cut and the type of the first corner, the wear adjustment coefficient of the first wear evaluation area where the shearing knife is worn during the current shearing operation is obtained from a preset mapping table; the wear adjustment coefficient corresponding to the sharp corner is greater than the wear adjustment coefficient corresponding to the chamfer and is greater than one; According to the wear adjustment coefficient, the wear value of the first wear evaluation area where the shearing knife is worn in the current shearing operation is adjusted.

8. An articulated shearing system according to claim 7, characterized in that: Generate the hardness value of the cut target according to the power output curve corresponding to the time interval [t1, t2] in the power output curve; including: The hardness value of the cut target is generated according to the power increase slope of the power output curve corresponding to [t1, t2].

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